Determination of phenolic compounds in apple and pear pulp by ultra-high performance liquid chromatography coupled with mass spectrometry

João David Guimarães Teixeira Determination of phenolic compounds in apple and pear pulp by ultra-high performance liquid chromatography coupled with mass spectrometry outubro de 2022 UMinho | 2022 David Teixeira Determination of phenolic compounds in apple and pear pulp by ultra-high performance liquid chromatography coupled with mass spectrometry Universidade do Minho Escola de Ciências João David Guimãraes Teixeira Determination of phenolic compounds in apple and pear pulp by ultra-high performance liquid chromatography coupled with mass spectrometry Dissertação de Mestrado Mestrado em Química Medicinal Trabalho efetuado sob a orientação do Professor Doutor Pier Parpot e da Doutora Ana Sanches Silva Universidade do Minho Escola de Ciências outubro de 2022 i DIREITOS DE AUTOR E CONDIÇÕES DE UTILIZAÇÃO DO TRABALHO POR TERCEIROS Este é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e boas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada. Caso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições não previstas no licenciamento indicado, deverá contactar o autor, através do Repositório da Universidade do Minho. Licença concedida aos utilizadores deste trabalho Atribuição-NãoComercial-SemDerivações CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/ ii Agradecimentos Durante a elaboração desta tese de mestrado, tive a sorte de obter o apoio de várias pessoas e entidades, às quais desejo deixar uma palavra de apreço: À Universidade do Minho, e todos os docentes que me acompanharam no percurso académico e contribuíram para o meu desenvolvimento pessoal e profissional. Ao INIAV, I.P., pela oportunidade que me deu e por tornar possível, com as melhores condições, a realização do trabalho desta tese. Aos meus orientadores, Doutora Ana Sanches Silva, por toda a disponibilidade, conselhos, ajuda e preocupação que exibiu durante este período e ao Prof. Doutor Pier Parpot, meu orientador do trabalho de tese de mestrado, pela preocupação, rigor e apoio demonstrados ao longo deste trabalho. À Doutora Claudia Sanchez e ao Doutor Miguel Leão pela disponibilização das amostras regionais e comerciais de maçã e regionais de pera, indispensáveis à realização deste projeto. Aos meus pais, por todos os esforços que fazem por mim, por todos os conselhos que melhoram o meu dia-a-dia, e por serem sempre os meus maiores admiradores. Ao meu irmão, por estar sempre presente e por me tornar uma figura de exemplo para si. À minha Dani, por toda a ternura, carinho, apoio e amor que me transmite. Muito obrigado por estares sempre presente, pronta a ajudar e mais especialmente por me fazeres acreditar em mim. A todos os meus amigos, mas em especial à Flávia, à Cátia, ao Esperança, à Inês, ao Marcelo, ao Marco, ao Miguel, ao Nené, ao Rodrigo, ao Tiago e ao Xavi, por durante estes anos manterem uma amizade próxima comigo e por serem protagonistas em belas memórias. Agradeço também a todos os colegas do INIAV,I.P., pelo excelente ambiente que proporcionaram, com especial ênfase à Ana, à Carmen, ao João, à Margarida, à Marta, ao Ricardo e à Rita. Este trabalho foi realizado no âmbito do projeto clabel+: Alimentos inovadores “clean label” naturais, nutritivos e orientados para o consumidor, com a referência POCI-01-0247-FEDER046080 financiado pelo Programa Operacional Competitividade e Internacionalização (POCI), sob o acordo de parceria COMPETE2020, PORTUGAL2020, através do co-financiamento do Fundo Europeu de Desenvolvimento Regional (FEDER). iii STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Code of Ethical Conduct of the University of Minho. iv Determination of phenolic compounds in apple and pear pulp by ultra-high performance liquid chromatography coupled with mass spectrometry Apples ( Malus domestica ) and pears ( Pyrus L.). are two of the most widely grown and consumed fruits on the planet. They provide phenolic compounds, which are considered to have positive effects on human health such as antioxidant and anti-hypertensive properties. The byproducts of these fruits are not currently fully utilized; thus, it is critical to characterize them, particularly with regard to their antioxidant capabilities, in order to identify potential uses and prevent their waste. The goal of this study was to identify the antioxidant properties of by-products and edible parts of six national cultivars of pears — Bela-Feia, Torres Novas, Carapinheira, Carapinheira Roxa, Lambe-os-Dedos, and Amorim — and five different Portuguese apple cultivars — Pardo Lindo, Pêro de Borbelo, Noiva, Pêro Coimbra, and Repinau — produced in the Alcobaça region (Portugal) and compare them with commercially available cultivars, also from this region. The antioxidant properties were assessed employing antioxidant capacity tests (DPPH radical scavenging and - carotene bleaching) as well as total flavonoids and total phenolic content assays. In general byproducts from apples and pears, performed better in terms of antioxidant capacity, TFC, and TPC, indicating their potential as sources of beneficial antioxidant compounds. It is interesting to note that in some cultivars such as the Carapinheira pear, the maximum antioxidant properties can be found in seeds while in other cultivars like Noiva apple can be found in peels. A UHPLC-ToF-MS method was also developed for the determination of 20 individual phenolics in the mesocarp (pulp) and by-products of apples and pears. The analytical method was evaluated regarding linearity, limit of detection, limit of quantification, accuracy, showing its suitability for the quantification of phenolic compounds. This method was applied to four samples of the regional cultivars of fruits, providing their phenolic composition. Vanillic acid and caffeic acid were the phenolics with a higher concentration in the fruit samples. The principal component analysis (PCA) technique was used to reduce the data dimensionality while maintaining the contribution of the original variables. Fruits were successfully differentiated into groups based on their provenience. Keywords: Apples; PCA; Pears; Phenolic compounds; UHPLC-ToF-MS v Determinação de compostos fenólicos em polpas de maçãs e peras por cromatografia líquida de ultra resolução acoplada a espectrometria de massas Maçãs e peras são duas das frutas mais produzidas e consumidas em todo o mundo. Estas frutas são fontes de compostos fenólicos, que são considerados como tendo efeitos positivos para a saúde humana, tais como propriedades antioxidantes e anti-hipertensivas. Os sub-produtos destes frutos não estão a ser devidamente aproveitados; portanto é essencial caracterizá-los, particularmente em relação às suas propriedades antioxidantes, de maneira a identificar potenciais usos e prevenir o seu desperdício. O objetivo deste trabalho foi identificar as propriedades antioxidantes dos sub-produtos e parte edível de seis cultivares nacionais de peras — Bela-Feia, Torres Novas, Carapinheira, Carapinheira Roxa, Lambe-os-Dedos, e Amorim — e cinco cultivares portugueses de maçãs Pardo Lindo, Pêro de Borbelo, Noiva, Pêro Coimbra, e Repinau — produzidos na região de Alcobaça (Portugal) e compará-los com cultivares comercialmente disponíveis, também desta região. As capacidades antioxidantes foram avaliadas aplicando testes de capacidade antioxidante (método do radical DPPH e branqueamento do -caroteno) e também pela determinação do teor total de compostos fenólicos e de flavonoides. No geral, os sub-produtos obtiveram melhores resultados na capacidade antioxidante, TPC e TFC, mostrando o seu potencial como fonte de antioxidantes. É interessante referir que em alguns cultivares, como na pera Carapinheira, os resultados são melhores nas sementes e noutros, como na maçã Noiva, nas cascas. Também foi desenvolvido um método UHPLC-ToF-MS para a determinação de 20 compostos fenólicos individuais no mesocarpo (polpa) e sub-produtos destas matrizes. O método analítico foi avaliado quanto à linearidade, limite de deteção, limite de quantificação, precisão, mostrando ser adequado para a determinação de compostos fenólicos. O método foi aplicado a 4 amostras dos cultivares regionais, fornecendo a sua composição fenólica. Os ácidos vanílico e cafeico foram encontrados em maiores concentrações nestas matrizes. Uma técnica de análise de componentes principais (PCA) foi utilizada para diminuir a dimensão de dados, mantendo o contributo das variáveis iniciais. Os frutos foram separados com sucesso com base na sua proveniência. Palavras-chave: Compostos fenólicos, Maçã, PCA, Pera, UHPLC-ToF-MS vi Table of Contents Agradecimentos ...................................................................................................................................... ii STATEMENT OF INTEGRITY ................................................................................................................... iii Abstract ................................................................................................................................................. iv Resumo .................................................................................................................................................. v Table of Contents ................................................................................................................................... vi List of Figures ........................................................................................................................................ ix List of Tables ........................................................................................................................................ xiii List of Abbreviations .............................................................................................................................. xvi 1. Introduction ................................................................................................................................... 1 1.1. INIAV, I.P. .............................................................................................................................. 2 1.2. Apple ..................................................................................................................................... 4 1.3. Pear ....................................................................................................................................... 6 1.4. Phenolic compounds .............................................................................................................. 8 1.4.1. Flavonoids ..................................................................................................................... 9 1.4.1.1. Flavonols .............................................................................................................10 1.4.1.2. Flavan-3-ols .........................................................................................................11 1.4.1.3. Flavones ..............................................................................................................12 1.4.1.4. Isoflavones ..........................................................................................................13 1.4.1.5. Flavanones ..........................................................................................................14 1.4.1.6. Anthocyanidins ....................................................................................................14 1.4.2. Phenolic Acids .............................................................................................................15 1.4.2.1. Hydroxycinnamic acids ........................................................................................16 1.4.2.2. Hydroxybenzoic acids ..........................................................................................16 1.4.3. Stilbenes ......................................................................................................................17 1.4.4. Lignans........................................................................................................................18 1.5. Methods for the quantification of bioactive compounds .........................................................19 1.5.1. Spectrophotometric methods .......................................................................................20 1.5.1.1. DPPH free radical inhibition assay ........................................................................20 1.5.1.2. Total content of phenolic compounds assay .........................................................21 1.5.2. Total content of flavonoids assay ..................................................................................21 1.5.2.1. β-carotene bleaching assay ..................................................................................22 1.5.3. Chromatographic separation methods ..........................................................................22 1.5.3.1. High-Performance Liquid Chromatography (HPLC) ...............................................23 1.5.3.2. Ultra-High-Performance Liquid Chromatography (UHPLC) .....................................26 1.5.3.3. Gas Chromatography (GC) ...................................................................................26 xiii List of Tables Chapter 1 Table 1.1: Portugal national production of apples. Source: GPP 2021. ........................ 5 Table 1.2: International trade balance regarding apple commerce (x 1000€). Source: GPP 2021 ................................................................................................................................ 5 Table 1.3: Portugal national production of pears. Source: GPP 2021........................... 7 Table 1.4: International trade balance regarding pear commerce (x 1000€). Source: GPP 2021 ........................................................................................................................................ 7 Chapter 3 Table 3.1: Reagents and their characteristics used for the determination of the antioxidant properties and the quantification of fructose in apple and pear samples. .................................. 36 Table 3.2: Materials and their characteristics used for the determination of the antioxidant properties and the quantification of fructose in apple and pear samples. .................................. 38 Table 3.3: Equipments used for the determination of the antioxidant properties and the quantification of fructose in apple and pear samples. .............................................................. 