# Fruit Metabolism and Metabolomics

Annick Moing, Sonia Osorio, Pierre Pétriacq

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Annick Moing, Sonia Osorio, Pierre Pétriacq. Fruit Metabolism and Metabolomics. MDPI, 2020, 978-3-03943-014-7. 10.3390/books978-3-03943-015-4. hal-05211017

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metabolites

Fruit Metabolism
and Metabolomics

Edited by

Annick Moing, Sonia Osorio and Pierre Pétriacq

Printed Edition of the Special Issue Published in *Metabolites*
Fruit Metabolism and Metabolomics
<figure><img src="image_3.png" /></figure>
Fruit Metabolism and Metabolomics

Editors

Annick Moing
Sonia Osorio
Pierre Pétriacq

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

<figure><img src="image_4.png" /></figure>
Editors

Annick Moing

INRAE

France

Sonia Osorio

University of Málaga

Spain

Pierre Pétriacq

Univ. Bordeaux

France

Editorial Office

MDPI

St. Alban-Anlage 66

4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Metabolites* (ISSN 2218-1989) (available at: https://www.mdpi.com/journal/metabolites/special- issues/Fruit_Metabolism).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

Last Name, A.A.; Last Name, B.B.; Last Name, C.C. Article Title. *Journal Name* **Year**, *Article Number*, *Page Range*.

ISBN 978-3-03943-014-7 (Hbk)

ISBN 978-3-03943-015-4 (PDF)

Cover image courtesy of Annick Moing.

© 2020 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.
# Contents

<table><tr><td>About the Editors</td><td>vii</td></tr><tr><td>Annick Moing, Pierre Pétriacq and Sonia Osorio<br>Special Issue on "Fruit Metabolism and Metabolomics"<br>Reprinted from: Metabolites 2020, 10, 230, doi:10.3390/metabo10060230</td><td>1</td></tr><tr><td>Elenilson Alves Filho, Lorena Mara Silva, Ynayara Lima, Paulo Ribeiro, Ebenézer Silva, Guilherme Zocolo, Kirley Canuto, Selene Morais, Ana Cecília Castro and Edy de Brito<br>Metabolomic Variability of Different Genotypes of Cashew by LC-Ms and Correlation with Near-Infrared Spectroscopy as a Tool for Fast Phenotyping<br>Reprinted from: Metabolites 2019, 9, 121, doi:10.3390/metabo9060121</td><td>7</td></tr><tr><td>Annick Moing, J. William Allwood, Asaph Aharoni, John Baker, Michael H. Beale, Shifra Ben-Dor, Benoît Biais, Federico Brigante, Yosef Burger, Catherine Deborde, and et al.<br>Comparative Metabolomics and Molecular Phylogenetics of Melon (Cucumis melo, Cucurbitaceae) Biodiversity<br>Reprinted from: Metabolites 2020, 10, 121, doi:10.3390/metabo10030121</td><td>27</td></tr><tr><td>Gabriella Dono, Jose Luis Rambla, Sarah Frusciante, Antonio Granell, Gianfranco Diretto and Andrea Mazzucato<br>Color Mutations Alter the Biochemical Composition in the San Marzano Tomato Fruit<br>Reprinted from: Metabolites 2020, 10, 110, doi:10.3390/metabo10030110</td><td>53</td></tr><tr><td>Felipe Cervantes-Hernández, Paul Alcalá-González, Octavio Martínez and José Juan Ordaz-Ortiz<br>Placenta, Pericarp, and Seeds of Tabasco Chili Pepper Fruits Show a Contrasting Diversity of Bioactive Metabolites<br>Reprinted from: Metabolites 2019, 9, 206, doi:10.3390/metabo9100206</td><td>75</td></tr><tr><td>Simona Nardozza, Janine Cooney, Helen L. Boldingh, Katrin G. Hewitt, Tania Trower, Dan Jones, Amali H. Thrimawithana, Andrew C. Allan and Annette C. Richardson<br>Phytohormone and Transcriptomic Analysis Reveals Endogenous Cytokinins Affect Kiwifruit Growth under Restricted Carbon Supply<br>Reprinted from: Metabolites 2020, 10, 23, doi:10.3390/metabo10010023</td><td>91</td></tr><tr><td>Muhammad Maulana Malikul Ikram, Sobir Ridwani, Sastia Prama Putri and Eiichiro Fukusaki<br>GC-MS Based Metabolite Profiling to Monitor Ripening-Specific Metabolites in Pineapple (Ananas comosus)<br>Reprinted from: Metabolites 2020, 10, 134, doi:10.3390/metabo10040134</td><td>109</td></tr><tr><td>Carla Lenore F. Calumpang, Tomoki Saigo, Mutsumi Watanabe and Takayuki Tohge<br>Cross-Species Comparison of Fruit-Metabolomics to Elucidate Metabolic Regulation of Fruit Polyphenolics Among Solanaceous Crops<br>Reprinted from: Metabolites 2020, 10, 209, doi:10.3390/metabo10050209</td><td>125</td></tr><tr><td>Delphine M. Pott, José G. Vallarino and Sonia Osorio<br>Metabolite Changes during Postharvest Storage: Effects on Fruit Quality Traits<br>Reprinted from: Metabolites 2020, 10, 187, doi:10.3390/metabo10050187</td><td>143</td></tr></table>
Anastasiya Kuhalskaya, Micha Wijesingha Ahchige, Leonardo Perez de Souza, José Vallarino, Yariv Brotman and Saleh Alseekh

Network Analysis Provides Insight into Tomato Lipid Metabolism

Reprinted from: *Metabolites* 2020, 10, 152, doi:10.3390/metabo10040152 167

Estrella Luna, Amélie Flandin, Cédric Cassan, Sylvain Prigent, Chloé Chevanne, Camélia Feyrouse Kadiri, Yves Gibon and Pierre Pétriacq

Metabolomics to Exploit the Primed Immune System of Tomato Fruit

Reprinted from: *Metabolites* 2020, 10, 96, doi:10.3390/metabo10030096 185

Gilda D'Urso, Jurriaan J. Mes, Paola Montoro, Robert D. Hall and Ric C.H. de Vos

Identification of Bioactive Phytochemicals in Mulberries

Reprinted from: *Metabolites* 2020, 10, 7, doi:10.3390/metabo10010007 205

Vuanghao Lim, Sara Ghorbani Gorji, Venea Dara Daygon and Melissa Fitzgerald

Untargeted and Targeted Metabolomic Profiling of Australian Indigenous Fruits

Reprinted from: *Metabolites* 2020, 10, 114, doi:10.3390/metabo10030114 221

Álvaro Cuadros-Inostroza, Claudio Verdugo-Alegría, Lothar Willmitzer, Yerko Moreno-Simunovic and José G. Vallarino

Non-Targeted Metabolite Profiles and Sensory Properties Elucidate Commonalities and Differences of Wines Made with the Same Variety but Different Cultivar Clones

Reprinted from: *Metabolites* 2020, 10, 220, doi:10.3390/metabo10060220 241
## About the Editors

**Annick Moing** (Dr) gained an Engineer Diploma in Agronomy (ENSA Montpellier, France) and a Ph.D. in Plant Science (University of Bordeaux, France) and has subsequently completed more than 30 years research experience. After working on whole plant physiology, quantitative genetics, and fruit metabolism, her primary research activities have been centered on developing and using metabolomic strategies for the study of fruit and grain quality and metabolism, and the search for metabolite markers of plant response or performance. She has led the Metabolome Facility of Bordeaux Functional Genomics Centre for about 12 years, and is involved in MetaboHUB, the French Infrastructure in Metabolomics and Fluxomics. She has completed about 100 publications in peer reviewed journals and eight book chapters.

**Sonia Osorio** (Dr) graduated in Chemistry and attained a Ph.D. in Plant Science from the University of Malaga (Spain). She was a postdoc fellow at the Max Planck Institute of Molecular Plant Physiology. She is currently a Professor at the University of Malaga leading the laboratory of Biotechnology of Fruits (https://www.ihsm.uma-csic.es/investigadores/118). Her research interests center on uncovering the molecular mechanisms underlying the production of plant metabolites related to aroma, taste, and postharvest properties. Her activities are centered on understanding early events in the regulation of the ripening transition using strawberry and tomato as a model, and exploring the conservation of ripening mechanisms in those fruits. Her lab combines cutting-edge metabolomics in fruit development and postharvest. She has made a series of breakthrough achievements in fruit development and maturity molecule regulation networks, published in more than 80 papers in international mainstream journals such as *Nature*, *Nature Communications*, *The Plant Cell*, and *Plant Physiology* and more than 15 book chapters.

**Pierre Pétriacq** (Dr) obtained his Ph.D. in Plant Science from Paris-Sud University (France) where he was then appointed Assistant Professor in Plant Physiology. After a Leverhulme Trust postdoctoral fellowship at Sheffield University (UK), he became a Fellow of the Higher Education Academy then Group leader in Plant Metabolomics at P3 Institute, Sheffield (UK). Since 2017, he teaches as an Associate Professor at Bordeaux University and studies Plant Biochemistry at the Fruit Biology and Pathology Unit (Bordeaux, France). He is also head of the Bordeaux Metabolome facility, which provides tools and methods to investigate plant metabolism. His research focuses on redox biology and metabolomics applications for the study of plant performance. He is involved in the French Metabolomics and Fluxomics Infrastructure MetaboHUB, and has published valuable research in high-impact journals such as *Nature Chemical Biology*, *Plant Physiology*, and *The Plant Journal*.
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Editorial

# Special Issue on “Fruit Metabolism and Metabolomics”

Annick Moing<sup>1,2,*</sup>, Pierre Pétriacq<sup>1,2</sup> and Sonia Osorio<sup>3</sup>

Received: 29 May 2020; Accepted: 1 June 2020; Published: 3 June 2020

**Abstract:** Over the past 10 years, knowledge about several aspects of fruit metabolism has been greatly improved. Notably, high-throughput metabolomic technologies have allowed quantifying metabolite levels across various biological processes, and identifying the genes that underlie fruit development and ripening. This Special Issue is designed to exemplify the current use of metabolomics studies of temperate and tropical fruit for basic research as well as practical applications. It includes articles about different aspects of fruit biochemical phenotyping, fruit metabolism before and after harvest, including primary and specialized metabolisms, and bioactive compounds involved in growth and environmental responses. The effect of genotype, stages of development or fruit tissue on metabolomic profiles and corresponding metabolism regulations are addressed, as well as the combination of other omics with metabolomics for fruit metabolism studies.

**Keywords:** fruit metabolomics; developmental metabolomics; stress metabolomics; spatial metabolomics; central metabolism; specialized metabolism; mass spectrometry; nuclear magnetic resonance spectroscopy; omics; multi-omics integration

The growth and ripening of fruit are multifaceted and highly regulated developmental processes which yield colorful and flavorful tissues for organisms that consume and disperse the seeds therein [1]. Fruits are economically essential and vital for human nutrition and health owing to their content in sugars, organic acids, pigments, volatiles and other nutraceutical compounds [2,3], the metabolisms of which have been widely studied (e.g., [4–6]). The shift from single-metabolite analyses to analytical platforms that provide information on hundreds of metabolites has allowed researchers to better describe the links both within metabolites and between metabolism and important agronomic-associated traits. Metabolomics has permitted the identification of changes in the chemical composition of transgenic plants, mutants or populations and has allowed for identifying genomic regions associated with metabolite traits of agronomical value in model fruit species such as tomato and strawberry [7]. Tomato metabolomic studies have been numerous in the past decade [8,9] and have allowed the development of a comprehensive understanding of primary and specialized metabolism pathways and their interplay during fruit growth and development, and in diverse environmental conditions.

The number of teams involved in, and of articles published on, fruit metabolomics has been regularly and progressively increasing. Searching for articles in the Web of Science core collection for metabolomic(s) or metabolome, and fruit in the last decade (Figure 1) revealed an increment from about 20 articles per year in 2010 to about 200 in 2019. The application domains are also increasingly diverse, as illustrated by very recent works ranging, for instance, from the study of the regulation of steroidal

<footnote n="1">1 UMR Biologie du Fruit et Pathologie, Centre INRAE de Nouvelle Aquitaine-Bordeaux, University Bordeaux, INRAE, 71 av Edouard Bourlaux, 33140 Villenave d'Ornon, France; pierre.metriacq@inrae.fr</footnote>

<footnote n="2">2 Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, Centre INRAE de Nouvelle Aquitaine-Bordeaux, IBVM, 71 av Edouard Bourlaux, 33140 Villenave d'Ornon, France</footnote>

<footnote n="3">3 Department of Molecular Biology and Biochemistry, Instituto de Hortofruticultura Subtropical y Mediterránea "La Mayora", University of Málaga—Consejo Superior de Investigaciones Científicas (IHSM-UMA-CSIC), 29071 Málaga, Spain; sosorio@uma.es</footnote>

<footnote>* Correspondence: annick.moing@inrae.fr</footnote>
glycoalkaloids biosynthesis in Solanaceae [10] to that of the effect of scion/rootstock interaction on the metabolic composition in citrus fruit [11].

<figure><img src="image_7.png" /><figcaption>**Figure 1.** Number of articles published about fruit metabolomics over the past 10 years. Search in Web of Science core collection for TS = (metabolome OR metabolomics *) AND (TS = fruit) NOT (TS=mushroom * OR TS = fruit-fly).</figcaption></figure>

This Special Issue focuses on applications of metabolomics within the field of plant sciences for the study of fruit. It contains 12 original research articles and one review article. The contributed research articles are exemplary studies covering primary research applied to fruit species from a basic understanding of metabolism regulation to applications for phenotyping for breeding or defence priming (Figure 2).

This Special Issue covers a range of fruit species, including model fruit (tomato [12–14]), temperate (kiwifruit [15], mulberry [16]) or tropical fruit crops (pineapple [17], cashew [18]), genetic resources (melon [19]), and indigenous fruit (Davidson’s plum, finger lime and native pepper berry [20]). Different analytical approaches are covered also: near-infrared spectroscopy (NIRS) [18], gas chromatography coupled to mass spectrometry (GC-MS) [17], liquid chromatography coupled to mass spectrometry (LC-MS) [16,18,21,22], LC-MS/MS [20], and a combination of several analytical strategies including nuclear magnetic resonance spectroscopy (NMR), GC-MS and LC-MS [19] or several omics [15].

<figure><img src="image_8.png" /><figcaption>**Figure 2.** Summary of the works presented in the Special Issue on “Fruit Metabolism and Metabolomics”. GC-MS, gas chromatography coupled to mass spectrometry; ¹H-NMR, proton nuclear magnetic resonance spectroscopy; LC-MS, liquid chromatography coupled to mass spectrometry; NIRS, near-infrared spectroscopy.</figcaption></figure>
The original research articles can be classified according to scientific domains: biochemical phenotyping of genetic resources for collection maintenance or intraspecific classification, generating knowledge about fruit growth, ripening and post-harvest, understanding metabolism, characterizing defence priming, identifying bioactive compounds for human nutrition and health.

Biochemical phenotyping of cashew apple based on LC-MS and near-infrared spectroscopy [18] allowed the identification of a group of accessions, as a proof of concept for their use for the maintenance of a germplasm bank. For melon genetic resources [19], a combination of metabolome approaches enabled an evaluation of metabolomic relationship and its correlation with the genetic genome-by-sequencing distance of melon accessions, revealing that several melon groups, such as Inodorous, grouped in parallel with the genetic classifications, while other genome to metabolome and mineral element associations appeared less clear. A multi-approach study of color mutations introduced in a tomato Italian landrace [12] revealed unexpected biochemical changes. The double mutants expanded the effect on the metabolism of the single mutations by revealing additional additive or epistatic effects that could be of interest for the improvement and diversification of the landrace.

Fruit are complex organs constituted of different tissues. The distribution of metabolites throughout the different fruit tissues, pericarp, placenta, and seeds, was described in chilli pepper [22]. Pericarp showed a higher content of glycosides and terpenoids compared with other parts of the fruit. Placenta was the tissue having the highest content in alkaloids related to capsaicinoids and in tocopherols.

The mechanisms underlying fruit growth during kiwifruit development were investigated using phytohormone profiling with transcriptomics under two carbon supply levels [15]. The results suggest that cytokinins, known to be implicated in cell division, are also involved in fruit cell expansion and growth in kiwifruit. Pineapple ripening was characterized using GC-MS of crown, flesh, and peel samples [17]. Samples could be separated according to ripening phases from early-ripening to late-ripening phases for flesh and peel, which allowed highlighting metabolites correlated to ripening.

Concerning metabolism regulation, a cross-species study using LC-MS was performed to clarify the metabolic regulation of fruit phenolics among three Solanaceae crops, tomato, eggplant and pepper [21]. It allowed identifying the metabolic signatures of phenolics in each species from different fruit tissue-types and ripening stages and provided information for future functional genomics or breeding approaches. The metabolic changes during fruit postharvest storage were reviewed [23], emphasizing the roles that metabolomic platforms can play for better understanding the biochemical bases of postharvest physiology and identifying the numerous, often species-dependent, pathways affected during fruit senescence.

Metabolic correlation networks covering a variety of metabolic traits such as lipophilic and volatile compounds in tomato fruit have shown how the individual metabolic classes are related to yield-related phenotypic traits [13]. Moreover, metabolite-transcript correlation analysis exposed crucial putative genes involved in the biosynthesis of lipids.

Fruit metabolism is tuned in interaction with the abiotic and biotic environments. Immune priming of tomato fruit by the defence inducer β-aminobutyric acid (BABA) was investigated against three different fruit pathogens using a combination of targeted and untargeted metabolomics [14]. While BABA resulted in a long-lasting induced resistance in tomato fruit against those pathogenic microbes, the primed responses were likely specific to the infection nature, rather than characterizing a common pattern of BABA-induced priming. Further, modelling approaches established that soluble sugars were essential to predict resistance to fruit pathogens.

In relation to human health, in white and black mulberry fruits, combining an LC-MS method with assays for (i) α-glucosidase inhibitory activity and (ii) antioxidant activity [16], allowed for identifying a list of key bioactive compounds with antioxidant and/or α-glucosidase inhibitory activities. Similarly, based on targeted and untargeted metabolite profiling and antioxidant assays on selected Australian native fruits, a number of compounds that provide the antioxidant activities were identified [20].
The latter results open new possibilities of using these indigenous fruits in the nutraceutical and food industries.

Wine is a famous product derived from fruit for which a range of metabolomic studies have been performed [24,25]. Here, combining non-targeted metabolite profiling by UPLC-Orbitrap-MS/MS profiling and sensory analyses revealed commonalities and differences of wines made with different grape cluster types obtained each from three *Vitis vinifera* cv. Pinot noir putative-clones grown in identical field environments [26]. In addition, a set of molecular-feature (metabolite) markers was selected as being associated with these sensorial attributes.

Overall, these 13 articles demonstrate that metabolomics is a powerful and insightful tool to address numerous biological issues that link to fruit biology before or after harvest. The future improvement of metabolite annotation workflows will certainly help to decipher more ambitious questions when metabolomics biomarker discovery broadens our understanding of fruit processes. A greater number of fruit metabolomics studies, especially for tropical species or species other than major crops, will allow for a better depiction of fruit mechanisms that govern development and responses to environmental fluctuations.

To conclude, we would like to thank each of the authors who contributed to this Special Issue on fruit metabolism and metabolomics, the peer reviewers who allowed for improving the quality of submitted manuscripts, and the staff members of the Metabolite Editorial Office for their support.

**Funding:** A.M. and P.P. were partly funded by MetaboHUB (ANR-11-INBS-0010) and PHENOME (ANR-11-INBS-0012) French Agence Nationale de la Recherche projects. S.O. was partially supported by grants RTI2018-099797-B-100 (Ministerio de ciencia, Innovación y Universidades, Spain) and UMA18-DEDERJA-179 (Consejería de Economía, Conocimiento, Empresas y Universidades, Junta de Andalucía, Spain).

**Conflicts of Interest:** The authors declare no conflict of interest.

## References

1. van der Knaap, E.; Østergaard, L. Shaping a fruit: Developmental pathways that impact growth patterns. *Semin. Cell Dev. Biol.* **2018**, *79*, 27–36. [CrossRef] [PubMed]

2. Pott, D.M.; Osorio, S.; Vallarino, J.G. From central to specialized metabolism: An overview of some secondary compounds derived from the primary metabolism for their role in conferring nutritional and organoleptic characteristics to fruit. *Front. Plant Sci.* **2019**, *10*. [CrossRef] [PubMed]

3. Rodriguez-Casado, A. The health potential of fruits and vegetables phytochemicals: Notable examples. *Crit. Rev. Food Sci. Nutr.* **2016**, *56*, 1097–1107. [CrossRef] [PubMed]

4. Sun, P.; Schuurink, R.C.; Caissard, J.-C.; Hugueney, P.; Baudino, S. My way: Noncanonical biosynthesis pathways for plant volatiles. *Trends Plant Sci.* **2016**, *21*, 884–894. [CrossRef]

5. Beauvoit, B.; Belouah, I.; Bertin, N.; Cakpo, C.B.; Colombié, S.; Dai, Z.; Gautier, H.; Génard, M.; Moing, A.; Roch, L.; et al. Putting primary metabolism into perspective to obtain better fruits. *Ann. Bot.* **2018**, *122*, 1–21. [CrossRef]

6. Ilahy, R.; Tlili, I.; Siddiqui, M.W.; Hdider, C.; Lenucci, M.S. Inside and beyond color: Comparative overview of functional quality of tomato and watermelon fruits. *Front. Plant Sci.* **2019**, *10*, 769. [CrossRef]

7. Hanhineva, K.; Aharoni, A. Metabolomics in Fruit Development. In *Molecular Techniques in Crop Improvement*, 2nd ed.; Jain, S.M., Brar, D.S., Eds.; Springer: Heidelberg, Germany, 2010; pp. 675–693.

8. de Vos, R.C.H.; Hall, R.; Moing, A. *Metabolomics of a model fruit: Tomato In Biology of Plant Metabolomics*; Hall, R., Ed.; Wiley-Blackwell Ltd.: Oxford, UK, 2011; Volume 43, pp. 109–155.

