Metabolomic selection for enhanced fruit flavor

Metabolomic analysis combined with machine learning algorithms enables the evaluation of postharvest pecan color stability

Nut kernel color is a crucial quality indicator affecting the consumers first impression of the product. While growing evidence suggests that plant phenolics and their derivatives are linked to nut kernel color, the compounds (biomarkers) responsible for kernel color stability during storage remain elusive. Here, pathway-based metabolomics with machine learning algorithms were employed to identify key metabolites of postharvest pecan color stability. Metabolites in phenylpropanoid, flavonoid, and anthocyanin biosynthetic pathways were analyzed in the testa of nine pecan cultivars using liquid chromatography-mass spectrometry. With color measurements, different machine learning models were compared to find relevant biomarkers of pecan color phenotypes. Results revealed potential marker compounds that included flavonoid precursors and anthocyanidins as well as anthocyanins (e.g., peonidin, delphinidin-3-O-glucoside). Our findings provide a foundation for future research in the area, and will help select genes/proteins for the breeding of pecans with stable and desirable kernel color.

Enhancing post-harvest quality of tomato fruits with chitosan oligosaccharide-zinc oxide nanocomposites: A study on biocompatibility, quality improvement, and carotenoid enhancement

In this study, a new composite with combination of chitosan oligosaccharide (COS) and zinc oxide nanoparticles (ZnO NPs), termed Chitosan Oligosaccharide-Zinc Oxide Nanocomposites (COS-ZnO NC), was designed to enhance the quality of tomato fruits during postharvest storage. SEM analysis showed a uniform distribution of COS-ZnO NC films on tomato surfaces, indicating high biocompatibility, while the FTIR spectrum confirmed the interaction of COS and ZnO NPs via hydrogen bonds. The COS-ZnO NC exerts positive effects on post-harvest quality of tomato fruits, including significantly reduced water loss, fewer skin wrinkles, increased sugar-acid ratio, and enhanced vitamin C and carotenoids accumulation. Furthermore, COS-ZnO NC induces transcription of carotenoid biosynthesis genes and promotes carotenoids storage in the chromoplast. These results suggest that the COS-ZnO NC film can significantly improve the quality traits of tomato fruits, and therefore is potential in post-harvest storage of tomato fruits.

Genome-wide analysis of the passion fruit invertase gene family reveals involvement of PeCWINV5 in hexose accumulation

BACKGROUND

Invertases (INVs) are key enzymes in sugar metabolism, cleaving sucrose into glucose and fructose and playing an important role in plant development and the stress response, however, the INV gene family in passion fruit has not been systematically reported.

RESULTS

In this study, a total of 16 PeINV genes were identified from the passion fruit genome and named according to their subcellular location and chromosome position. These include six cell wall invertase (CWINV) genes, two vacuolar invertase (VINV) genes, and eight neutral/alkaline invertase (N/AINV) genes. The gene structures, phylogenetic tree, and cis-acting elements of PeINV gene family were predicted using bioinformatics methods. Results showed that the upstream promoter region of the PeINV genes contained various response elements; particularly, PeVINV2, PeN/AINV3, PeN/AINV5, PeN/AINV6, PeN/AINV7, and PeN/AINV8 had more response elements. Additionally, the expression profiles of PeINV genes under different abiotic stresses (drought, salt, cold temperature, and high temperature) indicated that PeCWINV5, PeCWINV6, PeVINV1, PeVINV2, PeN/AINV2, PeN/AINV3, PeN/AINV6, and PeN/AINV7 responded significantly to these abiotic stresses, which was consistent with cis-acting element prediction results. Sucrose, glucose, and fructose are main soluble components in passion fruit pulp. The contents of total soluble sugar, hexoses, and sweetness index increased significantly at early stages during fruit ripening. Transcriptome data showed that with an increase in fruit development and maturity, the expression levels of PeCWINV2, PeCWINV5, and PeN/AINV3 exhibited an up-regulated trend, especially for PeCWINV5 which showed highest abundance, this correlated with the accumulation of soluble sugar and sweetness index. Transient overexpression results demonstrated that the contents of fructose, glucose and sucrose increased in the pulp of PeCWINV5 overexpressing fruit. It is speculated that this cell wall invertase gene, PeCWINV5, may play an important role in sucrose unloading and hexose accumulation.

CONCLUSION

In this study, we systematically identified INV genes in passion fruit for the first time and further investigated their physicochemical properties, evolution, and expression patterns. Furthermore, we screened out a key candidate gene involved in hexose accumulation. This study lays a foundation for further study on INV genes and will be beneficial on the genetic improvement of passion fruit breeding.

Understanding the genetic basis of blueberry postharvest traits to define better breeding strategies

Blueberry (Vaccinium spp.) is among the most-consumed soft fruit and has been recognized as an important source of health-promoting compounds. Highly perishable and susceptible to rapid spoilage due to fruit softening and decay during postharvest storage, modern breeding programs are looking to maximize the quality and extend the market life of fresh blueberries. However, it is uncertain how genetically controlled postharvest quality traits are in blueberries. This study aimed to investigate the prediction ability and the genetic basis of the main fruit quality traits affected during blueberry postharvest to create breeding strategies for developing cultivars with an extended shelf life. To achieve this goal, we carried out target genotyping in a breeding population of 588 individuals and evaluated several fruit quality traits after 1 day, 1 week, 3 weeks, and 7 weeks of postharvest storage at 1°C. Using longitudinal genome-based methods, we estimated genetic parameters and predicted unobserved phenotypes. Our results showed large diversity, moderate heritability, and consistent predictive accuracies along the postharvest storage for most of the traits. Regarding the fruit quality, firmness showed the largest variation during postharvest storage, with a surprising number of genotypes maintaining or increasing their firmness, even after 7 weeks of cold storage. Our results suggest that we can effectively improve the blueberry postharvest quality through breeding and use genomic prediction to maximize the genetic gains in the long term. We also emphasize the potential of using longitudinal genomic prediction models to predict the fruit quality at extended postharvest periods by integrating known phenotypic data from harvest.

