A comprehensive Vis-NIRS equation for rapid quantification of seed glucosinolate content and composition across diverse Brassica oilseed chemotypes

Using near-infrared reflectance spectroscopy (NIRS) to predict the nitrogen levels in the stem and root tissues of Brassica juncea (Indian mustard)

Brassica juncea depends heavily on nitrogen (N) fertilizers for growth and accumulation of seed protein. However, it is an inefficient mobilizer of applied N which leads to accumulation of excess N in the soil, posing environmental risks. Hence, it is imperative to systematically examine spatial-temporal pattern of crop N to efficiently manage N application. The Kjeldahl method is commonly used to estimate N status of crops but it is a destructive method that entails the use of perilous and expensive chemicals. Near-infrared reflectance spectroscopy (NIRS) offers a safe, accurate, and non-destructive alternative for large-scale screening of seed metabolites. Currently, no NIRS model exists to quickly estimate N content in shoots and roots from large germplasm sets in any rapeseed-mustard crop. Developing such a model is essential to breed for enhanced nitrogen use efficiency (NUE). We used 738 shoot and 346 root samples from a B. juncea diversity set to construct the NIRS models. A diverse range of genetic variation in N content was recorded in the stem (0.21-6.61%) and root (0.15-3.04%) tissues of the crop raised on two different N levels (N0 and N100). Modified partial least squares (MPLS) method was employed to establish a regression equation linking reference N values with spectral changes. The developed models exhibited strong associations with reference values, with RSQ values of 0.884 for stem and 0.645 for roots. Furthermore, external validation confirms the reliability of the developed models. The developed models have strong predictive capabilities for rapid and reliable N estimation in various tissues of B. juncea plants.

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades

During food quality control, NIR technology enables the rapid and non-destructive determination of the typical quality characteristics of food categories, their origin, and the detection of potential counterfeits. Over the past 20 years, the NIR results for a variety of food groups-including meat and meat products, milk and milk products, baked goods, pasta, honey, vegetables, fruits, and luxury items like coffee, tea, and chocolate-have been compiled. This review aims to give a broad overview of the NIRS processes that have been used thus far to assist researchers employing non-destructive techniques in comparing their findings with earlier data and determining new research directions.

Targeted editing of multiple homologues of GTR1 and GTR2 genes provides the ideal low-seed, high-leaf glucosinolate oilseed mustard with uncompromised defence and yield

Glucosinolate content in the two major oilseed Brassica crops-rapeseed and mustard has been reduced to the globally accepted Canola quality level (<30 μmoles/g of seed dry weight, DW), making the protein-rich seed meal useful as animal feed. However, the overall lower glucosinolate content in seeds as well as in the other parts of such plants renders them vulnerable to biotic challenges. We report CRISPR/Cas9-based editing of glucosinolate transporter (GTR) family genes in mustard (Brassica juncea) to develop ideal lines with the desired low seed glucosinolate content (SGC) while maintaining high glucosinolate levels in the other plant parts for uncompromised plant defence. Use of three gRNAs provided highly efficient and precise editing of four BjuGTR1 and six BjuGTR2 homologues leading to a reduction of SGC from 146.09 μmoles/g DW to as low as 6.21 μmoles/g DW. Detailed analysis of the GTR-edited lines showed higher accumulation and distributional changes of glucosinolates in the foliar parts. However, the changes did not affect the plant defence and yield parameters. When tested against the pathogen Sclerotinia sclerotiorum and generalist pest Spodoptera litura, the GTR-edited lines displayed a defence response at par or better than that of the wild-type line. The GTR-edited lines were equivalent to the wild-type line for various seed yield and seed quality traits. Our results demonstrate that simultaneous editing of multiple GTR1 and GTR2 homologues in mustard can provide the desired low-seed, high-leaf glucosinolate lines with an uncompromised defence and yield.

Determination of glucosinolates and isothiocyanates in glucosinolate-rich vegetables and oilseeds using infrared spectroscopy: A systematic review

Cruciferous vegetables and oilseeds are rich in glucosinolates that can transform into isothiocyanates upon enzymic hydrolysis during post-harvest handling, food preparation and/or digestion. Vegetables contain glucosinolates that have beneficial bioactivities, while glucosinolates in oilseeds might have anti-nutritional properties. It is therefore important to monitor and assess glucosinolates and isothiocyanates content through the food value chain as well as for optimized crop production. Vibrational spectroscopy methods, such as infrared (IR) spectroscopy, are used as a nondestructive, rapid and low-cost alternative to the current and common costly, destructive, and time-consuming techniques. This systematic review discusses and evaluates the recent literature available on the use of IR spectroscopy to determine glucosinolates and isothiocyanates in vegetables and oilseeds. NIR spectroscopy was used to predict glucosinolates in broccoli, kale, rocket, cabbage, Brussels sprouts, brown mustard, rapeseed, pennycress, and a combination of Brassicaceae family seeds. Only one study reported the use of NIR spectroscopy to predict broccoli isothiocyanates. The major limitations of these studies were the absence of the critical evaluation of errors associated with the reference method used to develop the calibration models and the lack of interpretation of loadings or regression coefficients used to predict glucosinolates.