9. Tohge, T.; Fernie, A.R. Metabolomics-inspired insight into developmental, environmental and genetic aspects of tomato fruit chemical composition and quality. *Plant Cell Physiol.* **2015**, *56*, 1681–1696. [CrossRef]

10. Yu, G.; Li, C.; Zhang, L.; Zhu, G.; Munir, S.; Shi, C.; Zhang, H.; Ai, G.; Gao, S.; Zhang, Y.; et al. An allelic variant of GAME9 determines its binding capacity with the GAME17 promoter in the regulation of steroidal glycoalkaloid biosynthesis in tomato. *J. Exp. Bot.* **2020**, *71*, 2527–2536. [CrossRef]

11. Tietel, Z.; Srivastava, S.; Fait, A.; Tel-Zur, N.; Carmi, N.; Raveh, E. Impact of scion/rootstock reciprocal effects on metabolomics of fruit juice and phloem sap in grafted *Citrus reticulata*. *PLoS ONE* **2020**, *15*, e0227192. [CrossRef]
12. Dono, G.; Rambla, J.L.; Frusciante, S.; Granell, A.; Diretto, G.; Mazzucato, A. Color mutations alter the biochemical composition in the San Marzano tomato fruit. *Metabolites* **2020**, *10*, 110. [CrossRef]

13. Kuhalskaya, A.; Wijesingha Ahchige, M.; Perez de Souza, L.; Vallarino, J.; Brotman, Y.; Alseekh, S. Network analysis provides insight into tomato lipid metabolism. *Metabolites* **2020**, *10*, 152. [CrossRef] [PubMed]

14. Luna, E.; Flandin, A.; Cassan, C.; Prigent, S.; Chevanne, C.; Kadiri, C.F.; Gibon, Y.; Pétriacq, P. Metabolomics to exploit the primed immune system of tomato fruit. *Metabolites* **2020**, *10*, 96. [CrossRef] [PubMed]

15. Nardozza, S.; Cooney, J.; Boldingh, H.L.; Hewitt, K.G.; Trower, T.; Jones, D.; Thrimawithana, A.H.; Allan, A.C.; Richardson, A.C. Phytohormone and transcriptomic analysis reveals endogenous cytokinins affect kiwifruit growth under restricted carbon supply. *Metabolites* **2020**, *10*, 23. [CrossRef] [PubMed]

16. D'Urso, G.; Mes, J.J.; Montoro, P.; Hall, R.D.; de Vos, R.C.H. Identification of bioactive phytochemicals in mulberries. *Metabolites* **2020**, *10*, 7. [CrossRef]

17. Ikram, M.M.M.; Ridwani, S.; Putri, S.P.; Fukusaki, E. GC-MS based metabolite profiling to monitor ripening-specific metabolites in pineapple (*Ananas comosus*). *Metabolites* **2020**, *10*, 134. [CrossRef]

18. Alves Filho, E.; Silva, L.M.; Lima, Y.; Ribeiro, P.; Silva, E.; Zocolo, G.; Canuto, K.; Morais, S.; Castro, A.C.; de Brito, E. Metabolomic variability of different genotypes of cashew by LC-MS and correlation with near-infrared spectroscopy as a tool for fast phenotyping. *Metabolites* **2019**, *9*, 121. [CrossRef]

19. Moing, A.; Allwood, J.W.; Aharoni, A.; Baker, J.; Beale, M.H.; Ben-Dor, S.; Biais, B.; Brigante, F.; Burger, Y.; Deborde, C.; et al. Comparative metabolomics and molecular phylogenetics of melon (*Cucumis melo*, Cucurbitaceae) biodiversity. *Metabolites* **2020**, *10*, 121. [CrossRef]

20. Lim, V.; Gorji, S.G.; Daygon, V.D.; Fitzgerald, M. Untargeted and targeted metabolomic profiling of Australian indigenous fruits. *Metabolites* **2020**, *10*, 114. [CrossRef]

21. Calumpang, C.L.F.; Saigo, T.; Watanabe, M.; Tohge, T. Cross-species comparison of fruit-metabolomics to elucidate metabolic regulation of fruit polyphenolics among Solanaceous crops. *Metabolites* **2020**, *10*, 209. [CrossRef]

22. Cervantes-Hernández, F.; Alcalá-González, P.; Martínez, O.; Ordaz-Ortiz, J.J. Placenta, pericarp, and seeds of tabasco chili pepper fruits show a contrasting diversity of bioactive metabolites. *Metabolites* **2019**, *9*, 206. [CrossRef]

23. Pott, D.M.; Vallarino, J.G.; Osorio, S. Metabolite changes during postharvest storage: Effects on fruit quality traits. *Metabolites* **2020**, *10*, 187. [CrossRef] [PubMed]

24. Cozzolino, D. Metabolomics in grape and wine: Definition, current status and future prospects. *Food Anal. Methods* **2016**, *9*, 2986–2997. [CrossRef]

25. Pinu, F.R. Grape and wine metabolomics to develop new insights using untargeted and targeted approaches. *Fermentation* **2018**, *4*, 92. [CrossRef]

26. Cuadros-Inostroza, A.; Verdugo-Alegria, C.; Willmitzer, L.; Moreno, Y.; Vallarino, J.G. Non-targeted metabolite profiles and sensory properties elucidate commonalities and differences of wines made with the same variety but different cultivar clones. *Metabolites* **2020**, *10*, 220. [CrossRef] [PubMed]

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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Article

# Metabolomic Variability of Different Genotypes of Cashew by LC-Ms and Correlation with Near-Infrared Spectroscopy as a Tool for Fast Phenotyping

Elenilson Alves Filho¹, Lorena Mara Silva², Ynayara Lima³, Paulo Ribeiro², Ebenézer Silva², Guilherme Zocolo², Kirley Canuto², Selene Morais³, Ana Cecília Castro² and Edy de Brito ²,*

¹ Department of Food Engineering, Universidade Federal do Ceará, Fortaleza-CE 60020-181, Brazil; elenilson.godoy@yahoo.com.br

² Embrapa Agroindústria Tropical, Rua Dra Sara Mesquita, 2270, Pici, Fortaleza-CE 60511-110, Brazil; lorena.mara@embrapa.br (L.M.S.); paulo.riceli@embrapa.br (P.R.); ebenezer.silva@embrapa.br (E.S.); guilherme.zocolo@embrapa.br (G.Z.); kirley.canuto@embrapa.br (K.C.); cecilia.castro@embrapa.br (A.C.C.)

³ Centro de Ciências e Tecnologia, Universidade Estadual do Ceará, Fortaleza-CE 60020-181, Brazil; yna.colares@gmail.com (Y.L.); selenaiademorais@gmail.com (S.M.)

* Correspondence: edy.brito@embrapa.br; Tel.: +55-082-33917393

Received: 6 May 2019; Accepted: 20 June 2019; Published: 25 June 2019

**Abstract:** The objective of the present work was to develop an advanced fast phenotyping tool to explore the cashew apple compositions from different genotypes, based on a portable near-infrared (MicroNIR) spectroscopy. This will be in addition to associating the variability of the respective cashew apple pulps with the genotypes by ultra-performance liquid chromatography (UPLC), coupled with high-resolution mass spectrometry (HRMS). The NIR analysis is a non-destructive, low-cost procedure that provides prompt results, while considering the morphology of different cashew apples (shape, size, and color). The UPLC-HRMS analysis is characterized by specific bioactive compounds, such as the derivatives of hydroxybutanoic acid, galloyl, and flavonoids. Furthermore, both techniques allowed the identification of a group of accessions, which presented similarities among the chemical profiling. However, to improve the understanding of cashew chemical and physical variability, further variables related to the cashew apple composition, such as edaphoclimatic conditions, should be considered for future studies. These approaches lead to the conclusion that these two tools are useful for the maintenance of BAG-Caju (Cashew Germplasm Bank) and for the cashew-breeding program.

**Keywords:** Anacardium occidentale; fast phenotyping; NIR; UPLC-HRMS; chemometrics

## 1. Introduction

Cashew (*Anacardium occidentale* L.) is a fruit tree originating from the north of South America, with greater dispersion in the coastal regions, from the state of Rio de Janeiro to the Amazon, where it is possible to find spontaneous populations with great genetic and, consequently, phenotypic variability. To conserve part of this variability, Embrapa maintains a germplasm bank (BAG-Caju) holding almost seven hundred accessions with important variability [1].

Nuts are true fruits with hard shells that are inedible due to the presence of cardol and cardanol (caustic substances). The cashew nut consists of nut kernels that are edible and commercially known as cashew nuts. This complete fruit forms on the distal end of the false fruit that is also edible (called cashew apple or peduncle) [2]. Cashew nut (kernel) is the main product of this crop marketed globally. However, Brazil, by tradition and culture, has the habit of consuming the cashew apple (false fruit) in different ways, e.g., as fresh fruit, juice, or sweets. Thus, BAG-Caju that was initially established based on the size and weight of the cashew nut began to include the variability related to the false fruit, such as vitamin C, sugars, acidity, astringency, color, and bioactive compounds [3].
Due to the elevated number of cashew apple accessions, we have investigated the applicable potential of various technologies to develop simple, precise, and low-cost methods for studying the physico-chemical and nutritional features of this fruit. Among the technologies, the near infrared (NIR) spectroscopy is considered to be an economical, fast, and efficient technique for the evaluation of foodstuffs [4–6]. The NIR analysis produces an electromagnetic spectrum (reflectance or transmittance) between 780 nm and 2,500 nm, where the wavelength depends on the scattering and absorption processes according to the chemical compositions: molecular functional groups, C-H, N-H, S-H, or O-H bonds. A MicroNIR spectrometer is a portable NIR device for real-time, in situ, and non-destructive chemical and physical analyses [7].

In particular, the cashew apple exhibits beneficial characteristics to human health, such as antitumor, antimicrobial, urease inhibitory, and lipoxygenase activities [3,8–10]. The genetic enhancement of the quality and attributes of the cashew apple is still under development in Brazil, although the knowledge of genetic diversity for the construction of germplasm banks exists. Most cashew breeding programs are based on traditional selection approaches, including the size and weight of nuts or yield of a cashew tree, and efforts have also been applied in the prospection of dwarf genotypes with enhanced fruit quality and resistance to pests and diseases [11]. Therefore, the identification of the characteristics with economic interest, related to conserved or cultivated accessions, may be appropriated by breeding programs aimed at launching high-performance cultivars to meet specific demands, such as cultivars with high levels of bioactive compounds. The analysis of unique chemical fingerprints, based on ultra-performance liquid chromatography (UPLC), coupled with high-resolution mass spectrometry (HRMS) has been successfully applied for the study of fruit pulps. In such foodstuff studies, a large number of samples have to be screened for identifying metabolites and their classification according to the geographic origin, climate conditions, genotype, and cultural practices using multivariate statistics [3,12–15]. Usually, multivariate analyses are applied, in order to explore complex matrices, such as foodstuff to determine the variations and relationships among the compositions of the samples [16,17]. This untargeted approach is advantageous when the compounds and degradation products are not known.

From the foregoing studies, the aim of the present study was to evaluate the potential of a MicroNIR spectrometer to explore the composition of different genotypes of intact cashew apple from the Embrapa germplasm bank, as well as to create multivariate regression models considering °Brix, total acidity, and concentrations of ascorbic acid (vitamin C). Furthermore, due to the need to investigate plants that produce fruits with numerous compounds beneficial to human health, the study of metabolite profiling (nutritional and functional compounds) to reveal the variability of pulps from different genotypes of cashew apple was also investigated.

## 2. Results

The results were divided according to the analytical technique applied to evaluate the cashew apple variability: Section 2.1 for MicroNIR and Section 2.2 for UPLC-HRMS.

### 2.1. Exploratory Multivariate Analysis of the MicroNIR Dataset

The NIR analysis contains undesirable sources of error (intrinsic imperfections) for multivariate studies, and therefore, pre-treatments can correct many systematic or random errors. The information from the cashew apple composition was partly obscured (overlapped), and the distribution of the variables was highly skewed by usual and expected spectral imperfections. Three pre-treatments of the MicroNIR dataset were tested to assess the features regarding the cashew apple composition, minimizing irrelevant variations within the spectra to obtain quality data: Multiplicative scatter correction (MSC), standard normal variate (SNV), and first derivatives, using Savitzky-Golay filter with a second order polynomial for five points (Figure S1 in Supporting Information). Therefore, the pre-processing by MSC was chosen as an input for the following chemometric analysis.
To comprehend and classify the variability of the organic compounds in intact cashew apples according to the genotype, via detecting the tendencies related to the composition, a method based on hierarchical clustering was developed to segregate the samples in groups according to the composition similarity. Figure 1 presents a 3D dendrogram in heat map form: Genotypes with the harvest year in columns; wavelength in rows, and the signals intensities illustrated in colors. The relatively deep red represents the high relative intensity (wavelength); the relatively deep blue, the low relative intensity; and white, the intermediate intensity. Important tendencies for four cluster formations were observed in the heat map based on genotype dissimilarity (Figure S3 in Supporting Information). Cluster 4 presented the most dissimilar genotypes (zero similarity). The results reflected the natural differences among the groups, which were formed by different spectroscopic information from cashew genotypes pulp composition.

<figure><img src="image_12.png" /><figcaption>**Figure 1.** Three-dimensional (3D) dendrogram (sample × wavelength from MicroNIR × intensity) representing the chemical composition similarity relationships among the genotypes.</figcaption></figure>

## Multivariate Regression Analysis of the MicroNIR Dataset

Prior to performing the partial least squares (PLS) analysis, the °Brix value, total acidity, and concentrations of ascorbic acid (vitamin C) were determined in 31 cashew apples randomly chosen from the set of accessions. Three regression models were created for each dependent variable (°Brix, total acidity, and ascorbic acid) with spectroscopic data to evaluate the association between the chemical variability, genotype and NIR profile. Sequential to these supervised regression modeling, the PLS by intervals, known as iPLS (interval PLS), was developed to maximize the covariance between the independent variables on the MicroNIR dataset (X matrix) and each dependent variable. This method realizes individuals PLS models for each pre-defined spectra intervals, optimizing the predictive capacity of the model, while assisting the interpretation by reducing the number of variables, thereby providing superior prediction capacity using all the variables [18]. Figure 2 illustrates the relevant intervals (highlighted in green color) for regression modeling, based on RMSECV using the Brix values (a) and total acidities (b). The modeling using the concentrations of ascorbic acid was weakly adjusted based on statistical parameters (data not shown).
<figure><img src="image_13.png" /><figcaption>**Figure 2.** Spectral average (black line) of the MicroNIR spectra from different genotypes of cashew. The relevant absorbance selected by the iPLS model (green) for the genotype discrimination based on (a) °Brix and (b) total acidity.</figcaption></figure>

The merit graph obtained by regression modeling, using °Brix and total acidities, were evaluated to assess the quality of the calibration models, which are illustrated in Supplementary Information (Figures S4 and S5). The Hotelling's T² × Q residuals graph indicates that the sample did not negatively influence the modeling [19]. The leverage plot revealed the influence of each genotype on models based on Hotelling's T², and the studentized residuals (mean zero and unit variance) indicated the lack of fit of some quantitative parameters [20]. However, despite the high leverage and high residuals, the respective genotypes expressed a very low studentized Y residual, and therefore, the regression model was still able to sufficiently predict the °Brix and total acidity based on selected MicroNIR spectra (green region in Figure 2). The robustness of both prediction models (°Brix and total acidity) was achieved by the proximity between both regression curves from calibration and cross-validation (red and green lines). Furthermore, the statistical parameters used to assess the modeling quality (Table 1) indicated a well-adjusted models for both °Brix and total acidity, according to the high total variance cumulated using three latent variables (LVs) (higher than 94%), low bias model, low calibration and cross-validation errors, and proximity between the calibration and cross-validation errors. Additionally,
the root mean square error of calibration (RMSEC) and root mean square error of cross-validation (RMSECV) ratio, close to 0.75, is indicative of a well-adjusted model [21,22].

<table><caption>**Table 1.** Statistical parameters obtained by multivariate regression modeling of MicroNIR spectra with Brix and total acidity using 3 LV.</caption><thead><tr><th>Model</th><th>3 LV<sup>1</sup> (%)</th><th>r<sup>2</sup> cal<sup>2</sup></th><th>RMSEC<sup>3</sup></th><th>r<sup>2</sup> CV<sup>4</sup></th><th>RMSECV<sup>5</sup></th><th>RMSEC/RMSECV<sup>6</sup></th><th>Bias<sup>7</sup></th><th>CV Bias<sup>8</sup></th></tr></thead><tbody><tr><td><sup>6</sup>Brix</td><td>96.5</td><td>0.74</td><td>0.11</td><td>0.46</td><td>0.16</td><td>0.69</td><td>3.3 × 10<sup>-15</sup></td><td>-0.004</td></tr><tr><td>Acidity</td><td>97.1</td><td>0.66</td><td>1.19</td><td>0.46</td><td>1.53</td><td>0.78</td><td>1.8 × 10<sup>-15</sup></td><td>0.064</td></tr></tbody></table>

## 2.2. UPLC-HRMS

Commonly, in UPLC-HRMS analysis, some organic compounds may preferentially ionize in the positive or negative ionization mode, as phenolic and carboxylic derivatives ionized well in negative ionization mode, while flavonoids and alkaloids ionize better in positive ionization mode. Therefore, the cashew apple pulps were analyzed under a negative ionization mode to screen the aforementioned compounds and differentiate each cashew apple pulp according to the genotypes. Due to the complexity of the chromatographic data, visual differentiation of the sample composition could not be achieved. Therefore, non-targeted multivariate analyses by hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to comprehend the variability of the secondary metabolites in cashew apple pulps according to the genotype.

Initially, the unsupervised method HCA was applied: Segregating the pulps in groups according to similarity. Important tendencies for four cluster formations were observed in the dendrogram based on the genotype at a similarity index of 0.362 (Figure S3 in Supporting information). Cluster 4 presented the most distant samples included in the study, with zero similarity (as also observed in Figure 1). The results reflected the natural differences among the groups, which were formed as a function of pulp composition, dependent on the cashew genotype. In addition, the PCA method was applied to assist the modeling and interpretation of the multivariate data, with the scores graph presented in Figure 3a, and the respective loadings (plotted in lines) in Figure 3b. The tentatively identified biomarkers are presented in Table S1 (Supporting information). The main separation tendencies of the samples were observed with respect to PC1 and PC2 axes with a total explained the variance of 36.7%. The clusters observed in HCA were important for identifying the resultant grouping in the scores graph. The pulps were assigned symbols according to the clustering tendency: Blue triangles for negative scores of PC1 and PC2; red squares for positive scores of PC1 and negative scores of PC2; black stars for positive scores of PC1 and PC2; and green circles for positive scores of PC2. The samples with no relevant result were symbolized by gray circles considering the unrepresentative replicates according to the year.

<footnote n="1">¹ Percentage variance captured by the regression model; ² Coefficient of correlation between the real and predicted values during the calibration; ³ Root mean square error of calibration; ⁴ Coefficient of correlation between the real and predicted values during the cross-validation; ⁵ Root mean square error of cross-validation; ⁶ Similarity criterion; ⁷ Average difference between the estimator and real values during the calibration; ⁸ Average difference between the estimator and real values during the cross-calibration.</footnote>
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>**Figure 3.** Principal component analysis (PCA) multivariate analysis of UPLC-HRMS data from cashew apple pulps of different genotypes: a) PC1 × PC2 scores coordinate system for the cashew apple pulps from different genotypes; b) respective loadings plotted in lines form. The samples were assigned symbols according to the clustering tendency: blue triangles for negative values of PC1 and PC2; red squares for positive values of PC1 and negative values of PC2; black stars for positive values of PC1 and PC2; green circles for positive values of PC2. The samples that did not present relevant results were symbolized by gray circles.</figcaption></figure>

Compounds with significant changes, based on genotypes according to the chemometric evaluation, and not exhibiting overlapping signals, were integrated (details in Experimental Section 4.2.2), and their variations were expressed as the relative contribution. The relative peaks areas were calculated for quantitative expression of the chemical properties, with the differences evaluated by ANOVA single factor. Figure 4 illustrates the relative contributions of the total ion abundance of the peaks in the pulps, since the relative amplitude of the peaks provided the relative population of the isotopic forms in the chromatograms. The results from the signal area of the base peak intensity (BPI) corroborated the PCA results, considering the deviation of the method from the three replicates of sampling during two years, which totaled 6 pulps for each genotype, with the hydroxybutanoic acid ethyl ester-hexoside (at 3.28 min) being responsible for the pulps clustering at the red group, and galloylhexose I (at 1.60 min) and digalloylhexoside I (at 2.82 min) for the pulps clustering in the blue group.
<figure><img src="image_16.png" /><figcaption>**Figure 4.** Relative contributions of the isotopic forms in chromatograms (UPLC-HRMS) for the compounds at retention times of 1.60, 2.82, 3.23, 3.82, and 4.25 min.</figcaption></figure>

### 2.2.1. Multivariate Classification Analysis of the UPLC-HRMS Dataset

Based on the non-targeted chemometrics results and the relative quantification, the classification modeling by partial least squares-discriminant analysis (PLS-DA) was employed to improve the association of the chemical variability of the pulps according to the cashew genotype, which is illustrated in Figure 5. The model presented a classification ability of 88.05% using 3 LVs, considering the year replicate. The statistical parameters used to assess the quality of the modeling (Table 2) indicated a well-adjusted classification, with an RMSEC/RMSECV ratio close to 0.75 (similar values) [22]. The low calibration and cross-validation errors expressed a suitable predictive performance of the model estimated as a function of the global error, samples leverage, and the sample residual X-variance. The low error values indicated the similarity between the pulps used for prediction and those used to make the calibration model.

<table><caption>**Table 2.** Parameters from partial least squares-discriminant analysis (PLS-DA) classification model of UPLC-HRMS data from cashew apple pulps of different genotypes.</caption><thead><tr><th>Model</th><th>LV1+LV2+LV3<sup>1</sup></th><th>r<sup>2</sup> cal<sup>2</sup></th><th>RMSEC<sup>3</sup></th><th>r<sup>2</sup> val<sup>4</sup></th><th>RMSECV<sup>5</sup></th><th>RMSEC/RMSECV<sup>6</sup></th></tr></thead><tbody><tr><td>PLS-DA</td><td>88.05%</td><td>0.88</td><td>0.298</td><td>0.85</td><td>0.341</td><td>0.874</td></tr></tbody></table>

<footnote n="1">¹ Total variance percent in X matrix refer to 3 LVs; ² Coefficient of correlation between the real and predicted groups during the calibration; ³ Root mean square error of calibration; ⁴ Coefficient of correlation between the real and predicted groups during the cross-validation; ⁵ Root mean square error of cross-validation; ⁶ Similarity criterion.</footnote>
<figure><img src="image_17.png" /><figcaption>**Figure 5.** PLS-DA classification model of UPLC-HRMS data from cashew apple pulps of different genotypes: (a) LV1 × LV2 × LV3 scores 3D plot; (b) respective loadings potted in lines form (c) from PLS-DA for classification of the cashew apple pulps. Legend: 1-galloylhexose I (1.60 min); 2-digalloylhexoside I (2.82 min); 3-hydroxybutanoic acid ethyl ester-hexoside (3.28 min); 4-myricetin-3-O-glucoside (3.83 min); and 5-myricetin-3-O-rhamnoside (4.25 min).</figcaption></figure>

**3. Discussion**

It is known that NIR spectroscopy is a non-destructive, low-cost, and non-invasive procedure providing prompt results for a sample composition: Any molecule containing C-H, N-H, S-H, or O-H bonds. However, due to the intrinsic overlapping of the signals, pre-processing is mandatory. The MSC and SNV algorithms presented similar effects, which were considered as exchangeable (Figure S1 in Supporting Information). After the application of the MSC algorithm, the spectra were adjusted to present the same scatter level estimated by a mean spectrum. Conversely, the SNV algorithm treated
each spectrum separately by absorbance autoscaling [23]. The derivative algorithm emphasized steep peaks and enhanced the overlapping peaks, as well as reduced the measurement variations, thereby improving the differentiation of the bands. The first and/or second derivatives are more commonly applied, and the second derivative NIR spectrum may result in sharp signals [24,25]. Therefore, the MSC algorithm was chosen for the development of the experiments.

According to the HCA-heat map (Figure 1), important tendencies for four main cluster formations were observed based on the genotype, even with differences in fruit morphology, such as shape, size, and peel color. Another disadvantage of NIR analysis was the penetration of radiation into the tissues of fruits, which skin may reduce the light penetration that decreases with the depth [26]. The results reflected the natural differences among the cashew apple groups formed as a function of the composition intrinsically related to the genotype. Cluster 4 presented the most dissimilar samples compared to those of the other groups. The main absorbance peaks, related to the clustering, were located between 1150 and 1340 nm, 1370 and 1850 nm, and 1900 and 2020 nm. The absorbance from 2040 to 2170 nm was characterized as indicative of non-relevant functional groups. The absorbance from 1400 to 1620 nm and 1900 to 2020 nm were characterized as the second and first overtone of the OH stretch, respectively. The absorbance from 1150 to 1340 nm and 1650 to 1850 nm were due to the C-H stretches related to the second, and first overtone, respectively, which may be from carbohydrates and other organic compounds present in the cashew apple skin and/or pulp. The band at 1340 nm is attributed to the CH group from cellulose [27]. Additionally, it was highlighted that the O-H group from monomeric organic acids presented the first overtone at 1445 nm; and a characteristic overtone around 1890 nm indicative of the O-H stretching combined with C-O stretching from organic acids [26,28]. However, it is known that all these absorption bands are close to the stronger water absorption regions, hindering the signals observation [29].