Fruit-In-Sight: A deep learning-based framework for secondary metabolite class prediction using fruit and leaf images

Fruits produce a wide variety of secondary metabolites of great economic value. Analytical measurement of the metabolites is tedious, time-consuming, and expensive. Additionally, metabolite concentrations vary greatly from tree to tree, making it difficult to choose trees for fruit collection. The current study tested whether deep learning-based models can be developed using fruit and leaf images alone to predict a metabolite's concentration class (high or low). We collected fruits and leaves (n = 1045) from neem trees grown in the wild across 0.6 million sq km, imaged them, and measured concentration of five metabolites (azadirachtin, deacetyl-salannin, salannin, nimbin and nimbolide) using high-performance liquid chromatography. We used the data to train deep learning models for metabolite class prediction. The best model out of the seven tested (YOLOv5, GoogLeNet, InceptionNet, EfficientNet_B0, Resnext_50, Resnet18, and SqueezeNet) provided a validation F1 score of 0.93 and a test F1 score of 0.88. The sensitivity and specificity of the fruit model alone in the test set were 83.52 ± 6.19 and 82.35 ± 5.96, and 79.40 ± 8.50 and 85.64 ± 6.21, for the low and the high classes, respectively. The sensitivity was further boosted to 92.67± 5.25 for the low class and 88.11 ± 9.17 for the high class, and the specificity to 100% for both classes, using a multi-analyte framework. We incorporated the multi-analyte model in an Android mobile App Fruit-In-Sight that uses fruit and leaf images to decide whether to 'pick' or 'not pick' the fruits from a specific tree based on the metabolite concentration class. Our study provides evidence that images of fruits and leaves alone can predict the concentration class of a secondary metabolite without using expensive laboratory equipment and cumbersome analytical procedures, thus simplifying the process of choosing the right tree for fruit collection.

Genomic selection optimization in blueberry: Data-driven methods for marker and training population design

Genomic prediction is a modern approach that uses genome-wide markers to predict the genetic merit of unphenotyped individuals. With the potential to reduce the breeding cycles and increase the selection accuracy, this tool has been designed to rank genotypes and maximize genetic gains. Despite this importance, its practical implementation in breeding programs requires critical allocation of resources for its application in a predictive framework. In this study, we integrated genetic and data-driven methods to allocate resources for phenotyping and genotyping tailored to genomic prediction. To this end, we used a historical blueberry (Vaccinium corymbosun L.) breeding dataset containing more than 3000 individuals, genotyped using probe-based target sequencing and phenotyped for three fruit quality traits over several years. Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data-driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long-term implication, we carried out a simulation study and emphasized that data-driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. Altogether, we provided a data-oriented decision-making approach for breeders by demonstrating that critical breeding decisions associated with resource allocation for genomic prediction can be tackled through a combination of statistics and genetic methods.

Multi-omic applications for understanding and enhancing tropical fruit flavour

Consumer trends towards nutrient-rich foods are contributing to global increasing demand for tropical fruit. However, commercial cultivars in the breeding pipeline that are tailored to meet market demand are at risk of possessing reduced fruit flavour qualities. This stems from recurrent prioritised selection for superior agronomic traits and not fruit flavour, which may in turn reduce consumer satisfaction. There is realisation that fruit quality traits, inclusive of flavour, must be equally selected for; but currently, there are limited tools and resources available to select for fruit flavour traits, particularly in tropical fruit species. Although sugars, acids, and volatile organic compounds are known to define fruit flavour, the specific combinations of these, that result in defined consumer preferences, remain unknown for many tropical fruit species. To define and include fruit flavour preferences in selective breeding, it is vital to determine the metabolites that underpin them. Then, objective quantitative analysis may be implemented instead of solely relying on human sensory panels. This may lead to the development of selective genetic markers through integrated omics approaches that target biosynthetic pathways of flavour active compounds. In this review, we explore progress in the development of tools to be able to strategically define and select for consumer-preferred flavour profiles in the breeding of new cultivars of tropical fruit species.

A Review of Transcriptomics and Metabolomics in Plant Quality and Environmental Response: From Bibliometric Analysis to Science Mapping and Future Trends

Transcriptomics and metabolomics offer distinct advantages in investigating the differentially expressed genes and cellular entities that have the greatest influence on end-phenotype, making them crucial techniques for studying plant quality and environmental responses. While numerous relevant articles have been published, a comprehensive summary is currently lacking. This review aimed to understand the global and longitudinal research trends of transcriptomics and metabolomics in plant quality and environmental response (TMPQE). Utilizing bibliometric methods, we presented a comprehensive science mapping of the social structure, conceptual framework, and intellectual foundation of TMPQE. We uncovered that TMPQE research has been categorized into three distinct stages since 2020. A citation analysis of the 29 most cited articles, coupled with a content analysis of recent works (2020-2023), highlight five potential research streams in plant quality and environmental responses: (1) biosynthetic pathways, (2) abiotic stress, (3) biotic stress, (4) development and ripening, and (5) methodologies and tools. Current trends and future directions are shaped by technological advancements, species diversity, evolving research themes, and an environmental ecology focus. Overall, this review provides a novel and comprehensive perspective to understand the longitudinal trend on TMPQE.

Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program

Sweet corn breeding programs, like field corn, focus on the development of elite inbred lines to produce commercial hybrids. For this reason, genomic selection models can help the in silico prediction of hybrid crosses from the elite lines, which is hypothesized to improve the test cross scheme, leading to higher genetic gain in a breeding program. This study aimed to explore the potential of implementing genomic selection in a sweet corn breeding program through hybrid prediction in a within-site across-year and across-site framework. A total of 506 hybrids were evaluated in six environments (California, Florida, and Wisconsin, in the years 2020 and 2021). A total of 20 traits from three different groups were measured (plant-, ear-, and flavor-related traits) across the six environments. Eight statistical models were considered for prediction, as the combination of two genomic prediction models (GBLUP and RKHS) with two different kernels (additive and additive + dominance), and in a single- and multi-trait framework. Also, three different cross-validation schemes were tested (CV1, CV0, and CV00). The different models were then compared based on the correlation between the estimated breeding values/total genetic values and phenotypic measurements. Overall, heritabilities and correlations varied among the traits. The models implemented showed good accuracies for trait prediction. The GBLUP implementation outperformed RKHS in all cross-validation schemes and models. Models with additive plus dominance kernels presented a slight improvement over the models with only additive kernels for some of the models examined. In addition, models for within-site across-year and across-site performed better in the CV0 than the CV00 scheme, on average. Hence, GBLUP should be considered as a standard model for sweet corn hybrid prediction. In addition, we found that the implementation of genomic prediction in a sweet corn breeding program presented reliable results, which can improve the testcross stage by identifying the top candidates that will reach advanced field-testing stages.

Predicting and improving complex beer flavor through machine learning

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

Metabolomics to understand metabolic regulation underpinning fruit ripening, development, and quality

Classically fruit ripening and development was studied using genetic approaches, with understanding of metabolic changes that occurred in concert largely focused on a handful of metabolites including sugars, organic acids, cell wall components, and phytohormones. The advent and widespread application of metabolomics has, however, led to far greater understanding of metabolic components that play a crucial role not only in this process but also in influencing the organoleptic and nutritive properties of the fruits. Here we review how the study of natural variation, mutants, transgenics, and gene-edited fruits has led to a considerable increase in our understanding of these aspects. We focus on fleshy fruits such as tomato but also review berries, receptacle fruits, and stone-bearing fruits. Finally, we offer a perspective as to how comparative analyses and machine learning will likely further improve our comprehension of the functional importance of various metabolites in the future.

