Development of Monoclonal Antibodies against Pea Globulins for Multiplex Assays Targeting Legume Proteins

High-throughput near-infrared spectroscopy for detection of major components and quality grading of peas

Pea (Pisum sativum L.) is a nutrient-dense legume whose nutritional indicators influence its functional qualities. Traditional methods to identify these components and examine the relationships between their contents could be more laborious, hindering the quality assessment of the varieties of peas. This study conducted a statistical analysis of data about the sensory and physicochemical nutritional attributes of peas acquired using traditional techniques. Additionally, 90 sets of spectral data were obtained using a portable near-infrared spectrometer, which were then integrated with chemical values to create a near-infrared model for the basic ingredient content of peas. The correlation analysis revealed significant findings: pea starch displayed a substantial negative correlation with moisture, crude fiber, and crude protein, while showing a highly significant positive correlation with pea seed thickness. Furthermore, pea protein exhibited a significant positive correlation with crude fiber and crude fat. Cluster analysis classified all pea varieties into three distinct groups, successfully distinguishing those with elevated protein content, high starch content, and low-fat content. The combined contribution of PC1 and PC2 in the principal component analysis (PCA) was 51.2%. Partial least squares regression (PLSR) and other spectral preprocessing methods improved the predictive model, which performed well with an external dataset, with calibration coefficients of 0.89-0.99 and prediction coefficients of 0.71-0.88. This method enables growers and processors to efficiently analyze the composition of peas and evaluate crop quality, thereby enhancing food industry development.

Enzymatic Hydrolysis and Fermentation of Pea Protein Isolate and Its Effects on Antigenic Proteins, Functional Properties, and Sensory Profile

Combinations of enzymatic hydrolysis using different proteolytic enzymes (papain, Esperase®, trypsin) and lactic fermentation with Lactobacillus plantarum were used to alter potential pea allergens, the functional properties and sensory profile of pea protein isolate (PPI). The order in which the treatments were performed had a major impact on the changes in the properties of the pea protein isolate; the highest changes were seen with the combination of fermentation followed by enzymatic hydrolysis. SDS-PAGE, gel filtration, and ELISA results showed changes in the protein molecular weight and a reduced immunogenicity of treated samples. Treated samples showed significantly increased protein solubility at pH 4.5 (31.19-66.55%) and at pH 7.0 (47.37-74.95%), compared to the untreated PPI (6.98% and 40.26%, respectively). The foaming capacity was significantly increased (1190-2575%) compared to the untreated PPI (840%). The treated PPI showed reduced pea characteristic off-flavors, where only the treatment with Esperase® significantly increased the bitterness. The results from this study suggest that the combination of enzymatic hydrolysis and lactic fermentation is a promising method to be used in the food industry to produce pea protein ingredients with higher functionality and a highly neutral taste. A reduced detection signal of polyclonal rabbit anti-pea-antibodies against the processed protein preparations in ELISA furthermore might indicate a decreased immunological reaction after consumption.

Improved Sensitivity of Allergen Detection by Immunoaffinity LC-MS/MS Using Ovalbumin as a Case Study

Food allergies are caused by severe hypersensitivity to specific food allergens such as the egg protein ovalbumin. It is therefore important to test food products for the presence of allergens to protect allergic people from accidental ingestion. For egg detection, ELISA is the only reasonable commercially available test format, although the recognition of target allergens can be affected by food processing, which may lead to false negative results. Current mass spectrometry-based detection methods may overcome this issue, but these approaches are often less sensitive. Here we combined the advantages of antibody-based and MS-based methods by developing an immunoaffinity LC-MS/MS technique to detect the common egg allergen Gal d 2. We investigated the principal functionality of this method with incurred cookie material containing whole egg powder. We found that the new method matched easily the sensitivity of egg specific ELISA tests. Further western blot experiments indicated that this strategy may be unaffected by food processing, providing an important alternative strategy for the detection and quantification of allergens in food.