The iPLS evaluation highlighted the region between 1250 and 1400 nm as the most important for fruit discrimination, based on the calibration modeling by °Brix values and total acidity (statistical parameters, described in Table 1. The modeling for ascorbic acid was weakly adjusted based on statistical parameters. In particular, the absorbance range between 1570 and 1650 nm was important for the °Brix model, while the absorbance between 1800 and 1900 nm was important for the total acidity. The absorbance between 1650 and 1850 nm may be related to the C-H stretches from the second, and first overtone, respectively (Figure 1), which may be from carbohydrates and other organic species in cashew apple skin and/or pulp; and between 1800–1900 nm may be related to carbonyl and carboxyl groups from carboxylic acids [27]. The regression modeling °Brix values (a) and total acidities (b) were well adjusted based on statistical parameters, and the modeling for ascorbic acid was weakly adjusted. This may be because the MicroNIR was operated between 1150–2170 nm, and some organic acids found in fruits typically show bands from O-H group related to the second and third overtones approximately at 1000, and 800 nm, respectively, as well as starch and sugars to the second (920 nm) and the third (720 nm) overtones of O-H stretching, and the third (910 nm) and the fourth (750 nm) overtones of C-H stretching [28].

The relatively low correlation coefficients (r²) for both models (°Brix values and total acidity) express the rather weak calibration performances estimated as a function of the global model error, samples leverage, and the sample residual X-variance. Therefore, the cross-validation results indicated that further parameters related to the fruits morphology (shape, size, and color), non-homogeneous distribution of particles into the fruits (density variations), and environmental factors that may affect the instrument performance as the illumination (since all the analysis was developed during the year), must be taken into account for improving the comprehension of the chemical and physical variability of the cashew apples from the germplasm bank of Embrapa. For instance, small physical variations from sample to sample may lead to light scattering that influences the MicroNIR measurement, resulting in baseline shifts and scaling variations (intensity variations), and consequently, disturbing the future predictions evaluation.
Prior to the chemometric analysis of the HPLC-HRMS analysis, a valuable feature of chromatograms acquisition was taken into account, since the retention times of the chromatographic peaks are sensitive to minor fluctuations in temperature, pH, flow, pump operation, etc. To solve the problem of small peaks shifts related to the same compound inter-chromatograms, some different peak alignment methods were tested. The alignment practice can be performed manually, using COW (correlation optimized warping) [30], or using the bucketing method, which reduces the chromatogram dimensionally by slicing it into equal sized regions [31], making it easier to analyze the respective loadings. Therefore, all the chromatogram peaks were previously aligned using the COW method, which is illustrated in Figure S2 in Supporting Information.

Significant composition variability was detected in PCA regarding cashew genotypes despite the low cumulated variance, which indicated the existence of further factors beyond the scope of this study, such as seasonality. An examination of the loadings provided evidence of the variables (compounds) responsible for the separations or clustering observed in scores. The signals from galloylhexose I (at 1.60 min, 331.0650 *m/z*), digalloylhexoside I (at 2.82 min, 483.0741 *m/z*), hydroxybutanoic acid ethyl ester-hexoside (at 3.28 min, 293.1242 *m/z*), myricetin-3-O-glucoside (at 3.83 min, 479.0826 *m/z*), myricetin-3-O-rhamnoside (at 4.25 min, 463.0875 *m/z*), and a mixture of unknown compounds (at 4.82 min) were responsible for the pulps placement based on genotypes. Minor compounds were irrelevant due to the pretreatments of mean centering applied over the samples [16]. According to Figure 3, the negative loadings of PC1 are the pulps symbolized by blue triangles (2001/3, 2001/6, 2005/122, B 963, and B967) with relatively high amounts of galloylhexose I and digalloylhexoside I. This latter compound is a trihydroxybenzoic acid derivative, which provides astringent flavor and can contribute to the characteristic bitter taste of immature cashew apple [32]. The pulps symbolized by red squares (CP 06, CP 09, B 393, and BRS 275) and black stars (2005/127, 2005/133, 2005/102, BRS 226, CP 76, 98/101) exhibited tendencies of having relatively high amounts of hydroxybutanoic acid ethyl ester-hexoside at a retention time of 3.28 min according to positive loadings of PC1. Hydroxybutanoic acid ethyl ester-hexoside has been previously described in melon fruit and it is considered a precursor of volatile compounds [33]. The presence of this compound has been associated with some amino acids, such as alanine, glutamine, isoleucine, phenylalanine, tryptophan, and tyrosine. Alanine has been reported as one of the key amino acids of the characteristic profile of cashew apple. Phenylalanine and tyrosine have also been detected but in small amounts [34]. The positive loadings of PC2 exhibited the tendency of the cashew apple pulps symbolized by green circle (2005/111, 2005/223, 2001/13, 98/116, and B 741) and black stars (2005/127, 2005/133, 2005/102, BRS 226, CP 76, 98/101) to have relatively high amounts of the flavonoids, myricetin-3-O-glucoside, myricetin-3-O-rhamnoside, and an unknown mixture of compounds between the retention times of 4.79 and 4.85 min. The presence of myricetin-derived and other flavonoids compounds in pulps may offer biological benefits, including the reduction of cardiovascular disease and risks of cancer [3], as well as antihyperglycemic property [35]. Furthermore, these compounds have been reported in methanol-water extracts of cashew apple [3]. The corroborative results between quantitative and chemometric analyses confirm the advantages of an untargeted multivariate analysis, since the compounds and degradation products are not always known, and it is sometimes difficult to find certified standards.

According to Figure 5, the LV1 axis was most relevant in clustering the cashew apple pulps in black color (2005/102, 2005/111, 2005/127, 2005/133, 2005/223, 2001/13, 98/101, 98/116, BRS 266, B 741, CP 76), and in separating them from those in red (CP 06, CP 09, B 393) and blue (2001/3, 2001/6, 2005/122, B 963, B 967). In addition, the LV2 axis was important in clustering the red samples at the positive scores, and LV3 was relevant in the separation of the red and blue cashew apple pulps. The interpretation of the loadings revealed that the pulps in black had higher amounts of galloylhexose I and digalloylhexoside I than those of in red and blue. The results corroborated that the pulps in red color have a higher amount of hydroxybutanoic acid ethyl ester-hexoside than those in the black and blue pulps; in addition, opposite behavior between the hydroxybutanoic acid ethyl ester-hexoside and galloylhexose I was presented. Finally, the LV3 axis confirmed the relatively high
amount of the flavonoids, myricetin-3-O-glucoside and myricetin-3-O-rhamnoside, in the pulps in blue and presented the opposite behavior between these flavonoids and hydroxybutanoic acid ethyl ester-hexoside. Therefore, the statistics data indicated that the model could be acceptable to classify new or unknown cashew apple pulps based on the main secondary metabolites.

# 4. Materials and Methods

## 4.1. *Sampling and Experimental Planning*

Based on experimental viability, 764 cashew apples (*Anacardium occidentale*, L.) from 24 different accessions were randomly harvested at the Embrapa experimental station (Pacajus, Ceará, Brazil)—coordinates 4°11'07"S; 38°30'07"W; altitude: 70 m. The region has a tropical climate, average temperatures of 26 to 28 °C, and 1020 mm average annual rainfall. The soil is classified as Ultisol and has a sandy/medium texture with low organic matter content. The cashew samplings were collected between August and December in two years. Table 3 presents the different genotypes with their respective accession numbers and morphoagronomic characteristics.

<table><caption>**Table 3.** Illustration of the cashew genotypes associated with the accession number and morphoagronomic characteristics: plant size, tree appearance, fruits color and shape, and origin (county-state).</caption><thead><tr><th>Accession Number</th><th>Plant Size</th><th>Tree Appearance</th><th>Fruit Color</th><th>Fruit Shape</th><th>Sampling Origin</th><th>Illustration</th></tr></thead><tbody><tr><td>CP 76</td><td>tall</td><td>Open erect</td><td>orange</td><td>pyriform</td><td>crop*/Maranguape-CE</td><td></td></tr><tr><td>Clone 98/101</td><td>semi tall</td><td>compact erect</td><td>orange</td><td>pyriform</td><td>breeding program*/Pacajus-CE</td><td></td></tr><tr><td>Progeny 2005/127</td><td>semi tall</td><td>compact erect</td><td>dark red</td><td>pyriform</td><td>breeding program/Beberibe-CE</td><td></td></tr><tr><td>Progeny 2005/133</td><td>semi tall</td><td>open erect</td><td>orange</td><td>spherical</td><td>breeding program/Cruz-CE</td><td></td></tr></tbody></table>

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<table><caption>Table 3. *Cont.*</caption><thead><tr><th>Accession Number</th><th>Plant Size</th><th>Tree Appearance</th><th>Fruit Color</th><th>Fruit Shape</th><th>Sampling Origin</th><th>Illustration</th></tr></thead><tbody><tr><td>BRS 226</td><td>dwarf</td><td>compact erect</td><td>orange</td><td>pyriform</td><td>crop/Pio IX-PI</td><td></td></tr><tr><td>Clone 2005/102</td><td>tall</td><td>compact erect</td><td>orange</td><td>pyriform</td><td>breeding program/Beberibe-CE</td><td></td></tr><tr><td>CP 09</td><td>semi tall</td><td>compact erect</td><td>orange</td><td>pyriform</td><td>crop/Maranguape-CE</td><td></td></tr><tr><td>B 393</td><td>tall</td><td>compact erect</td><td>light red</td><td>spherical</td><td>germplasm*/Aracati-CE</td><td></td></tr><tr><td>BRS 275</td><td>semi tall</td><td>open erect</td><td>orange</td><td>pyriform</td><td>crop/Pacajus-CE and Maranguape-CE</td><td></td></tr><tr><td>B 963</td><td>tall</td><td>open erect</td><td>yellow orange</td><td>pyriform</td><td>germplasm/Maranguape-CE</td><td></td></tr><tr><td>Hybrid 2001/3</td><td>semi tall</td><td>compact erect</td><td>orange</td><td>pyriform</td><td>breeding program/Maranguape-CE and Pio IX-PI</td><td></td></tr></tbody></table>

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<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>
<table><caption>Table 3. *Cont.*</caption><thead><tr><th>Accession Number</th><th>Plant Size</th><th>Tree Appearance</th><th>Fruit Color</th><th>Fruit Shape</th><th>Sampling Origin</th><th>Illustration</th></tr></thead><tbody><tr><td>Hybrid 2001/6</td><td>semi tall</td><td>compact erect</td><td>yellow orange</td><td>pyriform</td><td>breeding program/Maranguape-CE and Pio IX-PI</td><td></td></tr><tr><td>B 967</td><td>tall</td><td>open erect</td><td>orange</td><td>cylindrical</td><td>germplasm/Pacajus-CE</td><td></td></tr><tr><td>CP 06</td><td>tall</td><td>open erect</td><td>yellow</td><td>conical obovate</td><td>crop/Pacajus-CE</td><td></td></tr><tr><td>Progeny 2005/122</td><td>semi tall</td><td>open erect</td><td>yellow</td><td>spherical</td><td>breeding program/Beberibe-CE</td><td></td></tr><tr><td>Hybrid 2001/13</td><td>semi tall</td><td>open erect</td><td>orange</td><td>pyriform</td><td>breeding program/Pacajus-CE</td><td></td></tr><tr><td>Clone 2005/111</td><td>semi tall</td><td>open erect</td><td>orange</td><td>pyriform</td><td>breeding program/Serra do Mel-RN</td><td></td></tr><tr><td>Clone 98/116</td><td>semi tall</td><td>open erect</td><td>orange</td><td>pyriform</td><td>breeding program/São Luiz do Curu-CE</td><td></td></tr></tbody></table>

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>
<table><caption>Table 3. *Cont.*</caption><thead><tr><th>Accession Number</th><th>Plant Size</th><th>Tree Appearance</th><th>Fruit Color</th><th>Fruit Shape</th><th>Sampling Origin</th><th>Illustration</th></tr></thead><tbody><tr><td>B 741</td><td>semi tall</td><td>compact erect</td><td>orange</td><td>pyriform</td><td>breeding program (CP 76 x A. microcarpum)/Maranguape-CE</td><td></td></tr><tr><td>Progeny 2005/223</td><td>semi tall</td><td>open erect</td><td>orange</td><td>pyriform</td><td>breeding program/Beberibe-CE</td><td></td></tr><tr><td>Embrapa 51</td><td>semi tall</td><td>open erect</td><td>orange</td><td>pyriform</td><td>crop/Pacajus-CE</td><td></td></tr><tr><td>M 886</td><td>tall</td><td>open erect</td><td>yellow</td><td>spherical</td><td>breeding program/Beberibe-CE</td><td></td></tr><tr><td>1001</td><td>tall</td><td>open erect</td><td>orange</td><td>pyriform</td><td>crop/Pacajus-CE</td><td></td></tr><tr><td>Clone 2003/102</td><td>semi tall</td><td>compact erect</td><td>orange</td><td>pyriform</td><td>breeding program/Pio IX -PI</td><td></td></tr></tbody></table>

## 4.2. Portable NIR Spectrometer Analysis

The portable NIR (MicroNIR) analysis of the cashew apple composition was divided into two stages. First, all the 764 intact fruits were analyzed by MicroNIR. After that, 31 fruits were randomly selected for the determination of quantitative parameters, such as °Brix, total acidity, and concentration of ascorbic acid (vitamin C) to develop multivariate regression models.

The NIR experiment was acquired using a portable NIR spectrometer (MicroNIR 1700, Viavi, Milpitas, CA, USA), which operated in a range between 1150 and 2170 nm (spectral resolution of 10 nm), with dimensions of 45 mm diameter × 42 mm high, two tungsten sources for reflectance measurements, and a continuous monochromator based on a linear variable filter. The parameters for

<footnote>* Legends: crop means registered product on market; breeding program means plant before crossbreed; germplasm means plant collected and conserved in the germplasm bank.</footnote>
spectral data acquisition were set at 50 ms integration time and an average of 100 scans. The reference spectrum for the absorbance calculation was obtained from a piece of Spectralon™, and the dark signal was obtained by pointing the measurement window of the instrument to the ambient environment.

### 4.2.1. Determination of °Brix, Total Acidity, and Concentration of Ascorbic Acid

The °Brix, total acidity, and concentration of ascorbic acid (vitamin C) were determined in 31 cashew apples randomly chosen from the set of accessions. These experiments were carried out to use the quantitative results as categorical variables (Y column) to develop multivariate regression models by partial least square (PLS) analyses by maximizing the covariance between X matrix (NIR spectral data) and Y responses (°Brix, total acidity, and ascorbic acid as dependent variables).

The °Brix (concentration of sucrose w/w) was determined following the AOAC method (2005) [36] (soluble solids content), which was obtained by refractometry using a digital refractometer (ATAGO™ N1, Kirkland-WA-USA) with automatic temperature compensation.

The total acidity was determined as follows: Total titratable acidity (TTA) determined by titration with NaOH solution (0.1 N) in 1 g of the pulp diluted to approximately 50 mL of distilled water, containing 3 drops of 1% phenolphthalein until pink coloration, was observed. The results were expressed as the percentage of malic acid according to IAL (1985) [36].

The concentrations of ascorbic acid were determined by titration with 0.02% 2,6-dichloro-indophenol (DFI) as reported by Strohecker and Henning (1967) [37]. One gram of pulp was diluted to 100 mL with 0.5% oxalic acid and homogenized. Subsequently, 5 mL of this solution was diluted to 50 mL with distilled water and titrated. The results were expressed as mg·100 g⁻¹ FW (fresh weight).

### 4.2.2. Chemometric Analysis of the MicroNIR Dataset

Different multivariate approaches were performed on the numerical matrix from 764 cashew apple fruits (Section 2.1). The averaging method was applied on those fruits from the same accession (from four to six replicates), resulting in 135 mean spectra. The spectral region between 1150 and 2170 nm was used for the modeling, and a matrix with the dimensionality of 16,875 data points (135 spectra × 125 variables into each spectrum) was generated. The samples were named according to the accession number and year of harvest.

For numerical matrix construction, each spectrum was converted to an American Standard Code for Information Interchange (ASCII) file and imported by the Origin™ program (version 9.4). To reduce the dimensionality of the original data and to assist the interpretation of the multivariate dataset, the matrix was averaged along variables by a factor of 2 using the PLS-Toolbox™ program (version 8.6.2, Eigenvector Research Incorporated, Manson, WA USA), and imported by GENE-E program (https://software.broadinstitute.org/GENE-E/index.html) for pattern recognition through the hierarchical clustering algorithm by heat map. The Euclidean distance was used to measure the proximity between the samples (columns), and the average linkage method (sum-of-squares approach in calculating intercluster distances) was applied. The results were presented as heat maps (three-dimensional (3D) dendrogram = sample × wavelength × intensity) [22].

In addition to the unsupervised analysis, supervised methods by PLS were developed using the °Brix values, total acidity, and concentrations of ascorbic acid previously calculated in Section 2.2.1 to improve the identification of chemical changes according to genotypes by MicroNIR. The simplified PLS (SIMPLS) algorithm was applied to build the models and the LVs were selected in accordance with the statistical parameters based on the full cross-validation method: RMSEC, RMSEV, and calibration and cross-validation coefficients (r²) [16].

## 4.3. UPLC-HRMS Analysis

Due to the health benefits of the cashew apple, additional experiments were developed in parallel by non-targeted UPLC-HRMS analysis. A total of 24 different genotypes of cashew apple (as described
in Section 2.1) were evaluated. The cashew apples were manually pressed to obtain the resultant pulp, followed by centrifuged for 5 min at 804.6 g (IEC clinical centrifuge, Damon/IEC Division, Needham, MA, USA). The samples were preserved at -80 °C until the analysis.

Prior to the UPLC-HRMS analysis, the samples were filtered using PTFE membranes of 0.22 µm. The analysis was performed on an Acquity system (Waters) coupled with quadrupole/TOF (Waters) equipped with an ESI source operated in the positive ion mode. The chromatographic separation was performed using Waters Acquity UPLC BEH (150.0 × 2.1 mm, 1.7 µm) column with the temperature set at 40 °C. Water and acetonitrile were used for the mobile phase, both with 0.1% formic acid. The gradient ranged from 2% to 95% of water in 15 min in a flow of 0.4 mL·min⁻¹ and injection volume of 5.0 µL per sample. The desolvation gas was N₂. The desolvation temperature was set at 350 °C at a flow rate of 350 L·h⁻¹ and a source temperature of 120 °C. The capillary voltage was set to 3200 V. The collision energies/cone voltages were set at 6 eV/15 V (low) and 30–50 eV/30 V (high) to achieve sufficient fragmentation. Data were collected using the negative ionization mode between 100 Da and 1180 Da, and the mode tandem was MS⁵.

### 4.3.1. Chemometric Analysis of the HPLC-HRMS Dataset

Chemometric analysis was performed on the numerical matrix from 24 cashew apples harvested in duplicates this year, and analytical triplicate, resulting in 144 chromatograms. The chromatographic region between 0.65 and 7.12 min was selected. The samples were named according to the accession numbers (Table 3).

The same procedure applied for numerical matrix construction from MicroNIR (Section 4.2.2) dataset was also applied for the UPLC-HRMS dataset. Therefore the chromatograms were converted to an American Standard Code for Information Interchange (ASCII) file and imported by the Origin™ program (version 9.4) in order to build the matrix. The final matrix was exported for chemometric analyses by HCA, PCA, and PLS-DA using PLS Toolbox™ program (version 8.6.2, Eigenvector Research Incorporated, Manson, WA, USA).

The normalized scaling parameter and baseline correction, using linear fit algorithms, were applied over the variables, and mean-centered processing was applied over the samples, which reduced the noise and minor analytical errors [38,39]. For HCA, the matrix was mean-centered, and the incremental linkage method (sum-of-squares approach in calculating the inter-cluster distances) was applied. The Euclidian distance was used for distance metric. The PCA was performed using singular value decomposition (SVD) algorithm. To improve the identification of the chemical constituents associated with cashew genotype, a supervised method by PLS-DA was employed using the SIMPLS algorithm. The number of LVs were selected in accordance with the statistical parameters: RMSEC; RMSECV; calibration and cross-validation coefficients ($r^2$); and similarity criterion RMSEC/RMSECV ratio above 0.75 [16,21].

### 4.3.2. Relative Contribution

The peaks detected as exactly as possible, in both m/z and retention time, were used for determining the peak area for achieving the relative contribution of the compounds with less overlapped signals in the chromatograms. The relative contribution of the areas was calculated based on the total ion abundance from the peaks in the samples, since the relative amplitude of the peaks provides the relative abundance of the isotopic forms in the chromatograms. Therefore, the normalized means in the base peak intensity (BPI) at the retention times of 1.60, 2.82, 3.28, 3.83, and 4.25 min were determined.

The results were evaluated using the analysis of variance ANOVA single factor (significance level of 0.05; means comparison using Tukey test; Levene's test for the homogeneity of the variance) to statistically certify the differences among the relative contributions. The deviation of the method was estimated based on the null hypothesis (*p*-value) from the three replicates of sampling for two years, totaling 6 samples for each cashew genotype.
**5. Conclusions**

It was demonstrated that MicroNIR spectroscopic analyses of the cashew apple composition provided a non-destructive and low-cost method for obtaining prompt results. In addition, important composition tendencies were observed with four fruits clusterings according to their composition similarity, and genotype, even considering the morphologic differences, including shape, size, and color. The multivariate regression results, obtained using °Brix and total acidity, showed that it is possible to satisfactorily predict °Brix and total acidity within the cashew genotypes. However, the parameters related to the fruit composition, and the environmental factors that affect the instrument performance must be taken into account to improve the comprehension of the chemical and physical variability of the cashew apples from the germplasm bank of Embrapa.

Additionally, the chemometrics evaluation of the UPLC-HRMS dataset was suitable to follow changes in the composition of cashew apple pulps according to genotype. The current study resulted in the identification of relatively high amounts of different bioactive compounds, including galloylhexose I, digalloylhexoside I, hydroxybutanoic acid ethyl ester-hexoside, and the flavonoids, myricetin-3-O-glucoside and myricetin-3-O-rhamnoside, in different genotypes. This information is useful for breeding programs to establish accessions with higher concentrations of important compounds for human health.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2218-1989/9/7/121/s1. Figure S1: (a) Raw absorbance spectra from 135 cashew apples obtained using the portable NIR spectrometer, and the same spectra after the following treatments, (b) MSC, (c) SNV, and (d) first derivative using the Savitzky-Golay filter with a second order polynomial for five points. Figure S2: The total ion chromatograms from 24 different genotypes of cashew apple pulps acquired under negative ionization mode. Figure S3: Dendrogram representing the chemical composition similarity relationships among the cashew apple pulps. Figure S4: Regression modeling using the MicroNIR dataset from different genotypes of cashew apples based on °Brix values: a) influence plot of Hotelling's T² × Q residuals, (b) leverage × studentized residuals, c) Y calibration × cross-validated Y with 95% confidence limits, and d) scores on LV1 × LV2. Figure S5: Regression modeling using the MicroNIR dataset from different genotypes of cashew apples based on total acidity: a) influence plot of Hotelling's T² × Q residuals, (b) leverage × studentized residuals, c) Y calibration × cross-validated Y with 95% confidence limits, and d) scores on LV1 × LV2.