Accumulation mechanism of metabolite markers identified by machine learning between Qingyuan and Xiushui counties in Polygonatum cyrtonema Hua

Polygonatum cyrtonema Hua is a traditional Chinese medicinal plant acclaimed for its therapeutic potential in diabetes and various chronic diseases. Its rhizomes are the main functional parts rich in secondary metabolites, such as flavonoids and saponins. But their quality varies by region, posing challenges for industrial and medicinal application of P. cyrtonema. In this study, 482 metabolites were identified in P. cyrtonema rhizome from Qingyuan and Xiushui counties. Cluster analysis showed that samples between these two regions had distinct secondary metabolite profiles. Machine learning methods, specifically support vector machine-recursive feature elimination and random forest, were utilized to further identify metabolite markers including flavonoids, phenolic acids, and lignans. Comparative transcriptomics and weighted gene co-expression analysis were performed to uncover potential candidate genes including CHI, UGT1, and PcOMT10/11/12/13 associated with these compounds. Functional assays using tobacco transient expression system revealed that PcOMT10/11/12/13 indeed impacted metabolic fluxes of the phenylpropanoid pathway and phenylpropanoid-related metabolites such as chrysoeriol-6,8-di-C-glucoside, syringaresinol-4'-O-glucopyranosid, and 1-O-Sinapoyl-D-glucose. These findings identified metabolite markers between these two regions and provided valuable genetic insights for engineering the biosynthesis of these compounds.

Chemical and genetic basis of orange flavor

Sweet orange (Citrus sinensis) exhibits limited genetic diversity and high susceptibility to Huanglongbing (HLB). Breeding HLB-tolerant orange-like hybrids is in dire need. However, our understanding of the key compounds responsible for orange flavor and their genetic regulation remains elusive. Evaluating 179 juice samples, including oranges, mandarins, Poncirus trifoliata, and hybrids, distinct volatile compositions were found. A random forest model predicted untrained samples with 78% accuracy and identified 26 compounds crucial for orange flavor. Notably, seven esters differentiated orange from mandarin flavor. Cluster analysis showed six esters with shared genetic control. Differential gene expression analysis identified C. sinensis alcohol acyltransferase 1 (CsAAT1) responsible for ester production in orange. Its activity was validated through overexpression assays. Phylogeny revealed the functional allele was inherited from pummelo. A SNP-based DNA marker in the coding region accurately predicted phenotypes. This study enhances our understanding of orange flavor compounds and their biosynthetic pathways and expands breeding options for orange-like cultivars.

Volatile metabolomics reveals the characteristics of the unique flavor substances in oats

Oats is a cereal well known for its high nutritional value and unique flavor. This study investigated the metabolomics data from oats, wheat, and barley using broadly targeted GC-MS metabonomic techniques. A total of 437 volatile organic compounds (VOCs) were identified, of which 414 were shared metabolites, with three metabolites unique to oats. Three hundred and seven differentially accumulated metabolites (DAMs) were screened from all the comparison groups, of which 27 metabolites were shared by oats and barley, and 121 shared by oats and wheat. Terpenoids and esters were the key metabolites determining the differences in flavor. A KEGG analysis indicated that the alpha-linolenic acid and phenylalanine pathways were the most significant metabolic pathways. The 42 DAMs found may be the main substances leading to the flavor differences between the different varieties. Overall, this study reveals the main reasons for the unique flavor of oats through metabolomic evidence.

Biochemical and molecular changes in peach fruit exposed to cold stress conditions

Storage or transportation temperature is very important for preserving the quality of fruit. However, low temperature in sensitive fruit such as peach can induce loss of quality. Fruit exposed to a specific range of temperatures and for a longer period can show chilling injury (CI) symptoms. The susceptibility to CI at low temperature varies among cultivars and genetic backgrounds. Along with agronomic management, appropriate postharvest management can limit quality losses. The importance of correct temperature management during postharvest handling has been widely demonstrated. Nowadays, due to long-distance markets and complex logistics that require multiple actors, the management of storage/transportation conditions is crucial for the quality of products reaching the consumer.Peach fruit exposed to low temperatures activate a suite of physiological, metabolomic, and molecular changes that attempt to counteract the negative effects of chilling stress. In this review an overview of the factors involved, and plant responses is presented and critically discussed. Physiological disorders associated with CI generally only appear after the storage/transportation, hence early detection methods are needed to monitor quality and detect internal changes which will lead to CI development. CI detection tools are assessed: they need to be easy to use, and preferably non-destructive to avoid loss of products.

Adapting enzymes to improve their functionality in plants: why and how

Synthetic biology creates new metabolic processes and improves existing ones using engineered or natural enzymes. These enzymes are often sourced from cells that differ from those in the target plant organ with respect to, e.g. redox potential, effector levels, or proteostasis machinery. Non-native enzymes may thus need to be adapted to work well in their new plant context ('plantized') even if their specificity and kinetics in vitro are adequate. Hence there are two distinct ways in which an enzyme destined for use in plants can require improvement: In catalytic properties such as substrate and product specificity, kcat, and KM; and in general compatibility with the milieu of cells that express the enzyme. Continuous directed evolution systems can deliver both types of improvement and are so far the most broadly effective way to deliver the second type. Accordingly, in this review we provide a short account of continuous evolution methods, emphasizing the yeast OrthoRep system because of its suitability for plant applications. We then cover the down-to-earth and increasingly urgent issues of which enzymes and enzyme properties can - or cannot - be improved in theory, and which in practice are the best to target for crop improvement, i.e. those that are realistically improvable and important enough to warrant deploying continuous directed evolution. We take horticultural crops as examples because of the opportunities they present and to sharpen the focus.

Machine learning applications to improve flavor and nutritional content of horticultural crops through breeding and genetics

Over the last decades, significant strides were made in understanding the biochemical factors influencing the nutritional content and flavor profile of fruits and vegetables. Product differentiation in the produce aisle is the natural consequence of increasing consumer power in the food industry. Cotton-candy grapes, specialty tomatoes, and pineapple-flavored white strawberries provide a few examples. Given the increased demand for flavorful varieties, and pressing need to reduce micronutrient malnutrition, we expect breeding to increase its prioritization toward these traits. Reaching this goal will, in part, necessitate knowledge of the genetic architecture controlling these traits, as well as the development of breeding methods that maximize their genetic gain. Can artificial intelligence (AI) help predict flavor preferences, and can such insights be leveraged by breeding programs? In this Perspective, we outline both the opportunities and challenges for the development of more flavorful and nutritious crops, and how AI can support these breeding initiatives.

A metabolome and transcriptome survey to tap the dynamics of fruit prolonged shelf-life and improved quality within Greek tomato germplasm

Introduction

Tomato is a high economic value crop worldwide with recognized nutritional properties and diverse postharvest potential. Nowadays, there is an emerging awareness about the exploitation and utilization of underutilized traditional germplasm in modern breeding programs. In this context, the existing diversity among Greek accessions in terms of their postharvest life and nutritional value remains largely unexplored.

