Chickpea is a highly nutritious pulse crop with low digestible carbohydrates (40–60%), protein (15–22%), essential fats (4–8%), and a range of minerals and vitamins. The fatty acid composition of the seed adds value because fats govern the texture, shelf-life, flavor, aroma, and nutritional composition of chickpea-based food products. Therefore, the biofortification of essential fatty acids has become a nutritional breeding target for chickpea crop improvement programs worldwide. This paper examines global chickpea production, focusing on plant lipids, their functions, and their benefits to human health. In addition, this paper also reviews the chemical analysis of essential fatty acids and possible breeding targets to enrich essential fatty acids in chickpea (Cicer arietinum) biofortification. Biofortification of chickpea for essential fatty acids within safe levels will improve human health and support food processing to retain the quality and flavor of chickpea-based food products. Essential fatty acid biofortification is possible by phenotyping diverse chickpea germplasm over suitable locations and years and identifying the candidate genes responsible for quantitative trait loci mapping using genome-wide association mapping.

Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy is a high‐throughput, cost‐effective method to quantify nutritional traits, such as total protein and sulfur‐containing amino acid (SAA) concentrations, in plant matter. This study used the spectroscopic technique FT‐MIR coupled with attenuated total internal reflectance sampling interface to develop multivariate models for total protein concentration in chickpea (Cicer arietinum L.), dry pea (Pisum sativum L.), and lentil (Lens culinaris Medik.), in addition to SAA concentration in lentil. Total nitrogen data from combustion analysis and SAA data from high‐performance liquid chromatography analysis following acid hydrolysis were used for model calibration and validation. Models for the total protein concentration of chickpea (calibration root mean square error [RMSE] = 0.093, R2 = 0.948, prediction RMSE = 0.10), dry pea (calibration RMSE = 0.096, R2 = 0.845, prediction RMSE = 0.093), and lentil (calibration RMSE = 0.13, R2 = 0.845, prediction RMSE = 0.11) utilized infrared regions associated with protein structures, namely amide bands A, I, and II. In sulfur‐related models for lentil total SAA (calibration RMSE = 0.014, R2 = 0.827, prediction RMSE = 0.022) and methionine (calibration RMSE = 0.0075, R2 = 0.815, prediction RMSE = 0.014) models utilized the C‐S and S‐CH3 stretching and bending bands. Study findings support the conclusion that FT‐MIR spectroscopy is a promising high‐throughput and cost‐effective phenotyping technique that will allow quantifying protein traits quickly and easily in pulse crops.