39 Table 3.4: Preparation of the trolox standard solutions. ............................................. 40 Table 3.5: Preparation of the gallic acid standard solutions. ...................................... 41 Table 3.6: Preparation of the epicatechin standard solutions. .................................... 42 Table 3.7: Preparation of the fructose standard solutions. ......................................... 43 Table 3.8: Preparation of the different phenolic compounds’ standards. .................... 45 Table 3.9: Mobile phase gradient used for the separation of phenolic compounds. .... 53 Table 3.10: GC column temperature gradient for pesticide separation. ..................... 54 Chapter 4 Table 4.1: Solvent study using the DPPH free radical inhibition system. (IP= Inhibiting percentage) ............................................................................................................................ 55 Table 4.2: Solvent study using the total flavonoid content assay. (ECE= Epicatechin equivalents) ............................................................................................................................ 56 Table 4.3: Solvent study using the β-carotene bleaching assay. (AAC= Antioxidant Activity) .............................................................................................................................................. 56 Table 4.4: Results of DPPH free radical inhibition assay of different regional apple cultivars collected in 2021 at Alcobaça region (Portugal). ........................................................ 57 xiv Table 4.5: DPPH free radical inhibition assay results of different regional pear cultivars collected in 2021 at Alcobaça region (Portugal). ...................................................................... 57 Table 4.6: DPPH free radical inhibition assay results on the commercial apple cultivars. .............................................................................................................................................. 60 Table 4.7: β-carotene bleaching assay results on the regional apple cultivars. Harvested in 2021 in Alcobaça (Portugal). ............................................................................................... 64 Table 4.8: β-carotene bleaching assay results on the pear cultivars harvested in 2021 in Alcobaça (Portugal). ................................................................................................................ 65 Table 4.9: β-carotene bleaching assay results on the commercial apple cultivars harvested in 2021 in Alcobaça (Portugal). ............................................................................... 67 Table 4.10: Total phenolics content of the regional apple cultivars harvested in 2021 in Alcobaça (Portugal). ................................................................................................................ 72 Table 4.11: Total phenolics content of the regional pear cultivars harvested in 2021 in Alcobaça (Portugal). ................................................................................................................ 72 Table 4.12: Total phenolics content of commercial apple cultivars harvested in 2021 in Alcobaça (Portugal). ................................................................................................................ 75 Table 4.13: Total flavonoids content of the apple cultivars harvested in 2021 in Alcobaça (Portugal). .............................................................................................................................. 80 Table 4.14: Total flavonoids content of the pears cultivars harvested in 2021 in Alcobaça (Portugal). .............................................................................................................................. 80 Table 4.15: Total flavonoids content of the commercial apple cultivars harvested in 2021 in Alcobaça ............................................................................................................................. 83 Table 4.16: Total fructose content of the apple cultivars harvested in 2021 in Alcobaça (Portugal). .............................................................................................................................. 88 Table 4.17: Total fructose content of the pear cultivars harvested in 2021 in Alcobaça (Portugal). .............................................................................................................................. 88 Table 4.18: Total fructose content assay results on the commercial apple cultivars harvested in 2021 in Alcobaça (Portugal). ............................................................................... 91 Table 4.19: Linear range, calibration curves, determination coefficients and retention time of the studied phenolic compounds. .............................................................................. 103 Table 4.20: Limit of quantification and limit of detection of the studied phenolic compounds. ......................................................................................................................... 105 xv Table 4.21: Recovery of the analytical method for each of the studied phenolic compounds at two fortification levels. .................................................................................... 108 Table 4.22: Phenolic compounds concentration in the studied fruit samples. .......... 110 xvi List of Abbreviations AAC Antioxidant activity coefficient DPPH 2,2-diphenyl-1-picrylhydrazyl ECE Epicatechin equivalents GAE Gallic acid equivalents GC Gas chromatography HS-SPME Headspace solid phase microextraction HPLC High performance liquid chromatography INIAV National Institute of Agrarian and Veterinary Research IP Inhibiting percentage LC Liquid chromatography LOD Limit of detection LOQ Limit of quantification MeOH Methanol MS Mass spectrometry PCA Principal component analysis PDMS Polydimethylsiloxane QuECHERS Quick, Easy, Cheap, Effective, Rugged and Safe r Coefficient of correlation r2 Coefficient of determination RSA Radical scavenging activity rt Retention time SLE Solid-liquid extraction SPME Solid phase microextraction TE Trolox equivalents TFC Total flavonoids content ToF Time of Flight TPC Total phenolics content xvii UHPLC Ultra-high performance liquid chromatography UV-B Ultraviolet B radiation UV-Vis Ultraviolet-visible spectroscopy 1 1. Introduction Food loss and waste is defined as “a decrease, at all stages of the food system from production to consumption, in mass and/or quality, of food that was originally intended for human consumption, regardless of the cause”.1 Therefore, addressing the systemic causes (demands and consumption patterns that are influenced by commerce, affluence, and culture), permitting changes in consumer behavior, and promoting responsible food valorization are the essential components of preventing food waste. cLabel+ (cLabel+ Innovative natural, nutritious, and consumer-oriented clean label food) is a research and technological development project aimed at addressing the issues the food industry faces. The cLabel+ project is promoted by a consortium led by Sumol + Compal Marcas SA and comprised of 20 entities: 8 companies with diverse and complementary areas of activity and 12 non-business entities from the R&I System with a strong experience in development of projects in the scientific areas of the project. cLabel+ project focuses on "clean label" concept, which is emerging as one of the major current trends in the sector given the growing number of customers who are increasingly conscious and ready for information and who are looking for alternative, more transparent, and natural food products. Focusing the clean label concept, a book chapter2 dedicated to the Regulatory framework and guidelines for the development of clean label products was accepted for publication, where I am the first author. In the frame of the workpackage 2 of cLabel+ (PPS2), new solutions for reducing and modeling the sweetening power in foods are developed. In this line, the National Institute of Agrarian and Veterinary Research (INIAV I.P.), as partner of the project, planned to study the composition of regional cultivars of apples and pears produced at the Alcobaça campus of INIAV, as an alternative to reduce sugar in some food formulations. In this context, this dissertation aimed to characterize the antioxidant capacity and fructose content, of regional cultivars of apples and pears, for which there are still scarce data on components, and compare these results with those of commercial cultivars. Moreover, different parts of the fruits (peels, seeds and mesocarp) were analyzed individually in order to evaluate the potential of by-products to be used a valuable source of natural antioxidants. Moreover, an ultrahigh resolution liquid chromatography coupled to time-of-flight mass spectrometry (UHPLC-ToFMS) method was developed and validated to determine individual phenolics and it was applied to four selected samples. 2 Also, a pesticide detection study was made to evaluate if the regional cultivars were contaminated with these compounds. Principal component analysis was also carried out in this study in order to visualize the relationship between samples which consist of different apple and pear varieties and their chemical properties. 1.1. INIAV, I.P. The presented research study was developed in the Vairão campus (Vila do Conde) of the INIAV, I.P, INIAV is the State Laboratory, in the area of competences of Agriculture, Forestry and Rural Development, which develops research activities in the agronomic and veterinary areas. It is positioned at the interface between the national scientific and technological system, companies, and territorial agents. INIAV has two main areas of activity. The first is the national reference laboratories, laboratories at the forefront of technology in the European Union. There is only one of these laboratories per country, and in the case of Portugal, INIAV holds the reference laboratories for animal diseases, plant diseases and pests, and food safety. The other major area of activity is research and innovation in agriculture, food, and forestry. Figure 1.1: Distribution of INIAV´s infrastructures in Portugal. 3 INIAV is spread throughout the national territory (Figure 1.1), where there are scientific and technological infrastructures that constitute an innovation network at the service of the agrofood sector. It has a team of over 600 collaborators, 300 of whom are highly specialized in these areas of knowledge. It works in cooperation with 500 partners in Portugal, seeking to respond to their needs and opportunities. Some of INIAV´s mission and assignments are: o Develop the technological and scientific foundations for the sector-specific public policies. o Promote research, experimentation, and demonstration activities in the areas of agroforestry, crop protection, food production, animal and plant health, food safety, as well as in the area of food technologies and biotechnology with application in these areas, in accordance with the public policies defined for the respective sectors, which ensure technical and scientific support leading to development and innovation and improvement of competitiveness. o Ensure that the National Reference Laboratory performs its duties, particularly those related to food safety, animal health, and plant health. o Cooperate with similar scientific and technological institutions, whether domestic or foreign, take part in science and technology activities, such as consortia, networks, and other forms of collaborative work, and promote knowledge transfer with domestic and international public and private entities, particularly through the conclusion of cooperation agreements and protocols, all without compromising the purview of the Ministry of Foreign Affairs. o Participate in the creation of official control plans for food safety, plant health, and animal health. o Ensure that laboratory analyses are completed within the parameters of official control plans coordinated by the Ministry of Agriculture, specifically through the networking of existing accredited laboratories, in the areas of its purview. 4 1.2. Apple Apples ( Malus domestica ), a member of the rosaceae family, are found all over the world, but are particularly common in the northern hemisphere, since they are native from Central Asia.3 There are over 7500 different cultivars, each with an unique set of characteristics comprising size, color, firmness, shape, texture, flavor (including sweet, sour, and bitter sensations), juiciness, aroma, and nutritional value.4 Only a small portion of these cultivars are produced and sold worldwide, with particular high shares of production in the Asian continent (Figure 1.2).5 In 2020, almost 47% of the 86 million tons of apple produced worldwide, where produced in China alone, with the United States and Turkey coming in second and third with around 5% of the world production each.6 In 2020, 286 thousand tons of apples were produced in Portugal, far from the peak of the 370 thousand tons that were produced in the year before (Table 1.1). Figure 1.2: Production share of apples by region, in 2020 6. 5 Table 1.1: Portugal national production of apples. Source: GPP 2021. Year Orchard Area (ha) Total Production (tons) 2012 12 903 220 761 2013 13 661 287 314 2014 13 847 273 721 2015 14 006 324 994 2016 14 159 254 321 2017 13 851 329 371 2018 13 612 263 961 2019 14 311 370 708 2020 14 313 286 075 Apple is the fresh fruit that, by far, presents a higher production in tons, therefore represents a significant sector in agricultural production in Portugal. In the last five years exports have been increasing, in such a way that the trade balance turned positive (representing a profit of 8 398 000€). (Table 1.2) Table 1.2: International trade balance regarding apple commerce (x 1000€). Source: GPP 2021 Year 2012 2013 2014 2015 2016 2017 2018 2019 2020 Expense 31 360 44 029 29 702 32 720 43 049 45 812 40 227 30 204 32 435 Sales 14 091 14 853 21 819 26 765 24 729 30 079 31 789 39 737 40 833 Balance -17 269 29 176 -7 883 -5 955 -18 321 -15 733 -8 438 9 533 8 398 The main suppliers to the domestic market are Spain, France, Chile, and Brazil. The majority of exports go to the European Union, with Spain standing out. Brazil and the United Kingdom also account for a sizable portion of exports (Source: GPP 2021). According to the Portuguese Food Composition database from the National Institute of Health Dr Ricardo Jorge, apples are mostly composed of water (83%, w/w), sugar (13.4%, w/w), dietary fiber (2.1%, w/w), and minerals, the most significant of which being potassium, phosphorus, sodium, and calcium. The lipid content is 0.5% (w/w), and the protein content is less than 0.3% (w/w) (Figure 1.3). (Source: INSA, 2022). Moreover, the energy value of an apple is 64 kcal (269 kJ) per 100g of edible weight (Source: INSA, 2022). 