Special Issue on “fruit metabolism and metabolomics”

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 underly 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. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.partly funded by MetaboHUB (ANR‐11‐INBS‐0010) and PHENOME (ANR‐11‐ INBS‐0012) French Agence Nationale de la Recherche projects. S.O. was parcially 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).Peer reviewe

Special Issue on “fruit metabolism and metabolomics”

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 underly 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. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.partly funded by MetaboHUB (ANR‐11‐INBS‐0010) and PHENOME (ANR‐11‐ INBS‐0012) French Agence Nationale de la Recherche projects. S.O. was parcially 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).Peer reviewe

Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction.

Phenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability

Geochemical and spectroscopic fingerprinting for authentication and geographical traceability of high-quality lemon fruits.

Geochemical (mineral element and Sr isotope ratio) and spectroscopical fingerprinting (Near Infrared Spectroscopy) were proposed to authenticate and track the two high-quality lemon fruits from the Campania region (Limone di Sorrento PGI and Limone Costa d'Amalfi PGI) to protect them from frauds. Considering the geochemical indicators, we built different chemometric discriminant models based on mineral profile and 87Sr/86Sr isotope ratio. These two techniques were applied to discriminate fruits from different territorial scales, small territorial scales (region scale), and large territorial scales. The results of different discriminant models applied on mineral profiles of lemon juices, both on a small and large territorially scale, showed good discrimination according to provenance, especially for non-essential elements as Rb, Ba, Sr, Ti, and Co. These same elements have shown a good correlation with cultivation soils and stability between the two production years. It is worth noting that although, the performance of the whole elemental profile gave a better result than the profile of the non-essential elements, the reliability of the two models, calculated as the ratio between the percentage of correctly validated and classification samples, was similar. In addition, the Sr isotope ratio had shown a clear differentiation among the fruits from the Campania region and extra-regional samples, and by analysis of 86Sr/87Sr of soils, it was clear that the strontium isotope ratio of lemon juices was closely related to that of the bioavailable fractions of the soil. Furthermore, combining both isotopic and mineral profiles in lemon juices by a low-level data fusion approach, the results showed a better clustering according to geographical origins than the two-determination taken separately, although on an explorative level. In addition, the spectroscopical data (NIR) on intact lemon fruits showed the strong influence of environmental growing conditions on the samples. For this, the application of Linear Discriminant Analysis (LDA) models suggested building the discrimination models according to origins (PGI and not PGI productions) based on one production year. In the same way, the application of MLR models, that showed a strong relationship between quality properties of lemon fruits and NIR spectra, suggested the applicability of this technique to build predictive models for the quality properties. In addition, on a part of the total samples collected only in 2019 (intact lemons and juices), have been successfully applied two different chemometrics models i.e., LDA and Partial Least Square Discriminant Analysis (PLS-DA). The results showed better provenance discrimination using the lemon juices than the intact lemons. Comparing the results obtained, of the two approaches used, the results of geochemical fingerprinting have shown more stability for discriminate lemon fruits derived from two different production years, especially for not essential elements. However, considering the various vantages of the application of NIR spectroscopy (non-destructive, rapid, and cheap) and the results obtained, this technique can be used for rapid screening of samples in order to verify the quality and origins of lemon fruits during the year. The study of the pedoclimatic features was fundamental to understand the nature of discriminating variables, in both approaches. Additional research should be conducted to include a greater number of lemon farms (or sampling points) in the PGI area and to enlarge the existing database including lemon samples from other regions and validate the models built. These discriminant models based on geochemical and spectroscopical profiles of lemon fruits could substantially contribute to implementing a blockchain system for Campanian lemon traceability, providing real-time information not only to the final consumers but also to manufacturers, distributors, and retailers