**Author Contributions:** All authors provided critical feedback and helped shape the research, analysis, and manuscript writing. Particularly, the researchers E.d.B. (Chemist) and E.S. (Agronomist) conceived the presented idea, theory development, and investigated the study viability. The researchers S.M. (Chemist), G.Z. (Chemist) and K.C. (Chemist) helped to plan and supervise the project. The researchers A.C.C. (Biologist) and E.S. (Agronomist) planned and designed the agronomical experiments. The authors L.M.S. (Chemist) and P.R. (Chemist) verified and developed the analytical methods. The author Y.L. (graduate) helped in chemical experiments and contributed to sample preparation. The author E.A.F. (Chemist) developed the chemometric experiments and wrote the manuscript together with L.M.S. under the E.d.B supervising.

**Funding:** This research was funded by CNPq, grant number 314737/2018-9; and FUNCAP, grant number DCR-0024-01686.01.00/15.

**Conflicts of Interest:** The authors declare no conflict of interest.

**References**

1. Caramês, E.T.; Alamar, P.D.; Poppi, R.J.; Pallone, J.A.L. Quality control of cashew apple and guava nectar by near infrared spectroscopy. *J. Food. Compos. Anal.* **2017**, *56*, 41–46. [CrossRef]

2. Ribeiro, L.P.D.; da Silva, A.P.M.; de Lima, A.A.; de Oliveira Silva, E.; Rinnan, A.; Pasquini, C. Non-destructive determination of quality traits of cashew apples (*Anacardium occidentale* L.) using a portable near infrared spectrophotometer. *J. Near. Infrared. Spectrosc.* **2016**, *24*, 77–82. [CrossRef]

3. De Brito, E.S.; de Araújo, M.C.P.; Lin, L.Z.; Harnly, J. Determination of the flavonoid components of cashew apple (*Anacardium occidentale*) by LC-DAD-ESI/MS. *Food Chem.* **2007**, *105*, 1112–1118. [CrossRef]

4. Mathlouthi, M. Water content, water activity, water structure and the stability of foodstuffs. *Food Control* **2001**, *12*, 409–417. [CrossRef]
5. Nicolai, B.M.; Beullens, K.; Bobelyn, E.; Peirs, A.; Saeys, W.; Theron, K.I.; Lammertyn, J. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. *Postharvest Biol. Technol.* **2007**, *46*, 99–118. [CrossRef]

6. Connolly, C. NIR spectroscopy for foodstuff monitoring. *Sens Rev.* **2005**, *25*, 192–194. [CrossRef]

7. Dos Santos, C.A.T.; Lopo, M.; Páscoa, R.N.; Lopes, J.A. A review on the applications of portable near-infrared spectrometers in the agro-food industry. *Appl. Spectrosc.* **2013**, *67*, 1215–1233. [CrossRef]

8. Cavalcante, A.A.M.; Rübensam, G.; Erdtmann, B.; Brendel, M.; Henriques, J.A. Cashew (*Anacardium occidentale*) apple juice lowers mutagenicity of aflatoxin B1 in *S. typhimurium* TA102. *Genet. Mol. Biol.* **2005**, *28*, 328–333. [CrossRef]

9. Kubo, I., Nihei, K.I.; Tsujimoto, K. Antibacterial action of anacardic acids against methicillin resistant *Staphylococcus aureus* (MRSA). *J. Agric. Food Chem.* **2003**, *51*, 7624–7628. [CrossRef]

10. Ha, T.J.; Kubo, I. Lipoxygenase inhibitory activity of anacardic acids. *J. Agric. Food Chem.* **2005**, *53*, 4350–4354. [CrossRef]

11. Chaves, N.; Castro, A.; Vidal, R.; Albuquerque, A.; Moura, C.; Barros, L. Characterization of cashew accessions for ornamental purposes. In *Proceedings of the X International Symposium on Postharvest Quality of Ornamental Plants* 1060, Liege, Belgium, 19–24 May 2019; pp. 315–320.

12. Scherer, R.; Rybka, A.C.P.; Ballus, C.A.; Meinhart, A.D.; Filho, J.T.; Godoy, H.T. Validation of a HPLC method for simultaneous determination of main organic acids in fruits and juices. *Food Chem.* **2012**, *135*, 150–154. [CrossRef]

13. Zepka, L.Q.; Mercadante, A.Z. Degradation compounds of carotenoids formed during heating of a simulated cashew apple juice. *Food Chem.* **2009**, *117*, 28–34. [CrossRef]

14. Pereira, A.L.F.; Almeida, F.D.L.; de Jesus, A.L.T.; da Costa, J.M.C.; Rodrigues, S. Storage stability and acceptance of probiotic beverage from cashew apple juice. *Food Bioprocess Tech.* **2013**, *6*, 3155–3165. [CrossRef]

15. Assunção, R.B.; Mercadante, A.Z. Carotenoids and ascorbic acid composition from commercial products of cashew apple (*Anacardium occidentale* L.). *J. Food Compos. Anal.* **2003**, *16*, 647–657. [CrossRef]

16. Beebe, K.R.; Pell, R.J.; Seasholtz, M.B. *Chemometrics, a Practical Guide*; Wiley: New York, NY, USA, 1998; Volume 4.

17. Hotelling, H. Analysis of a complex of statistical variables into principal components. *J. Educ. Psychol.* **1933**, *24*, 417–441. [CrossRef]

18. Andersen, C.M.; Bro, R. Variable selection in regression—A tutorial. *J. Chemom.* **2010**, *24*, 728–737. [CrossRef]

19. Ballabio, D. A MATLAB toolbox for Principal Component Analysis and unsupervised exploration of data structure. *Chemom. Intell. Lab. Syst.* **2015**, *149*, 1–9. [CrossRef]

20. Wise, B.M.; Gallagher, N.; Bro, R.; Shaver, J.; Windig, W.; Koch, R.S. PLS Toolbox 4.0; Eigenvector Research Incorporated: Wenatchee, WA, USA, 2007.

21. Wold, S.; Sjöström, M.; Eriksson, L. PLS-regression, a basic tool of chemometrics. *Chemom. Intell. Lab. Syst.* **2001**, *58*, 109–130. [CrossRef]

22. Freitas, J.V.B.; Alves Filho, E.G.; Silva, L.M.A.; Zocolo, G.J.; de Brito, E.S.; Gramosa, N.V. Chemometric analysis of NMR and GC datasets for chemotype characterization of essential oils from different species of Ocimum. *Talanta* **2018**, *180*, 329–336. [CrossRef]

23. Varmuza, K.; Filzmoser, P. *Introduction to Multivariate Statistical Analysis in Chemometrics*; CRC Press: Boka Raton, FL, USA, 2016.

24. Press, W.H.; Teukolsky, S.A.; Vetterling, W.T.; Flannery, B.P. *Numerical recipes. The Art of Scientific Computing*, 3rd ed.; Cambridge University Press: Cambridge, UK, 2007; Volume 3.

25. Gorry, P.A. General least—Squares smoothing and differentiation by the convolution (Savitzky–Golay) method. *Anal. Chem.* **1990**, *62*, 570–573. [CrossRef]

26. Bureau, S.; Ruiz, D.; Reich, M.; Gouble, B.; Bertrand, D.; Audergon, J.M.; Renard, C.M.G.C. Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy. *Food Chem.* **2009**, *113*, 1323–1328. [CrossRef]

27. Shenk, J.S.; Workman, J.J.; Westerhaus, M.O. Application of NIR spectroscopy to agricultural products. *Pract. Spectroscopy Ser.* **2001**, *27*, 419–474.

28. Malegori, C.; Nascimento Marques, E.J.; de Freitas, S.T.; Pimentel, M.F.; Pasquini, C.; Casiraghi, E. Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms. *Talanta* **2017**, *165*, 112–116. [CrossRef]
29. Magwaza, L.S.; Opara, U.L.; Nieuwoudt, H.; Cronje, P.J.; Saeys, W.; Nicolaï, B. NIR spectroscopy applications for internal and external quality analysis of citrus fruit—A review. *Food Bioprocess Tech.* **2012**, *5*, 425–444.
[CrossRef]

30. Nielsen, N.-P.V.; Carstensen, J.M.; Smedsgaard, J. Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping. *J. Chromatogr. A* **1998**, *805*, 17–35. [CrossRef]

31. Alves Filho, E.G.; Almeida, F.D.; Cavalcante, R.S.; de Brito, E.S.; Cullen, P.J.; Frias, J.M.; Bourke, P.; Fernandes, F.A.; Rodrigues, S. 1H NMR spectroscopy and chemometrics evaluation of non-thermal processing of orange juice. *Food Chem.* **2016**, *204*, 102–107. [CrossRef]

32. Cunha, A.G.; Brito, E.S.; Moura, C.F.; Ribeiro, P.R.; Miranda, M.R.A. UPLC-qTOF-MS/MS-based phenolic profile and their biosynthetic enzyme activity used to discriminate between cashew apple (*Anacardium occidentale* L.) maturation stages. *J. Chromatogr. B* **2017**, *1051*, 24–32. [CrossRef]

33. Bernillon, S.; Biais, B.; Deborde, C.; Maucourt, M.; Cabasson, C.; Gibon, Y.; Hansen, T.H.; Husted, S.; de Vos, R.C.; Mumm, R. Metabolomic and elemental profiling of melon fruit quality as affected by genotype and environment. *Metabolomics* **2013**, *9*, 57–77. [CrossRef]

34. Okpanachi, U.; Attah, S.; Shaahu, D. A Comparative study between vitamins and amino acid profile of sun-dried red and yellow cashew pulp. *Int. J. An. Biol* **2015**, *1*, 23.

35. Arumugam, B.; Palanisamy, U.D.; Chua, K.H.; Kuppusamy, U.R. Potential antihyperglycaemic effect of myricetin derivatives from *Syzygium malaccense*. *J. Funct. Foods* **2016**, *22*, 325–336. [CrossRef]

36. Lutz, I.A. *Normas Analíticas, Métodos Químicos e Físicos para Análise de Alimentos*; Instituto Adolfo Lutz: São Paulo, Brazil, 1985; Volume 1.

37. Strohecker, R.; Henning, H.M. *Análisis de Vitaminas Métodos Comprobados*; Madrid Paz Montalvo: Madrid, Spain, 1967.

38. Silva, L.M.A.; Alves Filho, E.G.; Choze, R.; Lião, L.M.; Alcantara, G.B. 1H HRMAS NMR spectroscopy and chemometrics for evaluation of metabolic changes in citrus sinensis Caused by *Xanthomonas axonopodis* pv. citri. *J. Braz. Chem. Soc.* **2012**, *23*, 1054–1061. [CrossRef]

39. Costa, D.P.; Alves Filho, E.G.; Silva, L.M.A.; Santos, S.C.; Passos, X.S.; Silva, M.d.R.R.; Seraphin, J.C.; Ferri, P.H. Influence of fruit biotypes on the chemical composition and antifungal activity of the essential oils of *Eugenia uniflora* leaves. *J. Braz. Chem. Soc.* **2010**, *21*, 851–858. [CrossRef]

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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
<figure><img src="image_37.png" /></figure>
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Article

Comparative Metabolomics and Molecular
Phylogenetics of Melon (*Cucumis melo*,
*Cucurbitaceae*) Biodiversity

Annick Moing ¹, J. William Allwood ², Asaph Aharoni ³, John Baker ⁴, Michael H. Beale ⁴,
Shifra Ben-Dor ³, Benoît Biais ¹, Federico Brigante ⁵,⁶,⁷, Yosef Burger ⁸, Catherine Deborde ¹,
Alexander Erban ⁵, Adi Faigenboim ⁸, Amit Gur ⁹, Royston Goodacre ¹⁰, Thomas H. Hansen ¹¹,
Daniel Jacob ¹, Nurit Katzir ⁹, Joachim Kopka ⁵, Efraim Lewinsohn ⁹, Mickael Maucourt ¹,
Sagit Meir ³, Sonia Miller ⁴, Roland Mumm ¹², Elad Oren ⁹, Harry S. Paris ⁹, Ilana Rogachev ³,
Dominique Rolin ¹, Uzi Saar ⁹, Jan K. Schjoerring ¹¹, Yaakov Tadmor ⁹, Galil Tzuri ⁹,
Ric C.H. de Vos ¹², Jane L. Ward ⁴, Elena Yeselson ⁸, Robert D. Hall ¹²,¹³,† and
Arthur A. Schaffer ⁸,*,†

1 INRAE, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Bordeaux Metabolome Facility MetaboHUB, Centre INRAE de Nouvelle Aquitaine - Bordeaux, 33140 Villenave d'Ornon, France; annick.moing@inrae.fr (A.M.); bbiais@free.fr (B.B.); catherine.deborde@inrae.fr (C.D.); daniel.jacob@inrae.fr (D.J.); mickael.maucourt@inrae.fr (M.M.); dominique.rolin@inrae.fr (D.R.)

2 The James Hutton Institute, Environmental & Biochemical Sciences, Invergowrie, Dundee, DD2 5DA
Scotland, UK; william.allwood@hutton.ac.uk

3 Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel; asaph.aharoni@weizmann.ac.il (A.A.); sagit.meir@weizmann.ac.il (S.M.); llana.rogachov@weizmann.ac.il (I.R.); Shifra.ben-dor@weizmann.ac.il (S.B.-D.)

4 Rothamsted Research, Harpenden, Herts AL5 2JQ, UK; john.baker@pfizer.com (J.B.); mike.beale@rothamsted.ac.uk (M.H.B.); sonia.miller83@yahoo.co.uk (S.M.); jane.ward@rothamsted.ac.uk (J.L.W.)

5 Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm 14476, Germany; federicobrigante@outlook.com (F.B.); Erban@mpimp-golm.mpg.de (A.E.); Kopka@mpimp-golm.mpg.de (J.K.)

6 Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Dto. Química Orgánica, Córdoba 5000, Argentina

7 CONICET, ICYTAC (Instituto de Ciencia y Tecnologia de Alimentos Córdoba), Córdoba 5000, Argentina

8 Institute of Plant Science, Agricultural Research Organization—Volcani Center, Rishon LeZiyyon 7515101, Israel; surgery@agri.gov.il (Y.B.); adif@agri.gov.il (A.F.); elenae@agri.gov.il (E.Y.)

9 Newe Ya'ar Research Center, Agricultural Research Organization, P. O. Box 1021, Ramat Yishay 3009500, Israel; amitgur@volcani.agri.gov.il (A.G.); katzim@agri.gov.il (N.K.); twefraim@agri.gov.il (E.L.); elad.oren@mail.huji.ac.il (E.O.); hsparis@agri.gov.il (H.S.P.); yarden@agri.gov.il (U.S.); tadmory@agri.gov.il (Y.T.); galilt@agri.gov.il (G.T.)

10 Department of Biochemistry, Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK; roy.goodacre@liverpool.ac.uk

11 Department of Plant and Environmental Sciences & Copenhagen Plant Science Center, Faculty of Science, University of Copenhagen, DK-1871 Frederiksberg C, Denmark; thh@plen.ku.dk (T.H.H.); jks@plen.ku.dk (J.K.S.)

12 Business Unit Bioscience, Wageningen University & Research, Post Box 16, 6700AA, Wageningen, Netherlands; roland.mumm@wur.nl (R.M.); ric.devos@wur.nl (R.C.H.d.V.); robert.hall@wur.nl (R.D.H.)

13 Department of Plant Physiology, Wageningen University & Research, Laboratory of Plant Physiology, Post Box 16, 6700AA, Wageningen, Netherlands

* Correspondence: vcaris@volcani.agri.gov.il; Tel.: + 972(3)9683646

† These authors have contributed equally to this work.

Received: 27 February 2020; Accepted: 20 March 2020; Published: 24 March 2020

**Abstract:** The broad variability of *Cucumis melo* (melon, Cucurbitaceae) presents a challenge to conventional classification and organization within the species. To shed further light on the
infraspecific relationships within *C. melo*, we compared genotypic and metabolomic similarities among 44 accessions representative of most of the cultivar-groups. Genotyping-by-sequencing (GBS) provided over 20,000 single-nucleotide polymorphisms (SNPs). Metabolomics data of the mature fruit flesh and rind provided over 80,000 metabolomic and elemental features via an orchestra of six complementary metabolomic platforms. These technologies probed polar, semi-polar, and non-polar metabolite fractions as well as a set of mineral elements and included both flavor- and taste-relevant volatile and non-volatile metabolites. Together these results enabled an estimate of “metabolomic/elemental distance” and its correlation with the genetic GBS distance of melon accessions. This study indicates that extensive and non-targeted metabolomics/elemental characterization produced classifications that strongly, but not completely, reflect the current and extensive genetic classification. Certain melon Groups, such as Inodorous, clustered in parallel with the genetic classifications while other genome to metabolome/element associations proved less clear. We suggest that the combined genomic, metabolic, and element data reflect the extensive sexual compatibility among melon accessions and the breeding history that has, for example, targeted metabolic quality traits, such as taste and flavor.

**Keywords:** genetic resources; melon; genotype by sequencing; elemental analysis; metabolome; *Cucumis melo*

# 1. Introduction

*Cucumis melo* L., melon, is a phenotypically highly variable species with respect to fruit characteristics [1]. Melon fruits vary not only in size and shape but also in the accumulation of various metabolites, the most obvious of which include the horticulturally important metabolites of external and internal pigmentation, volatiles responsible for fruit aroma, and carbohydrates and organic acids accounting for sweetness and acidity [2]. Melon fruit range in sizes up to 20 kg; in shape from spherical to very long; in taste from insipid to sweet, acidic, or bitter; have external colors of green, yellow, orange, and red, with internal flesh colors of white, green, orange, or cream; and encompass a broad range, from highly aromatic to almost non-aromatic types. The species is unique in that it has representatives of both climacteric and non-climacteric ripening physiology [3], which further impacts on the metabolite components of the ripe fruit.

The extreme fruit variation within *Cucumis melo* does not easily lend itself to conventional infraspecific classification. Kirkbride [4] suggested that the species should be considered to consist of two subspecies, based on the pubescence of ovaries and young fruits. Accordingly, ovaries and young fruits having appressed short hairs were assigned to *C. melo* subspecies *agrestis* and those having pilose or lanate, spreading, long hairs were assigned to *C. melo* subsp. *melo*. However, it is often not easy to reconcile young fruit pubescence with the melon fruits seen in marketplaces in various parts of the world. Although markets in some regions feature young cucumber-like melons, most markets feature mature ripe sweet dessert melons, others ripe and highly aromatic but insipid “duda’im” melons, and yet others fully grown but unripe “snap” melons [1,5].

Attempts to classify melons according to fruit characteristics date to the first half of the 19th century, and over the past two centuries, a great number of proposed infraspecific classifications for melons have accumulated (reviewed in [6]). Among these proposed classifications are “lumpers”, which have not indicated enough divisions within the species to adequately reflect its wide variation, and “splitters”, which have fragmented the species into an unwieldy number of units. To be feasible, any useful infraspecific classification must at once be reflective of genetic relationships and also be easily observed by those for whom the classification is intended, which should be a wide international audience that includes scientists and non-scientists alike. For melons, this means that the characteristics of fruits should serve as the basis for any such classification. Given all of the above considerations, our view is that the infraspecific breakdown to 16 varietas first proposed by Pitrat et al. [6] or cultivar-groups
("Groups") (adopted by Burger et al., [2]) is currently the most useful. Groups are lower ranking than the subspecies, but each Group can consist of more than one market type.

The economically most important Groups are the Reticulatus (climacteric, netted), Cantalupensis (climacteric, non-netted), Inodorus (non-climacteric), and Ameri (climacteric, dryland cultivation, Asian), all of which have sweet flesh when ripe and belong to subsp. *melo*. Other Groups include the Flexuosus (snake melon), Duda'im (pocket melon), Momordica (snap melon), and Khandalak, and the East Asian Conomon (Oriental pickling), Makuwa, and Chinensis Groups. However, even the placement of the various Groups into the two subspecies is not fully agreed upon, as may be expected from a somewhat subjective classification based on the single trait of ovary pubescence. The recent attempts at making order within the melon species, at the subspecies level, literally focused on hair-splitting. For example, while the *agrestis* subspecies proposed by Pitrat et al. [6] encompasses five Groups (Conomon, Makuwa, Chinensis, Momordica, and Acidulus), the *agrestis* as described by Decker-Walters et al. [7] combines Momordica, Conomon, Duda'im, and Chito. The latter two Groups, Duda'im and Chito, were placed in subsp. *melo* by Pitrat et al. [6], and Zhao et al. [8] recently traced the Momordica Group to subsp. *melo*. Furthermore, the distinction between Reticulatus (climacteric, netted) and Cantalupensis (climacteric, non-netted) based on netting is particularly unclear since the netting phenotype is expressed as a continuum of what is basically a quantitative trait. Thus, semi-netted Charentais-type melons have vacillated between the two infraspecific Groups [6,9,10].

The difficulty of clear infraspecific classification of the species is further confounded by the ease of crossbreeding within the species, as well as the undocumented history, both early and more recent, of most accessions, and the widespread occurrence of feral melons. Even the natural range of melon species has been the subject of ongoing studies. Traditionally, melon has been grouped with other *Cucumis* spp. associated with African origin [4]. However, recent studies indicate a closer relationship to an Australian/Asian clade, suggesting that *C. melo* has origins in Asia [11,12]. Inodorous Group melons have a Central Asian origin, traceable to the mid-9th century and had arrived in Spain by the 11th century [13]; Duda'im Group melons have been traced to mid-9th century Persia [14]; and Flexuosus Group melons have been traced back 4000 years to ancient Egypt [15,16].

Molecular-based phylogenetics contributed to the infraspecific classification of *C. melo*, consistently supporting a subspecies separation, with small differences in the placement of some of the Groups [17,18]. Recently, Endl et al. [19] pointed to the complexity within the *agrestis* subspecies and showed that there is likely a complex African/Asian/Australian origin of the *agrestis* types of *C. melo*, based on polymorphisms in seven DNA regions. The most recent and encompassing phylogenetic classification of the species was reported by Zhao et al. [8], which corroborated the subspecies classification but placed the Indian Momordica Group within the *melo* subspecies. Interestingly, Leida et al. [20], based on ca. 200 single nucleotide polymorphisms (SNPs), identified what they refer to as Spanish and European Inodorus Group landraces within the *agrestis* subspecies, distinct from the well-known cultivated Inodorus Group, suggesting that the non-climacteric genetic trait may have evolved independently multiple times.

Metabolomic-based phylogeny, or chemosystematics, has regularly been attempted from the early part of the last century [21], and was especially popular in the 1970s. However, these studies focused on intra-family classification at the species level and were generally based on the measurement of individual components of single biochemical families, frequently alkaloids, as allowed by the technologies of the period. For example, the chemical systematics of the family Rutaceae and the order Rutales attracted much research attention. This chemosystematic classification has been compared to the phylogeny determined by molecular polymorphism strategies and found to be generally confirmatory [22]. However, chemical systematics has not been attempted at the infraspecific level for the obvious reason that a limited number of chemicals are unlikely to be adequate for describing the extent of varietal differences. Furthermore, relatively minor differences due to few, or even single, genes may still be causal of large differences in metabolic components. For example, the genetic polymorphism controlling ripening physiology in melons, whether climacteric or non-climacteric,
although essentially a simply inherited trait [3], will likely be accompanied by major changes in the
primary and specialized metabolomes.

While small subsets of metabolites may not be useful for infraspecific systematics and phylogenetics, the number of metabolites available that can be analyzed by large-scale metabolomic strategies has potential discriminatory power. The advances in metabolomics technologies now allow for these techniques to be used and to attempt to analyze infraspecific relationships [23]. Recently, we described a metabolomic comparison of two melon cultivars [24], and the results were encouraging regarding the relevance of such analyses to a larger infraspecific classification. Furthermore, we performed a targeted metabolomic characterization of 77 metabolites, primarily of quality-determining volatiles, sugars, and carotenoids, of a novel segregating genetic population derived from random introgressions between two distantly related melon accessions [25]. The results indicated the potential of metabolomic analyses of genetic variability for the discovery of associations between metabolites and metabolic pathways. Esteras et al. [26] also recently surveyed the volatile and carotenoid profiles of broad genetic variability of melon and emphasized the value of unidentified and untapped diversity for melon fruit quality improvement.

