Effect of magnetic field-assisted fermentation on the in vitro protein digestibility and molecular structure of rapeseed meal.

BACKGROUND: There has been a significant growth in demand for plant-derived protein, and this has been accompanied by an increasing need for sustainable animal-feed options. The aim of this study was to investigate the effect of magnetic field-assisted solid fermentation (MSSF) on the in vitro protein digestibility (IVPD) and functional and structural characteristics of rapeseed meal (RSM) with a mutant strain of Bacillus subtilis. RESULTS: Our investigation demonstrated that the MSSF nitrogen release rate reached 86.3% after 96 h of fermentation. The soluble protein and peptide content in magnetic field feremented rapeseed meal reached 29.34 and 34.49 mg mL-1 after simulated gastric digestion, and the content of soluble protein and peptide in MF-FRSM reached 61.81 and 69.85 mg mL-1 after simulated gastrointestinal digestion, which significantly increased (p > 0.05) compared with the fermented rapeseed meal (FRSM). Studies of different microstructures - using scanning electron microscopy (SEM) and atomic force microscopy (AFM) - and protein secondary structures have shown that the decline in intermolecular or intramolecular cross-linking leads to the relative dispersion of proteins and improves the rate of nitrogen release. The smaller number of disulfide bonds and conformational alterations suggests that the IVPD of RSM was improved. CONCLUSIONS: Magnetic field-assisted solid fermentation can be applied to enhance the nutritional and protein digestibility of FRSM. © 2024 Society of Chemical Industry.

Rapid and nondestructive watermelon (Citrullus lanatus) seed viability detection based on visible near-infrared hyperspectral imaging technology and machine learning algorithms.

The improper storage of seeds can potentially compromise agricultural productivity, leading to reduced crop yields. Therefore, assessing seed viability before sowing is of paramount importance. Although numerous techniques exist for evaluating seed conditions, this research leveraged hyperspectral imaging (HSI) technology as an innovative, rapid, clean, and precise nondestructive testing method. The study aimed to determine the most effective classification model for watermelon seeds. Initially, purchased watermelon seeds were segregated into two groups: One underwent sterilization in a dehydrator machine at 40°C for 36 h, whereas the other batch was stored under favorable conditions. Watermelon seeds' spectral images were captured using an HSI with a charge-coupled device camera ranging from 400 to 1000 nm, and the segmented regions of all samples were measured. Preprocessing techniques and wavelength selection methods were applied to manage spectral data workload, followed by the implementation of a support vector machine (SVM) model. The initial hybrid-SVM model achieved a predictive accuracy rate of 100%, with a test set accuracy of 92.33%. Subsequently, an artificial bee colony (ABC) optimization was introduced to enhance model precision. The results indicated that, with kernel parameters (c, g) set at 13.17 and 0.01, respectively, and a runtime of 4.19328 s, the training and evaluation of the dataset achieved an accuracy rate of 100%. Hence, it was practical to utilize HSI technology combined with the PCA-ABC-SVM model to detect different watermelon seeds. As a result, these findings introduce a novel technique for accurately forecasting seed viability, intended for use in agricultural industrial multispectral imaging. PRACTICAL APPLICATION: The traditional methods for determining the condition of seeds primarily emphasize aesthetics, rely on subjective assessment, are time-consuming, and require a lot of labor. On the other hand, HSI technology as green technology was employed to alleviate the aforementioned problems. This work significantly contributes to the field of industrial multispectral imaging by enhancing the capacity to discern various types of seeds and agricultural crop products.