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

The Health Benefits of Fruits and Vegetables

This Special Issue gathers 14 original research papers to disseminate new data on phytochemicals from vegetables and fruits, which are recommended for their health-promoting properties. Epidemiological, toxicological and nutritional studies suggest an association between fruit and vegetable consumption and lower incidence of chronic diseases, such as coronary heart problems, cancer, diabetes, and Alzheimer’s disease. In this Special Issue the following topics have been addressed: (i) the protective roles, antioxidant and others bioactivities such as genotoxic and antigenotoxic effects in the Drosophila melanogaster animal genetic model and pro-apoptotic capacities against cancer processes, including cytotoxicity and clastogenic DNA activity, using an in vitro human cancer model (HL-60 cell line, (ii), new sustainable approaches based on near-infrared spectroscopy to determine the quality, (iii) broad-scale metabolomic investigation for the development of functional food and, (iv) processing techniques that can modify the initial nutritional and antioxidant content of fruits, vegetables, and additives. In summary, the information in this Special Issue will be interesting for researchers in this field and the general public interested in the relationship between vegetables and health

A review of hyperspectral image analysis techniques for plant disease detection and identif ication

Plant diseases cause signif icant economic losses in agriculture around the world. Early detection, quantif ication and identif ication of plant diseases are crucial for targeted application of plant protection measures in crop production. Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. The analysis of the ref lection spectrum of plant tissue makes it possible to classify healthy and diseased plants, assess the severity of the disease, differentiate the types of pathogens, and identify the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. This review describes the basic principles of hyperspectral measurements and different types of available hyperspectral sensors. Possible applications of hyperspectral sensors and platforms on different scales for diseases diagnosis are discussed and evaluated. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which make it possible to simultaneously evaluate both physiological and morphological parameters. The review describes the main steps of the hyperspectral data analysis process: image acquisition and preprocessing; data extraction and processing; modeling and analysis of data. The algorithms and methods applied at each step are mainly summarized. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation and identif ication of diseases, estimation of disease severity, phenotyping of disease resistance of genotypes. A comprehensive review of scientif ic publications on the diagnosis of plant diseases highlights the benef its of hyperspectral technologies in investigating interactions between plants and pathogens at various measurement scales. Despite the encouraging progress made over the past few decades in monitoring plant diseases based on hyperspectral technologies, some technical problems that make these methods diff icult to apply in practice remain unresolved. The review is concluded with an overview of problems and prospects of using new technologies in agricultural production

Non-destructive determination of taste-related compounds in tomato using NIR spectra

[EN] Near infrared (NIR) diffuse reflectance was used to predict the contents of taste-related compounds of tomato. Models were obtained for several varietal types including processing tomato, cherry and cocktail tomato, mid-sized tomato and tomato landraces, with a wide range of varieties. Good performance was obtained for the prediction of soluble solids, sugars and acids, considering a non-destructive methodology applied to fruits with different internal structure. Specific models averaged RMSEP (%mean) values lower than 6.1% for SSC, 13.3% for fructose, 14.1% for glucose, 12.7% for citric acid, 13.8% for malic acid and 21.9% for glutamic acid. The performance was dependent on varietal type. General models with a higher number of samples and variation did not improve the performance of specific models. The models obtained, either specific or general, couldn't be extrapolated to external assays and an internal calibration would be required for each assay in order to provide a reliable performance.This research was performed despite the lack of direct public funding for its development and thanks to the enthusiasm of the authors. The authors thank Dr. Lahoz and Dr. Campillo for providing processing tomato samples and Dr. Moreno for providing samples from tomato landraces. G. Ibanez thanks Universitat Jaume I for funding his pre-doctoral grant (PREDOC/2015/45).Ibáñez, G.; Cebolla Cornejo, J.; Martí-Renau, R.; Roselló, S.; Valcárcel-Germes, M. (2019). Non-destructive determination of taste-related compounds in tomato using NIR spectra. Journal of Food Engineering. 263:237-242. https://doi.org/10.1016/j.jfoodeng.2019.07.004S23724226