12 when considering only the peels. On the same study it was also reported that the level of catechin was between 0.8 and 3.6 mg per 100 g of fresh fruit and 2.0-5.4 mg in the peels.50 Figure 1.9: Structure of the epicatechin and catechin molecules (adapted from Del Rio et al. 51). 1.4.1.3. Flavones Flavones vary from other flavonoids regarding the flavonoid skeleton, which in this case it has a double bond between C2 and C3, there is no substitution at the C3 position, and the C4 position is oxidized. A growing body of evidence suggests that flavones including apigenin, luteolin, and luteolin-8-C-glucoside (Figure 1.10) can protect skin cells from UVB radiation.25,51 Flavones can also protect plants from parasites and fungal infections by acting as natural pesticides.52 Reports on flavone consumption in adults in Europe show results that ranges from 0.64 to 9.04 mg/day.53,54 Plant flavones are usually conjugated as 7-O-glycosides. The most prevalent flavone C-glycosides found are 6-C and 8-C-glucosides. 13 Figure 1.10: Structure of flavone aglycones and some derivatives (adapted from Hostetler et al 52). 1.4.1.4. Isoflavones Isoflavones may be found in a cultivar of plants, mainly in the roots and seeds. They're found in many different species of Fabaceae (Leguminosae) plants, including soybeans (Genus: Glycine), chickpeas (Genus: Cicer), lupine (Genus: Lupinus), fava beans (Genus: Vicia), Poaceae, barley (Genus: Hordeum), and of Brassicaceae, like broccoli (Genus: Brassica), and cauliflower (Genus: Brassica).55 Isoflavones have also been found in plants other than legumes, such as linseed and red clover.56 Soybeans and their products are among the richest and most prevalent sources of isoflavones, and their human health benefits have been extensively researched, leading to the discovery that they promote bone 57 and prostate health 58 as well as possess antibacterial activity.59 Daidzein and genistein are two of the main aglycones. They are the two most recognized compounds in this subclass, and they contribute significantly to the overall quantity of isoflavones 14 found in various food matrices, which may reach 126 mg/100 g of product in some fermented foods like miso (product obtained by fermenting soybeans).56 1.4.1.5. Flavanones In general, these molecules appear as flavonoid glycosides.60 In citrus fruits, the location of glycosylation is found at position 7 on the flavanone unit – these are known as citrus flavanones. The most common flavanone-7-O-glycosides are eriocitrin, naringin, naringenin, and hesperidin (Figure 1.11).61 In recent years several studies have shown that citrus flavanones may be useful in the treatment of diabetes 62 and that they can enhance the bioavailability of carotenoids 63 as well as possess antiviral and anti-tumoral activities.64 1.4.1.6. Anthocyanidins Anthocyanidins are a prominent subclass of flavonoids that might be exploited as potent inhibitors of α-glucosidase. Anthocyanidins have been found to be particularly plentiful in colored berries such as blueberries and blackcurrants. As a result, these brightly colored berry fruits may be rich in α-glucosidase inhibitory anthocyanidins.65 Figure 1.11: Structure of common flavanone-7-O-glycosides in fruits. 15 Anthocyanidin is generated by the hydrolysis of anthocyanin and has the same flavonoid structure as anthocyanin but lacks the ketone groups. As a result, it is anthocyanin in its aglycone form.66 The bulk (more than 65 percent) of anthocyanins are glycosidic anthocyanins, which include glycoside ligands and so reduce anthocyanin bioactivities such as antioxidant activity.67 Some of the most common anthocyanidins and anthocyanins are cyanidin, pelargonidin, delphinidin, petunidin, peonidin and malvidin (Figure 1.12). Figure 1.12: Structures of common anthocyanidins and anthocyanins.69 1.4.2. Phenolic Acids Phenolic acids are phenolic compounds that have a carboxylic acid group in their structure and are divided in two major subclasses, hydroxycinnamic (C6-C3 structure) and hydroxybenzoic (C6-C1 structure) acids (Figure 1.13), because of their two different carbon structures, as well as the number and location of hydroxyl groups on the aromatic ring.68 They are the most prominent class of phenolic compounds, responsible for giving food their organoleptic properties, including flavor, color, harshness and astringency.69 Figure 1.13: Basic skeleton structure of phenolic acids (adapted from Gutiérrez-Grijalva et al.69) 16 1.4.2.1. Hydroxycinnamic acids Many vegetables and fruits contain hydroxycinnamic acids, a group of naturally occurring phenylpropenoic acid compounds that are derivatives of cinnamic acid. These include caffeic acid, chlorogenic acid, coumaric acid, sinapic acid, and ferulic acid (Figure 1.14).70 They have a wide variety of medicinal effects, such as anti-inflammatory, antioxidant, neuroprotective, and antiamyloid aggregation, which are relevant to the treatment of conditions like Alzheimer's disease.71 Compared to hydroxybenzoic acids, hydroxycinnamic acids are more common in nature and typically exist in a cultivar of conjugated forms. Clifford 72 observed that people that consume a small amount of fruits and vegetables will ingest 25 mg of hydroxycinnamic acids per day, but people who are regular coffee consumers will have a daily intake of 500-800 mg (mostly chlorogenic and caffeic acid). In the work of Pérez-Jiménez et al. 73 the registered daily intake of hydroxycinnamic acids (600 mg/day) was significantly higher than that of hydroxybenzoic acids (41 mg/day). 1.4.2.2. Hydroxybenzoic acids Hydroxybenzoic acids can be found in two different forms: bound to the components of cells or conjugated (soluble form) with sugars or organic acids. Red fruits, onions, and black radish are a few exceptions that have slightly higher levels of the hydroxybenzoic acid than other edible plants. These typically accumulate very low levels of this compound.69 Gallic, ellagic, gentisic, protocatechuic, syringic, salicylic, and vanillic acids, as well as 3-hydroxybenzoic and 4Figure 1.14: Chemical structure of ferulic acid.72 17 hydroxybenzoic acids are the hydroxybenzoic acid derivatives (Figure 1.15) that are primarily found in fruits and vegetables. 74,75 Recently, health benefits have been found to be related to derivatives of hydroxybenzoic acid, like anti-cancer properties in gallic acid 76 and ellagic acid 77 and hepatoprotective, cardioprotective, anti-apoptotic, and antiproliferative properties in vanillic acid.78 In the study reported by Pérez-Jiménez et al. 73 , the intake of hydroxybenzoic acids of 4942 participants was found to be 41 ± 39 mg/day. 1.4.3. Stilbenes The polyphenolic substances known as stilbenes (Figure 1.16), which have a C6-C2-C6 structure, are produced by the secondary metabolism of plants. They are made up of a trans or cis -ethene double bond that has had phenyls substituted on both of its carbon atoms.79 They are highly regarded for their antimicrobial qualities in particular,80 but also for their anti-fungal,81,82 anticancer, anti-inflammatory and antioxidant properties. Figure 1.15: Structure of some common hydroxybenzoic acid derivatives. Figure 1.16: Basic structure of stilbenes (adapted from Pavan et al. 85). 18 Stilbenes and their derivatives, stilbenoids - natural, biologically active substances, form a multidisciplinary field that combines many important branches of chemistry. It is possible to find this group of compounds in nature (Figure 1.17). Both monomers and progressively more complex oligomers of stilbenoids exist. The trans isomer of the monomeric stilbene aglycone is the most prevalent configuration, and its fairly simple skeleton consists of two aromatic rings connected by an ethylene bridge. The majority of stilbenes that are found in nature have multiple phenol groups.83 Fruits like grapes and blueberries are their principal food sources.79 Figure 1.17: General skeleton of common stilbenoids (Adapted from Rivière et al.86) 1.4.4. Lignans Lignans are bioactive polyphenolic compounds with highly complex structures that are produced when two coniferyl alcohol residues combine. They possess neuroprotective84, antiviral 85 and antioxidant 86 properties. They can be broadly divided into plant lignans and mammalian lignans depending on where they originated from.87 While mammalian lignans have hydroxyl groups in -meta position, plant lignans primarily have oxygenated substituents in -para positions. Some plant lignans found in many edible plants are transformed into mammalian lignans like enterodiol and enterolactone (Figure 1.18) by intestinal microbiota.88 On the basis of their structural characteristics, such as their carbon skeletons, the manner in which oxygen is incorporated into the skeletons, and the (E)-Stilbene Z)-Stilbene 2-arylbenzofuran bis(bibenzyl) type A phenanthrene 9,10-dihydrophenanthrene Dihydrostilbene or bibenzyl 19 pattern of cyclization, lignans are divided into eight classes, arylnaphthalene, aryltetralin, dibenzocyclooctadiene, dibenzylbutane, dibenzylbutyrolactol, dibenzylbutyrolactone, furan, and furofuran. 89 Figure 1.18: Metabolism of plant lignans to mammalian lignans.91 The majority of plants that are high in fiber have lignans in their composition, including sesame seeds, pumpkin seeds, grains like rye, barley, and wheat, legumes like lentils and beans, and vegetables like broccoli, asparagus, garlic, and carrots. Foods often have modest lignan levels, usually less than 2 mg/100g. Sesame seeds and flaxseed are the exceptions since they have lignan contents that are several times higher than those of other food sources. 90 1.5. Methods for the quantification of bioactive compounds Due to the wide cultivar of bioactive chemicals found throughout plant tissues and the cultivar of their chemical structures, numerous analytical procedures for their identification and quantification have had to be developed. The earliest methods created were spectrophotometric methods, which are particularly interesting from a quality control perspective since they offer quick, efficient, and affordable solutions. It has become required to adopt more precise techniques, like chromatographic techniques, which allow the individual identification of each of the bioactive substances of nutritional relevance, because spectrophotometric techniques, despite having these advantages, have some limitations. 20 1.5.1. Spectrophotometric methods For the purpose of determining the amount of bioactive chemicals present in plant matrices, numerous spectrophotometric techniques have been developed. These techniques enable the quantification of a certain group of bioactive substances , such as the total content of phenolic compounds,91 as well as the quantification of a single bioactive substance's quantity, such as βcarotene 92 or two at the same time, like β-carotene and lycopene,93 in different food matrices. Despite the cultivar of spectrophotometric techniques currently available, they have some drawbacks. When complex samples are present, certain matrix elements may interfere with the analysis and cause the analysis to overestimate the amount of determined substances. Reduced specificity of the methods is therefore a significant limitation.94,95 1.5.1.1. DPPH free radical inhibition assay The DPPH assay is a well-known technique that is commonly used since it is straightforward, inexpensive, and just uses a basic spectrophotometer. There are a few standards that can be used to assess the antioxidant capacity in foods, plant extracts, and drinks, including ascorbic acid, gallic acid, and Trolox.96,97 In the DPPH free radical inhibition assay, the addition of the extract, that has antioxidants, reduces an unpaired valence electron of the N (nitrogen) atom in DPPH, by giving a hydrogen atom, causing the formation of DPPH-H and a change of color from pale purple to a yellow-colored product, in a concentration-dependent way (Figure 1.19).98 Antioxidants' ability to reduce can thus be assessed by monitoring the absorbance's decline, on a wavelength of 515nm. Figure 1.19: Reduction of DPPH in the DPPH free radical inhibition assay.101 21 1.5.1.2. Total content of phenolic compounds assay Quantification of phenolic compounds can be accomplished using a number of methods, such as the Folin-Ciocalteu assay and CuO oxidation-GC and a high-performance liquid chromatography (HPLC) method.99 In this work, we opted for the Folin-Ciocalteu, because when compared to the CuO oxidation-GC and the HPLC procedures, this test is more affordable and less complicated. This method relies on the Folin-Ciocalteu reagent's (composed of oxides of tungsten and molybdenum). In the fully oxidized 6+ valence state of the metal, the isopolyphosphotungstates are colorless while the molybdenum compounds are yellow.100 They exist in an acidic solution and combine to form mixed heteropolyphosphotungstates-molybdates. Reversible one or two electron sequential reductions lead to blue species 101 (Figure 1.20), due to the addition of an electron to a nonbonding orbital. Figure 1.20: Reaction between polyphenols and the Folin Ciocalteu reagent.102 1.5.1.3. Total content of flavonoids assay The total flavonoids content (TFC) in plants is typically determined calorimetrically. One of the most common methods for determining TFC in plant and fruit extracts is the aluminum chloride colorimetric assay, in which Al(III) is used as a complexing agent (Figure 1.21), forming a complex with the hydroxyl group of flavonoids.103 The total flavonoids content can be accessed with the aid 28 The packed columns are made of signalized glass and packed with solid particles coated with the liquid that makes up the mobile phase.127 The analyte's polarity, molecular weight, solubility, volatility, and quantity in the sample should all be taken into account while selecting the column. The higher the molecular weight and polarity of the substance, the lower its volatility and consequently the greater the difficulty to analyze.124,128 A detector is also part of a chromatograph. This instrument amplifies the electrical signal, identifies, and quantifies the components separated by the column; the analyte to be studied should be taken into consideration when choosing this instrument. There are various detector types, with the following being the most popular in gas chromatography: • Flame-ionization detector (FID)129 • Electron capture detector (ECD)130 • Thermal conductivity detector (TCD)131 • Mass spectrometry detector (MS)132 Essentially, there are two considerations in choosing a detector. The two aspects are sensitivity, i.e., the signal the detector is capable of creating, and the noise, which describes the detector's instability. The sensitivity decreases as noise level increases. 