Considering the presence of hundreds of thousands of primary and specialized metabolites in the plant kingdom, no single metabolomics platform is capable of describing the plant metabolome; however, the combination of multiple platforms can approach that goal. In this study, the metabolite profile of 51 melon accessions, representing the broad genetic variability in the species, was characterized in depth using an orchestra of metabolomic strategies. The metabolomic technologies used comprised flow injection electrospray mass spectrometry (FIE-MS) fingerprinting of semi-polar extracts, untargeted proton nuclear magnetic resonance (¹H-NMR) profiling of polar or semi-polar extracts, liquid chromatography coupled to QTOF (quadrupole time-of-flight) mass spectrometry (LC-QTOF-MS) of semi-polar compounds, gas chromatography coupled to mass spectrometry (GC-MS) of polar extracts, solid-phase microextraction (SPME) GC-MS of volatiles, and inductively coupled plasma mass spectrometry (ICP-MS) of mineral elements [27]. This broad array of technologies collected over 80,000 metabolite signatures, i.e., molecular features.

The analyses detailed in this report are based on the comparison of the infraspecific classification as determined by metabolomic profiling of an extensive collection of accessions representing most of the cultivar-groups of *Cucumis melo*, with the classification derived from extensive comparative genotyping. The unique combination of >80,000 specific molecular features and >20,000 genetic polymorphisms obtained in this study allowed us to compare the inferred infraspecific phylogenies derived from each of the strategies and to test the relationships between metabolomic and genetic distance in this complex widely cultivated species.

**2. Results**

2.1. *Infraspecific Structure of Cucumis melo*

Fifty-one accessions of *Cucumis melo*, representing the cultivar-groups Reticulatus, Cantalupensis, Inodorus, Ameri, Flexuosus, Duda'im, Momordica, Khandalak, Conomon, Makuwa, and Chinensis, were grown in Israel during a spring-summer season and flesh and rind samples of mature fruit were harvested and distributed to the collaborating laboratories for the respective metabolomics analyses (Figure 1, Table 1; Table S1). In our classification into subspecies and cultivar-groups, we followed the proposed classifications of Pitrat et al. [6] and Burger et al. [2], with the exception of the placement of the single Momordica accession PI414723, which we included among the subspecies *melo*.

In parallel, 44 accessions could be genotyped by sequencing (GBS), which, in brief, is based on high-throughput sequencing of restriction enzyme fragments of sample DNA. For the present study, GBS provided 23,931 informative SNPs, which were selected for genome-wide analyses and phylogenetic classification. The accessions not included in the GBS analysis were provisionally
genotyped by direct sequencing of over 11,000 bps, derived from 20 PCR reactions, yielding 116 informative SNPs (Figure S1).

<figure><img src="image_39.png" /><figcaption>**Figure 1.** Fruits of the accessions used for this study, representing most of the cultivar-groups of *Cucumis melo* (listed in Table 1 and Table S1): Cantalupensis, Reticulatus, Inodorus, Ameri, Flexuosus, Dudaim, Momordica, Khandalak, Conomon, Chinensis, and Makuwa.</figcaption></figure>

<table><caption>**Table 1.** List of melon accessions used in this study. All accessions were analyzed for metabolites. The seven accessions not included in the GBS analysis are noted as NI. Further details regarding the accessions, including classification of climacteric behavior, are presented in Table S1.</caption><thead><tr><th>Accession Name</th><th>Subspecies</th><th>Cultivar Group</th><th>Accession Abbreviation</th><th>Included in GBS</th><th>Clade (as in Figure 2)</th></tr></thead><tbody><tr><td>Batley's Hermaphrodite</td><td>agrestis</td><td>Chinensis</td><td>BAH</td><td>x</td><td>I</td></tr><tr><td>PI161375</td><td>agrestis</td><td>Chinensis</td><td>PI161375</td><td>x</td><td>I</td></tr><tr><td>Black Skin</td><td>agrestis</td><td>Conomon</td><td>BSK</td><td>NI</td><td></td></tr><tr><td>Freeman's Cucumber</td><td>agrestis</td><td>Conomon</td><td>FRC</td><td>x</td><td>I</td></tr><tr><td>Tokyo Giant</td><td>agrestis</td><td>Conomon</td><td>TOG</td><td>x</td><td>I</td></tr><tr><td>Early Silver Line</td><td>agrestis</td><td>Makuwa</td><td>ESL</td><td>x</td><td>I</td></tr><tr><td>Ohgon</td><td>agrestis</td><td>Makuwa</td><td>OHG</td><td>x</td><td>I</td></tr><tr><td>PI157071</td><td>agrestis</td><td>Makuwa</td><td>PI157071</td><td>x</td><td>I</td></tr><tr><td>PI157080</td><td>agrestis</td><td>Makuwa</td><td>PI157080</td><td>x</td><td>I</td></tr><tr><td>Sakata's Sweet Ananas</td><td>melo</td><td>Ameri</td><td>AYO</td><td>x</td><td>IIa2</td></tr><tr><td>Yoqne'am</td><td>melo</td><td>Ameri</td><td>EDO</td><td>x</td><td>IIa2</td></tr><tr><td>Ananas 'En Dor Bellegarde</td><td>melo</td><td>Cantalupensis</td><td>BEL</td><td>x</td><td>IIc2</td></tr><tr><td>Charentais Fom 2 Res.</td><td>melo</td><td>Cantalupensis</td><td>CHF</td><td>x</td><td>IIc2</td></tr><tr><td>Charentais</td><td>melo</td><td>Cantalupensis</td><td>CHT</td><td>x</td><td>IIc2</td></tr><tr><td>Doublon</td><td>melo</td><td>Cantalupensis</td><td>DOU</td><td>x</td><td>IIc2</td></tr><tr><td>Noy Yizre'el</td><td>melo</td><td>Cantalupensis</td><td>NOY</td><td>x</td><td>IIb1</td></tr><tr><td>Ogen</td><td>melo</td><td>Cantalupensis</td><td>OGE</td><td>x</td><td>IIb1</td></tr><tr><td>PH 406</td><td>melo</td><td>Cantalupensis</td><td>PH406</td><td>x</td><td>IIb1</td></tr><tr><td>Védrantais</td><td>melo</td><td>Cantalupensis</td><td>VEP</td><td>x</td><td>IIc2</td></tr></tbody></table>
<table><caption>Table 1. *Cont.*</caption><thead><tr><th>Accession Name</th><th>Subspecies</th><th>Cultivar Group</th><th>Accession Abbreviation</th><th>Included in GBS</th><th>Clade (as in Figure 2)</th></tr></thead><tbody><tr><td>Indian Best</td><td>melo</td><td>Khandalak</td><td>INB</td><td>x</td><td>II</td></tr><tr><td>Duda'im</td><td>melo</td><td>Duda'im</td><td>DUD</td><td>x</td><td>II</td></tr><tr><td>PI435288</td><td>melo</td><td>Flexuosus</td><td>PI435288</td><td>NI</td><td></td></tr><tr><td>Armenian Yard</td><td>melo</td><td>Flexuosus</td><td>AYL</td><td>x</td><td>IIa3</td></tr><tr><td>Long</td><td>melo</td><td>Flexuosus</td><td>DOY</td><td>x</td><td>IIa3</td></tr><tr><td>Doya</td><td>melo</td><td>Flexuosus</td><td>FAQ</td><td>x</td><td>IIa3</td></tr><tr><td>Faqqous</td><td>melo</td><td>Flexuosus</td><td>STA</td><td>NI</td><td></td></tr><tr><td>Striped</td><td>melo</td><td>Flexuosus</td><td>AMP</td><td>x</td><td>IIa1</td></tr><tr><td>Armenian</td><td>melo</td><td>Inodorus</td><td>ARO</td><td>x</td><td>IIa1</td></tr><tr><td>Amarillo Pipa</td><td>melo</td><td>Inodorus</td><td>CRE</td><td>NI</td><td></td></tr><tr><td>Blanca</td><td>melo</td><td>Inodorus</td><td>GOB</td><td>x</td><td>IIa2</td></tr><tr><td>Amarillo Oro</td><td>melo</td><td>Inodorus</td><td>GOK</td><td>x</td><td>IIa1</td></tr><tr><td>Crenshaw</td><td>melo</td><td>Inodorus</td><td>HDG</td><td>x</td><td>IIa1</td></tr><tr><td>Golden Beauty</td><td>melo</td><td>Inodorus</td><td>NZA</td><td>x</td><td>IIa1</td></tr><tr><td>Gold King</td><td>melo</td><td>Inodorus</td><td>PDS</td><td>x</td><td>IIa1</td></tr><tr><td>Honey Dew</td><td>melo</td><td>Inodorus</td><td>PSR</td><td>x</td><td>IIa2</td></tr><tr><td>Green</td><td>melo</td><td>Inodorus</td><td>ROC</td><td>x</td><td></td></tr><tr><td>Noy Zahov</td><td>melo</td><td>Inodorus</td><td>TVT</td><td>x</td><td></td></tr><tr><td>Piel de Sapo</td><td>melo</td><td>Inodorus</td><td>YCA</td><td>x</td><td></td></tr><tr><td>PI 334107, Kirkagac</td><td>melo</td><td>Inodorus</td><td>PI414723</td><td>x</td><td></td></tr><tr><td>PI 201581b, Kirkagac</td><td>melo</td><td>Inodorus</td><td>PI201581</td><td>NI</td><td></td></tr><tr><td>Piel de Sapo</td><td>melo</td><td>Inodorus</td><td>PSR</td><td>x</td><td></td></tr><tr><td>Redon</td><td>melo</td><td>Inodorus</td><td>ROC</td><td>x</td><td></td></tr><tr><td>Rochet</td><td>melo</td><td>Inodorus</td><td>TVT</td><td>x</td><td></td></tr><tr><td>Tendral Verde</td><td>melo</td><td>Inodorus</td><td>YCA</td><td>x</td><td></td></tr><tr><td>Tardio</td><td>melo</td><td>Inodorus</td><td>PI414723</td><td>x</td><td></td></tr><tr><td>Yellow Canary</td><td>melo</td><td>Inodorus</td><td>PI414723</td><td>x</td><td></td></tr><tr><td>PI414723</td><td>melo</td><td>Momordica</td><td>BES</td><td>x</td><td></td></tr><tr><td>Bender's Surprise</td><td>melo</td><td>Reticulatus</td><td>DUL</td><td>x</td><td></td></tr><tr><td>Dulce</td><td>melo</td><td>Reticulatus</td><td>FOG</td><td>x</td><td></td></tr><tr><td>Fordhook Gem</td><td>melo</td><td>Reticulatus</td><td>YCA</td><td>x</td><td></td></tr><tr><td>Hale's Best Jumbo</td><td>melo</td><td>Reticulatus</td><td>PSR</td><td>x</td><td></td></tr><tr><td>Krymka</td><td>melo</td><td>Reticulatus</td><td>KRY</td><td>x</td><td></td></tr><tr><td>Magyar Kincs</td><td>melo</td><td>Reticulatus</td><td>MAK</td><td>x</td><td></td></tr><tr><td>PMR45</td><td>melo</td><td>Reticulatus</td><td>PMR</td><td>NI</td><td></td></tr><tr><td>Top Mark</td><td>melo</td><td>Reticulatus</td><td>TPM</td><td>x</td><td></td></tr><tr><td>PI149169</td><td>undecided</td><td>undecided</td><td>PI149169</td><td>NI</td><td></td></tr><tr><td>Qishu</td><td>(outlier)</td><td>(feral)</td><td>QME</td><td>x</td><td></td></tr><tr><td>Meshullash</td><td>(outlier)</td><td>(feral)</td><td>QME</td><td>x</td><td></td></tr></tbody></table>

Based on 23,931 genetic polymorphisms, the 44 accessions could be classified into two well-defined clusters (referred to as I and II) clearly distinguishing between the subspecies *agrestis* and *melo* (Figure 2). A more distant accession designated Qishu Meshullash (QME), either *Cucumis trigonus* or *C. colossus*, both of which have been included in *agrestis* by Endl et al. [19], was included in the GBS analysis as an outlier. The smaller cluster I consists entirely of the subsp. *agrestis* accessions of the Chinensis, Makuwa, and Conomon Groups. Within the Conomon Group, accessions FRC and TOG are weakly separated from the accessions of the Chinensis and Makuwa Groups, which are interspersed among themselves. The Momordica and Duda'im accessions are distinct from the *agrestis* subspecies but are also separated from the rest of cluster II, which contains accessions of subsp. *melo* and consists of a number of sub-clusters (Figure 2). Ten accessions of the Inodorus Group form a sub-cluster IIa1, which is distinct
from, but closely related to, sub-cluster IIa2 comprising the Kirkagac-type Inodorus accessions and the two Ananas-type Ameri Group accessions. The Flexuosus Group accessions (IIa3) also Group together and are most closely related to the Inodorus and Ameri accessions. An additional cluster (IIb) includes both the green-fleshed Ha'Ogen-type Cantalupensis accessions (IIb1) and the closely related, yet distinct, green-fleshed Galia-type Reticulatus (IIb2). A third cluster IIc consists of two sub-clusters of orange-fleshed cultivars, one of which consists of the five orange-fleshed American accessions of the Reticulatus Group (IIc1) and, the other of four European accessions of the orange-fleshed Charentais-type Cantalupensis Group. The most unexpected placement was that of BEL, which we assumed to be a Charentais-type Cantalupensis melon. However, it was genetically distinct from the other subspecies melo types in clades IIa,b,c.

<figure><img src="image_40.png" /><figcaption>**Figure 2.** Phylogram representing the phylogenetic relationships of the 44 accessions of *C. melo* used for GBS analysis in this study. The feral QME serves as outlier. Bootstrap values are based on 100 iterations.</figcaption></figure>

The seven accessions, which were solely characterized metabolomically, are PI2015801, STA, PI435288, BSK, PMR, PI149169, and CRE, and these were classified based on 116 informative SNPs as shown in Figure S1 and Table S2. PI2015801 is a Kirkagac type of Inodorus and the PCR-based sequencing placed it near the Kirkagac-type PI334107. CRE is a unique orange-fleshed Inodorus accession and was genotypically closer to the two Kirkagac Inodorus accessions than to the more typical Inodorus accessions, HDG and PSR. BSK was classed together with the two other Conomon accessions, FRC and OHG. PI435288 has the appearance of a characteristic long-fruited Flexuosus type (Figure 1) and was genotypically classed as being closely related to the FAQ variety. PMR was most similar to the American netted Reticulatus accessions, both in appearance and based on the genetic polymorphisms. STA and PI149169 classed closely together, and related to PI435288 and FAQ.

## 2.2. Combined Analysis of All Metabolomic and Elemental Data

We analyzed fruit flesh and rind samples of the 51 melon accessions (Table 1). An independent duplicate of one accession, namely subsp. *melo* Duda'im (DUD), was included for control purposes, i.e., for internal validation of profile similarity.

A total of >80,000 molecular features were collected from the combination of nine analytical profiling strategies applied to the samples of melon flesh and rind, yielding more than 36,000 and 46,000 features for the flesh and rind, respectively (Table 2). The vast majority of features were collected by the two non-targeted MS-TOF techniques, UPLC profiling of semi-polar metabolites, and GC profiling of a polar fraction enriched for primary metabolites, including taste-relevant sugars and organic acids. For the metabolic classification purpose of this study, these molecular features remained non-annotated. However, for the selection of molecular features that were relevant to build a classification model of melon accessions (refer to Section 2.4.), we used a subset of annotated features [27]. In addition, three global variables of fruit quality were included, percent dry matter (%DM), total soluble solids
(TSS), and pH, with mean values of 11.5 %DM, 9.7 °Brix, and pH 5.9, These showed considerable variability among the accessions as illustrated by their coefficients of variation of 10.4%, 26.2%, and 11.0%, respectively (n = 52, Table S1, Figure S2).

<table><caption>**Table 2.** Summary of the metabolome and elemental data measured using MS or NMR analytical strategies in extracts of fruit flesh or rind samples from of 52 melon accessions.</caption><thead><tr><th>Analytical Strategies and Corresponding Examples of Covered Compounds or Compound Families</th><th colspan="2">Number of Molecular Features</th></tr><tr><th></th><th>Flesh</th><th>Rind</th></tr></thead><tbody><tr><td>GC-MS of polar extracts:</td><td>12 397</td><td>13 200</td></tr><tr><td>soluble sugars, sugar-alcohols, organic acids, amino acids, polyamines</td><td></td><td></td></tr><tr><td><sup>1</sup>H-NMR fingerprints of polar extracts:</td><td>40</td><td>28</td></tr><tr><td>major soluble sugars, organic acids, amino acids and other amino compounds</td><td></td><td></td></tr><tr><td><sup>1</sup>H-NMR quantitative profiles of polar extracts:</td><td>108</td><td>108</td></tr><tr><td>major soluble sugars, organic acids, amino acids and other amino compounds</td><td></td><td></td></tr><tr><td><sup>1</sup>H-NMR fingerprints of semi-polar extracts:</td><td>839</td><td>819</td></tr><tr><td>major soluble sugars, organic acids, amino acids and major semi-polar specialized metabolites</td><td></td><td></td></tr><tr><td>DI-ESI-MS of semi-polar extracts:</td><td></td><td></td></tr><tr><td>positive ionization mode</td><td>931931</td><td>931931</td></tr><tr><td>negative ionization mode</td><td></td><td></td></tr><tr><td>semi-polar major and specialized metabolites</td><td></td><td></td></tr><tr><td>LC-QTOF-MS of semi-polar extracts:</td><td></td><td></td></tr><tr><td>negative ionization mode</td><td>20 785</td><td>30 695</td></tr><tr><td>non-volatile specialized metabolites and their conjugates including the flavonoid- and hydroxycinnamate-families</td><td></td><td></td></tr><tr><td>SPME GC-MS of volatiles:</td><td>282</td><td>-</td></tr><tr><td>volatile specialized metabolites including alcohols, aldehydes, terpenoids</td><td></td><td></td></tr><tr><td>ICP-MS of mineral elements:</td><td>20</td><td>-</td></tr><tr><td>mineral elements including P, K, Fe, Ni, and low-abundant trace elements</td><td></td><td></td></tr></tbody></table>

We combined the data sets of the profiled molecular features (Table 2) after 0.1–0.9 quantile range normalization of each of the sub-datasets, separately. Unique features of melon accessions were given high weight in our analyses through missing value substitution by zero. We combined all normalized sub-datasets into a single matrix of melon accessions characterized by all available molecular features of both flesh and rind samples.

Preliminary analyses by principal components analyses (PCA) and independent components analyses (ICA) verified the complex nature of the combined dataset and revealed that only 23.3% of the total variance was represented by the first three principal components. ICA of the first three principal components (Figure 3A,B) relaxed the orthogonality criterion of PCA and instead optimized independent component kurtosis as a measure of bi- or multi-modality of the scores' values of the resulting ICA axes [28]. The ICA scores plot array indicated separate clusters of subsp. *agrestis* and subsp. *melo*, with full or partial separation of respective accession Groups.

To transform the multi-dimensional ICA clusters of melon accessions into a mono-dimensional tree structure comparable to conventional genetic distance representations, e.g., by phylogenetic or genetic distance trees, we created a covariance matrix of the complete accession profiles. For this purpose, we did not preselect features and did not apply dimension reduction methods to consider the complete molecular variance reflected by the data set. The covariance matrix was subsequently subjected to hierarchical cluster analysis (HCA) and bootstrapped to generate an HCA support tree (Figure 3C), with the node confidence validated by the bootstrap values. These values ranged from 2 to as high as 92 on a scale of 0–100. High bootstrap support was given to the terminal nodes of similar melon accessions with two of the Duda im profiles giving an estimate of a bootstrap threshold of 82 that was indicative of the level of metabolic similarity of genetically identical melon accessions. The basal nodes of the HCA tree, however, received low bootstrap support. These results indicated that alternative basal classifications may be present in the data set.
<figure><img src="image_41.png" /><figcaption>**Figure 3.** Metabolic classification of *Cucumis melo* accessions using the combined dataset of melon flesh and rind analyzed by multiplexed metabolome and elemental profiling. (A,B) ICA of the first three PCs obtained from the complete data set of >80,000 molecular features. The first three PCs comprised 23.3% of the total variance of the data set. (C) Covariance matrix of the complete molecular profiles of *C. melo* accessions, with HCA of the matrix using covariance distance metrics and complete linkage for clustering. HCA included support analysis by bootstrapping with 1000 iterations. Bootstrap support values of all HCA nodes and a scale of node height are included. Boxed regions of the covariance matrix indicate examples of additional alternative metabolic associations described in detail in the text. Cluster cones were arbitrarily set at the 75% tree height.</figcaption></figure>

This interpretation was supported by alternative highly correlated groups of melon accessions that became apparent in the covariance matrix (Figure 3C). Multiple instances of alternative associations were apparent. One example was provided by the accessions of the Cantalupensis Group. This Group split between two HCA sub-branches, but alternative associations of the two Cantalupensis splits were also detected. In one HCA branch, the Cantalupensis accessions OGE, NOY, and PH406 clustered with the Reticulatus accessions KRY and MAK and the Ameri accessions AYO and EDO. In the second HCA branch, the remaining Cantalupensis accessions clustered with Inodorus accession CRE, Khandalak INB, and Reticulatus BES.

We concluded that the reduction of all paired metabolic similarities into a tree structure perhaps only reflects in part the complex metabolic similarities that were generated by the melon breeding process. This breeding process may have included crossbreeding beyond the Group or subspecies boundaries within *Cucumis melo*. Such breeding events may cause genetic introgressions with transfers of large gene sets. In addition, breeding for metabolic quality traits may have caused partial convergence of metabolic profiles from genetically distant accessions. We found a matrix representation of accession similarity more adequate in representing a complex intra-species breeding history. Consequently, we
took this approach to also compare and correlate metabolic to genetic distance matrices instead of
analyzing tree similarities only (Figures S3 and S4).

The highly supported nodes of our current HCA tree (Figure 3C) were useful to evaluate the closest metabolic neighbors among the GBS-characterized and the seven PCR-characterized accessions (Table 1). The Conomon cultivar BSK (Black Skin) of subsp. *agrestis* grouped at a medium bootstrap support of 44 with TOG, a Conomon Group accession of subsp. *agrestis*. A higher-level metabolic cluster with a bootstrap support of 32 contained all five Flexuosus accessions of subsp. *melo*, including non-characterized accession STA, PI435288, and accession PI149169 of undecided Group affiliation. A well-supported cluster consistently contained 13 Inodorus accessions. This cluster included both Kirkagac-type accessions, PI201581 and PI334107, and their closest neighbor, GOB. As reported above, non-characterized CRE, provisionally classified as Inodorus, grouped to a high-level cluster of diverse subsp. *melo* accessions and was metabolically most similar to INB of the Khandalak Group. Finally, PMR membership to the Reticulatus Group was confirmed with nearest neighbor TPM (Figure 3C). PMR and TPM had high similarity to a highly supported (bootstrap value 68) cluster containing three other Reticulatus accessions.

2.3. *Platform-Specific Metabolomic Analyses of Melon Flesh or Rind Tissue*

For each analytical strategy, an HCA of accession samples was separately performed, based on
the range-normalized levels of all features detected per strategy (Table 2). This HCA was performed
for both the flesh (eight technological platforms, Figure S3) and rind (six technological platforms,
Figure S4) of the fruits, and the similarity of the grouping of the 52 accession samples based on the
observed variation in their metabolome or element composition was compared to the results based on
the variation in their genome (Figure 2, Table S3).