Methods

Herein, a detailed evaluation of 130 tomato Greek accessions for postharvest and nutritional characteristics was performed, using metabolomics and transcriptomics, leading to the selection of accessions with these interesting traits.

Results

The results showed remarkable differences among tomato Greek accessions for overall ripening parameters (color, firmness) and weight loss. On the basis of their postharvest performance, a balance between short shelf life (SSL) and long shelf life (LSL) accessions was revealed. Metabolome analysis performed on 14 selected accessions with contrasting shelf-life potential identified a total of 206 phytonutrients and volatile compounds. In turn, transcriptome analysis in fruits from the best SSL and the best LSL accessions revealed remarkable differences in the expression profiles of transcripts involved in key metabolic pathways related to fruit quality and postharvest potential.

Discussion

The pathways towards cell wall synthesis, polyamine synthesis, ABA catabolism, and steroidal alkaloids synthesis were mostly induced in the LSL accession, whereas those related to ethylene biosynthesis, cell wall degradation, isoprenoids, phenylpropanoids, ascorbic acid and aroma (TomloxC) were stimulated in the SSL accession. Overall, these data would provide valuable insights into the molecular mechanism towards enhancing shelf-life and improving flavor and aroma of modern tomato cultivars.

Molecular bases of strawberry fruit quality traits: Advances, challenges, and opportunities

The strawberry is one of the world's most popular fruits, providing humans with vitamins, fibers, and antioxidants. Cultivated strawberry (Fragaria × ananassa) is an allo-octoploid and highly heterozygous, making it a challenge for breeding, quantitative trait locus (QTL) mapping, and gene discovery. Some wild strawberry relatives, such as Fragaria vesca, have diploid genomes and are becoming laboratory models for the cultivated strawberry. Recent advances in genome sequencing and CRISPR-mediated genome editing have greatly improved the understanding of various aspects of strawberry growth and development in both cultivated and wild strawberries. This review focuses on fruit quality traits that are most relevant to the consumers, including fruit aroma, sweetness, color, firmness, and shape. Recently available phased-haplotype genomes, single nucleotide polymorphism (SNP) arrays, extensive fruit transcriptomes, and other big data have made it possible to locate key genomic regions or pinpoint specific genes that underlie volatile synthesis, anthocyanin accumulation for fruit color, and sweetness intensity or perception. These new advances will greatly facilitate marker-assisted breeding, the introgression of missing genes into modern varieties, and precise genome editing of selected genes and pathways. Strawberries are poised to benefit from these recent advances, providing consumers with fruit that is tastier, longer-lasting, healthier, and more beautiful.

Machine Learning for Improved Detection of Pathogenic E. coli in Hydroponic Irrigation Water Using Impedimetric Aptasensors: A Comparative Study

Reuse of alternative water sources for irrigation (e.g., untreated surface water) is a sustainable approach that has the potential to reduce water gaps, while increasing food production. However, when growing fresh produce, this practice increases the risk of bacterial contamination. Thus, rapid and accurate identification of pathogenic organisms such as Shiga-toxin producing Escherichia coli (STEC) is crucial for resource management when using alternative water(s). Although many biosensors exist for monitoring pathogens in food systems, there is an urgent need for data analysis methodologies that can be applied to accurately predict bacteria concentrations in complex matrices such as untreated surface water. In this work, we applied an impedimetric electrochemical aptasensor based on gold interdigitated electrodes for measuring E. coliO157:H7 in surface water for hydroponic lettuce irrigation. We developed a statistical machine-learning (SML) framework for assessing different existing SML methods to predict the E. coliO157:H7 concentration. In this study, three classes of statistical models were evaluated for optimizing prediction accuracy. The SML framework developed here facilitates selection of the most appropriate analytical approach for a given application. In the case of E. coliO157:H7 prediction in untreated surface water, selection of the optimum SML technique led to a reduction of test set RMSE by at least 20% when compared with the classic analytical technique. The statistical framework and code (open source) include a portfolio of SML models, an approach which can be used by other researchers using electrochemical biosensors to measure pathogens in hydroponic irrigation water for rapid decision support.

Fruit Quality and Metabolomic Analyses of Fresh Food Accessions Provide Insights into the Key Carbohydrate Metabolism in Blueberry

Blueberry is a nutrient-rich berry, and its taste and flavor directly determine the consumer preference. Until now, few studies have focused on the comparison of fresh food quality and the key metabolites in superior fresh-eating blueberry cultivars. Herein, fruit quality indicators of 10 highbush blueberry cultivars were evaluated using 'Bluerain' as the control. Appearance quality analysis of fruits showed that 'Brigitta' had a larger fruit size and 'Anna' was the smallest. 'Anna' fruits, followed by 'O'Neal', had the highest ratio of soluble solids to acidity because of their lowest titratable acidity content. Despite the high soluble sugar content, the antioxidants in 'Anna' fruits such as total flavonoids, anthocyanins and vitamin C were lowest among all cultivars, while 'Duke' seemed to have opposite patterns. Furthermore, a total of 553 and 557 metabolites were identified by non-targeted metabolomics liquid chromatography-tandem mass spectrometry (LC-MS/MS) in positive and negative ion mode, respectively. Particularly, the numbers of differentially accumulated metabolites (DAMs) were the most between the 'O'Neal' vs. 'Bluerain' group. The DAMs involved in the metabolic pathways, sesquiterpenoid and triterpenoid biosynthesis, monoterpenoid biosynthesis, galactose metabolism, starch and sucrose metabolism, may be mainly related to the synthesis of flavor and carbohydrate substances. Moreover, the expression patterns of genes involved in sugar metabolism were verified by quantitative real-time PCR (qRT-PCR) analysis in different cultivars. Therefore, the systematical comparison of the quality characteristics, metabolites and expression profiles of related genes in highbush blueberries with good flavor could provide some basis for further research on fresh fruit breeding of blueberries.

Variation in ripe fruit volatiles across the tomato clade: An evolutionary framework for studying fruit scent diversity in a crop wild relative

PREMISE

The scents of volatile organic compounds (VOCs) are an important component of ripe fleshy fruit attractiveness, yet their variation across closely related wild species is poorly understood. Phylogenetic patterns in these compounds and their biosynthetic pathways offer insight into the evolutionary drivers of fruit diversity, including whether scent can communicate an honest signal of nutrient content to animal dispersers. We assessed ripe fruit VOC content across the tomato clade (Solanum sect. Lycopersicon), with implications for crop improvement since these compounds are key components of tomato flavor.

METHODS

We analyzed ripe fruit volatiles from 13 species of wild tomato grown in a common garden. Interspecific variations in 66 compounds and their biosynthetic pathways were assessed in 32 accessions, and an accession-level phylogeny was constructed to account for relatedness.