1.5.3. Extraction techniques Due to the complexity of the matrix, its incompatibility with chromatographic systems, and the fact that many of the substances to be studied are in trace amounts, chromatographic analysis of food samples frequently requires previous treatment of the sample 133. There are numerous sample pretreatment methods available today for the extraction of bioactive substances from food matrices. The most significant methods that have been frequently employed for the extraction of phenolic compounds from various food matrices are solid phase extraction (SPE)134,135, QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe)136,137 and solid-liquid extraction (SLE).138 29 1.5.3.1. Solid-Liquid Extraction (SLE) The most popular analytical method for preparing solid samples is known as solid-liquid extraction (SLE), which involves separating the analytes of interest from the other compounds in the matrix. Three key mechanisms control the SLE process: the penetration of the extractant into the solid matrix, the diffusivity of the analytes to the outer space and the solubility of the analytes in the extractant.139 SLE has historically been characterized by a lack of quantitative efficiency, and several measures have been taken to improve its functionality. The use of high temperature with high pressure and the aid with auxiliary energies, particularly microwaves and ultrasound, stand out as the most effective methods for improving SLE quantitatively. These approaches have given rise to sample preparation methods like superheated solvent extraction, microwaves-assisted extraction, and ultrasound-assisted extraction. These methods enable improved performance in addition to shorter extraction times, automation of the procedure, and less consumption of organic solvents. 1.5.3.2. Solid Phase MicroExtraction (SPME) SPME is a very straightforward sorptive extraction-based sample extraction and preconcentration technique.140 Currently, this method is frequently used to examine the volatile profile of various meals and drinks, including wine,141,142 and fruits.143,144 Generally, analytes present in aqueous matrices are extracted by being absorbed or adsorbed onto a thin fused silica fiber covered with a polymeric layer,145 which is housed in a syringe-shaped apparatus (Figure 1.25). Figure 1.25: SPME device. 30 SPME can be carried out using direct immersion (DI-SPME), headspace (HS-SPME), or with the aid of a membrane (M-SPME) (Figure 1.26), depending on the type of analytes being investigated and the complexity of the sample. HS-SPME exposes the fiber to the vapor phase above the sample, whereas DI-SPME places the fiber in direct contact with the sample. Membrane extraction is comparable to DI-SPME with the sole exception that the fiber is shielded by a semipermeable membrane. Figure 1.26: Schematic representation of SPME modes: (A) direct immersion, (B) headspace, (C) membrane assisted (Adapted from Pawliszyn151). Since HS-SPME is more selective than direct immersion and, avoiding additional interferences such as high molecular weight molecules that may be present in the matrix, this extraction mode is the most frequently employed. 1.6. Fructose Fructose is a ketonic sugar present, in large quantities, in the juices of plants and fruits, where it frequently forms the disaccharide sucrose when linked to glucose. It is one of the three dietary monosaccharides that are directly absorbed into the blood during digestion, along with galactose and glucose. However, it does not enter the bloodstream in large amounts because it is primarily converted into glycogen or triglycerides once it reaches the liver. 146 Sample headspace Fiber Membrane A Sample Coating Coating Sample B C 31 Figure 1.27: Relative sweetness (%) of natural sugars and sweeteners. Sucrose is reference and is set at 100 (Adapted from Basso et al.153) One of the main assets of fructose is its sweetening power. In a study developed by Basso et al. 147 (Figure 1.27), it was stated that all the sweeteners tested had a relative sweetness lower than sucrose, and therefore are less sweet than sucrose. On the other hand, fructose proved to be almost twice as sweet as sucrose. However, it is well established that this is only true, when the 6membered ring form of fructose is present, because when heated, this ring forms a 5-membered ring form and gives only as much sweetening power as regular sucrose.148 Some of the biggest natural sources of fructose are fruits, particularly, grapes149 and apples,150 and vegetables.151 It is also abundantly used in the process of making high-fructose corn syrup (HFCS), a sweetener produced with corn syrup that is commonly used in the beverage and in cereals and processed foods industries.152 1.6.1. Total content of fructose assay There are a variety of methods to determine the total fructose content in food matrixes, such as the one developed by Moreira et al., 153 in which a electrosynthesized molecularly imprinted polymer is used, and also the method reported by Rongtong et al., 154 using near-infrared spectroscopy to determine different constituents of food products, like fructose. A more established colorimetric method is the one developed by Ashwell, 155 based on the principle that the 0 20 40 60 80 100 120 140 160 180 200 Relative Sweetness (%) 32 hydroxymethyl furfural formed from fructose in acid medium156 (Figure 1.28) reacts with resorcinol to give a red color product. Figure 1.28: Hydroxymethyl furfural formation, from fructose.156 1.7. Multivariate data analysis (Chemometrics) Chemometrics is described by some people as “the chemical discipline that uses mathematical, statistical, and other methods employing formal logic to design or select optimal measurement procedures and experiments, and to provide maximum relevant chemical information by analyzing chemical data”.157 Despite the broad definition of chemometrics, it is obvious that the use of multivariate data analysis in data obtained from analytical chemical procedures is its most useful instrument. The multivariate data analysis has gained recognition as a potent method for structuring and analyzing data sets in the fields of chemistry and biochemistry.157 In these fields of research, fundamentally in analytical chemistry, and food science, the information retrieved from instrumental measurements is highly complex and frequently made up of thousands of variables for each sample. Multivariate data analysis is required to understand/solve the information in the data due to its complexity. Both quantitative and qualitative analysis can be used to assess the experimental data. There are two types of qualitative analytic methods: supervised learning and unsupervised learning.158 Principal component analysis (PCA) fits under the unsupervised learning category. 33 Figure 1.29: Multivariate data analysis methods used in the evaluation of Agro-Food products. MLR: Multiple Linear Regression, PCR: Principle Component Regression, PLSR: Partial Least Square Regression, ANN: Artificial Neural Network, SVM: Support Vector Machine, Multivariate data analysis methods used in the evaluation of Agro-Food products. MLR: Multiple Linear Regression, PCR: Principle Component Regression, PLSR: Partial Least Square Regression, ANN: Artificial Neural Network, SVM: Support Vector Machine, LDA: Linear Discriminant Analysis, PLSDA: Partial Least Squares–Discriminant Analysis, kNN: kNearest Neighbor, PCA: Principal Component Analysis.159 1.7.1. Principal component analysis (PCA) Principal component analysis is a statistical tool that allows to reduce the dimensionality of the data which contain a set of variables using linear combinations. It is used with sample results to demonstrate how and how much the examined variables affect the range of the measured values.160 PCA is a tool that is used to decrease redundancy and the number of variables used in the system observation, by establishing a new database whose components are linearly independent of the primary components indicated by the first set of variables. These new components are then ordered so that they represent most of the original variance.161 1.8. Pesticides The use of pesticides is still a reality and, in fact, is essential to prevent food loss even while efforts to decrease or find alternatives are rapidly developing. The use of pesticides, however, also has negative impacts on the environment for food consumers. As a result, it is crucial to manage pesticide residues in food, and in the European Union, this is supported by regulation to safeguard public safety as well as domestic and international trade. 34 Effective sample preparation and trace-level detection and identification are vital components of analytical methods due to the low detection limits demanded by regulatory bodies and the complex structure of food matrices in which the target compounds are contained. 35 2. Objectives The main objectives of this research study are: • Determination of antioxidant properties of both by-products (peels and seeds) and edible part (mesocarp) of five different regional apples (Pêro de Borbelo, Pardo Lindo, Repinau, Pêro Coimbra and Noiva) and six regional pears (Bela-Feia, Torres Novas, Carapinheira, Carapinheira Roxa, Lambe-os-Dedos e Amorim), in two consecutive harvest years (2021 and 2022) in order to characterize Portuguese cultivars of these fruits concerning their bioactives’ composition, for which there are scarce data, and to contribute for conservation of fruit biodiversity and consumption of diverse diets; • Determination of antioxidant properties of 22 commercial cultivars of apples produced in the same region (Alcobaça, Portugal) in order to compare them with regional cultivars; • Evaluate fructose content of both regional and commercial cultivars of apples and pears to conclude about their potential to substitute sugar in food formulations; • Optimization and validation of an analytical method for the determination of 23 individual phenolic compounds in fruits by ultra-high performance liquid chromatography coupled to time of flight mass spectrometry (UHPLC-ToF-MS), according to international guidelines. • Application of the UHPLC-ToF-MS analytical method to selected pear and apple samples. • Carry out a PCA in order to evaluate the relationship between the apple and pear samples and their chemical properties. • Evaluation of the pesticide residues content in apple and pear cultivars targeted for study and to conclude about their safety. • Discussion of the results about the potential of the different apple and pear cultivars to be used as source of natural antioxidants or as a source of fructose. 36 3. Experimental Part This section describes the materials, equipment and reagents used, the characterization of the samples, as well as all the experimental procedures performed throughout this work. The experimental procedures were carried out mostly in National Institute of Agrarian and Veterinary Research - Polo de Vairão (Vila do Conde, Portugal), and in University of Minho (Braga, Portugal) (pesticide analysis). 3.1. Reagents The reagents used and their respective characteristics are described in Table 3.1. Note that all reagents are of analytical grade and the ultrapure MilliQ® water was obtained from a MilliQ® Direct Water Purification System. Table 3.1: Reagents and their characteristics used for the determination of the antioxidant properties and the quantification of fructose in apple and pear samples. Reagents Chemical formula State of matter Purity level (%) Brand Ethanol C2H6O Liquid 96.0 Honeywell Trolox C14H18O4 Solid 97.0 Sigma-Aldrich DPPH C18H12N5O6 Solid 95.0 Sigma-Aldrich Methanol CH4O Liquid 99.9 Honeywell Folin-Cioucalteu reagent C6H6O Liquid - Sigma-Aldrich Sodium carbonate Na2CO3 Solid 99.5 Sigma-Aldrich Epicatechin C15H14O6 Solid 90.0 Sigma-Aldrich Sodium nitrite NaNO2 Solid 95.0 Supelco Aluminum chloride AlCl3 Solid 98.0 Sigma-Aldrich Sodium hydroxide NaOH Solid 97.0 Sigma-Aldrich β-Carotene C40H56 Solid 93.0 Sigma-Aldrich Chloroform CHCl3 Liquid 99.5 Sigma-Aldrich Tween® 40 C62H122O26 Liquid - Sigma-Aldrich Linoleic acid C18H32O2 Liquid 98.0 Sigma-Aldrich Fructose C6H12O6 Solid 99.0 Sigma-Aldrich Resorcinol C6H6O2 Solid 99.0 Sigma-Aldrich Thiourea CH4N2S Solid 99.0 Sigma-Aldrich 37 Reagents Chemical formula State of matter Purity level (%) Brand Acetic acid C2H4O2 Liquid 99.7 Honeywell Hydrochloric acid HCl Liquid 37.0 Honeywell o-Coumaric acid C9H8O3 Solid 97.0 Sigma-Aldrich Chlorogenic acid C16H18O9 Solid 95,0 Sigma-Aldrich Hyperoside C21H20O12 Solid 99.0 Sigma-Aldrich p-Coumaric acid C9H8O3 Solid 98.0 Sigma-Aldrich Caffeic acid C9H8O4 Solid 98.0 Sigma-Aldrich Rutin Hydrate C27H30O16 . xH2O Solid 94.0 Sigma-Aldrich Trans-ferulic acid C10H10O4 Solid 99.0 Sigma-Aldrich Luteolin C15H10O6 Solid 97.0 Supelco Quercetin 3-β-D-glucoside C21H20O12 Solid 90.0 Sigma-Aldrich Quercetin C15H10O7 Solid 95.0 Sigma-Aldrich Gentisic acid C7H6O4 Solid 98.0 Sigma-Aldrich Sinapic acid C11H12O5 Solid 98.0 Sigma-Aldrich Apigenin C15H10O5 Solid 99.0 Supelco 4-Hydroxybenzoic acid C7H6O3 Solid 99.0 Sigma-Aldrich Syringic acid C9H10O5 Solid 95.0 Sigma-Aldrich (−)-Epicatechin C15H14O6 Solid 98.0 Sigma-Aldrich Vanillic acid C8H8O4 Solid 97.0 Sigma-Aldrich (+)-Catechin C15H14O6 Solid 99.0 Supelco Phloridzin C21H24O10 Solid 98.0 Sigma-Aldrich Quercitrin C21H20O11 Solid 99.0 Sigma-Aldrich (±)-Naringenin C15H12O5 Solid 95.0 Supelco Eriodictyol C15H12O6 Solid 95.0 Sigma-Aldrich 3.2. Materials In order to successfully perform the analytical procedure, certain materials were used that were properly calibrated according to the internal laboratory protocols and checked whenever used (Table 3.2). 44 3.4.1.13. Sodium nitrite solution (50 mg/mL) The sodium carbonate solution was prepared by using a precision scale to weight 0.5 g of sodium nitrite into a 10 mL volumetric flask and making up the volume with ultrapure MilliQ® water. 3.4.1.14. Aluminum chloride solution (100 mg/mL) The aluminum chloride solution was prepared by using a precision scale to weight 2.0 g of aluminum chloride into a 20 mL volumetric flask and making up the volume with ultrapure MilliQ® water. The preparation of this solution should be dealt carefully, because this is an exothermic reaction. 