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Lentil yield is a complicated quantitative trait; it is significantly influenced by the environment. It is crucial for improving human health and nutritional security in the country as well as for a sustainable agricultural system. The study was laid out to determine the stable genotype through the collaboration of G × E by AMMI and GGE biplot and to identify the superior genotypes using 33 parametric and non-parametric stability statistics of 10 genotypes across four different conditions. The total G × E effect was divided into two primary components by the AMMI model. For days to flowering, days to maturity, plant height, pods per plant, and hundred seed weight, IPCA1 was significant and accounted for 83%, 75%, 100%, and 62%, respectively. Both IPCA1 and IPCA2 were non-significant for yield per plant and accounted for 62% of the overall G × E interaction. An estimated set of eight stability parameters showed strong positive correlations with mean seed yield, and these measurements can be utilized to choose stable genotypes. The productivity of lentils has varied greatly in the environment, ranging from 786 kg per ha in the MYM environment to 1658 kg per ha in the ISD environment, according to the AMMI biplot. Three genotypes (G8, G7, and G2) were shown to be the most stable based on non-parametric stability scores for grain yield. G8, G7, G2, and G5 were determined as the top lentil genotypes based on grain production using numerical stability metrics such as Francis’s coefficient of variation, Shukla stability value (σi2), and Wrick’s ecovalence (Wi). Genotypes G7, G10, and G4 were the most stable with the highest yield, according to BLUP-based simultaneous selection stability characteristics. The findings of graphic stability methods such as AMMI and GGE for identifying the high-yielding and stable lentil genotypes were very similar. While the GGE biplot indicated G2, G10, and G7 as the most stable and high-producing genotypes, AMMI analysis identified G2, G9, G10, and G7. These selected genotypes would be used to release a new variety. Considering all the stability models, such as Eberhart and Russell’s regression and deviation from regression, additive main effects, multiplicative interactions (AMMI) analysis, and GGE, the genotypes G2, G9, and G7 could be used as well-adapted genotypes with moderate grain yield in all tested environments.

The effects of hydrolysis by commercial food-grade proteases on the physicochemical and techno-functional properties of lentil protein concentrate were investigated. Lentil protein concentrate was hydrolysed with Alcalase, Novozym 11028 or Flavourzyme, and a control was prepared without enzyme addition under the same conditions. Differences in specificity between the three proteases were evident in the electrophoretic protein profile, reversed-phase HPLC peptide profile, and free amino acid composition. Alcalase and Novozym were capable of extensively degrading all the major protein fractions. Alcalase or Novozym treatment resulted in considerably higher solubility under acidic conditions compared to the control. Flavourzyme treatment resulted in moderately improved solubility in the acidic range, but slightly lower solubility at pH 7. Alcalase treatment resulted in slightly larger particle size and slightly higher viscosity. The foaming properties of the protein concentrate were not significantly affected by hydrolysis. Increased solubility in acidic conditions with hydrolysis could broaden the range of food and beverage applications for lentil protein concentrate.

This book provides a comprehensive review of current lentil research. It contains 26 chapters covering topics on lentil global production, supply and demand; origin, phylogeny, domestication and spread; plant morphology, anatomy and growth habit; agroecology and adaptation; genetic resources collection, characterization, conservation and documentation; genetic enhancement for yield and yield stability; breeding for short season environments; improvement in Developed Countries; advances in molecular research; breeding and management to minimize the effects of drought and improve water use efficiency; soil nutrient management; cropping systems; biological nitrogen fixation and soil health improvement; mechanization; disease, pest and weed management; seed quality; postharvest processing and value addition; and food preparation and use. The last chapter presents field-based evidence of adoption of improved lentil cultivars from two cases: Bangladesh and Ethiopia.