RESULTS

Wild tomato species can be differentiated by their VOCs, with Solanum pennellii notably distinct. Phylogenetic conservatism exists to a limited extent. Major cladewide patterns corresponded to divergence of the five brightly colored-fruited species from the nine green-fruited species, particularly for nitrogen-containing compounds (higher in colored-fruited) and esters (higher in green-fruited), the latter appearing to signal a sugar reward.

CONCLUSIONS

We established a framework for fruit scent evolution studies in a crop wild relative system, showing that each species in the tomato clade has a unique VOC profile. Differences between color groups align with fruit syndromes that could be driven by selection from frugivores. The evolution of colored fruits was accompanied by changes in biosynthetic pathways for esters and nitrogen-containing compounds, volatiles important to tomato flavor.

Plant biochemical genetics in the multiomics era

Our understanding of plant biology has been revolutionized by modern genetics and biochemistry. However, biochemical genetics can be traced back to the foundation of Mendelian genetics; indeed, one of Mendel's milestone discoveries of seven characteristics of pea plants later came to be ascribed to a mutation in a starch branching enzyme. Here, we review both current and historical strategies for the elucidation of plant metabolic pathways and the genes that encode their component enzymes and regulators. We use this historical review to discuss a range of classical genetic phenomena including epistasis, canalization, and heterosis as viewed through the lens of contemporary high-throughput data obtained via the array of approaches currently adopted in multiomics studies.

Natural variations in the Sl-AKR9 aldo/keto reductase gene impact fruit flavor volatile and sugar contents

The unique flavors of different fruits depend upon complex blends of soluble sugars, organic acids, and volatile organic compounds. 2-Phenylethanol and phenylacetaldehyde are major contributors to flavor in many foods, including tomato. In the tomato fruit, glucose, and fructose are the chemicals that most positively contribute to human flavor preferences. We identified a gene encoding a tomato aldo/keto reductase, Sl-AKR9, that is associated with phenylacetaldehyde and 2-phenylethanol contents in fruits. Two distinct haplotypes were identified; one encodes a chloroplast-targeted protein while the other encodes a transit peptide-less protein that accumulates in the cytoplasm. Sl-AKR9 effectively catalyzes reduction of phenylacetaldehyde to 2-phenylethanol. The enzyme can also metabolize sugar-derived reactive carbonyls, including glyceraldehyde and methylglyoxal. CRISPR-Cas9-induced loss-of-function mutations in Sl-AKR9 significantly increased phenylacetaldehyde and lowered 2-phenylethanol content in ripe fruit. Reduced fruit weight and increased soluble solids, glucose, and fructose contents were observed in the loss-of-function fruits. These results reveal a previously unidentified mechanism affecting two flavor-associated phenylalanine-derived volatile organic compounds, sugar content, and fruit weight. Modern varieties of tomato almost universally contain the haplotype associated with larger fruit, lower sugar content, and lower phenylacetaldehyde and 2-phenylethanol, likely leading to flavor deterioration in modern varieties.

Aroma correlation assisted volatilome coupled network analysis strategy to unveil main aroma-active volatiles of Rosa roxburghii

To investigate the main aroma-active volatiles out from comprehensive chemical profile, we proposed an aroma correlation assisted volatilome coupled network analysis strategy and applied it to the study of Rosa roxburghii. Based on 475 detected volatiles with GC × GC-TOF/MS analysis, the volatilome was screened with both positive aroma activities and high contents to discover some aliphatic acids, alcohols, aldehydes and esters, terpenoids as well as some alkenes and ketones. Especially, a series of homologous C6- and C8- acids, alcohols, aldehydes, esters as well as some terpenoids like limonene take the predominant contributions to the aromas. Moreover, two aroma-active and aroma-contributing volatile groups including acid-aldehyde-alcohol-ester and terpenoid groups were clustered to integrally be responsible for the major aromas of R. roxburghii with network analysis. Additionally, the accumulation of C6- and C8-family homologous aliphatic volatiles was also elucidated with linoleic and linolenic acid derived pathways. This strategy is practical to investigate the main aroma-active volatiles based on volatilome.

The dissection of tomato flavor: biochemistry, genetics, and omics

Flavor and quality are the major drivers of fruit consumption in the US. However, the poor flavor of modern commercial tomato varieties is a major cause of consumer dissatisfaction. Studies in flavor research have informed the role of volatile organic compounds in improving overall liking and sweetness of tomatoes. These studies have utilized and applied the tools of molecular biology, genetics, biochemistry, omics, machine learning, and gene editing to elucidate the compounds and biochemical pathways essential for good tasting fruit. Here, we discuss the progress in identifying the biosynthetic pathways and chemical modifications of important tomato volatile compounds. We also summarize the advances in developing highly flavorful tomato varieties and future steps toward developing a "perfect tomato".

Identifying Early-Stage Changes in Volatile Organic Compounds of Ceratocystis fimbriata Ellis & Halsted-Infected Sweet Potatoes (Ipomoea batatas L. Lam) Using Headspace Gas Chromatography-Ion Mobility Spectrometry

Ceratocystis fimbriata Ellis & Halsted is the pathogen causing black rot in sweet potatoes that can lead to flavor change and toxin release. This study detected the volatile organic compounds (VOCs) of C. fimbriata-infected sweet potatoes in the early stages using headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS). A total of 55 VOCs were identified, including aldehydes, alcohols, esters, ketones, and others. The content of aldehydes and ketones showed a decreasing trend, while alcohols and esters showed an increasing trend. An increase in infection time elevated the content of malondialdehyde (MDA) and pyruvate, while the starch content decreased, the content of soluble protein initially increased, then decreased, and the activities of lipoxygenase (LOX), pyruvate decarboxylase (PDC), alcohol dehydrogenase (ADH), and phenylalanine ammonia-lyase (PAL) increased. The changes in VOCs were closely related to the content of MDA, starch, pyruvate, and the activities of LOX, PDC, ADH, and PAL. Sweet potatoes showed a good discrimination effect by principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) from 0 to 72 h. Twenty-five differential VOCs could be used as early-stage characteristic compounds of C. fimbriata-infected sweet potatoes for early disease monitoring.

Metabolic activity organizes olfactory representations

Hearing and vision sensory systems are tuned to the natural statistics of acoustic and electromagnetic energy on earth and are evolved to be sensitive in ethologically relevant ranges. But what are the natural statistics of odors, and how do olfactory systems exploit them? Dissecting an accurate machine learning model (Lee et al., 2022) for human odor perception, we find a computable representation for odor at the molecular level that can predict the odor-evoked receptor, neural, and behavioral responses of nearly all terrestrial organisms studied in olfactory neuroscience. Using this olfactory representation (principal odor map [POM]), we find that odorous compounds with similar POM representations are more likely to co-occur within a substance and be metabolically closely related; metabolic reaction sequences (Caspi et al., 2014) also follow smooth paths in POM despite large jumps in molecular structure. Just as the brain's visual representations have evolved around the natural statistics of light and shapes, the natural statistics of metabolism appear to shape the brain's representation of the olfactory world.