3.4.1.15. Sodium hydroxide solution (40 mg/mL) The sodium hydroxide solution was prepared by using a precision scale to weight 2.0 g of sodium hydroxide into a 50 mL volumetric flask and making up the volume with ultrapure MilliQ® water. 3.4.1.16. β-carotene solution (0.2 mg/mL) The β-carotene solution was prepared by using a precision scale to weight 1.0 mg of βcarotene into a 5 mL volumetric flask and making up the volume with chloroform. This solution must be prepared and used on the day of performing the assay. 3.4.1.17. β-carotene emulsion The β-carotene emulsion was prepared by using a precision scale to weight 20 mg of linoleic acid, 200 mg of Tween®40 and adding 2 mL of the β-carotene solution into a 250 mL volumetric flask. This mixture was evaporated on a rotary evaporator at 40 °C. Then, 100 mL of ultrapure MilliQ® water, that was agitated for 30 minutes to incorporate oxygen, were added, and vigorously agitated, until an emulsion was formed. 45 3.4.1.18. Resorcinol reagent The resorcinol reagent was prepared by using a precision scale to weight 1.0 g of resorcinol and 250 mg of thiourea into a 100 mL volumetric flask and making up the volume with glacial acetic acid. This solution must be prepared and used on the day of performing the assay. 3.4.1.19. Dilute hydrochloric acid solution The diluted HCl solution was prepared in a 250 mL volumetric flask, using a beaker to measure 50 mL of concentrated HCl and making up the volume with ultrapure MilliQ® water. 3.4.1.20. Phenolic compounds standard solutions Standard solutions of 23 phenolic compounds were prepared by using a precision scale to weight 2.5, 5.0 or 10.0 of the 23 phenolic compounds, into a 5or 10-mL volumetric flask and making up the volume with ethanol. The different concentrations of the stock solutions can be seen in Table 3.8. Table 3.8: Preparation of the different phenolic compounds’ standards. Standard Stock solution (mg/mL) Working solution (µg/mL) o-Coumaric acid 1.0 100 Chlorogenic acid 1.0 100 p-Coumaric acid 1.0 100 Caffeic acid 1.0 100 Rutin Hydrate 1.0 100 Trans-ferulic acid 1.0 100 Luteolin 0.5 50 Quercetin 3-β-D-glucoside 0.5 50 Quercetin 1.0 100 46 Standard Stock solution (mg/mL) Working solution (µg/mL) Gentisic acid 1.0 100 Sinapic acid 1.0 100 Apigenin 0.5 50 4-Hydroxybenzoic acid 1.0 100 Syringic acid 1.0 100 (−)-Epicatechin 0.5 50 Vanillic acid 1.0 100 (+)-Catechin 0.5 50 Phloridzin 1.0 100 Quercitrin 1.0 100 (±)-Naringenin 1.0 100 Eriodictyol 0.5 50 The standard solutions were stored at -24 °C for up to three months. 3.4.1.21. Phenolic compounds standard mixture solution After obtaining the standard solutions of the different phenolic compounds, a volume of 0.625 or 1.25 mL was transferred from each of the solutions into a 25 mL volumetric flask and the volume was prefixed with methanol. This mixture has a concentration of 2.5 µg/mL for each one of the phenolic compounds. This solution can be stored at -24 °C in dark conditions for up to three months. 3.4.2. Preparation of the samples In this study 11 samples of regional fruit cultivars were used, from the Alcobaça campus of INIAV, in which 5 were apple regional cultivars (Noiva, Repineau, Pêro Coimbra, Pardo Lindo and Pêro de Borbelo) (Figure 3.1) and 6 were pear regional cultivars (Carapinheira, Bela-Feia, Torres Novas, Carapinheira Roxa, Lambe-os-Dedos and Amorim) (Figure 3.2). 47 Noiva Repinau Pêro Coimbra Pardo Lindo Pêro de Borbelo Carapinheira Bela-Feia Torres Novas CarapinheiraRoxa Lambe-osDedos Amori The samples were collected between July and November 2021 and July and September 2022. The sampling technique consisted of randomly collecting 10 to 15 ripe fruits of each cultivar to ensure that the harvest was representative. The samples were stored in the absence of light and sent to the Innovation Campus of Vairão, INIAV, I.P. The samples were separated in 3 portions: peels, mesocarp and seeds. The 3 portions were homogenized with the help of a homogenizer and frozen at <-20°C. 3.4.2.1. For the determination of the antioxidant properties For each sample, 2.0 g were weighted before freezing into 50 mL Falcon tubes. Then, on the day of the assays for the determination of the antioxidant properties, 20 mL of solvent (ethanol) was added, and the solution was mixed on an Ultra-TURRAX homogenizer for 3 minutes. The samples were then centrifuged at 3600 rpm for 10 minutes at room temperature, leading to a good separation of the two phases. The supernatant was isolated and stored in the absence of light and in a low temperature environment (Figure 3.3). Figure 3.1: Regional apple cultivars. Figure 3.2: Pear regional cultivars. 48 3.4.2.2. For the determination of the fructose content First, the extracts that had been previously prepared for the study of antioxidant capacity and total content of phenolic compounds and total flavonoids were used to evaluate the fructose content. However, in order to study the influence of the solvent (ethanol) on the determination of the fructose content, some extracts of the fruit mesocarp were also prepared in ultrapure water. 3.4.3. Experimental procedure Comparative quantification of total phenolic compounds content and total flavonoids content was performed on the edible part and the by-products of the different samples. Furthermore, the antioxidant capacity of the extracts was also measured by DPPH radical and βcarotene bleaching methods. Also, the total fructose content was analyzed in these matrices. 3.4.3.1. DPPH free radical inhibition assay This method was carried out as described by Moure et al. 162 and modified by Andrade et al. 163 using Trolox as the standard. To determine the antioxidant capacity of the ethanolic extracts through the DPPH free radical inhibition assay, 50 µL of sample are mixed with 2 mL of the DPPH radical solution. Next, this mixture is kept in the dark for 30 minutes and read in a spectrophotometer at 515 nm (Figure 3.4). Figure 3.3: Separation process of the fruits’ portions/parts to be analyzed. 49 3.4.3.2. β-carotene bleaching assay This method was carried out as described by Miller.164 In the β-carotene bleaching method, 200 µL of ethanol were mixed with 5 mL of the β-carotene emulsion to prepare the "blank" assay and this was read in a spectrophotometer at 470 nm. After this, 200 µL of sample were also mixed with 5 mL of the emulsion and together with the blank were kept in a water bath at 55 °C for 2 hours. At the end of this period, both samples and the blank were read in a spectrophotometer at 470 nm (Figure 3.5). The Antioxidant Activity Coefficient (AAC) was calculated resorting to the following equation: 𝐴𝐴𝐶 =(𝐴 𝑠𝑎𝑚𝑝𝑙𝑒− 𝐴2 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝐴0 𝑐𝑜𝑛𝑡𝑟𝑜𝑙− 𝐴2 𝑐𝑜𝑛𝑡𝑟𝑜𝑙)×1000 Where 𝐴0 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 corresponds to the absorbance of the control at the initial time and 𝐴2 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 is the absorbance if the control after 2 hours at 50◦C and 𝐴 𝑠𝑎𝑚𝑝𝑙𝑒 is the absorbance of the sample, also after being submitted to 50◦C in a water bath for 2 hours. The solutions' absorbance was read at 470 nm. Figure 3.4: DPPH free radical inhibition system. (Source: Personal collection) 50 3.4.3.3. Total phenolics content (TPC) assay This method was carried out as described by Erkan et al. 165 using gallic acid as the standard. In this method, 1 mL of sample is mixed with 7.5 mL of the Folin-Cioucalteu solution and after 5 minutes, 7.5 mL of the sodium carbonate solution are added. After 2 hours, the samples are read in a spectrophotometer at 725 nm (Figure 3.6). 3.4.3.4. Total flavonoids content (TFC) assay This method was carried out as described by Barbosa et al. 166 using epicatechin as the standard. In this study, 1 mL of sample was mixed with 4 mL of ultrapure water and 0.3 mL of sodium nitrite. After 5 minutes, 0.6 mL of aluminum chloride was added to the mixture and after Figure 3.5: β -carotene bleaching assay. (Source: Personal collection) Figure 3.6: Determination of the total content of phenolic compounds (Source: Personal collection). 51 another 6 minutes, 2 mL of sodium hydroxide and 2.1 mL of ultrapure MilliQ® water were added. The samples were then read in a spectrophotometer at 510 nm (Figure 3.7). 3.4.3.5. Total content of fructose assay In this study total content of fructose was determined according to Ashwell,155 using fructose as the standard. Briefly, 2 mL of sample were mixed with 1 mL of the resorcinol reagent. After that, 7 mL of the hydrochloric acid solution were added to the mixture. The samples mixtures were kept in a water bath at 80 °C for 10 minutes. At the end of this period, all samples were cooled under tap water for 5 minutes and read in a spectrophotometer at 520 nm (Figure 3.8). Figure 3.8: Determination of the total fructose content. (Source: Personal collection) Figure 3.7: Determination of the total content of flavonoids (Source: Personal collection). 52 3.5. Determination of Phenolic Compounds by UHPLC-ToF A solid-liquid extraction methodology was used to extract phenolics from samples. Optimization of the extraction procedure led to the final extraction method, described in Figure 3.9. Two different extraction solvents were tested: methanol (MeOH) and MeOH:H2O:Formic acid (49.95:49.95:0.10 v/v/v). It should be noted that the optimization was carried out using an apple sample (Pardo Lindo mesocarp) that had been fortified with the standard solution mixture of phenolic compounds (1 and 10 g/kg). Detection and quantification were performed with a Nexera X2 Shimadzu UHPLC coupled with a 5600+ ToF-MS detector (SCIEX, Foster City, CA, USA) equipped with a Turbo Ion Spray electrospray ionization source working in positive mode (ESI+). The phenolic compounds were separated using an Acquity UPLC BEH C18 (2.1 mm × 100 mm, 1.7 μm) analytical column. The column temperature was maintained at 85ºC and the autosampler was programmed to a temperature of 20ºC. The chromatographic separation took place in gradient mode using an aqueous solution of 0.1% formic acid (eluent A) and methanol with 0.1% formic acid (eluent B) as the mobile phase. The injection volume for both standards and samples was adjusted to 20 µL. Figure 3.9: Extraction procedure for the determination of phenolic compounds by UHPLC-ToF.(Source: Personal collection) 53 Table 3.9: Mobile phase gradient used for the separation of phenolic compounds. Time (min) Mobile Phase A (%) Mobile Phase B (%) 0.01 90 10 0.50 90 10 8.00 20 80 Using the Analyst® TF software (SCIEX, Foster City, CA, USA) and the following parameters for mass spectrometry, the acquisition was carried out in full scan from 100 to 750 Da: ion source voltage of 5500 V; source temperature of 575 C; curtain gas (CUR) of 30 psi; Gas 1 and Gas 2 of 55 psi; and declustering potential (DP) of 100 V. To provide accurate mass resolution, the ToF-MS detector was calibrated every 7 injections in the method's mass range. PeakView™ and MultiQuant™ software (SCIEX, Foster City, CA, USA) were used for phenolic compound identification and data processing. PeakView™ software automatically presents the isotope match. Two parameters and their accompanying equations (Equations (1) and (2)) were employed for phenolic compound identification: (1) maximum retention time deviation (∆RRT) of 0.1 min (Equation (1)); and (2) exact mass deviation (m) with a tolerance of 5 ppm (Equation (2)). 𝑅𝑇=(𝑅𝑇spiked samples −𝑅𝑇standard 𝑅𝑇standard )×100 Equation (1) ∆𝑚 (𝑝𝑝𝑚)=(𝐸𝑥𝑎𝑐𝑡 𝑚𝑎𝑠𝑠− 𝐷𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝑚𝑎𝑠𝑠 𝐸𝑥𝑎𝑐𝑡 𝑚𝑎𝑠𝑠 )×106 Equation (2) 3.6. Determination of pesticides by GC-MS Analysis of pesticides in apple and pear pulps was performed on a 450-GC gas chromatograph from Varian coupled to a 4000 Performance ion-trap mass spectrometer also from Varian. 60 Table 4.6: DPPH free radical inhibition assay results on the commercial apple cultivars. Sample/Cultivar Portion %RSA Concentration (µg TE/g) Galafab Peels 72.2 892.5 Seeds 16.8 236.8 Mesocarp 10.9 167.6 Granny Smith Peels 37.0 476.2 Seeds 22.3 301.9 Mesocarp 6.52 115.7 Dalinette Peels 42.7 544.0 Seeds 16.9 238.1 Mesocarp 7.76 130.4 Venus Fengal Peels 51.3 645.1 Seeds 19.7 271.3 Mesocarp 9.45 150.3 Rubelite Peels 46.0 582.6 Seeds 5.51 103.8 Mesocarp 0.78 47.88 Rubin Fuji Peels 29.1 383.1 Seeds 19.7 271.3 Mesocarp 2.02 62.51 Story Inored Peels 62.1 772.8 Seeds 19.2 266.0 Mesocarp 6.97 121.0 Evelina Peels 38.6 494.6 Seeds 7.77 130.5 Mesocarp 0.14 15.60 Pixie Peels 50.1 630.7 Seeds 8.70 141.1 Mesocarp 0.30 42.11 Schinga Schnico Peels 54.6 683.7 Seeds 13.3 195.9 Mesocarp 0.00 0.000 Redlum Peels 41.3 526.4 Seeds 13.5 197.6 Mesocarp 0.00 0.000 61 Sample/Cultivar Portion %RSA Concentration (µg TE/g) Fuji Aztec Peels 28.1 370.9 Seeds 8.82 142.9 Mesocarp 0.00 0.000 Bigbucks Peels 68.8 851.6 Seeds 18.7 259.5 Mesocarp 0.00 0.000 Candine Peels 39.8 508.7 Seeds 10.8 165.8 Mesocarp 0.00 0.000 Decarli Peels 42.4 539.7 Seeds 17.8 249.0 Mesocarp 2.69 70.37 Schnico Red Peels 25.5 339.9 Seeds 10.4 161.2 Mesocarp 2.31 65.82 Redfeu Peels 55.3 692.6 Seeds 15.4 220.3 Mesocarp 2.69 70.37 Brookfield Peels 39.8 509.4 Seeds 17.4 244.5 Mesocarp 1.54 56.74 Jonaprince Peels 25.0 333.8 Seeds 7.94 132.4 Mesocarp 3.07 74.91 Fuji Spur Peels 53.3 668.4 Seeds 13.3 196.0 Mesocarp 4.87 96.10 Fuji Fubrax Peels 29.2 383.8 Seeds 19.9 273.2 Mesocarp 3.84 83.99 Fengapi Peels 59.3 739.6 Seeds 17.0 239.9 Mesocarp 4.61 93.08 62 0 20 40 60 80 100 120 140 160 180 200 TE (µg TE/g) 0 50 100 150 200 250 300 350 TE (µg TE/g) 0 100 200 300 400 500 600 700 800 900 1000 TE (µg TE/g) Figure 4.2: Graphical representation of the DPPH free radical inhibition assay results in commercial apples. A – Results of the peels. B – Results of the seeds. C – Results of the mesocarps. 