For flesh samples (Figure S3), the HCA showed a partial or complete clustering, depending
on the analytical strategy, of accessions from the Inodorus Group. Likewise, the accessions of the
Flexuosus Group were closely clustered, except for data from the NMR profiling and volatiles SPME
GC-MS platforms. Grouping of Cantaloupe accessions was also clear, with the four analytical strategies
detecting specialized metabolites (semi-polar extracts and volatiles) as well as for ¹H-NMR fingerprints
of polar extracts and the mineral element platform. Overall, grouping was less clear for the three
platforms, detecting mainly primary metabolites (polar extracts). The grouping of accessions based
on metabolites detected by SPME-GCMS and especially LC-QTOF-MS mostly showed a relatively
high correlation with the genetic distances between accessions, while grouping based on primary
metabolites targeted by NMR profiling data showed a relatively lower correlation. For rind samples
(Figure S4), the HCA showed a partial or complete clustering of accessions from the Inodorous Group
for all six platforms, except for LC-QTOF-MS of semi-polar extracts for which distances within and
between clusters were similar. Grouping of Cantaloupe accessions was also clear with GC-MS of polar
extracts, except for one accession.

For the 44 accessions having both GBS and metabolome or elemental data, the association between
the genetic distance matrix and each metabolomic or elemental distance matrix was measured using a
Mantel test, separately for flesh and rind data (Table 3). Among the 14 associations between genetic and
metabolomic/elemental distances that could be tested, namely 8 for flesh, and 6 for rind (volatiles and
microelements were not measured in rind), 13 including the flesh microelement-based matrix, showed
a significant association with a *p*-value <0.0001. Only the metabolomic distance matrix based on
LC-QTOF-MS of semi-polar extracts in rind was not related to genetic distances. For flesh, the highest
two correlations between the genetic and a metabolic/elemental distance matrix were observed for
specialized metabolites, i.e., for LC-QTOF-MS of semi-polar extracts (r = 0.56) and for SPME GC-MS of
volatiles (r = 0.39). For rind, the highest two correlations between the genetic and metabolic/elemental
distance matrix were observed for the ¹H-NMR fingerprints of semi-polar extracts (r = 0.56) and for
FIE-MS of semi-polar extracts (r = 0.47).
<table><caption>**Table 3.** Link between genetic and compositional distances measured from melon fruit flesh or rind. Mantel test between the molecular distance matrix and each metabolomic or elemental distance matrix for the 44 accessions genotyped and phenotyped. ‘’ indicates that these analyses were not performed on the rind samples.</caption><thead><tr><th>Analytical Strategy</th><th>GC-MS of Polar Extracts</th><th><sup>1</sup>H-NMR Fingerprints of Polar Extracts (0.04 ppm VS bucketting)</th><th><sup>1</sup>H-NMR Quantitative Profiles of Polar Extracts</th><th><sup>1</sup>H-NMR Fingerprints of Semi-Polar Extracts (0.01 ppm bucketting)</th><th>FIE-MS of Semi-Polar Extracts</th><th>LC-QTOF-MS of Semi-Polar Extracts</th><th>SPME-GC-MS of Volatiles</th><th>ICP-MS of Mineral Elements</th></tr></thead><tbody><tr><td>Flesh</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pearson correlation (r) between the molecular distance matrix and the metabolomic or elemental distance matrix</td><td>0.207</td><td>0.117</td><td>0.224</td><td>0.202</td><td>0.149</td><td>0.560</td><td>0.387</td><td>0.177</td></tr><tr><td>Correlation p-value <sup>a</sup></td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td></tr><tr><td>Rind</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pearson correlation (r) between the molecular distance matrix and the metabolomic or elemental distance matrix <sup>b</sup></td><td>0.315</td><td>0.273</td><td>0.267</td><td>0.561</td><td>0.468</td><td>-0.041</td><td>-</td><td>-</td></tr><tr><td>Correlation p-value <sup>b</sup></td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>0.189</td><td>-</td><td>-</td></tr></tbody></table>

<footnote><sup>a</sup> p-value calculated with 10000 Monte Carlo simulations. <sup>b</sup> Euclidian distance on range-normalized data for metabolomic or elemental distance matrices between accessions.</footnote>
## 2.4. Feature Selection by Random Forest Technology

To select molecular features that were relevant for the metabolic classification of melon accessions, we applied random forest (RF) machine learning technology. RF technology tuned towards metabolic feature selection enables the selection of small sets of molecular features that, if manually supervised, can be relevant for sample classification [29]. For this purpose, we used the set of 605 provisionally annotated molecular features from the combined >80,000 data set (Table S4). To create approximately balanced classes, we split the available melon accessions into six subsets, namely 1) all accessions of subsp. *agrestis*, 2) the Cantalupensis accessions, 3) the Flexuosus accessions, 4) the Inodorus accessions, 5) the Reticulatus accessions of subsp. *melo*, respectively, and 6) a class that contained all other melon Groups that were represented by only one or two accessions (Figure 4A). Because of the diverse nature of the sixth class, we did not expect good classification results, but we added this class to contrast with the remaining more populated classes that had 5–14 members (Figure 4).

### A

<table><thead><tr><th rowspan="2"></th><th rowspan="2">Class size</th><th colspan="6">Predicted class</th><th rowspan="2">Classification error</th></tr><tr><th>sp. <i>agrestis</i></th><th>sp. <i>melo</i> Cantalupensis</th><th>sp. <i>melo</i> Reticulatus</th><th>sp. <i>melo</i> Inodorus</th><th>sp. <i>melo</i> Flexuosus</th><th>sp. <i>melo</i> miscellaneous</th></tr></thead><tbody><tr><td>sp. <i>agrestis</i></td><td>10</td><td>8.50</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.10</td><td>0.10</td><td>0.02</td></tr><tr><td>sp. <i>melo</i> Cantalupensis</td><td>8</td><td>0.30</td><td>5.80</td><td>0.60</td><td>0.20</td><td>0.00</td><td>0.10</td><td>0.17</td></tr><tr><td>sp. <i>melo</i> Reticulatus</td><td>7</td><td>0.00</td><td>0.50</td><td>5.80</td><td>0.00</td><td>0.00</td><td>0.90</td><td>0.19</td></tr><tr><td>sp. <i>melo</i> Inodorus</td><td>14</td><td>0.00</td><td>0.00</td><td>0.00</td><td>11.90</td><td>0.00</td><td>0.60</td><td>0.05</td></tr><tr><td>sp. <i>melo</i> Flexuosus</td><td>5</td><td>0.30</td><td>0.00</td><td>0.00</td><td>1.00</td><td>0.90</td><td>2.20</td><td>0.80</td></tr><tr><td>sp. <i>melo</i> miscellaneous</td><td>8</td><td>0.20</td><td>0.40</td><td>2.00</td><td>0.00</td><td>1.50</td><td>2.10</td><td>0.66</td></tr></tbody></table>

### B

<figure><img src="image_42.png" /><figcaption>**Figure 4.** Random forest (RF) analysis (A) and decision tree classification (B) of six *C. melo* accession classes using a subset of 605 provisionally annotated molecular features. Classification of six pre-defined melon accession classes was performed. The classification table (A) lists classes, class size, the actual and predicted class membership, and the classification error (means of 10 iterations using hyperparameter-tuned RF settings). The decision tree uses the top 20 most informative molecular features ranked by the mean decrease in accuracy. The node information of the decision tree reports the used molecular feature code (Table S4) and threshold value. The branch information (colored ovals) lists the main class, the fraction of classified samples, left to right, subsp. *agrestis*, Cantalupensis, Flexuosus, Inodorus, miscellaneous, and Reticulatus accessions of subsp. *melo*. The percentage value indicates the fraction of the 52 accession samples that fall into each of the diagnostic categories.</figcaption></figure>
Hyperparameter tuning of the RF procedure was applied to the six melon classes and based on the annotated data subset, provided the parameter settings: Sampled fraction of 0.899 for RF-classification, minimal node size of 2, and mtry value of 59. The estimate of the overall error rate across 10 repeated RF classifications was 23.9% ± 2.9 %, mean ± standard deviation (SD). The classification error differed, however, between tested classes. Subsp. *agrestis* classified best, with an average classification error of 0.02, followed by subsp. *melo* Inodorus with an error of 0.05. Subsp. *melo* Cantalupensis and subsp. *melo* Reticulatus classified with errors of 0.17 and 0.19, respectively. Unexpectedly, Flexuosus accessions were difficult to classify using the annotated metabolites. Molecular features relevant for the classification of the Flexuosus Group may be present among the non-annotated features (Figure 3B,C). Using annotated molecular features, the Flexuosus classification failed and had a high classification error, similar to the class of miscellaneous accessions (Figure 4A).

The annotated mass features were ranked by RF analysis according to the variable selection parameter mean decrease in accuracy (Table S4). A decision tree based on the top 20 ranks of annotated mass features used only three features (Figure 4B). This decision tree had four classes but failed to define a diagnostic rule for the Flexuosus and miscellaneous classes. One decision rule defined a class that only contained subsp. *agrestis* accessions to 100%. A second class defined Cantalupensis accessions to 89% with a minor contribution of subsp. *agrestis* accessions (11%). A third class contained 87% Inodorus accessions with 6% each of subsp. *agrestis* and Flexuosus. The fourth class was, with 42%, enriched for Reticulatus accessions but contained, in addition, 21% Flexuosus and 37% miscellaneous accessions. The top 20 informative molecular features according to RF analysis were glucose, formic acid, glycerol, arabitol, galactinol, and raffinose in rind; xylose and 1-kestose in flesh; 10 volatile organic compounds detected in melon flesh; and Cr and As elements detected in melon flesh (Table S4). These features provide additional supporting information for melon classification.

**3. Discussion**

3.1. *Phylogenomic and Phytochemical Relationships Partly Coincide in C. melo*

Besides contributing to the genetic classification of the species, the goal of this research was to determine whether phylogenomic and phytochemical relationships in melon coincide and are mutually supportive. Studies of this nature have been carried out for over 50 years but have been limited in two significant respects. Firstly, due to the limiting nature of the targeted chemical analyses performed in earlier studies, chemosystematics was limited to interspecific classification where biochemical differences were large. Thus, interspecific chemosystematics, in conjunction with genomic classification were successfully implemented to distinguish, for example, among *Capsicum* (pepper) species [30], *Brassica* species [31], and *Citrus* species [32]. However, large-scale infraspecific classification based on chemosystematics has not been successful to date. Secondly, the comprehensiveness of metabolomic analyses, as is presented here, was made possible only recently, and earlier studies attempting chemosystematics focused on only single metabolite families, such as volatiles and carotenoids [26]. This inevitably not only limited the breadth of the characterization but also could cause strong bias in the results, especially if the metabolites most impacting the clustering have also been of selective value, either through natural selection or human selection via breeding. The variability of select metabolite families is generally controlled by few genes, which are strongly affected by selection pressure. Hence, for crop varieties, targeted analyses of metabolite variation may not reflect the genetic distance. For example, the impact of a trait, such as non-climacteric ripening, although determined by few genes, is expected to have wide-ranging pleiotropic effects on numerous secondary metabolites, including volatiles [33].

Our study, which is based on the combination of large-scale unbiased genetic and similarly broad characterization of metabolomic phenotypes, allowed us to determine the degree of coincidence between the phylogenetic and chemosystematic classification of the *C. melo* species. We expected to have improved representation of phylogenetic/genomic relationships in our data set by including
non-targeted metabolomic technologies, as compared to a multi-targeted approach that may cover predominantly those metabolic traits that were directly subject or linked to the selective breeding process. Human breeding in melon largely aims for metabolic traits and therefore, breeding can lead to convergence of such traits in cultivars of different genotypic backgrounds and evolutionary histories. We hypothesized that non-targeted metabolites, such as those detectable by the UPLC-TOF-MS and GC-TOF-MS technologies, were presumably of less selective value during domestication and breeding than the targeted metabolites of volatiles, sugar, and organic acid levels, which together comprise the major determinants of quality and hence selection. We therefore predicted that our broad non-targeted profiling would cover the potentially convergent breeding traits, but these would be diluted by metabolic and elemental traits that were not targeted by breeding and hence hypothesized to have higher discriminatory power. Thus, chemosystematics based on non-targeted metabolites would be more likely to reflect true genetic relationships.

Systemization of melon variation goes back at least as far as the 1832 monograph of Jacquin [34]. These early classifications and nearly all subsequent ones were based primarily on fruit traits. The introduction of molecular genetic technologies has helped greatly in evaluating prospective infraspecific relationships. For example, the infraspecific classification proposed for the extremely variable *Cucurbita pepo* L. (Cucurbitaceae) that was based mainly on fruit shape [35] was supported and clarified by polymorphisms in 134 simple sequence repeat loci [36]. Rapid development of new technologies has allowed for successively and increasingly more comprehensive and precise identification and analyses of genetic variability. Most recently, high throughput sequencing and massive GBS strategies have been applied to melon variability in representative GWAS populations [8,37], yielding 17,000 and 22,000 genetic polymorphisms, respectively. Such large-scale genotyping allows for a more precise assessment of genetic relationships within species. The genotype classification of 44 of the 51 accessions used in this study was derived from the GWAS GBS previously reported by Gur et al. [37]. The remaining accessions not included in that GWAS study but screened metabolomically in this study could be reliably placed within Groups based on an additional smaller set of polymorphisms identified by direct PCR product sequencing.

Our results present a mixed picture; on the one hand the metabolomics-based infraspecific classification indeed largely reflects the phylogenetic classification. The correlation between genetic and metabolomics distance increases with the addition of metabolite signals, as expected, and the >80,000 combined metabolite signals best mirror the genetic classification (Figure 3). However, on the other hand, there are exceptions to a straightforward correlation, as will be discussed here further, and the metabolomics-based classification at times may better reflect the market-type classification rather than the genotype classification. This may indicate that selection and breeding for particular fruit characteristics may have had a disproportional impact on metabolites for which breeding did not directly select.

The present phylogenetic results substantiate the dichotomy within *Cucumis melo* to subsp. *agrestis* and subsp. *melo* (Figure 2). Furthermore, the results suggest that only the East Asian cultivar-groupGroups, Chinensis, Makuwa, and Conomon, belong to subsp. *agrestis*. The Duda'im and Momordica accessions that were included in our study are allied with subsp. *melo* but form an outlying cluster within that subspecies, as recently suggested [8,17,19]. However, with respect to the comparison of phylogenetic and the combined metabolomics-based classification (Figures 2 and 3), the *agrestis* subspecies does not behave as a single Group. The two Chinensis and five Makuwa types of the *agrestis* subspecies are similar to each other based on their metabolite composition and clearly clade together. However, they are distinct metabolomically from the other *agrestis* subspecies Group Conomon. Instead, the three Conomon accessions, BSK, FRC, and TOG, clade with the Flexuosus Group, along with the single Momordica accession PI414723 and the undecided accession PI149169. Thus, the Flexuosus and Conomon accessions are metabolically more similar to each other than would be expected based on their genetic relationships. Fruits of both Groups are used primarily when young, either fresh or pickled, similar to cucumbers. The mature fruit of both Groups are characterized by an
acidic pH and low sugar content, in contrast to the dessert melons with low acidity and high sugar content in the edible mature fruit [38]. In light of the fact that these two traits are governed by only a few genes [38–40], we expected that the large-scale metabolomics studies would overcome the bias of these few targeted metabolites and would more closely reflect the unbiased genetic relationships combining the three *agrestis* Groups. Our results with respect to *agrestis* suggest otherwise.

## 3.2. *Cantalupensis* and *Reticulatus* Accessions are Separated from Each Other Both Genotypically and Metabolomically

With regard to the *Cantalupensis* and *Reticulatus* accessions, the combined metabolomics-based classification does strongly parallel the phylogenetic classification. The separation of *Reticulatus/Cantalupensis* into two individual clades, IIb and IIc (Figure 2), that we observed in the genetic cladogram is similarly evident in the metabolomics classification (Figure 3). The *Reticulatus* *Galia* types (MAK, KRY) and the *Cantalupensis* *Ha'Ogen* types (OGE, NY, PH406) have a distinct metabolite profile from the *Reticulatus/Cantalupensis* Groups of American *cantaloupes* and *Charantais*. Furthermore, within each of the two *Reticulatus/Cantalupensis* Groups, the *Cantalupensis* and *Reticulatus* accessions are separated from each other, both genotypically and metabolomically. This is evident in the separation between the *Ha'Ogen* and *Galia* types, as well as the American *cantaloupe* and *Charentais* types from each other. The *Ananas*-type *Ameri* accessions, *EDO* and *AYO*, group with the *Galia* and *Ha'Ogen* accessions, as they did in the phylogenetic classification. Thus, the results relating to the climacteric, netted, and semi-netted *melo* subspecies (*Reticulatus/Cantalupensis/Ameri*) do support the similarity between genetic and metabolomics-based classification.

The *Cantalupensis* and *Reticulatus* Groups are horticulturally distinguished largely according to the extent of rind netting or reticulation, which is actually a continuous trait rather than two distinct phenotypes of netted versus smooth. The trait of rind reticulation is presumed to be relatively simply inherited [39,41,42], but there remains the possibility that the netting trait, present in the different clades of *Reticulatus/Cantalupensis*, may be under separate genetic control. The most visual distinction between the different clades of *Reticulatus/Cantalupensis* is the flesh color. While the *Charentais*-type *Cantalupensis* are orange-fleshed, the *Ha'Ogen*-type *Cantalupensis* are green-fleshed. Similarly, the American *cantaloupe* *Reticulatus* varieties are orange-fleshed while the *Galia* *Reticulatus* are green-fleshed. Flesh color is also determined by relatively few genes [40,43–45], but the associated pleiotropic metabolomic effects due to chloroplast to chromoplast transformations are expected to be large.

## 3.3. *The Metabolomic-Based Classification May Indicate That Two Independent Evolutionary Events Led to Non-Climacteric Ripening*

The results of the GC-MS analysis of melon volatiles (Figure S3) indeed showed that volatile metabolites alone distinguish between the non-climacteric and climacteric groups. However, our comprehensive genomic and metabolomic results also indicate that the *Inodorus* Group is genetically distant from the other cultivar-groups and this large genetic distance cannot be attributed only to the limited climacteric-related genetic differences but also primarily to other unrelated genetic evolution.

Concerning the *Inodorus* Group, most accessions are clearly sorted on the basis of their combined metabolites, similar to their phylogenetic classification. The inclusion of the *Kirkagac* *Inodorus* accessions clearly within the *Inodorus* metabolite group even though they were genetically distinct from it further indicates that the relationship between genetic and metabolomics-based classification is not simple, even when the latter is based on a large number of metabolic signatures as in our study. The genetic distinction between the characteristic *Inodorus* Group melons and non-characteristic *Inodorus* types referred to as European types was also noted by Leida et al. [20].

These results regarding the genetic relationships between most of the *Inodorus* accessions of clade IIa (Figure 2) and those of the *Kirkagac* accessions in clade IIb are significant as they indicate that there may have been two independent evolutionary events leading to non-climacteric ripening, one
represented in the large Inodorus Group and the other more closely related to the clade IIb climacteric types. The genetic control of climacteric ripening in *C. melo* has been studied and found to be due to mutations in just a few genes [46,47]. However, these genetic studies were performed using typical Inodorus accessions as a genetic source for the non-climacteric trait and typical climacteric Cantalupensis (Charentais) accessions. The possibility remains that the genetic cause of non-climactericism in the IIa and IIb clades may not be identical and may even be due to mutations in different genes. In support of this possibility, Eduardo et al. [48] and Vegas et al. [49] reported that a supposed non-climacteric Conomon accession, crossed with the non-climacteric Inodorus PDS, yielded climacteric phenotypes, indicating independent genetic mutations. In fact, Saladie et al. [50] pointed out that classification of melon fruit ripening behavior into just two distinct types is an over-simplification, and that, in reality, there is a continuous spectrum of fruit ripening behavior. If so, further study of the relationship between the non-climacteric traits of Kirkagac and other Inodorus genotypes could broaden the genetic variability available for this important characteristic for genetically improving fruit quality by extending the harvest period as well as the shelf life of the horticultural product.

While practically all the Inodorus accessions metabolomically clade together, the CRE variety is a striking exception (Figure 3). CRE is most similar with respect to its metabolite signature to the single Khandalak accession INB, and together are most closely related to the Charentais Cantalupensis accessions. In fact, CRE is strikingly distinct phenotypically from the other Inodorus accessions, as can be seen in Figure 1. Whereas all the Inodorus lines studied are green-fleshed, as are non-climacteric Inodorus lines in general, the CRE variety is orange-fleshed and likely also climacteric. In fact, the GC-MS volatile results of flesh (Figure S4e) also distinguish between CRE and the other Inodorus lines and place CRE together with the BEL Ha'Ogen-type Cantalupensis. The CRE accession is a Crenshaw market type, which is considered to be a hybrid derivative of the non-climacteric, green-fleshed, smooth-skinned Casaba market-type melon and the climacteric cream/orange-fleshed Persian Cantalupensis melons and thus this may explain that metabolomically, it is similar to the latter.

The different analytical strategies and over all the different types of compounds targeted (primary or specialized metabolites, mineral elements) contributed differently to the classification of melon accessions. This may be related to the fact that some metabolite classes were under breeding selection and may represent convergence of metabolic traits rather than genetic distance. The latter seems probable for major primary metabolites, measured using ¹H-NMR or GC-MS of polar extracts in flesh and implicated in sweetness or acidity. Convergence may also be the case for different families of specialized metabolites measured using LC-MS of semi-polar extracts, such as phenolics or alkaloids with an astringent or bitter taste, or GC-MS of volatiles, such as esters and alcohols. Even mineral elements in flesh contributed to accession classification, possibly through a link with flesh acidity, for instance, for K, or metabolic links between metabolites and mineral elements as shown previously [27]. Selection may have been less stringent for rind than for flesh composition, as for five out of the six analytical strategies used for both flesh and rind analyses, the correlations between compositional distances and genetic distances were higher for rind than for flesh (Table 3).

The correlation between genetic and metabolomics distance increased with the addition of the >80,000 combined molecular features and best mirrored the genetic classification. Nevertheless, information reduction by RF analysis allowed pointing to 20 informative molecular features that differentiate several accession groups. In flesh, 10 volatiles and xylose are possibly linked with climactericity as discussed above. Of these top 10 volatiles, 6 could be reliably annotated and are well known components of melon fruit, including (Z,E)-3-hexen-ol and 1-octen-3-ol, which result from fatty acid degradation, and also two typical acetate esters (2-methyl butyl acetate and pentyl acetate) as well as alpha-ionene [27,51]. Xylose is a major non-cellulosic neutral sugar of cell walls in cucurbit fruit [52]. Fruit softening during maturation involves cell wall degradation. For cell wall-related genes, specific genes of each gene family can be categorized as totally ethylene dependent, totally ethylene independent, or partially ethylene dependent [3]. Surprisingly, two mineral elements were in the top 20 molecular features highlighted by the RF analysis. Among the six compounds highlighted in
peel, glycerol may be related with the fruit surface, such as cuticular waxes [53] or suberization [54]. Galactinol and raffinose linked classification to the raffinose family and to oligosaccharides metabolism in the peel (Table S4). Galactinol and raffinose linked with the raffinose family of oligosaccharide metabolism after phloem unloading into the melon fruit may be related to fruit sweetness [55].