Today, image classification methods are widely utilized on agricultural products or in agricultural applications. However, many of these methods based on traditional approaches remain unsatisfactory in terms of obtaining effective results. Within this context, this study aimed to classify lentil images by machine learning algorithms, a current and effective method. In line with this purpose, first of all, a camera system was prepared primarily and a dataset was created by recording lentil grains at 225 × 225 resolution via this system. The dataset contains a total of 33,938 data obtained from 3 lentil species as green, yellow, and red. SqueezeNet, InceptionV3, DeepLoc, and VGG16 architectures, among the CNN methods, were used in order to extract features from the recorded images. Lastly, Artificial Neural Network (ANN), Naive Bayes (NB), Random Forest (RF), Adaptive Boosting (AB), and Decision Tree (DT) algorithms were utilized with the aim of creating models for lentil images’ classification. The classification success of the created machine learning models was calculated and the results were analyzed. The highest classification success with the deep features obtained from the SqueezeNet model, 99.80%, was achieved in the ANN algorithm. The results also revealed that grain size and shape features in image classification can yield much more detailed and precise data than can be obtained practically with manual quality assessment.

Lentil is one of the most important food legumes consumed widely throughout the world. Lentils are produced in diverse agroecological regions, such as Asia, North and South America, Africa, and Oceania. During the last two decades (2001–2020), world production of lentils increased by 107%, from 3.15 to 6.54 million metric tons. Canada leads lentil producing countries (with 44% share of the global output), followed by India and Australia having 18% and 8% share, respectively. Being a rich source of protein, complex carbohydrates, dietary fiber, and folic acid, lentils are considered a healthy food nutritionally. Lentils also contain a number of bioactive phytochemicals, such as flavonoids, total phenolics, phytate, saponins, and tannins. Dehulling and splitting of lentils are the most‐commonly used basic processing methods. Additional value‐added operations include milling of whole or dehulled lentils and isolating fractions that are rich in protein and starch that can be used as ingredients in diverse food applications. Lentils are aligned well with the changing consumer trends towards meat alternatives, plant‐based diets, and healthy food options. Furthermore, due to increased environmental concerns for the production of meat, consumers are minimizing or even excluding meat consumption and opting for non‐meat foods produced in a sustainable manner. This review article provides an overview of lentil's production/trade, consumption trends, nutritional profile, value‐added processing, emerging research and development trends, and the role of lentil production in environmental sustainability.

Isoelectric precipitation and ultrafiltration were investigated for their potential to produce protein products from lentils. Higher protein concentrations were obtained when ultrafiltration was used (> 90%), whereas isoelectric precipitation resulted in higher contents of dietary fibre and some minerals (i.e., sodium and phosphorus). Differences in the functional properties between the two ingredients were found as the isoelectric precipitated ingredient showed lower protein solubilities over the investigated pH range (from 3 to 9) which can be linked to the slightly higher hydrophobicity values (2688.7) and total sulfhydryl groups (23.9 µM/g) found in this sample. In contrast, the protein ingredient obtained by ultrafiltration was superior with regard to its solubility (48.3%; pH 7), fat-binding capacity (2.24 g/g), water holding capacity (3.96 g/g), gelling properties (11%; w/w), and foam-forming capacity (69.6%). The assessment of the environmental performance showed that both LPIs exhibited promising properties and low carbon footprints in comparison to traditional dairy proteins.

Non-rhizobial endophytes (NREs) are active colonizers inhabiting the root nodules. Though their active role in the lentil agroecosystem is not well defined, here we observed that these NREs might promote the growth of lentils, modulate rhizospheric community structure and could be used as promising organisms for optimal use of rice fallow soil. NREs from root nodules of lentils were isolated and examined for plant growth-promoting traits, exopolysaccharide (EPS) and biofilm production, root metabolites, and the presence of nifH and nifK elements. The greenhouse experiment with the chosen NREs, i.e., Serratia plymuthica 33GS and Serratia sp. R6 significantly increased the germination rate, vigour index, development of nodules (in non-sterile soil) and fresh weight of nodules (33GS 94%, R6 61% growth) and length of the shoot (33GS 86%, R6 51.16%) as well as chlorophyll levels when compared to the uninoculated control. Scanning Electron Microscopy (SEM) revealed that both isolates could successfully colonize the roots and elicit root hair growth. The inoculation of the NREs resulted in specific changes in root exudation patterns. The plants with 33GS and R6 treatment significantly stimulated the exudation of triterpenes, fatty acids, and their methyl esters in comparison to the uninoculated plants, altering the rhizospheric microbial community structure. Proteobacteria dominated the rhizospheric microbiota in all the treatments. Treatment with 33GS or R6 also enhanced the relative abundance of other favourable microbes, including Rhizobium, Mesorhizobium, and Bradyrhizobium. The correlation network analysis of relative abundances resulted in numerous bacterial taxa, which were in cooperation with each other, having a possible role in plant growth promotion. The results indicate the significant role of NREs as plant growth promoters, which also includes their role in root exudation patterns, enhancement of soil nutrient status and modulation of rhizospheric microbiota, suggesting their prospects in sustainable, and bio-based agriculture.