A metabolic perspective of selection for fruit quality related to apple domestication and improvement

BACKGROUND

Apple is an economically important fruit crop. Changes in metabolism accompanying human-guided evolution can be revealed using a multiomics approach. We perform genome-wide metabolic analysis of apple fruits collected from 292 wild and cultivated accessions representing various consumption types.

RESULTS

We find decreased amounts of certain metabolites, including tannins, organic acids, phenolic acids, and flavonoids as the wild accessions transition to cultivated apples, while lysolipids increase in the "Golden Delicious" to "Ralls Janet" pedigree, suggesting better storage. We identify a total of 222,877 significant single-nucleotide polymorphisms that are associated with 2205 apple metabolites. Investigation of a region from 2.84 to 5.01 Mb on chromosome 16 containing co-mapping regions for tannins, organic acids, phenolic acids, and flavonoids indicates the importance of these metabolites for fruit quality and nutrition during breeding. The tannin and acidity-related genes Myb9-like and PH4 are mapped closely to fruit weight locus fw1 from 3.41 to 3.76 Mb on chromosome 15, a region under selection during domestication. Lysophosphatidylethanolamine (LPE) 18:1, which is suppressed by fatty acid desaturase-2 (FAD2), is positively correlated to fruit firmness. We find the fruit weight is negatively correlated with salicylic acid and abscisic acid levels. Further functional assays demonstrate regulation of these hormone levels by NAC-like activated by Apetala3/Pistillata (NAP) and ATP binding cassette G25 (ABCG25), respectively.

CONCLUSIONS

This study provides a metabolic perspective for selection on fruit quality during domestication and improvement, which is a valuable resource for investigating mechanisms controlling apple metabolite content and quality.

Natural variation of respiration-related traits in plants

Plant respiration is one of the greatest global metabolic fluxes, but rates of respiration vary massively both within different cell types as well as between different individuals and different species. Whilst this is well known, few studies have detailed population-level variation of respiration until recently. The last 20 years have seen a renaissance in studies of natural variance. In this review, we describe how experimental breeding populations and collections of large populations of accessions can be used to determine the genetic architecture of plant traits. We further detail how these approaches have been used to study the rate of respiration per se as well as traits that are intimately associated with respiration. The review highlights specific breakthroughs in these areas but also concludes that the approach should be more widely adopted in the study of respiration per se as opposed to the more frequently studied respiration-related traits.

Plant and microbial sciences as key drivers in the development of metabolomics research

This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al., Trends Biotech. 16, 373-378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al., Cell Metab. 34, 21-34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry-based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance-based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.

Targeting ripening regulators to develop fruit with high quality and extended shelf life

Fruit quality directly impacts fruit marketability and consumer acceptance. Breeders have focused on fruit quality traits to extend shelf life, primarily through fruit texture, but, in some cases, have neglected other qualities such as flavor and nutrition. In recent years, integrative biotechnology and consumer-minded approaches have surfaced, aiding in the development of flavorful, long-lasting fruit. Here, we discussed how specific transcription factors and hormones involved in fruit ripening can be targeted to generate high-quality fruit through traditional breeding and bioengineering. We highlight regulators that can be used to generate novel-colored fruit or biofortify fresh produce with health-promoting nutrients, such as vitamin C. Overall, we argue that addressing grower and industry needs must be balanced with consumer-based traits.

Identification and Analysis of Metabolites That Contribute to the Formation of Distinctive Flavour Components of Laoxianghuang

In addition to volatile compounds, metabolites also have a great effect on the flavour of food. Fresh finger citron cannot be eaten directly because of its spicy and bitter taste, so it is made into a preserved fruit product known as Laoxianghuang (LXH). To investigate the metabolites that have an effect on the flavour of LXH, untargeted metabolomics was performed using an ultrahigh-performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS), and the metabolites of the Laoxianghuang samples from different locations in the Chaoshan area were compared and analysed. A total of 756 metabolites were identified and distinct differences were revealed among the different Laoxianghuang samples. A total of 33 differential metabolites with the most significant changes were screened through further multivariate analytical steps, and each group of samples had unique metabolites. For instance, pomolic acid had the highest content in the JG sample, while L-glycyl-L-isoleucine was rich in the QS sample. Moreover, flavonoid metabolites made the greatest contribution to the unique flavour of Laoxianghuang. The metabolic pathways involved are the biosynthetic pathways of flavonoids, isoflavonoids, flavones, and flavonols. This study can provide some creative information for distinguishing the quality differences of Laoxianghuang from the perspective of metabolites and offer preliminary theoretical support to characterise the formation of flavour substances in Laoxianghuang.

Advances in Physiological, Transcriptomic, Proteomic, Metabolomic, and Molecular Genetic Approaches for Enhancing Mango Fruit Quality

Mango (Mangifera indica L.) is a nutritionally important fruit of high nutritive value, delicious in taste with an attractive aroma. Due to their antioxidant and therapeutic potential, mango fruits are receiving special attention in biochemical and pharmacognosy-based studies. Fruit quality determines consumer's acceptance, and hence, understanding the physiological, biochemical, and molecular basis of fruit development, maturity, ripening, and storage is essential. Transcriptomic, metabolomic, proteomic, and molecular genetic approaches have led to the identification of key genes, metabolites, protein candidates, and quantitative trait loci that are associated with enhanced mango fruit quality. The major pathways that determine the fruit quality include amino acid metabolism, plant hormone signaling, carbohydrate metabolism and transport, cell wall biosynthesis and degradation, flavonoid and anthocyanin biosynthesis, and carotenoid metabolism. Expression of the polygalacturonase, cutin synthase, pectin methyl esterase, pectate lyase, β-galactosidase, and ethylene biosynthesis enzymes are related to mango fruit ripening, flavor, firmness, softening, and other quality processes, while genes involved in the MAPK signaling pathway, heat shock proteins, hormone signaling, and phenylpropanoid biosynthesis are associated with diseases. Metabolomics identified volatiles, organic acids, amino acids, and various other compounds that determine the characteristic flavor and aroma of the mango fruit. Molecular markers differentiate the mango cultivars based on their geographical origins. Genetic linkage maps and quantitative trait loci studies identified regions in the genome that are associated with economically important traits. The review summarizes the applications of omics techniques and their potential applications toward understanding mango fruit physiology and their usefulness in future mango breeding.

Genetic architecture of fresh-market tomato yield

BACKGROUND

The fresh-market tomato (Solanum lycopersicum) is bred for direct consumption and is selected for a high yield of large fruits. To understand the genetic variations (distinct types of DNA sequence polymorphism) that influence the yield, we collected the phenotypic variations in the yields of total fruit, extra-large-sized fruit, small-sized fruit, or red-colored fruit from 68 core inbred contemporary U.S. fresh-market tomatoes for three consecutive years and the genomic information in 8,289,741 single nucleotide polymorphism (SNP) positions from the whole-genome resequencing of these tomatoes.

