Search Results (3)

Peanut meal (PM) is a by-product of extracting oil from peanut kernels. Although peanut meal contains protein, carbohydrates, minerals, vitamins, and small amounts of polyphenols and fiber, it has long been used as a feed in the poultry and livestock industries due to its coarse texture and unpleasant taste. It is less commonly utilized in the food processing industry. In recent years, there has been an increasing amount of research conducted on the deep processing of by-products from oil crops, resulting in the high-value processing and utilization of by-products from various oil crops. These include peanut meal, which undergoes treatments such as enzymatic hydrolysis in industries like food, chemical, and aquaculture. The proteins, lipids, polyphenols, fibers, and other components present in these by-products and hydrolysates can be incorporated into products for further utilization. This review focuses on the research progress in various fields, such as the food processing, breeding, and industrial fields, regarding the high-value utilization of peanut meal and its hydrolysates. The aim is to provide valuable insights and strategies for maximizing the utilization of peanut meal resources. Full article

Hot-pressed peanut meal protein hydrolysates are rich in Arg residue, but there is a lack of research on their α-glucosidase inhibitory activity. In this study, different proteases were used to produce hot-pressed peanut meal protein hydrolysates (PMHs) to evaluate the α-glucosidase inhibitory activity. All PMHs showed good α-glucosidase inhibitory activity with the best inhibition effect coming from the dual enzyme system of Alcalase and Neutrase with an IC₅₀ of 5.63 ± 0.19 mg/mL. The fractions with the highest inhibition effect were separated and purified using ultrafiltration and cation exchange chromatography. Four novel α-glucosidase inhibitory peptides (FYNPAAGR, PGVLPVAS, FFVPPSQQ, and FSYNPQAG) were identified by nano-HPLC-MS/MS and molecular docking. Molecular docking showed that peptides could occupy the active pocket of α-glucosidase through hydrogen bonding, hydrophobic interaction, salt bridges, and π-stacking, thus preventing the formation of complexes between α-glucosidase and the substrate. In addition, the α-glucosidase inhibitory activity of PMHs was stable against hot, pH treatment and in vitro gastrointestinal digestion. The study demonstrated that PMHs might be used as a natural anti-diabetic material with the potential to inhibit α-glucosidase. Full article

Peanut meal is the byproduct of high-temperature peanut oil extraction; it is mainly composed of proteins, which have complex tastes after enzymatic hydrolysis to free amino acids and small peptides. The enzymatic hydrolysis method was adopted by using two compound proteases of trypsin and flavorzyme to hydrolyze peanut meal aiming to provide a flavor base. Hence, it is necessary to assess the taste attributes and assign definite taste scores of peanut meal double enzymatic hydrolysis hydrolysates (DEH). Conventionally, sensory analysis is used to assess taste intensity in DEH. However, it has disadvantages because it is expensive and laborious. Hence, in this study, both taste attributes and taste scores of peanut meal DEH were evaluated using an electronic tongue. In this regard, the response characteristics of the electronic tongue to the DEH samples and standard five taste samples were researched to qualitatively assess the taste attributes using PCA and DFA. PLS and RBF neural network (RBFNN) quantitative prediction models were employed to compare predictive abilities and to correlate results obtained from the electronic tongue and sensory analysis, respectively. The results showed that all prediction models had good correlations between the predicted scores from electronic tongue and those obtained from sensory analysis. The PLS and RBFNN prediction models constructed using the voltage response values from the sensors exhibited higher correlation and prediction ability than that of principal components. As compared with the taste performance by PLS model, that of RBFNN models was better. This study exhibits potential advantages and a concise objective taste assessment tool using the electronic tongue in the assessment of DEH taste attributes in the food industry. Full article