Developing multifunctional crops by engineering Brassicaceae glucosinolate pathways

Glucosinolates (GSLs), found mainly in species of the Brassicaceae family, are one of the most well-studied classes of secondary metabolites. Produced by the action of myrosinase on GSLs, GSL-derived hydrolysis products (GHPs) primarily defend against biotic stress in planta. They also significantly affect the quality of crop products, with a subset of GHPs contributing unique food flavors and multiple therapeutic benefits or causing disagreeable food odors and health risks. Here, we explore the potential of these bioactive functions, which could be exploited for future sustainable agriculture. We first summarize our accumulated understanding of GSL diversity and distribution across representative Brassicaceae species. We then systematically discuss and evaluate the potential of exploited and unutilized genes involved in GSL biosynthesis, transport, and hydrolysis as candidate GSL engineering targets. Benefiting from available information on GSL and GHP functions, we explore options for multifunctional Brassicaceae crop ideotypes to meet future demand for food diversification and sustainable crop production. An integrated roadmap is subsequently proposed to guide ideotype development, in which maximization of beneficial effects and minimization of detrimental effects of GHPs could be combined and associated with various end uses. Based on several use-case examples, we discuss advantages and limitations of available biotechnological approaches that may contribute to effective deployment and could provide novel insights for optimization of future GSL engineering.

Glucosinolates and Omega-3 Fatty Acids from Mustard Seeds: Phytochemistry and Pharmacology

Seeds from mustard (genera Brassica spp. and Sinapsis spp.), are known as a rich source of glucosinolates and omega-3 fatty acids. These compounds are widely known for their health benefits that include reducing inflammation and lowering the risk of cardiovascular diseases and cancer. This review presented a synthesis of published literature from Google Scholar, PubMed, Scopus, Sci Finder, and Web of Science regarding the different glucosinolates and omega-3 fatty acids isolated from mustard seeds. We presented an overview of extraction, isolation, purification, and structure elucidation of glucosinolates from the seeds of mustard plants. Moreover, we presented a compilation of in vitro, in vivo, and clinical studies showing the potential health benefits of glucosinolates and omega-3 fatty acids. Previous studies showed that glucosinolates have antimicrobial, antipain, and anticancer properties while omega-3 fatty acids are useful for their pharmacologic effects against sleep disorders, anxiety, cerebrovascular disease, neurodegenerative disease, hypercholesterolemia, and diabetes. Further studies are needed to investigate other naturally occurring glucosinolates and omega-3 fatty acids, improve and standardize the extraction and isolation methods from mustard seeds, and obtain more clinical evidence on the pharmacological applications of glucosinolates and omega-3 fatty acids from mustard seeds.

Nutraceutical and Pharmaceutical Behavior of Bioactive Compounds of Miracle Oilseeds: An Overview

India plays an important role in the production of oilseeds, which are mainly cultivated for future extraction of their oil. In addition to the energic and nutritional contribution of these seeds, oilseeds are rich sources of bioactive compounds (e.g., phenolic compounds, proteins, minerals). A regular and moderate dietary supplementation of oilseeds promotes health, prevents the appearance of certain diseases (e.g., cardiovascular diseases (CVDs), cancers) and delays the aging process. Due to their relevant content in nutraceutical molecules, oilseeds and some of their associated processing wastes have raised interest in food and pharmaceutical industries searching for innovative products whose application provides health benefits to consumers. Furthermore, a circular economy approach could be considered regarding the re-use of oilseeds’ processing waste. The present article highlights the different oilseed types, the oilseeds-derived bioactive compounds as well as the health benefits associated with their consumption. In addition, the different types of extractive techniques that can be used to obtain vegetable oils rich from oilseeds, such as microwave-assisted extraction (MAE), ultrasonic-assisted extraction (UAE) and supercritical fluid extraction (SFE), are reported. We conclude that the development and improvement of oilseed markets and their byproducts could offer even more health benefits in the future, when added to other foods.