RESULTS

Genome-wide association (GWA) mapping using the SNP data with or without SNP filtering steps using the regularization methods, validated with quantitative trait loci (QTL) linkage mapping, identified 18 significant association signals for traits evaluated. Among them, 10 of which were not located within genomic regions previously identified as being associated with fruit size/shape. When mapping-driven association signals [558 SNPs associated with 28 yield (component) traits] were used to calculate genomic estimated breeding values (GEBVs) of evaluated traits, the prediction accuracies of the extra-large-sized fruit and small-sized fruit yields were higher than those of the total and red-colored fruit yields, as we tested the generated breeding values in inbred tomatoes and F2 populations. Improved accuracy in GEBV calculation of evaluated traits was achieved by using 364 SNPs identified using the regularization methods.

CONCLUSIONS

Together, these results provide an understanding of the genetic variations underlying the heritable phenotypic variability in yield in contemporary tomato breeding and the information necessary for improving such economically important and complex quantitative trait through breeding.

Postharvest chilling diminishes melon flavor via effects on volatile acetate ester biosynthesis

In postharvest handling systems, refrigeration can extend fruit shelf life and delay decay via slowing ripening progress; however, it selectively alters the biosynthesis of flavor-associated volatile organic compounds (VOCs), which results in reduced flavor quality. Volatile esters are major contributors to melon fruit flavor. The more esters, the more consumers enjoy the melon fruit. However, the effects of chilling on melon flavor and volatiles associated with consumer liking are yet to be fully understood. In the present study, consumer sensory evaluation showed that chilling changed the perception of melon fruit. Total ester content was lower after chilling, particularly volatile acetate esters (VAEs). Transcriptomic analysis revealed that transcript abundance of multiple flavor-associated genes in fatty acid and amino acid pathways was reduced after chilling. Additionally, expression levels of the transcription factors (TFs), such as NOR, MYB, and AP2/ERF, also were substantially downregulated, which likely altered the transcript levels of ester-associated pathway genes during cold storage. VAE content and expression of some key genes recover after transfer to room temperature. Therefore, chilling-induced changes of VAE profiles were consistent with expression patterns of some pathway genes that encode specific fatty acid- and amino acid-mobilizing enzymes as well as TFs involved in fruit ripening, metabolic regulation, and hormone signaling.

Metabolomics-centered mining of plant metabolic diversity and function: Past decade and future perspectives

Plants are natural experts in organic synthesis, being able to generate large numbers of specific metabolites with widely varying structures that help them adapt to variable survival challenges. Metabolomics is a research discipline that integrates the capabilities of several types of research including analytical chemistry, statistics, and biochemistry. Its ongoing development provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites. Metabolomics is usually performed by targeting either a specific cell, a specific tissue, or the entire organism. Considerable advances in science and technology over the last three decades have propelled us into the era of multi-omics, in which metabolomics, despite at an earlier developmental stage than genomics, transcriptomics, and proteomics, offers the distinct advantage of studying the cellular entities that have the greatest influence on end phenotype. Here, we summarize the state of the art of metabolite detection and identification, and illustrate these techniques with four case study applications: (i) comparing metabolite composition within and between species, (ii) assessing spatio-temporal metabolic changes during plant development, (iii) mining characteristic metabolites of plants in different ecological environments and upon exposure to various stresses, and (iv) assessing the performance of metabolomics as a means of functional gene identification , metabolic pathway elucidation, and metabolomics-assisted breeding through analyzing plant populations with diverse genetic variations. In addition, we highlight the prominent contributions of joint analyses of plant metabolomics and other omics datasets, including those from genomics, transcriptomics, proteomics, epigenomics, phenomics, microbiomes, and ion-omics studies. Finally, we discuss future directions and challenges exploiting metabolomics-centered approaches in understanding plant metabolic diversity.

Bioengineered intestinal tubules as a tool to test intestinal biological efficacy of lettuce species

Lettuce (Lactuca sativa) is one of the most consumed and cultivated vegetables globally. Its breeding is focused on the improvement of yield and disease resistance. However, potential detrimental or beneficial health effects for the consumer are often not targeted in the breeding programs. Here, a bioengineered intestinal tubule was used to assess the intestinal efficacy of extracts from five plant accessions belonging to four Lactuca species. These four species include the domesticated L. sativa, closely related wild species L. serriola, and phylogenetically more distant wild relatives L. saligna and L. virosa. We assessed the epithelial barrier integrity, cell viability, cell attachment, brush border enzyme activity, and immune markers. Extracts from L. sativa cv. Salinas decreased cell attachment and brush border enzyme activity. However, extracts from the non-edible wild species L. saligna and L. virosa reduced the epithelial barrier functions, cell attachment, cell viability, and brush border enzyme activity. Since wild species represent a valuable germplasm pool, the bioengineered intestinal tubules could open ways to evaluate the safety and nutritional properties of the lettuce breeding material originating from crosses with wild Lactuca species.

Impact of different cooking methods on the flavor and chemical profile of yellow-fleshed table-stock sweetpotatoes (Ipomoea batatas L.)

This study investigated the impact of baking, boiling, and steaming on the taste, flavor, and chemical profile of yellow-fleshed sweetpotatoes (YFSP). Baked YFSP were sweeter, more palatable, and more flavorful than both steamed and boiled YFSP. Baking increased the YFSP soluble sugar content from 9.12% to 36.65%. Specifically, maltose increased by 200-fold and this possibly accounted for the sweetness of baked YFSP. From the Gas Chromatography-Mass Spectrometry analysis, the contents of furans and terpenes increased with baking, endowing baked YFSP with an aroma. On the contrary, boiling retained more carotenoids than the other cooking methods. Although cooking clearly altered YFSP, bioactive substances were predominantly preserved as only 72 out of 706 metabolites were identified as differentially accumulated metabolites between cooked and raw samples. Taken together, baked YFSP had high levels of sugars and volatile compounds, and the three cooking methods had little effect on chemical compounds. This comprehensive evaluation of cooked YFSP is a basis for sweetpotato processing and consumer choice.

Starch and sugars as determinants of postharvest shelf life and quality: some new and surprising roles

Starch and sugars account for most of the dry weight of horticultural crops and in many species, are known determinants of quality. However, we posit that these carbohydrates often have less-obvious roles in plant tissues with direct implications for the postharvest quality and produce shelf life. The latter has not been given as much attention, but with the recent interest in reducing the scale of postharvest waste and loss, we highlight how dynamic changes in the spatial-temporal accumulation of carbohydrates, can influence myriads of biological processes affecting postharvest attributes. Versatile roles, some surprising, that carbohydrates play in determining produce of high value to consumers, are highlighted, and gene targets for biotechnological improvement are specified.

