Applicability of near-infrared reflectance spectroscopy (NIRS) for determination of crude protein content in cowpea (Vigna unguiculata) leaves

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.

Prediction of winter wheat leaf chlorophyll content based on VIS / NIR spectroscopy using ANN and PLSR

Visible-near-infrared spectroscopy is known for its rapid and nondestructive characteristics designed to predict leaf chlorophyll content (LCC) of winter wheat. It is believed that the nonlinear technique is preferable to the linear method. The canopy reflectance was applied to generate the LCC prediction model. To accomplish such an objective, artificial neural networks (ANN), along with partial least squares regression (PLSR), nonlinear, and linear evaluation methods have been employed and evaluated to predict wheat LCC. The wheat leaves reflectance spectra were initially preprocessed using Savitzky-Golay smoothing, differentiation (first derivative), SNV (Standard Normal Variate), MSC (Multiplicative Scatter Correction), and their combinations. Afterward, a model for LCC using the reflectance spectra was developed by means of the PLS and ANN. The vis/NIR spectroscopy samples at the 350-1400 nm wavelength were preprocessed using S. Golay smoothing, D₁, SNV, and MSC. The preprocessing with SNV-S.G, followed by PLS and ANN modeling, was able to achieve the most accurate prediction, with the correlation coefficient of 0.92 and 0.97, along with the root mean square error of 0.9131 and 0.7305 receptivity. The experimental findings also revealed that the suggested method utilizing the PLS and ANN model with SNV-S. G preprocessing was practically feasible to estimate the chlorophyll content of a particular winter wheat leaf area according to the visible and near-infrared spectroscopy sensors, achieving improved precision and accuracy. The nonlinear technique was proposed as a more refined technique for LCC estimating.

Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm

Cowpea (Vigna unguiculata (L.) Walp.) is one such legume that can facilitate achieving sustainable nutrition and climate change goals. Assessing nutritional traits conventionally can be laborious and time-consuming. NIRS is a technique used to rapidly determine biochemical parameters for large germplasm. NIRS prediction models were developed to assess protein, starch, TDF, phenols, and phytic acid based on MPLS regression. Higher RSQ_external values such as 0.903, 0.997, 0.901, 0.706, and 0.955 were obtained for protein, starch, TDF, phenols, and phytic acid respectively. Models for all the traits displayed RPD values of >2.5 except phenols and low SEP indicating the excellent prediction of models. For all the traits worked, p-value ≥ 0.05 implied the accuracy and reliability score >0.8 (except phenol) ensured the applicability of the models. These prediction models will facilitate high throughput screening of large cowpea germplasm in a non-destructive way and the selection of desirable chemotypes in any genetic background with huge application in cowpea crop improvement programs across the world.

Patch quality and habitat fragmentation shape the foraging patterns of a specialist folivore

Research on use of foraging patches has focused on why herbivores visit or quit patches, yet little is known about visits to patches over time. Food quality, as reflected by higher nutritional quality and lower plant defenses, and physical patch characteristics, which offer protection from predators and weather, affect patch use and hence should influence their revisitation. Due to the potentially high costs of moving between patches, fragmented habitats are predicted to complicate foraging decisions of many animals. We aimed to determine how food quality, shelter availability and habitat fragmentation influence tree reuse by a specialist folivore, the koala, in a fragmented agricultural landscape. We GPS-tracked 23 koalas in northern New South Wales, Australia and collated number of revisits, average residence time, and average time-to-return to each tree. We measured tree characteristics including food quality (foliar nitrogen and toxic formylated phloroglucinol compounds, FPCs concentrations), tree size, and tree connectedness. We also modeled the costs of locomotion between trees. Koalas re-visited isolated trees with high leaf nitrogen disproportionately often. They spent longer time in trees with high leaf nitrogen, and in large trees used for shelter. They took longer to return to trees with low leaf nitrogen. Tree connectivity reduced travel costs between patches, being either individual or groups of trees. FPC levels had no detectable effect on patch revisitation. We conclude that food quality and shelter drive koala tree re-visits. Scattered, isolated trees with nutrient-rich leaves are valuable resource patches for koalas despite movement costs to reach them.