## 4. Conclusions

The main objective of this research was to determine whether classification by large-scale, non-targeted metabolomic and element profiling technologies recapitulates or extends the phylogenomic relationships within *Cucumis melo*. Our results indicate that, in general, metabolomic/elemental means of classification can indeed significantly reflect the genetic relationships. Nevertheless, there are deviations of metabolomics and elemental groupings from genetic classifications. These differences indicate that the selection for major phenotypic quality traits by the breeding process has been influential. Some of the quality traits that can be controlled by single or few enzymatic or regulatory genes, include changes of metabolite levels that define color, taste, and flavor. In addition, some traits change fruit development and thereby have pleiotropic effects on fruit metabolomics.

Large-scale information on genomic sequence and metabolomic/elemental variation of a broad range of genetic diversity can serve for dissecting the genetic basis of metabolic diversity. This process has been recently referred to as mGWAS (metabolite genome wide association study) [56]. mGWAS presents a powerful tool for attributing metabolite variation to particular genetic regions. In melon, GWAS based on GBS was even capable of mapping traits to the single candidate gene level [37] and it is expected that the comprehensive metabolomics data presented here will allow for a large-scale mapping of metabolic traits.

Furthermore, this work sets the stage for an unlimited number of metabolic QTL (mQTL) analyses based on recombinant populations generated from selected metabolically characterized parental lines. The identification of valuable genetic and metabolic variability forms the basis for directed crop diversification and genetic improvement by breeding. The future combination of the results of this study with gene expression data of the developing melon fruit rind and flesh provides an 'omics' blend of genomics, metabolomics, and transcriptomics that will be especially useful for the identification of the genetic basis of metabolic diversity [44].

## 5. Materials and Methods

### 5.1. Plant Material Description, Cultivation, and Sampling

An initial core collection comprised of 51 *Cucumis melo* accessions representing a broad spectrum of melon genetic variation was grown as a spring-summer crop in an open field at the Newe Ya'ar Research Center in the Yizre'el Valley, northern Israel. The accessions prospectively represented both subspecies and 11 of the cultivar-groups (Table 1; Figure 1; Table S1). These 51 accessions were a representative subset of a larger collection of 177 accessions that were used for detailed characterization of fruit trait variation and evaluation of the potential of genome-wide association (GWA) for trait mapping in melon [37].

Of the 51 accessions used for metabolomics analysis, 7 were not included in the GBS study but were genotypically classified in a less encompassing sequencing project based on direct sequencing of 20 PCR products of known genes. This direct sequencing comprised a total of nearly 12,000 bp and provided 116 polymorphisms for which haplotypes were produced and used for the phylogenetic analysis (Table S2). This phylogenetic analysis is not as comprehensive as the GBS but was useful in placing those accessions for which GBS data was missing.

Among these 51 accessions were included two closely related Duda'im melons (DUD2, DUD3) for a total of 52 sampled accessions. Each accession was represented in three replicated plots of five plants each. A minimum of 10 ripe fruits were harvested from each accession. Fruit were photographed prior to sampling. For sample preparation, a midsection of each fruit was excised, and its seed
cavity removed. For the rind sample, the rind was removed using a vegetable peeler to a depth of approximately 2–3 mm and the rinds from all fruit of each accession were combined into a single sample. Similarly, the flesh samples of each accession were combined. Combined bulk samples were ground to a powder in liquid nitrogen. Falcon tubes were filled with approximately 50 mL of powdered sample and a total of 104 tubes (flesh and rind samples of 52 accessions) were stored at –80 °C until shipment under dry ice to each of the participating laboratories where two replicate samples were analyzed per accession/per platform.

## 5.2. Genotype and Phylogenetic Analysis

### 5.2.1. DNA Isolation for Genotype by Sequencing (GBS)

For the GBS of the melon collection, leaf tissue was taken from 44 of the 51 accessions, listed in Table 1. Total genomic DNA isolation was performed as described by Gur et al. [37] using the GenEluteTM Plant Genomic DNA miniprep kit (Sigma, St. Louis, MO, USA). The quality of the DNA was analyzed with an ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE) and by electrophoresis on agarose gel. The concentration of DNA was estimated using a Qubit® 2.0 Fluorometer (Life Technologies, City, Singapore) and a Qubit® dsDNA BR Assay Kit (Life Technologies, Eugene, OR, USA).

### 5.2.2. GBS Analysis

DNA was analyzed at the Institute for Genomic Diversity facility at Cornell University for GBS. GBS 96-plex libraries were prepared using the restriction enzyme ApeKI, following an established protocol [57]. Fragments were sequenced on an Illumina HiSeq 2500 as 100 bp, single-end reads and aligned to the reference genome of C. melo [58] available at https://melonomics.net/files/Genome/Melon_genome_v3.5.1/. TASSEL pipeline v3.0.173 was used for sequence alignment and single nucleotide polymorphism (SNP) calling [59]. Further filtration was performed using TASSEL v5.2.33 [60]; the SNP list was filtered to MAF > 0.05 and a maximum of 6% missing data per site.

### 5.2.3. Phylogenetic Analysis

TASSEL software (v5.2.33) was used to estimate the distance matrix for all pairwise combinations. The phylogenetic tree based on the GBS results of the 44 accessions was assembled using the neighbor-joining function. In addition, we constructed a more limited phylogenetic tree based on 116 polymorphic sites derived from direct sequencing of 11,950 bp derived from 23 genomic sequences (Table S2). The sites were concatenated to create a pseudo-sequence haplotype, which was then aligned (Figure S1a). The aligned sequences were used to build a neighbor-joining tree, and a Phylip distance matrix (Figure S1b). The final alignment, tree, and distance matrix were performed using Clustal Omega version 3.0, www.ebi.ac.uk/Tools/msa/clustalo.

## 5.3. Global Measurements of Fruit Quality

Fruit total soluble solids (TSS, as degrees Brix) and pH were measured on a set of the frozen powdered samples. Portions were allowed to defrost, and juice was measured using a hand-held refractometer (Atago A-10) and a pH meter for each of the 52 accessions used for metabolomic and elemental measurements. Percent of dry matter (%DM) was measured by drying a representative sample in a 60 °C oven and weighing before and after. Means per accession were calculated.

## 5.4. Metabolomics and Elementals Analysis

### 5.4.1. NMR-Based Metabolomic Analyses

For targeted ¹HNMR, polar metabolites were extracted from 50 mg of lyophilized powder using a hot ethanol/water series, and then analyzed, identified, and quantified by ¹H-NMR profiling as
previously described [27,61]. For the preparation of extracts and NMR acquisition parameters, special care was taken to allow absolute quantification of individual metabolites. Quantitative ¹H-NMR spectra were recorded at 500.162 MHz and 300 K on a Bruker Avance spectrometer (Wissembourg, France) with a 5-mm inverse probe using a 90° pulse angle and an electronic reference for quantification with calibration. Two replicates were extracted and analyzed for each accession. Unknown metabolites were named using the mid-value of the chemical shift and the multiplicity of the corresponding resonance group and quantified in arbitrary units. The same spectra issued from polar extracts were also processed as fingerprints: The spectra from δ9.40–0.40 ppm (without the residual water resonance) were binned to chemical shift regions of 0.04 ppm and data were scaled to the total signal intensity.

For untargeted NMR fingerprinting of semi-polar extracts, metabolite extraction was carried out according to the methods described by Ward et al. [62]. Briefly, triplicate aliquots (15 mg) of freeze-dried powder were extracted with an 80:20 D₂O:CD₃OD mixture (1 mL) containing d4-TSP as the internal standard (0.01% *w/v*). Samples were extracted for 10 min at 50 °C. Supernatants were transferred to a clean tube and heated to 90°C for 2 min. Samples were cooled and centrifuged. For ¹H-NMR, 200 µL of the supernatant was evaporated to dryness and reconstituted in 650 µL of deuterated sodium phosphate buffer solution (pH 6.0, 200 mM). Samples were mixed and allowed to stand at room temperature for 1 h after which 600 µL were then transferred to a 5-mm NMR tube for ¹H-NMR analysis. ¹H-NMR spectra were collected at 600 MHz on a Bruker Avance spectrometer equipped with a 5-mm selective inverse probe. Parameters for data acquisition are as described in Ward et al. [62]. ¹H-NMR data (δ9.395–0.505 ppm) were binned to chemical shift regions of 0.01 ppm and data were scaled to the total signal intensity. Regions corresponding to residual water and methanol were removed (H₂O 4.775–4.865; MeOH 3.285–3.335 ppm set to zero).

### 5.4.2. GC-MS-Based Metabolomic Analysis of Polar Compounds

GC-MS profiling analysis of a polar metabolite fraction enriched for primary metabolites, including sugars, amino acids, and organic acids, was performed as described earlier [63] with modifications reported by Moing and co-authors [27]. Samples were extracted, partitioned into a polar liquid phase, and dried for chemical derivatization, as described. Methoxymation was performed with 40 µL of pyridine containing 1 mg/mL methoxyaminohydrochloride and 80 µL silylation-mixture, containing 7:1 (v/v) N,O-bis(trimethylsilyl)-trifluoroacetamide (BSTFA, Macherey-Nagel, Düren, Germany) and a mixture of alkanes in pyridine. GC-MS was by splitless mode after injection of 1 µL of chemically derivatized sample. Evaluated mass features had intensities of ≥ 50 arbitrary intensity units. Peak heights of mass features defined by nominal mass to charge ratios (m/z) and *n*-alkane based retention indices were normalized to the sample fresh weight and the internal standard, succinic-d₄ acid (Sigma-Aldrich, Deisenhofen, Germany). The internal standard was added to the extraction solution. Normalized mass spectral features were aligned, correlated across all recorded samples, and placed into clusters and time groups using TagFinder [64]. Metabolite annotations of mass spectral features were manually supervised using TagFinder visualizations for mass spectral matching [64]. Metabolite annotation required a match of at least three co-eluting and correlated mass features and a retention index deviation < 5% [65] compared to the reference data of the Golm Metabolome Database, http://gmd.mpimp-golm.mpg.de/ [66].

### 5.4.3. GC-MS-Based Metabolomic Analysis of Volatile Compounds

For GC-MS of volatile organic compounds, headspace-solid phase micro extraction (SPME) with a 65-mm polydimethylsiloxane-divinylbenzene fiber (Supelco, Bellefonte, USA) was used as described previously [67]. In short, 200 mg of frozen powder was mixed with 4 mL of 4.6 M CaCl₂ containing 5 mM EDTA in a 10-mL vial. Volatiles in the sample headspace were trapped for 20 min at 50 °C with agitation (CombiPAL autosampler, CTC Analytics, Switzerland) and thermally desorbed in the GC injection port for 1 min at 250 °C. A HP-5 column, 30 m x 0.25 mm ID, 1.05 µm – film thickness (Hewlett Packard, Palo Alto, CA, USA) was used to separate the volatile compounds, applying a temperature
gradient from 44 to 250°C at a speed of 5 °C min⁻¹. All masses from m/z 35 to m/z 400 resulting from 70 eV electron impact ionization were recorded (MD800 electron impact MS, Fisons Instruments, Milan, Italy). Metalign software [68] was used to extract and align all mass features in an untargeted manner and masses originating from the same molecule were then clustered to reconstruct the relative intensity and mass spectrum of each detected compound, using MSClust software [69]. Volatiles were putatively annotated by matching the reconstructed mass spectra of detected compounds to the electron impact mass spectral libraries of NIST (National Institute of Standards and Technology, Gaithersburg, MD, USA) and Wiley. Subsequently, for the random forest (RF) machine learning analysis on the putatively annotated data (Supplementary Table S4), the top 10 volatiles arising as potential markers were then further manually checked for matching of the retention index and mass spectra. Annotations with an RI >20 and/or a match factor <750 were reclassified as 'unknowns'.

### 5.4.4. FIE- or LC-MS-Based Metabolomics Analysis

For FIE-MS, 50 µL of the semi-polar extract supernatant prepared for NMR fingerprinting was diluted with 80:20 H₂O:CH₃OH (950 µL). Samples were analyzed using an Esquire 3000 spectrometer (Bruker Daltonics, Coventry, UK) in both positive and negative ionization modes as described previously [24]. Spectral data were exported as ASCII files containing mass-intensity pairs and automatically reduced using AMIX software version 3.9.11 (Bruker Biospin, Coventry, UK), to a single CSV file for each ionization mode, containing integrated regions of equal width (m/z width = 1). Individual signal intensities were scaled to the total intensity and m/z regions relating to *d4*-TSP and its isotope peaks were removed from the data prior to statistical analysis.

For LC-QTOF-MS, ground frozen rind and flesh tissue (0.5 g) were extracted in 1.5 mL of methanol, containing 0.1% formic acid. The samples were vortexed vigorously, sonicated for 20 min, and centrifuged for 15 min at 4400 g. For rind samples, the supernatant was filtered through a 0.22-µm PVDF filter directly to HPLC vials. Flesh samples were further concentrated as follows: 1 mL of supernatant was freeze-dried and resuspended in 150 µL of 75% aqueous methanol, containing 0.1% formic acid, sonicated for 20 min, and filtered through a 0.22-µm PVDF filter directly to the insert of the HPLC vial. Melon samples were injected into a UPLC-QTOF-MS (HDMS-Synapt, Waters, Manchester, UK) in negative ionization mode as in [70] with some modifications: Short 9.5-min gradient was used for metabolite separation. The linear gradient program was as follows: 100% to 90% phase A over 1 min, 90% to 75% phase A over 3 min, 75% to 55% phase A over 2 min, and 55% to 0% phase A over 0.5 min; held at 100% phase B for 1 min; and then returned to the initial conditions (100% phase A) in 1 min and conditioning at 100% phase A for 1.0 min. A divert valve (Rheodine) excluded the first 1.3 min and last 1.8 min of injection. A mixture of 15 standard compounds, injected after each 10 samples, was used for quality control. XCMS software [71] was used for peak picking and peak alignment. Intensity values were log<sup>e</sup>-transformed.

### 5.4.5. Elemental Analysis

For microelements, frozen milled melon samples were freeze-dried and 200-250 mg dry material was wet digested (5 mL 65% HNO₃ and 5 mL 15% H₂O₂) at 210 °C in 100-mL closed vessels using a microwave oven (Multiwave 3000 software version 1.24, Anton Paar, Graz, Austria). Before analysis, the digests were diluted to a final concentration of 3.5% HNO₃. Multi-element analysis was performed using an ICP-MS (Agilent 7500ce, Agilent Technologies, Wokingham, UK) as described in Bernillon et al. [24]. The impact of spectral interferences was reduced using an octopole ion guide, pressurized with He or H₂ certified reference material (NIST 1515, apple leaves, particle size <75 µm; National Institute of Standards and Technology, Gaithersburg, MD, USA) were included. Only elements deviating less than ±10% from the certified reference values are reported.
5.4.6. Data Handling and Mining

Metabolome and elemental data, i.e., the means of at least two technical replicates per accession sample and of each fruit tissue, were gathered from the consortium of laboratories. Initial response/abundance values below zero were judged to be missing data and removed. Data of each molecular feature were log₁₀-transformed response ratios relative to the median across all samples. Subsets of each analytical technology, including both concatenated flesh and rind profiles, were 0.1 - 0.9 quantile range-normalized to harmonize the diverse linear response ranges of each profiling technology. Missing data were replaced by zero to give enhanced weight to subspecies- or group-specific molecular features rather than inferring values by more elaborate means, e.g., [72,73]. The resulting data set of > 80,000 molecular features was analyzed either in combined mode or divided into subsets.

Independent component analysis (ICA) of 52 samples included 51 accessions (Table 1) and a second independent sample of the Duda im accession. ICA was applied to the first three components of a preceding principal component analysis and was performed using the MetaGeneAlyse web-application, https://metagenealyse.mpimp-golm.mpg.de/ [28]. ICA scores values were plotted. The Multi Experiment Viewer software v 4.9 [74] generated the covariance matrix of the combined data set and a support hierarchical cluster analysis (support HCA) using covariance distance, complete linkage, and bootstrap values range from 1–100 and represent the frequency of tree-node occurrence across the iterations.

In order to compare accession clustering based on genetics with the ones based on compositional distances for each tissue and each analytical strategy separately, Euclidian distances were calculated using the corresponding metabolome or ionone sub-dataset with zeros for undetected metabolomic features for the 52 accession samples with Multi Experiment viewer v 4.9. Then, accession sample clustering based on compositional data was performed under R (https://www.R-project.org/) using the Euclidian distance matrices calculated with Multi Experiment viewer and with the "complete" method for sample hierarchical clustering. In order to compare distances between accessions based on genetics with the ones based on composition, each compositional Euclidian distance matrix was restricted to the 44 accessions having both molecular and fruit metabolome or elemental data. Then, a Mantel test was done to measure the association between the molecular distance matrix and each metabolomic or elemental distance matrix by calculating the Pearson correlation between these two matrices, with its *p*-value calculated with 10000 Monte Carlo simulations using XLSTAT (v2019, Addinsoft, Paris, France). When the *p*-value was below 0.001, the two matrices were considered as significantly correlated.

To select annotated metabolic features that are relevant to classifying the *C. melo* accessions, random forest (RF) analysis was performed using the subset of annotated molecular features [27] from the combined data set (Table S4). Feature selection by RF technology was as described earlier [29] using hyperparameter optimization proposed by [75]. The R-package randomForest v 4.6–14 [76,77] was used for training classification trees. The hyperparameter tuning was performed using the tuneRanger function from the tuneRanger package V 0.2 [75]. The optimized parameters were mtry, node size, and sample size. Other non-tuned RF parameter settings were ntree = 10,000, importance = TRUE, replace = TRUE. The decision tree was created by the functions rpart and rpart plot from the rpart V 4.1–13 and rpart plot V 2.2.0 packages, respectively, using the method "class". We selected the top 20 molecular features according to the mean decrease of accuracy from 10 repeated RF analyses. A decision tree was created, limiting the tree to only those branches that contain all members of a class. The averaged mean decrease of accuracy and mean decrease of the Gini index were correlated by Pearson's correlation coefficient (r²) 0.931 assuming linear association.

All data were visualized by the respective R-packages and the Multi Experiment Viewer [74] in combination with Microsoft Excel and PowerPoint.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2218-1989/10/3/121/s1, Figure S1: Phylogenetic tree and alignments based on direct PCR sequencing of melon accessions not included in GBS, Figure S2: Fruit quality global measurements of 52 accession samples, Figure S3: Hierarchical clustering analysis of 52 accession samples performed per analytical platform for fruit flesh, Figure S4: Hierarchical clustering
analysis of 52 accession samples performed per analytical platform for fruit rind, Table S1: Description of the melon accessions used in this study, Table S2: Genes and primers used for PCR-based genome sequencing and polymorphism identification, Table S3: Genetic distance matrix of the 44 GBS-sequenced accessions, Table S4: List of 605 provisionally-annotated molecular features from the combined data set with assignment of variable-importance obtained by RF technology.

**Author Contributions:** Conceptualization, A.A.S. and R.D.H.; sample preparation and biochemical analyses, A.A., J.W.A., B.B., J.B., M.H.B., C.D., A.E., R.G., T.H.H., D.R., M.M., S.M. (Sagit Meir), S.M. (Sonia Miller), R.M., I.R., J.K.S., R.C.H.d.V., J.L.W., E.Y.; genetic analyses, A.G., N.K., E.O., H.S.P., E.L., U.S., Y.T., Y.B., A.F., S.B.-D., G.T. data curation and mining, F.B., A.E., D.J., J.K., A.M.; writing and original draft preparation, A.E., R.D.H., J.K., A.M., H.S.P., A.A.S.; manuscript reviewing and editing, all authors; funding acquisition, R.D.H. All authors have read and agreed to the submitted version of the manuscript.

**Funding:** The data presented in this paper were generated within the META-PHOR project financed by the European Union Framework 6 programme [2006-F00DCT-036220].

**Acknowledgments:** A.E. and J.K. acknowledge technical and laboratory support of the GC-MS analyses by Ines Fehrle.

**Conflicts of Interest:** The authors declare no conflict of interest.

**References**

1. Pitrat, M. Melon. In *Handbook of Plant Breeding: Vegetable*; Prohens, J., Nuez, F., Eds.; Springer Science: New York, NY, USA, 2008; Volume I, pp. 283–316.

2. Burger, Y.; Paris, H.S.; Cohen, R.; Katzir, N.; Tadmor, Y.; Lewinsohn, E.; Schaffer, A.A. Genetic diversity of *Cucumis melo*. *Hortic. Rev.* **2010**, *36*, 165–198.

3. Pech, J.-C.; Bouzayen, M.; Latche, A. Climacteric fruit ripening: Ethylene-dependent and independent regulation of ripening pathways in melon fruit. *Plant Sci.* **2008**, *175*, 114–120. [CrossRef]

4. Kirkbride, J.H., Jr. *Biosystematic Monograph of the Genus Cucumis* [*Cucurbitaceae*]; Parkway Publishers: Boone, NC, USA, 1993.

5. Robinson, R.W.; Decker-Walters, D.S. *Cucurbits*; CAB International: Wallingford, Oxon, UK, 1997; pp. 65–70.

6. Pitrat, M.; Hanelt, P.; Hammer, K. Some comments on infraspecific classification of cultivars of melon. In *Proceedings of Cucurbitaceae 2000: The 7th Eucarpia Meeting on Cucurbit Genetics and Breeding, Ma'ale Ha Hamisha, Israel, 19–23 March 2000*; Acta Horticulturae No. 510; Katzir, N., Paris, H.S., Eds.; ISHS: Leuven, Belgium, 2000; pp. 29–36.