Rapid prediction method of α-Glycosidase inhibitory activity of Coreopsis tinctoria extract from different habitats by near infrared spectroscopy

α-Glucosidase is one of the main enzymes causing elevated blood glucose, and Coreopsis tinctoria extract can be used as a natural inhibitor of α-Glucosidase. Therefore, a new method was proposed for predicting the inhibitory activity on α-Glucosidase of Coreopsis tinctoria extract based on near infrared spectroscopy. The absorbance of the inhibitory system was measured by ultraviolet spectroscopy, which was used to study the inhibitory activity on a-glucosidase of Coreopsis tinctoria extract. The near infrared spectra of the solid samples were collected. By selecting spectral preprocessing and optimizing spectral bands, a rapid prediction model of the inhibitory activity was established by partial least squares regression. The root mean square error of cross-validation (RMSECV), correlation coefficient (R) value and the ratio of prediction to deviation (RPD) value were used as indicators of the evaluation model. The near infrared spectrum model was established by combining the best spectral preprocessing of the continuous wavelet transform (CWT) and the best spectral band. The root mean square error of cross-validation (RMSECV) of this model was 0.815%, the correlation coefficient (R) value was 0.942, and the ratio of prediction to deviation (RPD) was 3.0. The root mean square error of prediction (RMSEP) of the model by prediction set was 0.819%, the correlation coefficient (R) value was 0.950, and the RPD was 3.2. The model shows that the fitting relationship between the predicted inhibition value and the reference inhibition value of the near infrared spectral model is good. The results showed that there was a good correlation between near infrared spectroscopy and the inhibitory activity of Coreopsis tinctoria extract. Thus, the established model was robust and effective and could be used for rapid quantification of α-Glucosidase inhibitory activity. The prediction method is simple and rapid, and can be extended to study the inhibition of other medicinal plants.

Potential of Vis-NIR to measure heavy metals in different varieties of organic-fertilizers using Boruta and deep belief network

The quick identification of heavy metals is of major importance and is beneficial for controlling the fertilizer production process in the fertilizer industries. This work aimed to use visible and near-infrared spectroscopy (Vis-NIR), Boruta, and deep learning to establish rapid heavy metals screening methods. Boruta algorithm was used to extract appropriate wavelengths, and a deep belief network (DBN) was computed to determine the amounts of various heavy metals such as chromium (Cr), cadmium (Cd), lead (Pb), and mercury (Hg) for both the entire and selected wavelengths. To assess the model, coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD) were used to calculate the reliability of the model. The results of the selected wavelengths were excellent and much higher than the full wavelengths with R2p = 0.96, RMSEP = 0.2017 mg kg-1 and RPDpred = 5.0 for Cr; R2p = 0.91, RMSEP = 0.2832 mg kg-1 and RPDpred = 3.4 for Pb; R2p = 0.90, RMSEP = 0.2992 mg kg-1, and RPDpred = 3.3 for Hg. Descent prediction was obtained also for Cd (R2p = 0.87, RMSEP = 0.3435 mg kg-1, and RPDpred = 2.7). To further assess the robustness of the DBN, it was compared with conventional machine learning methods such as support vector machine for regression (SVR), k nearest neighbor (KNN), and partial least squares (PLS). The overall results indicated that the Vis-NIR technique coupled with Boruta and DBN could be reliable and accurate for screening heavy metals in organic fertilizers.

GTR1 and GTR2 transporters differentially regulate tissue-specific glucosinolate contents and defence responses in the oilseed crop Brassica juncea

GTR1 and GTR2 transporters are components of the source to sink translocation network of glucosinolates, which are major defence metabolites in the Brassicaceae. These transporters can be genetically manipulated for reduction of seed-glucosinolates without inhibiting glucosinolate biosynthesis, thereby maintaining the inherent defence potential of plants. However, the different roles of GTRs in influencing tissue-specific distribution of glucosinolates in agriculturally important Brassica crops are yet unknown. Here, we report functional characterization of two groups of glucosinolate transporters (GTR1 and GTR2) from Brassica juncea based on gene expression data, biochemical analysis, gene-complementation studies in GTR-deficient mutants and RNAi-based knockdown followed by insect feeding experiments. Although both GTRs showed ubiquitous expression patterns and broad substrate specificity, the single-gene knockdown lines displayed different phenotypes. The GTR2-knockdown plants showed a significant reduction of glucosinolates in seeds and a higher accumulation in leaves and pods, while the GTR1-knockdown plants displayed a smaller reduction of glucosinolates in seeds and significantly lower glucosinolate levels in leaves. Consequently, knockdown of GTR2 resulted in higher resistance towards the generalist pest, Spodoptera litura. Overall, our study highlights the distinctive roles of B. juncea GTRs in tissue-specific accumulation of glucosinolates and the potential for manipulating GTR2 for enhanced nutrition and plant defence.