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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.

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.

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.

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.

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.

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.

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.

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.

Legumes represent the second most important family of crop plants after grasses, accounting for approximately 27% of the world's crop production. Past domestication processes resulted in a high degree of relatedness between modern varieties of crops, leading to a narrower genetic base of cultivated germplasm prone to pests and diseases. Crop wild relatives (CWRs) harbor genetic diversity tested by natural selection in a range of environments. To fully understand and exploit local adaptation in CWR, studies in geographical centers of origin combining ecology, physiology, and genetics are needed. With the advent of modern genomics and computation, combined with systematic phenotyping, it is feasible to revisit wild accessions and landraces and prioritize their use for breeding, providing sources of disease resistances; tolerances of drought, heat, frost, and salinity abiotic stresses; nutrient densities across major and minor elements; and food quality traits. Establishment of hybrid populations with CWRs gives breeders a considerable benefit of a prebreeding tool for identifying and harnessing wild alleles and provides extremely valuable long-term resources. There is a need of further collecting and both ex situ and in situ conservation of CWR diversity of these taxa in the face of habitat loss and degradation and climate change. In this review, we focus on three legume crops domesticated in the Fertile Crescent, pea, chickpea, and lentil, and summarize the current state and potential of their respective CWR taxa for crop improvement

This study aims to investigate the role of red lentil flour gel in the development of dough and bread texture. The flour was obtained from untreated (FLU), blanched (FLS), and fermented (FLF) red lentil seeds. Subsequently, wheat flour was replaced with lentil flour in different percentages (0, 2, 4, 6, 8, and 10%), and the α-amylase activity of the flour samples was determined. The rheological properties of the dough during the fermentation process (dough development and gas formation and retention, elastic (G′) and viscous (G″) moduli) were also investigated. The hardness, resilience, cohesiveness, and elasticity of the bread samples were obtained using a TVT-6700 texturometer (Perten Instruments, Hägersten, Sweden). The results showed that α-amylase activity was stronger and the falling number decreased as the amount of lentil flour added increased (from 506 ± 2.50 s (control sample) to 386 ± 1.25 s for 10% FLU and to 403 ± 0.60 s for 10% FLF), except for the FLS samples (which ranged from 518 ± 2.92 to 559 ± 2.81 s). Lentils can disrupt the gluten network in dough, and it has been observed that dough quality was influenced by the addition and treatment of lentils: the maximum height of the dough decreased (from 53.8 mm (control sample) to less than 35 mm) as the percentage of wheat flour replaced by lentil flour increased. In contrast, the amount of gas formed was greater than in the control sample, demonstrating the positive effect of lentil flour on dough fermentation. Textural analysis showed positive effects at moderate concentrations of up to 6% lentil flour. Thus, bread hardness decreased from 1933 ± 0.13 (control sample) to 1849 ± 0.75 for 6% FLU and 1911 ± 0.56 for 6% FLF. The results showed that the use of 4% blanched or fermented lentil flour in dough gives it superior properties compared with regular dough, which leads to improved properties in baked goods.