7. Decker-Walters, D.S.; Chung, S.M.; Staub, J.E.; Quemada, H.D.; López-Sesé, A.I. The origin and genetic affinities of wild populations of melon [*Cucumis melo*, *Cucurbitaceae*] in North America. *Plant Syst. Evol.* **2002**, *233*, 183–197. [CrossRef]

8. Zhao, G.; Lian, Q.; Zhang, Z.; Fu, Q.; He, Y.; Ma, S.; Ruggieri, V.; Monforte, A.J.; Wang, P.; Julca, I.; et al. A comprehensive genome variation map of melon identifies multiple domestication events and loci influencing agronomic traits. *Nat. Genet.* **2019**, *51*, 1607–1615. [CrossRef]

9. Hadfield, K.A.; Rose, J.K.C.; Bennett, A.B. The respiratory climacteric is present in Charentais [*Cucumis melo* cv. Reticulatus F1 Alpha] melons ripened on or off the plant. *J. Exp. Bot.* **1995**, *46*, 1923–1925. [CrossRef]

10. Aubert, C.; Bourger, N. Investigation of volatiles in Charentais cantaloupe melons [*Cucumis melo* Var. *cantalupensis*. Characterization of aroma constituents in some cultivars. *J. Agric. Food Chem.* **2004**, *52*, 4522–4528. [CrossRef]

11. Renner, S.S.; Schaefer, H.; Kocyan, A. Phylogenetics of *Cucumis* [*Cucurbitaceae*]: Cucumber [*C. sativus*] belongs in an Asian/Australian clade far from melon [*C. melo*]. *BMC Evol. Biol.* **2007**, *7*, 58–69. [CrossRef]

12. Sebastian, P.; Schaefer, H.; Telford, I.R.H.; Renner, S.S. Cucumber [*Cucumis sativus*] and melon [*C. melo*] have numerous wild relatives in Asia and Australia, and the sister species of melon is from Australia. *Proc. Natl. Acad. Sci. USA* **2010**, *107*, 14269–14273. [CrossRef]

13. Paris, H.S.; Amar, Z.; Lev, E. Medieval emergence of sweet melons, *Cucumis melo* [*Cucurbitaceae*]. *Ann. Bot.* **2012**, *110*, 23–33. [CrossRef]

14. Paris, H.S.; Amar, Z.; Lev, E. Medieval history of the duda'im melon [*Cucumis melo*, *Cucurbitaceae*]. *Econ. Bot.* **2012**, *66*, 276–284. [CrossRef]

15. Janick, J.; Paris, H.S.; Parrish, D.C. The cucurbits of Mediterranean antiquity: Identification of taxa from ancient images and descriptions. *Ann. Bot.* **2007**, *100*, 1441–1457. [CrossRef]
16. Paris, H.S. Semitic-language records of snake melons [*Cucumis melo*, Cucurbitaceae] in the medieval period and the “piqquos” of the “faqqous”. *Genet. Resour. Crop Evol.* **2012**, *59*, 31–38. [CrossRef]

17. Deleu, W.; Esteras, C.; Roig, C.; González-To, M.; Fernández-Silva, I.; Gonzalez-Ibeas, D.; Blanca, J.; Aranda, M.A.; Arús, P.; Nuez, F.; et al. A set of EST-SNPs for map saturation and cultivar identification in melon. *BMC Plant Biol.* **2009**, *9*, 90. [CrossRef]

18. Blanca, J.M.; Cañizares, J.; Ziarsolo, P.; Esteras, C.; Mir, G.; Nuez, F.; Garcia-Mas, J.; Picó, M.B. Melon transcriptome characterization: Simple sequence repeats and single nucleotide polymorphisms discovery for high throughput genotyping across the species. *Plant Genome* **2011**, *4*, 118–131. [CrossRef]

19. Endl, J.; Achigan-Dako, E.G.; Pandey, A.K.; Monforte, A.J.; Pico, B.; Schaefer, H. Repeated domestication of melon [*Cucumis melo*] in Africa and Asia and a new close relative from India. *Am. J. Bot.* **2018**, *105*, 1662–1671. [CrossRef]

20. Leida, C.; Moser, C.; Esteras, C.; Sulpice, R.; Lunn, J.E.; de Langen, F.; Monforte, A.J.; Picó, B. Variability of candidate genes, genetic structure and association with sugar accumulation and climacteric behavior in a broad germplasm collection of melon [*Cucumis melo* L.]. *BMC Genet.* **2015**, *16*, 28. [CrossRef]

21. McNair, J. Angiosperm phylogeny on a Chemical Basis. *Bull. Torrey Bot. Club* **1935**, *62*, 515–532. [CrossRef]

22. Waterman, P.G. The current status of chemical systematics. *Phytochemistry* **2007**, *68*, 2896–2903. [CrossRef]

23. van Treuren, R.; van Eekelen, H.D.; Wehrens, R.; de Vos, R.C. Metabolite variation in the lettuce gene pool: Towards healthier crop varieties and food. *Metabolomics* **2018**, *14*, 146. [CrossRef]

24. Bernillon, S.; Biais, B.; Deborde, C.; Maucourt, M.; Cabasson, C.; Gibon, Y.; Moing, A. Metabolomic and elemental profiling of melon fruit quality as affected by genotype and environment. *Metabolomics* **2013**, *9*, 57–77. [CrossRef]

25. Freilich, S.; Lev, S.; Gonda, I.; Reuveni, E.; Portnoy, V.; Oren, E.; Lohse, M.; Galpaz, N.; Bar, E.; Tzuri, G.; et al. Systems approach for exploring the intricate associations between sweetness, color and aroma in melon fruits. *BMC Plant Biol.* **2015**, *15*, 71. [CrossRef] [PubMed]

26. Esteras, C.; Rambla, J.L.; Sánchez, G.; López-Gresa, M.P.; González-Mas, M.C.; Fernández-Trujillo, J.P.; Bellés, J.M.; Granell, A.; Picó, M.B. Fruit flesh volatile and carotenoid profile analysis within the *Cucumis melo* L. species reveals unexploited variability for future genetic breeding. *J. Sci. Food Agric.* **2018**, *98*, 3915–3925. [CrossRef] [PubMed]

27. Moing, A.; Aharoni, A.; Biais, B.; Rogachev, I.; Meir, S.; Brodsky, L.; Allwood, J.W.; Erban, A.; Dunn, W.B.; Kay, L.; et al. Extensive metabolic cross talk in melon fruit revealed by spatial and developmental combinatorial metabolomics. *New Phytol.* **2011**, *190*, 683–696. [CrossRef]

28. Daub, C.O.; Kloska, S.; Selbig, J. MetaGeneAlyse: Analysis of integrated transcriptional and metabolite data. *Bioinformatics* **2003**, *19*, 2332–2333. [CrossRef]

29. Erban, A.; Fehrle, I.; Martinez-Seidel, F.; Brigante, F.; Lucini Más, A.; Baroni, V.; Wunderlin, D.; Kopka, J. Discovery of food identity markers by metabolomics and machine learning technology. *Sci. Rep.* **2019**, *9*, 9697. [CrossRef]

30. Wahyuni, Y.; Ballester, A.R.; Tikunov, Y.; de Vos, R.C.; Pelgrom, K.T.; Maharijaya, A.; Sudarmonowati, E.; Bino, R.J.; Bovy, A.G. Metabolomics and molecular marker analysis to explore pepper [*Capsicum* sp.] biodiversity. *Metabolomics* **2013**, *9*, 130–144. [CrossRef]

31. De Vos, R.C.H.; Schipper, B.; Hall, R.D. High-performance liquid chromatography–mass spectrometry analysis of plant metabolites in Brassicaceae. In *Plant Metabolomics; Methods in Molecular Biology* [Methods and Protocols]; Hardy, N., Hall, R., Eds.; Humana Press: Totowa, NJ, USA, 2011; Volume 860.

32. Liu, C.; Jiang, D.; Cheng, Y.; Deng, X.; Chen, F.; Fang, L.; Ma, Z.; Xu, J. Chemotaxonomic study of *Citrus*, *Poncirus* and *Fortunella* genotypes based on peel oil volatile compounds—Deciphering the genetic origin of Mangshanyegan [*Citrus nobilis* Lauriro. PLoS ONE **2013**, *8*, e58411. [CrossRef]

33. Obando-Ulloa, J.M.; Moreno, E.; García-Mas, J.; Nicolai, B.; Lammertyn, J.; Monforte, A.J.; Fernández-Trujillo, J.P. Climacteric or non-climacteric behavior in melon fruit: 1. Aroma volatiles. *Postharvest Biol. Technol.* **2008**, *49*, 27–37. [CrossRef]

34. Jacquin, H. *Monographie Complète Du Melon*; Rousselon: Paris, France, 1832.

35. Paris, H.S. A proposed subspecific classification for *Cucurbita pepo*. *Phytologia* **1986**, *61*, 133–138.

36. Gong, L.; Paris, H.S.; Nee, M.H.; Stift, G.; Pachner, M.; Vollmann, J.; Lelley, T. Genetic relationships and evolution in *Cucurbita pepo* [pumpkin, squash, gourd] as revealed by simple sequence repeat polymorphisms. *Theor. Appl. Genet.* **2012**, *124*, 875–891. [CrossRef]
37. Gur, A.; Tzuri, G.; Meir, A.; Sa'ar, U.; Portnoy, V.; Katzir, N.; Schaffer, A.A.; Li, L.; Burger, J.; Tadmor, Y. Genome-wide linkage-disequilibrium mapping to the candidate gene level in melon [Cucumis melo]. *Sci. Rep.* **2017**, *7*, 9770. [CrossRef]

38. Cohen, S.; Itkin, M.; Yeselson, Y.; Tzuri, G.; Portnoy, V.; Harel-Baja, R.; Lev, S.; Sa'ar, U.; Davidovitz-Rikanati, R.; Baranes, N.; et al. The PH gene determines fruit acidity and contributes to the evolution of sweet melons. *Nat. Commun.* **2014**, *5*, 4026. [CrossRef]

39. Harel-Beja, R.; Tzuri, G.; Portnoy, V.; Lotan-Pompan, M.; Lev, S.; Cohen, S.; Dai, N.; Yeselson, L.; Meir, A.; Libhaber, S.E.; et al. A genetic map of melon highly enriched with fruit quality QTLs and EST markers. including sugar and carotenoid metabolism genes. *Theor. Appl. Genet.* **2010**, *121*, 511–533. [CrossRef]

40. Tzuri, G.; Zhou, X.; Chayut, N.; Yuan, H.; Portnoy, V.; Meir, A.; Sa'ar, U.; Baumkoler, F.; Mazourek, M.; Lewinsohn, E.; et al. A 'golden' SNP in CmOr governs the fruit flesh color of melon [Cucumis melo]. *Plant J.* **2015**, *82*, 267–279. [CrossRef]

41. Diaz, A.; Fergany, M.; Formisano, G.; Ziarsolo, P.; Blanca, J.; Fei, Z.; Staub, J.E.; Zalapa, J.E.; Cuevas, H.E.; Dace, G.; et al. A consensus linkage map for molecular markers and quantitative trait loci associated with economically important traits in melon [Cucumis melo L.]. *BMC Plant Biol.* **2011**, *11*, 111. [CrossRef]

42. Oren, E.; Tzuri, G.; Dafna, A.; Meir, A.; Kumar, R.; Katzir, N.; Elkind, Y.; Freilich, S.; Schaffer, A.A.; Tadmor, Y.; et al. High-density NGS-based map construction and genetic dissection of fruit shape and rind netting in *Cucumis melo*. *Theor. Appl. Genet.* **2020**, in press. [CrossRef] [PubMed]

43. Oren, E.; Tzuri, G.; Vexler, L.; Dafna, A.; Meir, A.; Faigenboim, A.; Kenigswald, M.; Portnoy, V.; Schaffer, A.A.; Levi, A.; et al. The multi-allelic APRR2 gene is associated with fruit pigment accumulation in melon and watermelon. *J. Exp. Bot.* **2019**, *70*, 3781–3794. [CrossRef]

44. Galpaz, N.; Gonda, I.; Shem-Tov, D.; Barad, O.; Tzuri, G.; Lev, S.; Fei, Z.; Xu, Y.; Mao, L.; Jiao, C.; et al. Deciphering genetic factors that determine melon fruit-quality traits using RNA-Seq-based high-resolution QTL and eQTL mapping. *Plant J.* **2018**, *94*, 169–191. [CrossRef]

45. Chayut, N.; Yuan, H.; Ohali, S.; Meir, A.; Sa'ar, U.; Tzuri, G.; Zheng, Y.; Mazourek, M.; Gepstein, S.; Zhou, X.; et al. Distinct mechanisms of the ORANGE protein in controlling carotenoid flux. *Plant Physiol.* **2017**, *173*, 376–389. [CrossRef]

46. Périn, C.; Gomez-Jimenez, M.; Hagen, L.; Dogimont, C.; Pech, J.C.; Latché, A.; Pitrat, M.; Lelievre, J.M. Molecular and genetic characterization of a non-climacteric phenotype in melon reveals two loci conferring altered ethylene response in fruit. *Plant Physiol.* **2002**, *129*, 300–309. [CrossRef]

47. Ríos, P.; Argyris, J.; Vegas, J.; Leida, C.; Kenigswald, M.; Tzuri, G.; Troadec, C.; Bendahmane, A.; Katzir, N.; Picó, B.; et al. ETHQV6.3 is involved in melon climacteric fruit ripening and is encoded by a NAC domain transcription factor. *Plant J.* **2017**, *91*, 671–683. [CrossRef]

48. Eduardo, I.; Arús, P.; Monforte, A.J. Development of a genomic library of near isogenic lines [NILs] in melon [Cucumis melo L.] from the exotic accession PI161375. *Theor. Appl. Genet.* **2005**, *112*, 139–148. [CrossRef]

49. Vegas, J.; Garcia-Mas, J.; Monforte, A.J. Interaction between QTLs induces an advance in ethylene biosynthesis during melon fruit ripening. *Theor. Appl. Genet.* **2013**, *126*, 1531–1544. [CrossRef]

50. Saladié, M.; Cañizares, J.; Phillips, M.A.; Rodriguez-Concepcion, M.; Larrigaudière, C.; Gibon, Y.; Stitt, M.; Lunn, J.E.; Garcia-Mas, J. Comparative transcriptional profiling analysis of developing melon [Cucumis melo L.] fruit from climacteric and non-climacteric varieties. *BMC Genom.* **2015**, *16*, 440. [CrossRef]

51. Buttery, R.G.; Seifert, R.M.; Ling, L.C.; Soderstrom, E.L.; Ogawa, J.M.; Turnbaugh, J.G. Additional aroma components of honeydew melon. *J. Agric. Food Chem.* **1982**, *30*, 1208–1211. [CrossRef]

52. Gross, K.C.; Sams, C.E. Changes in cell wall neutral sugar composition during fruit ripening: A species survey. *Phytochemistry* **1984**, *23*, 2457–2461. [CrossRef]

53. Jetter, R.; Kunst, L.; Samuels, A.L. Composition of plant cuticular waxes. *Biol. Plant Cuticle* **2008**, *23*, 145–181.

54. Cohen, H.; Dong, Y.; Szymanski, J.; Lashbrooke, J.; Meir, S.; Almekias-Siegl, E.; Zeisler-Diehl, V.V.; Schreiber, L.; Aharoni, A. A multilevel study of melon fruit reticulation provides insight into skin ligno-suberization hallmarks. *Plant Physiol.* **2019**, *179*, 1486–1501. [CrossRef]

55. Dai, N.; Cohen, S.; Portnoy, V.; Tzuri, G.; Harel-Beja, R.; Pompan-Lotan, M.; Carmi, N.; Zhang, G.; Diber, A.; Pollock, S.; et al. Metabolism of soluble sugars in developing melon fruit: A global transcriptional view of the metabolic transition to sucrose accumulation. *Plant Mol. Biol.* **2011**, *76*, 1–18. [CrossRef]

56. Fang, C.; Luo, J. Metabolic GWAS-based dissection of genetic bases underlying the diversity of plant metabolism. *Plant J.* **2019**, *97*, 91–100. [CrossRef]
57. Elshire, R.J.; Glaubitz, J.C.; Sun, Q.; Poland, J.A.; Kawamoto, K.; Buckler, E.S.; Mitchell, S.E. A robust, simple genotyping-by-sequencing [GBS] approach for high diversity species. *PLoS ONE* **2011**, *6*, 1–10. [CrossRef]

58. Garcia-Mas, J.; Benjak, A.; Sanseverino, W.; Bourgeois, M.; Mir, G.; González, V.M.; Hénaff, E.; Câmera, F.; Cozzuto, L.; Lowy, E.; et al. The genome of melon [*Cucumis melo* L.]. *Proc. Natl. Acad. Sci. USA* **2012**, *109*, 11872–11877. [CrossRef]

59. Glaubitz, J.C.; Casstevens, T.M.; Lu, F.; Harriman, J.; Elshire, R.J.; Sun, Q.; Buckler, E.S. TASSEL-GBS: A high capacity genotyping by sequencing analysis pipeline. *PLoS ONE* **2014**, *9*, e90346. [CrossRef]

60. Bradbury, P.J.; Zhang, Z.; Kroon, D.E.; Casstevens, T.M.; Ramdoss, Y.; Buckler, E.S. TASSEL: Software for association mapping of complex traits in diverse samples. *Bioinformatics* **2007**, *23*, 2633–2635. [CrossRef]

61. Biais, B.; Allwood, J.W.; Deborde, C.; Xu, Y.; Mickael, M.; Beauvoit, B.; Dunn, W.B.; Jacob, D.; Goodacre, R.; Rolin, D.; et al. ¹H NMR, GC–EI-TOFMS, and data set correlation for fruit metabolomics: Application to spatial metabolite analysis in melon. *Anal. Chem.* **2009**, *81*, 2884–2894. [CrossRef]

62. Ward, J.L.; Forcat, S.; Beckmann, M.; Bennett, M.; Miller, S.J.; Baker, J.M.; Hawkins, N.D.; Vermeer, C.P.; Lu, C.; Lin, W.; et al. The metabolic transition during disease following infection of *Arabidopsis thaliana* by *Pseudomonas syringae* pv. *tomato*. *Plant J.* **2010**, *63*, 443–457. [CrossRef]

63. Allwood, J.W.; Erban, A.; de Koning, S.; Dunn, W.B.; Luedemann, A.; Lommen, A.; Kay, L.; Löscher, R.; Kopka, J.; Goodacre, R. Inter-laboratory reproducibility of fast gas chromatography–electron impact–time of flight mass spectrometry [GC–EI–TOF/MS] based plant metabolomics. *Metabolomics* **2009**, *5*, 479–496. [CrossRef]

64. Luedemann, A.; Strassburg, K.; Erban, A.; Kopka, J. TagFinder for the quantitative analysis of gas chromatography-mass spectrometry [GC-MS] based metabolite profiling experiments. *Bioinformatics* **2008**, *24*, 732–737. [CrossRef]

65. Strehmel, N.; Hummel, J.; Erban, A.; Strassburg, K.; Kopka, J. Retention index thresholds for compound matching in GC-MS metabolite profiling. *J. Chromatogr. B* **2008**, *871*, 182–190. [CrossRef]

66. Kopka, J.; Schauer, N.; Krueger, S.; Birkemeyer, C.; Usadel, B.; Bergmueller, E.; Doermann, P.; Weckwerth, W.; Gibon, Y.; Stitt, M.; et al. GMD@CSB.DB: The Golm Metabolome Database. *Bioinformatics* **2005**, *21*, 1635–1638. [CrossRef]

67. Verhoeven, H.A.; Jonker, H.; De Vos, R.C.H.; Hall, R.D. Solid Phase Micro-Extraction GC–MS Analysis of Natural Volatile components in Melon and Rice. In *Book Humana, Methods for Plant Metabolomics*; Hardy, N.G., Hall, R.D., Eds.; Springer–Humana: London, UK, 2012; pp. 85–99.

68. Lommen, A. MetAlign: Interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. *Anal. Chem.* **2009**, *81*, 3079–3086. [CrossRef]

69. Tikunov, Y.M.; Laptenok, S.; Hall, R.D.; Bovy, A.; de Vos, R.C.H. MSClust: A tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data. *Metabolomics* **2012**, *8*, 714–718. [CrossRef]

70. Moussaieff, A.; Rogachev, I.; Brodsky, L.; Malitsky, S.; Toal, T.W.; Belcher, H.; Yativ, M.; Brady, S.M.; Benfey, P.N.; Aharoni, A. High-resolution metabolic mapping of cell types in plant roots. *Proc. Natl. Acad. Sci. USA* **2013**, *110*, E1232–E1241. [CrossRef] [PubMed]

71. Smith, C.A.; Want, E.J.; O'Maille, G.; Abagyan, R.; Siuzdak, G. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. *Anal. Chem.* **2006**, *78*, 779–787. [CrossRef]

72. Scholz, M.; Kaplan, F.; Guy, C.L.; Kopka, J.; Selbig, J. Non-linear PCA: A missing data approach. *Bioinformatics* **2005**, *21*, 3887–3895. [CrossRef]

73. Wehrens, R.; Hageman, J.A.; van Eeuwijk, F.; Kooke, R.; Flood, P.J.; Wijnker, E.; Keurentjes, J.J.; Lommen, A.; van Eekelen, H.D.; Hall, R.D.; et al. Improved batch correction in untargeted MS-based metabolomics. *Metabolomics* **2016**, *12*, 1–12. [CrossRef]

74. Saeed, A.I.; Sharov, V.; White, J.; Li, J.; Liang, W.; Bhagabati, N.; Braisted, J.; Klapa, M.; Currier, T.; Thiagarajan, M.; et al. TM4: A free, open-source system for microarray data management and analysis. *BioTechniques* **2003**, *34*, 374–378. [CrossRef]
75. Probst, P.; Wright, M.; Boulesteix, A.-L. Hyperparameters and tuning strategies for random forest. *WIREs Data Min. Knowl. Discov.* **2019**, *9*, e1301. [CrossRef]

76. Breiman, L. Random forests. *Mach. Learn.* **2001**, *45*, 5–32. [CrossRef]

77. Liaw, A.; Wiener, M. Classification and regression by randomForest. *R. News* **2002**, *2*, 18–22.

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Article

# Color Mutations Alter the Biochemical Composition in the San Marzano Tomato Fruit

Gabriella Dono <fnref n="1" />, Jose Luis Rambla <fnref n="2" />,<fnref n="3" />, Sarah Frusciante <fnref n="4" />, Antonio Granell <fnref n="2" />, Gianfranco Diretto <fnref n="4" /> and Andrea Mazzucato <fnref n="1" />,\*

Received: 14 February 2020; Accepted: 12 March 2020; Published: 15 March 2020

**Abstract:** San Marzano (SM) is a traditional Italian landrace characterized by red elongated fruits, originating in the province of Naples (Italy) and cultivated worldwide. Three mutations, *yellow flesh* (*r*), *green flesh* (*gf*) and *colorless fruit epidermis* (*y*) were introduced into SM by backcross and the resulting introgression lines (ILs) produced the expected yellow, brown and pink fruit variants. In addition, ILs carrying double combinations of those mutations were obtained. The six ILs plus the SM reference were analyzed for volatile (VOC), non-polar (NP) and polar (P) metabolites. Sixty-eight VOCs were identified, and several differences evidenced in the ILs; overall *gf* showed epistasis over *r* and *y* and *r* over *y*. Analysis of the NP component identified 54 metabolites; variation in early carotenoids (up to lycopene) and chlorophylls characterized respectively the ILs containing *r* and *gf*. In addition, compounds belonging to the quinone and xanthophyll classes were present in genotypes carrying the *r* mutation at levels higher than SM. Finally, the analysis of 129 P metabolites evidenced different levels of vitamins, amino acids, lipids and phenylpropanoids in the ILs. A correlation network approach was used to investigate metabolite–metabolite relationships in the mutant lines. Altogether these differences potentially modified the hedonistic and nutritional value of the berry. In summary, single and combined mutations in *gf*, *r* and *y* generated interesting visual and compositional diversity in the SM landrace, while maintaining its original typology.

**Keywords:** fruit pigmentation; introgression lines; metabolomics; mass spectrometry; San Marzano landrace; tomato

## 1. Introduction

In tomato (*Solanum lycopersicum* L.), fruit color is one of the most important traits affecting consumer liking, and it is the result of combined effect of carotenoids, flavonoids and eventually chlorophylls. The red color of ripe fruit comes from the accumulation of all-trans-lycopene; mutants affected in the carotenoid pathway have an altered carotenoid composition, resulting in different fruit colors [1,2]. Besides carotenoids, flavonoids play a role in determining the color of tomato fruit, particularly at the epidermal level [3]. One of the most abundant flavonoids in tomato fruit peel is the yellow-colored naringenin chalcone, often preceding and paralleling the production of lycopene in the pericarp [4]. Lastly, chlorophylls can eventually have a role in defining the color of the fruit; although they are normally degraded during ripening, “stay green” (SGR) mutants exist, that maintain

<footnote n="1">¹ DAFNE Dept. of Agriculture and Forest Sciences, University of Tuscia, Via S.C. de Lellis snc, 01100 Viterbo, Italy; gabriella.dono91@hotmail.it</footnote>

<footnote n="2">² IBMCP Institute for Plant Molecular and Cell Biology (CSIC-UPV), Carrer de l'Enginyer Fausto Elio, s/n, 46022 Valencia, Spain; jrambla@ibmcp.upv.es (J.L.R.); agranell@ibmcp.upv.es (A.G.)</footnote>

<footnote n="3">³ Department of Agricultural and Environmental Sciences, Jaume I University, Av. Vicent Sos Baynat, s/n. 12071 Castellón de la Plana, Spain</footnote>

<footnote n="4">⁴ ENEA, Casaccia Research Center, Via Anguillarese 301, S. Maria di Galeria, 00123 Rome, Italy; sarah.frusciante@enea.it (S.F.); gianfranco.diretto@enea.it (G.D.)</footnote>

<footnote>\* Correspondence: mazz@unitus.it; Tel.: +39-0761-357370</footnote>