Dissecting postharvest chilling injury through biotechnology

Paradoxically, refrigerating many fruits and vegetables destroys their quality, and may even accelerate their spoilage. This phenomenon, known as postharvest chilling injury (PCI), affects produce from tropical and subtropical regions and leads to economic and postharvest loss and waste. Low temperatures are used to pause the physiological processes associated with senescence, but upon rewarming, these processes may resume at an accelerated rate. Chilling-injured produce may be discarded for not meeting consumer expectations or may prematurely deteriorate. In this review, we describe progress made in identifying the cellular and molecular processes underlying PCI, and point to advances in biotechnological approaches for ameliorating symptoms. Further, we identify the gaps in knowledge that must be bridged to develop effective solutions to PCI.

An Exploration of Pepino (Solanum muricatum) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation

Flavor is one of the most important characteristics that directly determines the popularity of a food. Moreover, the flavor of fruits is determined by the interaction of multiple metabolic components. Pepino, an emerging horticultural crop, is popular for its unique melon-like flavor. We analyzed metabolomics data from three different pepino growing regions in Haidong, Wuwei, and Jiuquan and counted the status of sweetness, acidity, flavor, and overall liking ratings of pepino fruit in these three regions by sensory panels. The metabolomics and flavor ratings were also integrated and analyzed using statistical and machine learning models, which in turn predicted the sensory panel ratings of consumers based on the chemical composition of the fruit. The results showed that pepino fruit produced in the Jiuquan region received the highest ratings in sweetness, flavor intensity, and liking, and the results with the highest contribution based on sensory evaluation showed that nucleotides and derivatives, phenolic acids, amino acids and derivatives, saccharides, and alcohols were rated in sweetness (74.40%), acidity (51.57%), flavor (56.41%), and likability (33.73%) dominated. We employed 14 machine learning strategies trained on the discovery samples to accurately predict the outcome of sweetness, sourness, flavor, and liking in the replication samples. The Radial Sigma SVM model predicted with better accuracy than the other machine learning models. Then we used the machine learning models to determine which metabolites influenced both pepino flavor and consumer preference. A total of 27 metabolites most important for pepino flavor attributes to distinguish pepino originating from three regions were screened. Substances such as N-acetylhistamine, arginine, and caffeic acid can enhance pepino‘s flavor intensity, and metabolites such as glycerol 3-phosphate, aconitic acid, and sucrose all acted as important variables in explaining the liking preference. While glycolic acid and orthophosphate inhibit sweetness and enhance sourness, sucrose has the opposite effect. Machine learning can identify the types of metabolites that influence fruit flavor by linking metabolomics of fruit with sensory evaluation among consumers, which conduces breeders to incorporate fruit flavor as a trait earlier in the breeding process, making it possible to select and release fruit with more flavor.

Targeted approaches to improve tomato fruit taste

Tomato (Solanum lycopersicum) is the most valuable fruit and horticultural crop species worldwide. Compared with the fruits of their progenitors, those of modern tomato cultivars are, however, often described as having unsatisfactory taste or lacking flavor. The flavor of a tomato fruit arises from a complex mix of tastes and volatile metabolites, including sugars, acids, amino acids, and various volatiles. However, considerable differences in fruit flavor occur among tomato varieties, resulting in mixed consumer experiences. While tomato breeding has traditionally been driven by the desire for continual increases in yield and the introduction of traits that provide a long shelf-life, consumers are prepared to pay a reasonable premium for taste. Therefore, it is necessary to characterize preferences of tomato flavor and to define its underlying genetic basis. Here, we review recent conceptual and technological advances that have rendered this more feasible, including multi-omics-based QTL and association analyses, along with the use of trained testing panels, and machine learning approaches. This review proposes how the comprehensive datasets compiled to date could allow a precise rational design of tomato germplasm resources with improved organoleptic quality for the future.

Flavor and Other Quality Traits of Tomato Cultivars Bred for Diverse Production Systems as Revealed in Organic Low-Input Management

This study was conducted to determine the volatile organic compounds (VOCs) associated with fruit flavor in diverse tomato cultivars (salad and cocktail cultivars) under organic low-input production. For this objective, 60 cultivars deriving from very diverse breeding programs 1880-2015 were evaluated in 2015, and a subset of 20 cultivars was selected for further evaluation in 2016. The diversity of instrumentally determined traits, especially for VOCs concentration and sensory properties (fruit firmness, juiciness, skin firmness, sweetness, sourness, aroma, and acceptability), was investigated at two harvest dates. The evaluation of the cultivars exhibited a wide range of variation for all studied traits, with the exception of a few VOCs. Cultivar had the most important effect on all instrumentally determined traits, while the influence of cultivar × harvest date × year interaction was significant for 17 VOCs, but not for total soluble solid (TSS) and titratable acidity (TA). The VOCs with the highest proportion (>8%) were hexanal, 6-methyl-5-heptene-2-one, 2-isobutylthiazole, and (E)-2-hexenal, which were identified in all cultivars. Twelve VOCs significantly correlated with one or more sensory attributes and these VOCs also allowed differentiation of the fruit type. Among these VOCs, phenylethyl alcohol and benzyl alcohol positively correlated with acceptability in the cocktail cultivars, whereas 2-isobuthylthiazole and 6-methyl-5-hepten-2-ol negatively correlated with acceptability in the salad cultivars. As a result of this study, organic breeders are recommended to use cultivars from a wide range of breeding programs to improve important quality and agronomic traits. As examples, salad tomatoes "Campari F1", "Green Zebra", and "Auriga", as well as cocktail tomatoes "Supersweet 100 F1", "Sakura F1", and "Black Cherry" showed higher scores for the sensory attributes aroma and acceptability under organic low-input growing conditions. It remains a challenge for breeders and growers to reduce the trade-off of yield and quality.

Changes of Sensory Quality, Flavor-Related Metabolites and Gene Expression in Peach Fruit Treated by Controlled Atmosphere (CA) under Cold Storage

Controlled atmosphere (CA) has been used to alleviate chilling injury (CI) of horticultural crops caused by cold storage. However, the effects of CA treatment on peach fruit sensory quality and flavor-related chemicals suffering from CI remain largely unknown. Here, we stored peach fruit under CA with 5% O₂ and 10% CO₂ at 0 °C up to 28 d followed by a subsequent 3 d shelf-life at 20 °C (28S3). CA significantly reduced flesh browning and improved sensory quality at 28S3. Though total volatiles declined during extended cold storage, CA accumulated higher content of volatile esters and lactones than control at 28S3. A total of 14 volatiles were positively correlated with consumer acceptability, mainly including three C6 compounds, three esters and four lactones derived from the fatty acid lipoxygenase (LOX) pathway. Correspondingly, the expression levels of genes including PpLOX1, hyperoxide lyase PpHPL1 and alcohol acyltransferase PpAAT1 were positively correlated with the change of esters and lactones. CA elevated the sucrose content and the degree of fatty acids unsaturation under cold storage, which gave us clues to clarify the mechanism of resistance to cold stress. The results suggested that CA treatment improved sensory quality by alleviating CI of peach fruits under cold storage.