Expression of Nutritional Traits in Vegetable Cowpea Grown under Various South African Agro-Ecological Conditions

Cowpea (Vigna unguiculata L.), a traditional legume food crop indigenous to Africa, has potential as both a vegetable and grain crop in contributing to dietary diversity to support health and address malnutrition, especially for those relying heavily on wheat, maize, and rice. The expression of nutritional traits (protein content and concentrations of iron (Fe), zinc (Zn), and manganese (Mn)) in cowpea leaves was evaluated over diverse agro-ecologies of South Africa and typical agronomic practices of smallholder farmers. The genotypes evaluated displayed genetic variation for all four traits. The mean values of Fe, Zn, Mn and protein content varied from 33.11 to 69.03 mg.100.g^-1; 4.00 to 4.70 mg.100.g^-1; and 14.40 to 19.63 mg.100.g^-1 and 27.98 to 31.98%, respectively. The correlation analysis revealed significant degree of positive association between protein and Zn (r = 0.20), while negative associations were observed between Mn and protein (-0.46) and between Mn and Fe (r = -0.27). Furthermore, the expression of these important nutrient traits was influenced by the climatic conditions represented by six environments (location by year combinations) as is typical of 'quality' traits. Additionally, genotype-by-environment interaction effects were detected, suggesting that local soil properties and soil health may play a role in nutritional content in plants, perhaps particularly for legume crops that rely on symbiotic relationships with soil bacterial populations to fix nitrogen, which is crucial to protein formation. Further studies are needed to understand how to coordinate and align agronomic and soil management practices in vegetable cowpea production, especially those workable for the smallholder farmer, to realize the full genetic potential and nutritional value of improved vegetable cowpea varieties.

A simple and nondestructive approach for the analysis of soluble solid content in citrus by using portable visible to near-infrared spectroscopy

A simple and nondestructive method for the analysis of soluble solid content in citrus was established using portable visible to near-infrared spectroscopy (Vis/NIRS) in reflectance mode in combination with appropriate chemometric methods. The spectra were obtained directly by the portable Vis/NIRS without destroying samples. Outlier detection was performed by using leave-one-out cross-validation (LOOCV) with the 3σ criterion, and the calibration models were established by partial least squares (PLS) algorithm. Besides, different data pretreatment methods were used to eliminate noise and background interference before calibration, to determine the one that will lead to better model accuracy. However, the correlation coefficients are all <0.62 and the results of all pretreatments are still unsatisfactory. Variable selection methods were discussed for improving the accuracy, and variable adaptive boosting partial least squares (VABPLS) method was used to get higher robustness models. The results show that standard normal variate (SNV) transformation is the best pretreatment method, while VABPLS can significantly simplify the calculation and improve the result even without pretreatment. The correlation coefficient of the best prediction models is 0.82, while the value is 0.48 for the raw data. The high performance shows the feasibility of portable Vis/NIRS technology combination with appropriate chemometric methods for the determination of citrus soluble solid content.

Cowpea (Vigna unguiculata L. Walp), a renewed multipurpose crop for a more sustainable agri-food system: nutritional advantages and constraints

The growing awareness of the relevance of food composition for human health has increased the interest of the inclusion of high proportions of fruits and vegetables in diets. To reach the objective of more balanced diets, an increased consumption of legumes, which constitutes a sustainable source of essential nutrients, particularly low-cost protein, is of special relevance. However, the consumption of legumes also entails some constraints that need to be addressed to avoid a deleterious impact on consumers' wellbeing and health. The value of legumes as a source of nutrients depends on a plethora of factors, including genetic characteristics, agro-climatic conditions, and postharvest management that modulate the dietary effect of edible seeds and vegetative material. Thus, more comprehensive information regarding composition, especially their nutritional and anti-nutritional compounds, digestibility, and alternative processing procedures is essential. These were the challenges to write this review, which focusses on the nutritional and anti-nutritional composition of Vigna unguiculata L. Walp, an emerging crop all over the world intended to provide a rational support for the development of valuable foods and feeds of increased commercial value. © 2016 Society of Chemical Industry.