Starch is the most abundant storage carbohydrate and a major component in pea seeds, accounting for about 50% of dry seed weight. As a by-product of pea protein processing, current uses for pea starch are limited to low-value, commodity markets. The globally growing demand for pea protein poses a great challenge for the pea fractionation industry to develop new markets for starch valorization. However, there exist gaps in our understanding of the genetic mechanism underlying starch metabolism, and its relationship with physicochemical and functional properties, which is a prerequisite for targeted tailoring functionality and innovative applications of starch. This review outlines the understanding of starch metabolism with a particular focus on peas and highlights the knowledge of pea starch granule structure and its relationship with functional properties, and industrial applications. Using the currently available pea genetics and genomics knowledge and breakthroughs in omics technologies, we discuss the perspectives and possible avenues to advance our understanding of starch metabolism in peas at an unprecedented level, to ultimately enable the molecular design of multi-functional native pea starch and to create value-added utilization.

Pulses are a group of leguminous crops that are harvested solely for their dry seeds. As the demand for plant-based proteins grows, pulses are becoming important food crops worldwide. In addition to being a rich source of nutrients, pulses also contain saponins that are traditionally considered anti-nutrients, and impart bitterness and astringency. Saponins are plant secondary metabolites with great structural and functional diversity. Given their diverse functional properties and biological activities, both undesirable and beneficial, saponins have received growing attention. It can be expected that redirecting metabolic fluxes to control the saponin levels and produce desired saponins would be an effective approach to improve the nutritional and sensory quality of the pulses. However, little effort has been made toward understanding saponin biosynthesis in pulses, and, thus there exist sizable knowledge gaps regarding its pathway and regulatory network. In this paper, we summarize the research progress made on saponin biosynthesis in pulses. Additionally, phylogenetic relationships of putative biosynthetic enzymes among multiple pulse species provide a glimpse of the evolutionary routes and functional diversification of saponin biosynthetic enzymes. The review will help us to advance our understanding of saponin biosynthesis and aid in the development of molecular and biotechnological tools for the systematic optimization of metabolic fluxes, in order to produce the desired saponins in pulses.

There is a growing consumer interest in sources of dietary protein that are plant-based. Pulse crops, such as lentils, beans, chickpeas, and peas, are gaining popularity due to their environmental sustainability, nutrient density, and functional attributes. The protein content and quality of pulses vary across different pulse classes and processing methods. The biological properties of the protein and the physiologically active peptides make pulse crops attractive as potentially functional or health-promoting foods. This review highlights the nutritional quality of pulse proteins as determined by the Protein Efficiency Ratio and Protein Digestibility Corrected Amino Acid Score as well as bioactive properties of specific bioactive peptides related to amelioration of hypertension and diabetes. Additionally, the use of proteomics platforms, such as mass spectrometry, in combination with bioinformatics tools, enables the identification and characterization of bioactive peptides in pulse crops. These technologies facilitate the development of pulse-derived products with enhanced nutritional values. Overall, the high nutritional quality of pulse-based proteins supports the benefits of pulse inclusion in the diet, which can also exert beneficial bioactivities resulting in improving outcomes in non-communicable diseases.

Rapid and accurate assessment of nutritional quality, particularly crude protein content and essential nutrient concentrations, remains a major challenge in the food and feed industries. In this study, laser-induced breakdown spectroscopy (LIBS) was combined with advanced chemometric modeling to predict the levels of crude protein and key macro- and micronutrients (Ca, Mg, K, Na, Fe, Mn, P, Zn) in 61 barley forage samples composed of whole aerial plant parts ground prior to analysis. LIBS offers a compelling alternative to traditional analytical methods by enabling real-time analysis with minimal sample preparation. To minimize interference from atmospheric nitrogen, nitrogen spectral lines were excluded from the protein calibration model in favor of spectral lines from elements biochemically associated with proteins. We compared the performance of Partial Least Squares (PLSR) regression and Extreme Learning Machine (ELM) using fivefold cross-validation. ELM outperformed PLS in terms of prediction, achieving a coefficient of determination (R²) close to 1 and a ratio of performance to deviation (RPD) exceeding 2.5 for proteins and several nutrients. These results underscore the potential of LIBS-ELM integration as a robust, non-destructive, and in situ tool for rapid forage quality assessment, particularly in complex and heterogeneous plant matrices.

Rapid quantitative characterization of protein-rich agricultural materials remains challenging due to the molecular and elemental complexity of plant-derived matrices. In this study, external-cavity quantum cascade laser-based mid-infrared (QCL-MIR) spectroscopy and laser-induced breakdown spectroscopy (LIBS) were investigated as complementary approaches for predicting protein and elemental composition in pea flour (Pisum sativum L.) samples. QCL-MIR spectroscopy primarily probed molecular vibrational information, whereas LIBS directly monitored elemental composition through plasma-generated atomic and ionic emission signatures. To address the high dimensionality and nonlinear relationships present in both spectroscopic datasets, advanced chemometric strategies combining Uninformative Variable Elimination (UVE), Partial Least Squares (PLS) regression, and Extreme Learning Machine (ELM) modeling were implemented. Given the limited dataset size (N = 20), predictive performance was assessed using a concentration-stratified five-fold prediction strategy. The optimized UVE–PLS–ELM approach improved predictive performance compared with conventional linear PLS models for both techniques. QCL-MIR spectroscopy showed strong predictive performance within the present dataset for total protein content (R² = 0.97) and also provided promising predictions of phosphorus and potassium through indirect correlations with sample composition. LIBS also demonstrated strong calibration and predictive capability for total protein content (R² = 0.93) and good predictive performance for Na, Mg, Ca, Mn, and Fe. However, LIBS performance decreased at concentration extremes, particularly for high-concentration elements (K and P) and trace elements such as Zn and Cu. These results demonstrate the complementary strengths of QCL-MIR and LIBS and highlight the potential of nonlinear chemometric approaches for accurate compositional analysis of protein-rich agricultural materials within the present limited dataset.