63 It is important to highlight that all the cultivars, commercial and regional were produced under the same conditions, during the same time period and on INIAV´s orchard fields at Alcobaça and therefore minimizing the influence of edaphoclimatic conditions. Comparing the commercial apple cultivars with their regional counterparts, we can observe that, in general, the commercial cultivars have a higher DPPH free radical inhibition percentage. The Noiva regional cultivar, however, surpasses 16 of the commercial cultivars, regarding the peels and 15 regarding the mesocarp, but only 9 when accounting for the seeds. The highest percentage of DPPH free radical inhibition was reached in the peels by the Galafab cultivar, with a 72.2 inhibition percentage (corresponding to 892.48 µg TE/g of fresh fruit) and in the seeds by the Granny Smith cultivar, with 22.3% (301.93 µg TE/g of fresh fruit). It is also important to mention that the DPPH free radical inhibition assay was not able to detect antioxidant capacity in 5 samples corresponding to the edible portion (mesocarp) of Schinga Schnico, Evelina, Bigbucks, Fuji Aztec and Redlum. To further analyze the DPPH radical inhibiting properties of the regional cultivars, a comparison between the 2021 and 2022 harvests of regional apple and pear cultivars was made. Only 3 of the apple cultivars and 5 of the pear cultivars were accounted due to availability reasons. We can observe that regarding the TE concentration (µg TE/g), differences lower than 20% were found in the peels and seeds of both fruits. The seeds of the Lambe-os-Dedos pear cultivar 0 50 100 150 200 250 300 350 Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Pêro Coimbra Pardo Lindo Repinau Bela-Feia Torres Novas Carapinheira Roxa Lambe-osDedos Amorim TE (µg TE/g) 2021 2022 Figure 4.3: Antioxidant capacity according to the DPPH radical assay in apples and pears harvest in 2021 and 2022. 64 presented one of the highest increases (83%) of antioxidant capacity according to DPPH between 2021 and 2022 harvest. On the other hand, in all of the mesocarp portions, we could observe an increase of over 50% of TE (with the exception of the Pardo Lindo apple and Amorim pear cultivars, where the antioxidant capacity of the DPPH radical assay was lower in 2022). The two harvest years have different climatic conditions that greatly influence the antioxidant capacity of the samples. However, the climatic conditions can affect differently the diverse pear and apple cultivars analyzed because they are harvested in different periods (months of the year – the Bela-Feia, Torres Novas, Carapinheira Roxa, Lambe-os-Dedos and Amorim pear cultivars and the Pêro Coimbra apple were harvested in September and the other apple cultivars, in early October). 4.1.3. β-carotene bleaching assay The Antioxidant activity coefficients of the regional apples and pears is shown in Tables 4.7 and 4.8 and Figure 4.4. Table 4.7: β-carotene bleaching assay results on the regional apple cultivars. Harvested in 2021 in Alcobaça (Portugal). Sample/Cultivar Portion Antioxidant activity coefficient (AAC) Pêro de Borbelo Peels 143.4 Seeds 108.8 Mesocarp 71.70 Pardo Lindo Peels 124.2 Seeds 62.74 Mesocarp 76.82 Repinau Peels 89.63 Seeds 42.25 Mesocarp 81.95 Pêro Coimbra Peels 126.8 Seeds 147.2 Mesocarp 85.79 Noiva Peels 283.0 Seeds 195.9 Mesocarp 76.82 65 Table 4.8: β-carotene bleaching assay results on the pear cultivars harvested in 2021 in Alcobaça (Portugal). Sample/Cultivar Portion Antioxidant activity coefficient (AAC) Bela-Feia Peels 161.3 Seeds 121.6 Mesocarp 83.23 Torres Novas Peels 88.35 Seeds 85.79 Mesocarp 64.02 Carapinheira Peels 162.6 Seeds 165.2 Mesocarp 40.97 Carapinheira Roxa Peels 96.03 Seeds 60.18 Mesocarp 52.50 Lambe-os-Dedos Peels 129.3 Seeds 39.69 Mesocarp 60.18 Amorim Peels 216.4 Seeds 152.4 Mesocarp 76.82 In what concerns to β-carotene bleaching assay, it was found that although not in all cultivars, the by-products have a higher AAC than the mesocarp. The Pardo Lindo and Repinau apple cultivars and the Lambe-os-Dedos pear cultivar have AAC values for the mesocarp higher than the seeds, but never higher than the peels. It was also found than between the two portions of by-products, peels presented higher AAC in most the apples and pears cultivars. The Pêro Coimbra apple and Carapinheira pear cultivars, on the other hand, have higher AAC in the seeds. 66 Comparing the apple with the pear cultivars, it was found the peels of apples and pears presented similar AAC, with special attention to the Noiva apple, reaching a AAC of 283.0. In the same line, seeds present the same pattern, with Noiva apple cultivar reaching a AAC of 195.9. The AAC of mesocarp was found to be similar among all the studied samples, however Carapinheira and Carapinheira Roxa pear cultivars presented much lower AAC values than all the other cultivars. Among all the fruit cultivars,Noiva apple, as well as Amorim pear, and the Carapinheira pear by-products stand out regarding the AAC. 0 50 100 150 200 250 300 Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Bela-Feia Torres Novas Carapinheira Carapinheira Roxa Lambe-os-Dedos Amorim AAC 0 50 100 150 200 250 300 Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Pêro de Borbelo Pardo Lindo Repinau Pêro Coimbra Noiva AAC Figure 4.4: β-carotene bleaching assay results in apples and pears collected in 2021 in Alcobaça region (Portugal). A - Results of apples regional cultivars. B - Results of pears regional cultivars 67 Table 4.9: β-carotene bleaching assay results on the commercial apple cultivars harvested in 2021 in Alcobaça (Portugal). Sample/Cultivar Portion Antioxidant activity coefficient (AAC) Galafab Peels 241.7 Seeds 208.3 Mesocarp 83.33 Granny Smith Peels 366.7 Seeds 383.3 Mesocarp 125.0 Dalinette Peels 325.0 Seeds 375.0 Mesocarp 266.7 Venus Fengal Peels 225.0 Seeds 225.0 Mesocarp 225.0 Rubelite Peels 358.3 Seeds 325.0 Mesocarp 308.3 Rubin Fuji Peels 225.0 Seeds 325.0 Mesocarp 200.0 Story Inored Peels 441.7 Seeds 233.3 Mesocarp 116.7 Evelina Peels 347.4 Seeds 511.9 Mesocarp 164.5 Pixie Peels 330.9 Seeds 426.0 Mesocarp 96.89 Schinga Schnico Peels 325.4 Seeds 352.8 Mesocarp 82.27 Redlum Peels 309.0 Seeds 135.3 Mesocarp 126.1 68 Sample/Cultivar Portion Antioxidant activity coefficient (AAC) Fuji Aztec Peels 188.3 Seeds 435.1 Mesocarp 65.81 Bigbucks Peels 376.6 Seeds 343.7 Mesocarp 74.95 Candine Peels 248.6 Seeds 356.5 Mesocarp 111.5 Decarli Peels 323.0 Seeds 180.9 Mesocarp 68.09 Schnico Red Peels 241.3 Seeds 208.2 Mesocarp 71.98 Redfeu Peels 324.9 Seeds 371.6 Mesocarp 46.69 Brookfield Peels 365.8 Seeds 289.9 Mesocarp 83.66 Jonaprince Peels 336.6 Seeds 192.6 Mesocarp 101.2 Fuji Spur Peels 356.0 Seeds 389.1 Mesocarp 68.09 Fuji Fubrax Peels 249.0 Seeds 367.7 Mesocarp 40.86 Fengapi Peels 385.2 Seeds 241.3 Mesocarp 87.55 69 The comparison of apple cultivars with their regional counterparts, allows to conclude that, in general, the commercial cultivars have a much higher AAC. The Noiva regional cultivar, surpasses 9 of the commercial cultivars, regarding the mesocarp and 7 regarding the peels, and only 3 when comparing the seeds. The highest antioxidant activity was reached by the Story Inored cultivar, with a AAC of 441.7 in the peels and by the Evelina cultivar, with a AAC of 511.9 in the seeds. It is also important to mention, that other than the Noiva cultivar, the regional apple cultivars showed considerably lower AAC values, with less than 50% of the values showed by the commercial cultivars. 76 Sample/Cultivar Portion Content (µg GAE/g) Fuji Aztec Peels 814.8 Seeds 315.7 Mesocarp 106.3 Bigbucks Peels 1864 Seeds 539.0 Mesocarp 131.8 Candine Peels 1027 Seeds 406.1 Mesocarp 134.1 Decarli Peels 1087 Seeds 519.7 Mesocarp 139.6 Schnico Red Peels 729.8 Seeds 303.3 Mesocarp 138.0 Redfeu Peels 1417 Seeds 437.0 Mesocarp 130.3 Brookfield Peels 1199 Seeds 553.7 Mesocarp 120.2 Jonaprince Peels 815.6 Seeds 321.1 Mesocarp 143.4 Fuji Spur Peels 1526 Seeds 383.7 Mesocarp 162.0 Fuji Fubrax Peels 857.3 Seeds 504.2 Mesocarp 148.8 Fengapi Peels 1602 Seeds 388.3 Mesocarp 174.3 77 When comparing the total phenolics content of the commercial apple cultivars with the regional cultivars, a tendency is observed. The regional cultivars, particulary Noiva, Pêro de Borbelo and Pêro Coimbra cultivars, show a higher content of phenolics than most of the commercial cultivars in all the portions. Other regional cultivars surpass the commercial ones only concerning the seeds and mesocarps. All the commercial cultivars present a phenolics content in the peels lower than the Noiva cultivar, but Galafab, Story Inored, and Bigbucks commercial cultivars showed similar contents (1852, 1897, and 1864 µg GAE/g of fresh fruit, respectively). While the total phenolics content in the seeds ranges between 278.6 and 566.8 µg GAE/g of fresh fruit in the commercial cultivars, if Granny Smith cultivar is not taken into consideration, on the other hand, in the regional cultivars this content ranged between 586.9 – 689.6 µg GAE/g of fresh fruit, showing that all of the seeds of regional cultivars have higher phenolics content than all of the commercial cultivars, apart from the Granny Smith cultivar. The same can be concluded regarding the mesocarps, being Granny Smith cultivar (268.6 µg GAE/g of fresh fruit) the only surpassing the regional cultivars with lowest TPC, i.e., Noiva, with 260.9 µg GAE/g of fresh fruit. 78 0 50 100 150 200 250 300 GAE (µg GAE/g) 0 100 200 300 400 500 600 700 800 GAE (µg GAE/g) 0 200 400 600 800 1000 1200 1400 1600 1800 2000 GAE (µg GAE/g) Figure 4.8: Total phenolics content of commercial apples. APeels; BSeeds; C-Mesocarp. 79 The differences on total phenolics content between the 2021 and 2022 harvests of some of the regional cultivars are shown in Figure 4.9. In the total phenolics content assay, slight changes in phenolics content of peels are found in µg GAE/g, with the exception of the Lambe-os-Dedos pear cultivar, where a 46% increase was found. As for the seeds and mesocarps, the phenolics content presented higher differences, with the seeds obtaining values in 2022 ranging from 40-80% of those obtained in 2021, in most, and the mesocarps presenting values in 2022 ranging from 31-61% of those obtained in 2021 in the majority of the cultivars. 4.1.5. Total content of flavonoids assay Regarding the total flavonoids content (TFC) assay, a calibration curve of epicatechin was prepared in order to be able to present the results as Epicatechin Equivalents (ECE) per gram. For this purpose, calibration standards were prepared from an epicatechin stock solution in methanol with a concentration of 200 µg/mL. From this solution, the calibration standard solutions were prepared by dilution, and afterwards subjected to the procedure of total phenolics content assay method procedure described in section three. 0 200 400 600 800 1000 1200 1400 1600 Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Pêro Coimbra Pardo Lindo Repinau Bela-Feia Torres Novas Carapinheira Roxa Lambe-osDedos Amorim GAE (µg GAE/g) 2021 2022 Figure 4.9: Graphical comparison of the total phenolic content (µg GAE/g) between the 2021 and 2022 harvests of regional apple and pear cultivars. 80 The obtained first-degree equation curve for epicatechin, in the range 5150 µg/mL, was Abs = 0.0017 Cepicatechin + 0.0165 (Cepicatechin - Concentration of Epicatechin solution). The good linearity obtained (r2=0.9997) indicated suitability of the curve to be used in the quantification of ECE equivalents in the samples. The results of the samples in µg of epicatechin equivalents per gram of fruit (µg ECE/g) are presented in Tables 4.13 and 4.14 and Figure 4.8. Table 4.13: Total flavonoids content of the apple cultivars harvested in 2021 in Alcobaça (Portugal). Sample/Cultivar Portion Content (µg ECE/g) Pêro de Borbelo Peels 778.1 Seeds 228.1 Mesocarp 37.89 Pardo Lindo Peels 295.4 Seeds 210.5 Mesocarp 43.74 Repinau Peels 561.6 Seeds 272.0 Mesocarp 37.89 Pêro Coimbra Peels 748.9 Seeds 359.7 Mesocarp 70.07 Noiva Peels 1284 Seeds 356.8 Mesocarp 64.22 Table 4.14: Total flavonoids content of the pears cultivars harvested in 2021 in Alcobaça (Portugal). Sample/Cultivar Portion Content (µg ECE/g) Bela-Feia Peels 389.0 Seeds 154.9 Mesocarp 11.55 Torres Novas Peels 339.3 Seeds 216.4 Mesocarp 0.000 81 Sample/Cultivar Portion Content (µg ECE/g) Carapinheira Peels 371.4 Seeds 447.5 Mesocarp 61.29 Carapinheira Roxa Peels 257.3 Seeds 111.0 Mesocarp 0.000 Lambe-os-Dedos Peels 292.4 Seeds 125.7 Mesocarp 0.000 Amorim Peels 547.0 Seeds 236.8 Mesocarp 23.26 Regarding the results of total flavonoids content, mesocarp of the analyzed fruits is the part with the lowest content on these compounds. In fact, some of the pear cultivars showed null results when subjected to this assay. Moreover, we can also observe that most of flavonoids is located on fruits’ by-products. Again, it is in the peels that the content is higher, when taking the by-products in consideration. Among regional apples, Noiva cultivar stands out by having the highest TFC, with 1284 µg ECE/g of fresh fruit. Again, in this assay, the Carapinheira pear is the only sample that has a higher content in the seeds than in the peels, while all the other cultivars have at least twice as much total flavonoids in the peels than in the seeds (with the exception of the Pardo Lindo apple and the Torres Novas pear, where the flavonoid content is similar between the two portions). All apple cultivars, except Pardo Lindo, have a higher total flavonoids content in the peels than the pear cultivars. The same can be concluded about the TFC in the seeds, apart from Pêro de Borbelo apple, that has a lower TFC than some pear cultivars. The TFC on the Carapinheira pear seeds stand out from the other results because, despite being a pear cultivar, it shows the highest TFC value for seeds, with 447.5 µg ECE/g of fresh fruit. The TFC found in the mesocarp of pear cultivars is overall null, with the exception of the Carapinheira cultivar, that showed a TFC of 61.3 µg ECE/g of fresh fruit, making it the third highest among all the samples of both fruits, only losing out to the Pêro Coimbra and Noiva apple cultivars, with 70.1 and 64.2 µg ECE/g of fresh fruit, respectively. All the other cultivars have a TFC lower than 50 µg ECE/g of fresh fruit. 82 Total flavonoids content ranging from 7 to 1421 µg ECE/g were reported by Mignard et al. 169, in a comparative study among 155 apple samples. This shows a very similar range of concentrations in comparison to our results (38 to 1284 µg ECE/g). Regarding the study carried out by Guan et al. ,171 where the comparison between the total flavonoids content of 22 Asian pear cultivars is performed, the authors registered a TFC ranging between 23 and 104 µg ECE/g of fresh fruit, while the TPC of regional apple cultivars ranged from 0 µg ECE/g in some mesocarps, to 547 µg ECE/g found in the Xuehua cultivar. 0 200 400 600 800 1000 1200 Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Pêro de Borbelo Pardo Lindo Repinau Pêro Coimbra Noiva ECE (µg ECE/g) 0 200 400 600 800 1000 1200 Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Bela-Feia Torres Novas Carapinheira Carapinheira Roxa Lambe-os-Dedos Amorim ECE (µg ECE/g) Figure 4.10: Total flavonoids content of regional apples and pears collected in 2021 at Alcobaça region (Portugal). AResults of apples regional cultivars. BResults of pears regional cultivars. 