This study aimed to investigate the suitability of lentil protein and emulsions thereof for the formulation of a milk substitute. The effect of high-pressure homogenisation and heat treatments on functional and physico-chemical properties of lentil protein solutions (3.3% w/w) and the emulsions, containing fat contents similar to commercial cow’s milk, was studied. Dynamic high-pressure treatments of 180 and 900 bar greatly affected physical and structural properties of the lentil proteins: the particle size was reduced by 100-fold to 129.00 nm for samples homogenised at 900 bar, leading to an almost complete solubilisation. Surface properties of lentil protein changed, as shown in an increase of hydrophobicity and decrease of free sulfhydryl groups, while changes in secondary structure and aggregation did not develop. Little impact was observed of the heat-treatment at 65 or 85 °C, however, colour changed from a faint pink hue to be more white in appearance. The obtained emulsions exhibited good colloidal stability at both homogenisation pressures, while overall product quality was best when treated at 900 bar. Sensory analyses showed the formulated lentil-based milk substitute had textural and organoleptic profiles comparable to commercial plant-based milk substitutes, including soya-based products. Lentil protein isolates showed great potential to be used formulating milk substitutes with a high-protein content, similar to cow’s milk.

Grain legumes or pulses, including lentil (Lens culinaris Medik), have gained increasing popularity among consumers and food processors in recent years. This trend has been driven by the consumers opting for plant‐based proteins and environmentally sustainable food sources. Global production of lentils has more than doubled since 2001 (from 3.15 to 6.58 million metric tons in 2020), which signifies the commercial importance of this nutrient‐dense legume. As per the USDA's nutrients data (2022), lentil contains 24.6% protein, 63.4% carbohydrates, 2.7% ash content, and 1.1% total fat. High amount of dietary fiber, slowly digestible starch and potassium, and low sodium in lentil align well with consumer choices for healthy foods. Many studies have reported on the health benefits of consuming lentils, especially being effective in reducing various health conditions, such as hypertension, cardiovascular diseases, diabetes mellitus, and cancer. The relatively higher protein and lower carbohydrates content of lentil compared with cereal grains can help in expanding the utilization of lentils and lentil‐based ingredients to develop new products. Combined with high dietary fiber, resistant starch, and bioactive polyphenolic content, the demonstrated nutritional benefits can fill the ever‐increasing demand for plant‐based proteins well beyond traditional consumption of lentils in developing countries. This article reviews the composition and nutrient profile of raw and processed lentils, effect of various processing methods on composition and nutrient profile, and health benefits of lentils.

This study evaluated Mn concentration in the seeds of 120 RILs of lentil developed from the cross “CDC Redberry” × “ILL7502”. Micronutrient analysis using atomic absorption spectrometry indicated mean seed manganese (Mn) concentrations ranging from 8.5 to 26.8 mg/kg, based on replicated field trials grown at three locations in Turkey in 2012 and 2013. A linkage map of lentil was constructed and consisted of seven linkage groups with 5,385 DNA markers. The total map length was 973.1 cM, with an average distance between markers of 0.18 cM. A total of 6 QTL for Mn concentration were identified using composite interval mapping (CIM). All QTL were statistically significant and explained 15.3–24.1% of the phenotypic variation, with LOD scores ranging from 3.00 to 4.42. The high-density genetic map reported in this study will increase fundamental knowledge of the genome structure of lentil, and will be the basis for the development of micronutrient-enriched lentil genotypes to support biofortification efforts.