83 Table 4.15: Total flavonoids content of the commercial apple cultivars harvested in 2021 in Alcobaça Sample/Cultivar Portion Content (µg ECE/g) Galafab Peels 1472 Seeds 327.6 Mesocarp 61.29 Granny Smith Peels 933.2 Seeds 409.5 Mesocarp 61.29 Dalinette Peels 909.8 Seeds 245.6 Mesocarp 23.26 Venus Fengal Peels 1115 Seeds 233.9 Mesocarp 34.96 Rubelite Peels 1039 Seeds 181.3 Mesocarp 40.81 Rubin Fuji Peels 833.7 Seeds 327.6 Mesocarp 0.000 Story Inored Peels 1603 Seeds 301.2 Mesocarp 37.89 Evelina Peels 988.8 Seeds 277.8 Mesocarp 55.44 Pixie Peels 1311 Seeds 263.2 Mesocarp 61.29 Schinga Schnico Peels 1161 Seeds 231.0 Mesocarp 37.89 Redlum Peels 865.9 Seeds 184.2 Mesocarp 43.74 84 Sample/Cultivar Portion Content (µg ECE/g) Fuji Aztec Peels 775.2 Seeds 207.6 Mesocarp 23.26 Bigbucks Peels 1416 Seeds 365.6 Mesocarp 64.22 Candine Peels 1041 Seeds 368.5 Mesocarp 46.66 Decarli Peels 532.4 Seeds 272.0 Mesocarp 20.33 Schnico Red Peels 333.4 Seeds 175.4 Mesocarp 17.41 Redfeu Peels 880.5 Seeds 307.1 Mesocarp 8.628 Brookfield Peels 687.4 Seeds 304.1 Mesocarp 2.776 Jonaprince Peels 564.5 Seeds 143.2 Mesocarp 11.55 Fuji Spur Peels 795.7 Seeds 219.3 Mesocarp 49.59 Fuji Fubrax Peels 462.1 Seeds 286.6 Mesocarp 58.37 Peels 892.2 Fengapi Seeds 263.2 Mesocarp 61.29 85 The comparison of the total flavonoids content between commercial and regional apple cultivars allows to conclude there isn’t a considerable difference between the regional and the commercial cultivars regarding the seeds and the mesocarp portions. Regarding the seeds, Pêro Coimbra and Noiva regional cultivars show a higher total flavonoids content (359.7 and 356.8 µg ECE/g of fresh fruit, respectively) than all the commercial cultivars, with exception of the Granny Smith, Bigbucks and Candine cultivars (409.5, 365.6, and 368.5 µg ECE/g of fresh fruit, respectively). It is also possible to observe that the TFC in the peels’ ranges between 561.6 and 1284 µg GAE/g of fresh fruit in the regional cultivars and that these values are in general lower than the TFC values of the commercial cultivars. The Noiva regional cultivar stands out as the regional cultivar with the highest TFC, being only surpassed by 4 of the commercial cultivars (Galafab, Story Inored, Pixie and Bigbucks – reaching TFC values between 1311 and 1603 µg ECE/g of fresh fruit). 92 Sample/Cultivar Portion Content (mg/g) Fuji Aztec Peels 79.83 Seeds 100.0 Mesocarp 43.82 Bigbucks Peels 78.74 Seeds 99.85 Mesocarp 49.56 Candine Peels 61.82 Seeds 91.94 Mesocarp 47.54 Decarli Peels 78.59 Seeds 94.73 Mesocarp 56.39 Schnico Red Peels 99.85 Seeds 56.08 Mesocarp 78.59 Redfeu Peels 56.39 Seeds 91.32 Mesocarp 69.27 Brookfield Peels 56.70 Seeds 92.87 Mesocarp 50.96 Peels 50.96 Joanaprince Seeds 88.06 Mesocarp 43.35 Fuji Spur Peels 63.06 Seeds 108.6 Mesocarp 60.74 Fuji Fubrax Peels 86.50 Seeds 105.6 Mesocarp 58.25 Fengapi Peels 81.23 Seeds 108.4 Mesocarp 48.63 93 0 10 20 30 40 50 60 70 80 mg Fructose/g Fruit 0 20 40 60 80 100 120 140 160 mg Fructose/g Fruit 0 20 40 60 80 100 120 140 mg Fructose/g Fruit Figure 4.14: Total fructose content of commercial apples harvested in 2021 in Alcobaça (Portugal). 94 Comparing fructose content in the regional apple cultivars with its content in the commercial cultivars allows to conclude that the commercial cultivars, in general, have a higher fructose content than the Noiva apple cultivar (73.6 mg fructose/g fruit). However, 17 of the commercial apple cultivars have a lower content than the other 4 regional cultivars. It was possible to conclude that the fructose content in the peels was always lower in the commercial cultivars, except for the Pixie and Schnico Red cultivars, in which the content is 67.6 and 56.1 mg fructose/g fruit, respectively. It is also very interesting to conclude that all the regional cultivars possess a higher fructose content, ranging from 85.6 to 107 mg fructose/g fruit in the mesocarp than all of the commercial cultivars, whereas the fructose content varies between 37.6 – 78.6 mg fructose/g fruit. The differences between the fructose content of the regional cultivars in 2021 and 2022 harvests are shown in Figure 4.15. The fructose content in the parameter where the greatest differences were found in all the portions of both fruits between the 2021 and 2022 harvests. It is possible to observe that most of the peels of the pear cultivars, suffered a considerable decrease of their fructose content, with the Bela-Feia and Amorim cultivars being the most affected by this decrease, (238 and 187%, respectively). In a general way, it is possible to conclude that the other cultivars have also had a 0 50 100 150 200 250 Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Peels Seeds Mesocarp Pêro Coimbra Pardo Lindo Repinau Bela-Feia Torres Novas Carapinheira Roxa Lambe-osDedos Amorim mg Fructose/g fruit 2021 2022 Figure 4.15: Comparison of the total fructose content (mg Fructose/g fruit) of apples and pears of regional cultivars harvested in 2021 and 2022. 95 significant decrease on their fructose content, but that in some cases, like the Pardo Lindo and Repinau seeds, the fructose content has actually increased by 32 and 49%, respectively. 4.3. Principal component analysis The principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is always less than or equal to the number of original variables. The main objective is the dimensionality reduction keeping the contribution of all initial variables in order to provide a visual pattern recognition. In this context the biplot graph which project the samples (scores) and the variables (loadings) into a two or three dimensional graph, shows first of all how the samples are related to each other and in a second place how each variable affect each sample. To ensure the construction of representative descriptive indices with the highest possible capacity to discriminate between uses, the fruits were characterized as described above by means of 6 chemical parameters. These parameters are antioxidant activity (DPPH), antioxidant activity coefficient (Bcaroten), total phenolics content (Fenol), total flavonoids content (Flavon), total fructose content (Frutose) and DPPH free radical inhibiting percentage (PI). Figure 4.16: Dendrogram with hierarchical grouping of the samples. 96 The dendrogram shows that the Noiva apple peel (MNC) sample is very different chemically from the rest of the analyzed samples. In general, there are separate clusters of apples and pears with the exception of the Amorim pear seeds, Torres Novas pear seeds and Carapinheira Roxa pear seeds, that although belonging to the pear cultivars, still have significant similarities with some apple species. Figure 4.17: Correlation chart among the studied variables. The correlation chart shows that the fructose content is independent from all other parameters. All other parameters are correlated with particular emphasis on the DPPH-PI pair, which is to be expected because both of these parameters' results come from the same assay. PCA transforms the original variable into new ones called principal components using eigenvectors, which are calculated attributing a coefficient for each original variables proportional to their contribution into this transformation (rotation) in order to maximize the variances of the first few components. The main objective is the dimensionality reduction keeping at the same time the contribution of all initial variables in order to provide a visual pattern recognition. In this context 97 the biplot graph which project the samples (scores) and the variables (loadings) into a twoor threedimensional graph, shows first of all how the samples are related to each other and in a second place how each variable affect each sample. The samples situated close to each other are similar while the ones with higher distance between them are different considering the studied properties. The groups were formed using k-means clustering. Regarding to the loadings vectors an angle (θ) close to 0º provides a correlation coefficient of 1, since this last one is calculated from cos(θ) and means that there are highly correlated. An angle equal to 90º gives a correlation coefficient of 0 indicating that no correlation exists between them, meaning in other words that there is independent. Finally, an angle of 180 º which gives a correlation coefficient of -1 means that there are inversely proportional, indicating that an increase of the first one corresponds to a decrease of the second one. The loading situated close to a given sample has greater effect on the definition of the chemical/biological behavior of this sample. These two PCs explain 95 % of the observed variation in the original data. In this case the variable PI was not included in the study, since it is highly correlated with the DPPH parameter, and it has the same effect on the distribution of the samples. it was found a significant correlation among the DPPH parameter and the content on total flavonoids, total phenolics compounds, and the antioxidant activity coefficient. These have a higher influence in the samples of clusters 1 and 2. The fructose variable allows the differentiation between apple and pear species. Figure 4.18: Biplot chart PC2 vs PC1. 98 Since the Noiva apple peel sample has a very distinct set of parameters, we decided to remove it from the plotting and re-do the biplot chart. Four clusters were found in the samples where each cluster contains more chemically similar samples. The variable "total fructose content" allows to differentiate apples from pears. Additionally, with the commercial apple cultivars and the 2022 harvests of some regional cultivars, the fruits were again characterized as described above, this time by means of 5 chemical parameters. These parameters are antioxidant activity (DPPH), antioxidant activity coefficient (Bcar), total phenolic content (Phen), total flavonoid content (Flav), and total fructose content (Fruc). Figure 4.19: Biplot chart PC2 vs PC1. (After removing the Noiva apple peel sample) 99 This heatmap heatmap between samples and variables shows the impact of each variable on a given sample (Figure 4.20) and shows that, as stated previously, the fructose content in completely independent from the other parameters. Also, the antioxidant activity coefficient has a lower correlation with the other 3 parameters, than they have with each other. This can be confirmed by the correlation plot (Figure 4.21) below. Figure 4. 21: Correlation plot between the studied parameters. Figure 4.20: Hierarchical clustering heatmap of the samples and the studied parameters. 100 The correlation matrix is the variance/covariance matrix of the standardized data and shows the coefficients of the correlation between variables. The correlation between the phenolics content and the flavonoids content and between the flavonoids content and antioxidant capacity (0.93) are the highest among all the correlations observed. There is also a very high correlation between the total phenolics content and the antioxidant capacity (0.91). However, the correlation observed between the antioxidant activity coefficient and these three parameters (0.6, 0.54, 0.49) is not as high, as found before. The fructose content shows almost no correlation with the other parameters (<0.21). Figure 4.22: biplot chart PC2 vs PC1. These two PCs (Figure 4.22) explain 88 % of the observed variation in the original data. We can observe that 3 clusters emerge, with the left one (in red) consisting of only commercial apple cultivars. This result shows that the commercial apples can be separated from the other regional species and PCA can be used to certify the origin and the cultivar of the fruit samples. 4.4. Classification with machine learning algorithms Due to the availability of multivariate data matrices, it is possible to efficiently extract the most relevant information from data using statistical and mathematical methods, and group individuals in classes. In order to place new, unknown samples in one of the recognized classes based on their measurement pattern, supervised pattern recognition techniques use information 101 about the samples' membership in a given group (class or category). For our case, the samples were divided into 3 classes (1.00 – commercial apples; 2.0 – regional pears; 3.0 – regional apples). For the purpose of developing a model for the classification of the samples, supervised pattern recognition needs a training set of samples which belong to recognized categories. Since we had a small amount of data, the decision tree (DT) algorithm was applied, and the data were split into a training group (60% of the samples) and a test group (40% of the samples). The scores we obtained were 0.9660 for the training group, meaning that the model was able to state, with 97% confidence, that the samples belong to the stated group. Regarding the test group, as expected, the results were a bit lower, 0.8056, but still presenting a very acceptable confidence value of 80%. These results can be seen in Figure 4.23. The confusion matrix shows how the data were distributed among three classes, also showing the false positives. 4.5. Quantitative Analysis of Phenolic Compounds by UHPLC-ToF 4.5.1. Selection of the solvent for extraction The selection of the extraction solvent is a crucial step in order to obtain accurate analytical results. The extraction of polyphenols from plant material frequently involves the use of solvents such methanol or ethanol,174,175 sometimes with some amount of water. Figure 4.23: Confusion matrix of the training group (at the left) and the test group (at the right). 108 4.5.2.3. Accuracy of the method Due to the fact that most extraction techniques result in some analyte loss or can present some interference, the determination of the extraction efficiency of the analytical procedure is a required step for any analytical method. In the absence of reference materials, the accuracy of the method can be determined through recovery assays. The recovery study was performed by analyzing a sample before and after addition of a known concentration of the mixture of phenolic compounds. The recoveries were measured after spiking samples at two different fortification levels (0.1 and 1 mg/100 g). The results are shown on Table 4.21. Table 4.21: Recovery of the analytical method for each of the studied phenolic compounds at two fortification levels. Phenolic Compound [M+H]+ (m/z) Fortification level (mg/100g) Recovery Percentage (%) 4-Hydroxybenzoic Acid 139.03 0.1 91.75 1 97.29 Gentisic Acid 155.03 0.1 80.15 1 99.51 Caffeic Acid 181.04 0.1 95.75 1 87.93 Chlorogenic Acid 355.10 0.1 n.d. 1 78.27 o-Coumaric Acid 165.05 0.1 85.55 1 88.24 p-Coumaric Acid 165.05 0.1 81.48 1 87.54 Sinapic Acid 225.07 0.1 82.22 1 86.53 Syringic Acid 199.05 0.1 70.24 1 88.45 trans-Ferrulic Acid 195.06 0.1 n.d. 1 82.20 Vanillic Acid 169.04 0.1 75.99 1 88.92 109 Phenolic Compound [M+H]+ (m/z) Fortification level (mg/100g) Recovery Percentage (%) Phloridzin 437.14 0.1 75.35 1 85.83 Quercetin 303.04 0.1 84.36 1 86.68 Quercetin-3-B-D-Glucoside 465.10 0.1 93.97 1 88.05 Quercitrin 449.10 0.1 77.51 1 85.39 Rutin 611.15 0.1 80.46 1 87.11 (+-)-Naringenin 273.07 0.1 80.58 1 87.03 (+)-Catechin 291.08 0.1 75.20 1 76.86 Eriodyctiol 289.06 0.1 84.30 1 87.81 Apigenin 271.05 0.1 85.11 1 67.05 (-)-Epicatechin 291.08 0.1 72.36 1 83.28 Luteolin 287.05 0.1 87.00 1 75.15 n.d.- not determined The results showed that the recovery of the analytes ranged from 70.24 to 95.75% for the lowest concentration level, and from 67.05 to 99.51% for the highest concentration ranges. Recovery between 80 and 120% is considered acceptable, however, if the recovery percentage is over 70%, results can be considered satisfactory. In this perspective, the recovery of the different phenolics is acceptable, and the extraction process is suitable for the extraction of most of the phenolic compounds from apple and pear fruit matrices. 110 4.5.3. Application of the method to apple and pear samples After confirming that the method was appropriate to determine phenolic compounds in fruit matrices, it was applied to four selected regional fruit samples: peels of the Amorim pear, peels of the Noiva apple, mesocarp of the Pardo Lindo apple and mesocarp of Lambe-os-Dedos pear cultivars. These extracts were chosen among all the regional cultivars because they presented the best overall results, among the by-products and mesocarp, respectively, on the assays performed related to antioxidant properties. Table 4.22 shows the phenolic compounds concentrations evaluated in the four selected, two regional apples and two regional pears cultivars. Values have been corrected with recovery value (mean recovery for both spiking levels). Table 4.22: Phenolic compounds concentration in the studied fruit samples. (H.B.A – Hydroxybenzoic Acid. H.C.A. – Hydrocycinnamic Acid. DHC - Dihydrochalcone) Type of phenol Standard Concentration (µg/g fresh fruit) Noiva Peel Pardo Lindo Mesocarp Amorim Peel Lambe-osDedos Mesocarp H.B.A. 4-Hydroxybenzoic Acid n.d. n.d. n.d. n.d. H.B.A. Gentisic Acid 2.58 ± 0.41 < LOQ 2.06 ± 0.40 < LOQ H.C.A. Caffeic Acid n.d. n.d. 6.73 ± 0.55 1.98 ± 0.05 H.C.A. Chlorogenic Acid 0.08 ± 0.01 0.83 ± 0.01 0.41 ± 0.01 < LOQ H.C.A. o-Coumaric Acid < LOQ < LOQ n.d. n.d. H.C.A. p-Coumaric Acid 2.58 ± 0.04 n.d. 2.12 ± 0.02 n.d. H.C.A. Sinapic Acid 0.58 ± 0.06 n.d. 0.35 ± 0.01 0.56 ± 0.04 H.B.A. Syringic Acid < LOQ n.d. 0.93 ± 0.04 1.91 ± 0.01 H.C.A. trans-Ferrulic Acid < LOQ n.d. < LOQ < LOQ H.B.A. Vanillic Acid 0.99 ± 0.03 < LOQ 9.32 ± 0.59 9.77 ± 0.41 DHC Phloridzin 0.69 ± 0.07 n.d. n.d. n.d. Flavonol Quercetin 0.12 ± 0.01 n.d. < LOQ n.d. Isoflavone Quercetin-3-B-DGlucoside 0.10 ± 0.00 < LOQ 0.11 ± 0.01 < LOQ Flavone Quercitrin 0.30 ± 0.01 0.09 ± 0.00 0.09 ± 0.00 < LOQ Flavone Rutin n.d. n.d. < LOQ n.d. Flavanone (+-)-Naringenin 0.06 ± 0.01 n.d. < LOQ n.d. Flavan-3-ol (+)-Catechin < LOQ < LOQ < LOQ 0.15 ± 0.01 111 Type of phenol Standard Concentration (µg/g fresh fruit) Noiva Peel Pardo Lindo Mesocarp Amorim Peel Lambe-osDedos Mesocarp Flavone Apigenin n.d. n.d. n.d. n.d. Flavan-3-ol (-)-Epicatechin 0.08 ± 0.01 < LOQ 0.10 ± 0.01 0.09 ± 0.01 Flavone Luteolin < LOQ n.d. < LOQ n.d. Total 8.16 ± 0.69 0.92 ± 0.12 22.22 ± 1.66 14.46 ± 0.65 In general, the results obtained for almost all of the portions of the studied fruits are satisfactory, with the exception of the Pardo Lindo apple mesocarp, where most of the phenolic compounds under study could not be detected. These compounds might not have been present in the matrix for a cultivar of reasons, such as the fruit cultivar not having the phenolic compounds under study or the method not being sensitive enough to find the trace levels of these compounds in the matrix. Both the portions of the pear cultivars showed a higher total phenolic composition among the studied phenolic compounds, than the different portions of apple cultivars. This is particularly due to their high level of vanillic acid (9.32 ± 0.59 and 9.77 ± 0.41 µg/g of fresh fruit) and caffeic acid (6.73 ± 0.55 and 1.98 ± 0.05 µg/g of fresh fruit) – Figure 4.26 shows a UHPLC-Tof-MS chromatogram of the Amorim pear cultivar regarding this compound. Looking at the phenolic profile of the apple cultivars, gentisic acid was the one with the highest concentration found (2.58 µg/g of fresh fruit in the Noiva peels), and p-coumaric acid (2.58 µg/g of fresh fruit). Regarding the pear cultivars, vanillic acid was found to be the most prevalent Figure 4.26: Chromatogram of the Amorim pear sample regarding Caffeic Acid. 112 polyphenol in the studied matrices, followed by caffeic acid. It is important to point out that this high content of vanillic acid was not expected, since other studies, like the one reported by EsSbata et al., 180 presented lower results (<1.09 mg/L) than the results obtained in the present study. Vanillic acid was also found to be present on the peels of the Noiva cultivar apple, but in much lower quantities. Vanillic Acid is a hydroxybenzoic phenolic acid, widely distributed in a wide cultivar of fruits, such as apricots181 and apples.182 Phenolic acids were present in higher concentrations than all the other phenolic compounds subclasses. There is no data regarding regional cultivars in the scientific literature, so the comparison was made with commercial cultivars of apples and pears. However, the commercial cultivars found in the literature showed a higher concentration of individual phenolics, that may be due to diverse circumstances, including not also the differences in the cultivars, edaphoclimatic conditions, cultivation practices, fruit ripeness, and extraction method, as well as the fact that our fruit samples, due to the delayed arrival of the analytical standards and due to UHPLC system availability reasons, had to be stored for almost one year, at -20 oC. This may have led to the degradation of the phenolic compounds of the frozen samples. A recent study by Radenkovs et al. 183, presented apple by-products as a source of phenolic compounds for food applications. In this study, apples from the cultivar Gita were air dried and therefore the content of major polyphenols is expressed in µg/g (dry weight). The authors reported values of 137, 44 and 21 µg/g (dry weight) for chlorogenic acid, (−)-epicatechin and (+)-catechin, respectively, when using 96% ethanol (also different from the 90% ethanol selected in our study) for the extraction procedure. Ma et al. 184, also reported the phenolic composition of apple cultivars (Fuji) expressed in µg/g (dry weight), using three different drying methods - hot air drying, heat pump drying and freeze drying, some of which with different drying temperatures. The best results were obtained with the heat pump drying method for all of the analyzed phenolics, apart from chlorogenic acid, that was detected in a higher level, when the sample was freeze-dried. When the apple peel was dried by heat pump at 65 °C, it showed relatively high drying rate, exhibited high retention of phenolics, and presented high antioxidant capacity. Bílková et al. 185 , when recently studying the benefits of ultra-low oxygen conditions in longterm storage, reported a much higher phenolics content in apple cultivars (Angold, Gala, Nicoleta, Rucla, Golden Delicious, among others) particularly in regard to the chlorogenic acid content, that consisted in over 50% of the total phenolic composition of the studied cultivars, reaching a 113 concentration of 99.6 µg/g of fresh weight in the Angold cultivar. In our study though, the chlorogenic acid content was only residual, with 0.83 µg/g (fresh weight) in the mesocarp of the Pardo Lindo cultivar. The authors reported that the analysis was made right after harvesting the fruits, while our analysis occurred several months later due to the reasons stated earlier. This may explain the major difference seen in this phenolic compound concentration. Some of the cultivars (Rubin) presented similar total phenolics content to the cultivars evaluated in the present study. A recent study carried out by Salazar-Orbea et al .186, evaluated the effect of different processing techniques, in industrial set up, in the stability of phenolic compounds in Golden Delicious apples. The authors reported the results in mg/100g of fresh weight. However, the authors did not specify the individual content of each phenolic compound. Instead, they presented the sum of phenolic compounds. It is possible to observe that the total phenolics content determined by these authors was 86 mg/100g of fresh weight, showing relatively higher content than our regional samples. In a study conducted by Salta et al., 187 the phenolic composition, as well as the antioxidant activity, of pear cultivars (Rocha, Comice, Passe Crassane, General Leclerc and Abate) was evaluated. The results indicate that the content of chlorogenic acid was also the highest among the studied phenolics. In the Rocha pear, the content was 62.4 mg/100 g (fresh weight) of chlorogenic acid. In the other cultivars, caffeic acid was the highest among the studied phenolics, with the Abate cultivar showing a concentration of caffeic acid of 12.9 mg/100 g (fresh weight), while the Rocha pear presented 11.1 mg/100 g (fresh weight). 4.6. Pesticide detection analysis The use of pesticides is still a reality and, in fact, is essential to prevent food loss even while efforts to decrease or find alternatives are rapidly developing. The use of pesticides, however, also has negative impacts on the environment for food consumers. As a result, it is crucial to manage pesticide residues in food, and in the European Union, this is supported by regulation to safeguard public safety as well as domestic and international trade. Effective sample preparation and trace-level detection and identification are vital components of analytical methods due to the low detection limits demanded by regulatory bodies and the complex structure of food matrices in which the target compounds are contained. 114 In this study, we started by analyzing the individual samples of apple and pear matrices, recurring to the method described before. Figure 4.27: Chromatogram of the mesocarp of the Noiva regional apple. As we can see from Figure 4.27, the chromatogram shows that there isn´t a detectable amount of any pesticide in this sample of the mesocarp of the Noiva regional apple cultivar. Therefore, we thought about the possibility of repeating the study in a randomly bought commercial apple, and again, the chromatogram shows that no quantifiable amount of any of the pesticides is present in the mesocarp - Figure 4.28 nor in the peels – Figure 4.29. Figure 4.28: Chromatogram of the mesocarp of the commercial apple. 115 Figure 4.29: Chromatogram of the peels of the commercial apple. This led us to doubt if the extraction process was being effective. Many studies have proven that in some food matrices, method optimization is necessary so that the analytes are present in the extract. We decided to spike the sample with an alkaline fungicide widely used to control plant diseases, metalaxyl (methyl N-(methoxyacetyl)-N-(2,6-xylyl)-DL-alaninate). Results show that a single peak (Figure 4.30), with tr = 21.165 was observed and that that peak effectively corresponds to metalaxyl (Figure 4.31), so we can say that the extraction method is appropriate and that the regional apple and pear samples are not contaminated with traceable amounts of pesticides. Figure 4.30: Chromatogram of the peels of the regional apple spiked with metalaxyl. 116 Figure 4.31: Library search of the identified peak (corresponds to metalaxyl - methyl N-(methoxyacetyl)-N-(2,6xylyl)-DL-alaninate) and mass spectrum of the same compound. We also used a commercially available pesticide mixture solution, containing 155 pesticides (Attachment 1), to ensure that the method was capable of detecting these compounds (Figure 4.32). The chromatogram shows several peaks that correspond to pesticides known in our library. Figure 4.32: Chromatogram of the 155-pesticide mixture. 117 5. Conclusions Eating fruit is very important to have a balanced diet and healthy habits and also to prevent diseases. In fact, The World Health Organization (WHO) recommends consuming at least 400 g each day to obtain their health and nutrition benefits.188 This is a major contributing factor for the high demand and therefore high production values of some fruits, including apples and pears. However, only about 70% of the fruit is edible, and this originates a high volume of by-products of these fruits. Moreover, fruits by-products have a high content of phenolic compounds, therefore there is both a need and an asset to reuse these by-products. In this work, an analytical method for the determination of phenolic compounds in fruit pulps and by-products by ultra-high performance liquid chromatography coupled to time-of-flight mass spectrometry, was developed. The following analytical parameters were characterized for each phenolic compound: working range, linearity, limit of detection, limit of quantification, and accuracy. The method was applied to four samples of regional fruits (two mesocarps and two peels, one of each fruit). The linear ranges were between 0.25 and 1250 µg/mL. The limits of quantification obtained were all lower than 100 µg/mL in the case of phenolic acids and 5 µg/mL in the case of other phenolics. Recovery tests showed values between 70 and 96% for the lower fortification level (0.1 mg/100g) and 67 and 99% for the higher fortification level (1.0 mg/100g). The highest content of a phenolic compound found in the fruit samples was vanillic acid, with 9.77 µg/g of fresh Lambe-os-Dedos pear, followed by caffeic acid with 6.73 µg/g of fresh Amorim peel. The antioxidant capacity of the regional cultivars of apples and pears, as well as some commercial cultivars of apples, was compared. Between the regional cultivars of apples and pears, the apple cultivars present a higher content of total phenolics and total flavonoids, as well as a higher antioxidant capacity, in general. The same thing can be said regarding the total fructose content because the apple cultivars present higher values of fructose than the regional pear cultivars. The regional apple cultivars, however, do not have as high values of these parameters as the commercial cultivars do. 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