Physicochemical, functional and microstructural characteristics of vegetable soybean (Glycine max) as affected by variety and cooking process

ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness

The freshness of vegetable soybean (VS) is an important indicator for quality evaluation. Currently, deep learning-based image recognition technology provides a fast, efficient, and low-cost method for analyzing the freshness of food. The RGB (red, green, and blue) image recognition technology is widely used in the study of food appearance evaluation. In addition, the hyperspectral image has outstanding performance in predicting the nutrient content of samples. However, there are few reports on the research of classification models based on the fusion data of these two sources of images. We collected RGB and hyperspectral images at four different storage times of VS. The ENVI software was adopted to extract the hyperspectral information, and the RGB images were reconstructed based on the downsampling technology. Then, the one-dimensional hyperspectral data was transformed into a two-dimensional space, which allows it to be overlaid and concatenated with the RGB image data in the channel direction, thereby generating fused data. Compared with four commonly used machine learning models, the deep learning model ResNet18 has higher classification accuracy and computational efficiency. Based on the above results, a novel classification model named ResNet-R &H, which is based on the residual networks (ResNet) structure and incorporates the fusion data of RGB and hyperspectral images, was proposed. The ResNet-R &H can achieve a testing accuracy of 97.6%, which demonstrates a significant enhancement of 4.0% and 7.2% compared to the distinct utilization of hyperspectral data and RGB data, respectively. Overall, this research is significant in providing a unique, efficient, and more accurate classification approach in evaluating the freshness of vegetable soybean. The method proposed in this study can provide a theoretical reference for classifying the freshness of fruits and vegetables to improve classification accuracy and reduce human error and variability.

Effects of "nine steaming nine sun-drying" on proximate composition, protein structure and volatile compounds of black soybeans

Nine steaming nine sun-drying is a traditional processing technology for food or medicinal materials. The dynamic changes of the proximate composition, protein structure and volatile compounds during nine-time steaming and sun-drying of black soybeans (BS) were studied. The proximate composition results showed that the content of protein, carbohydrate and fat of BS decreased after processing, whereas the relative content of amino acids remained basically unchanged. Protein structure was evaluated using Fourier transform infrared spectroscopy (FT-IR), Ultraviolet absorption spectroscopy (UV) and Fluorescence spectroscopy. FT-IR result revealed that the relative contents of β-sheet and β-turn of the secondary structure of black soybean protein isolate (BSPI) decreased but the relative contents of α-helix and random coil increased after steaming and sun-drying. The results of UV and fluorescence spectroscopy confirmed changes in the protein conformation. In addition, SPME-GCMS analysis demonstrated that hydrocarbons, alcohols and aldehydes were the main volatile compounds. The relative contents of 1-octen-3-ol and hexanal, which are the main sources of beany flavor decreased significantly compared with raw BS. Principal component analysis (PCA) results showed that the volatile compounds of nine steamed and nine sun-dried BS could be well distinguished during the process. These findings may therefore provide a scientific basis for the application of nine-time steamed and sun-dried BS in food industry and contribute to the understanding of process-induced chemical transformations in this ancient processing technique.

Volatile Flavor Profile and Sensory Properties of Vegetable Soybean

The volatile flavor profiles and sensory properties of different vegetable soybean varieties popularized and cultivated in China for 20, 10, and 2 years (TW292, X3, and SX6, respectively) were investigated. Nutrient composition analysis revealed that TW292 had a high soluble protein and soluble sugar content but low fat content. The total free amino acid content (15.43 mg/g) and umami free amino acid content (6.08 mg/g) of SX6 were significantly higher (p < 0.05) than those of the other varieties. An electronic tongue effectively differentiated between the umami and sweetness characteristics of the vegetable soybeans. Differences in sensory evaluation results were mainly reflected in texture and taste. A total of 41 volatile compounds were identified through HS-SPME-GC-MS, and the main flavor compounds were 1-octen-3-ol, hexanal, (Z)-2-heptenal, 2-octene, nonanal, (Z)-2-decenal, and 3,5-octadien-2-one. However, the volatile composition of different vegetable soybean varieties exhibited large variability in type and relative contents. Considerable differences in nutritional, organoleptic, and aroma characteristics were found among different varieties. The results of this study will provide a good basis for the assessment and application of the major vegetable soybean varieties grown in China.

Effect of different drying techniques on drying kinetics, nutritional components, antioxidant capacity, physical properties and microstructure of edamame

The present study aimed to investigate the effect of hot air drying (HD), microwave rolling-bed drying (MRD), hot air microwave rolling-bed drying (HMRD), pulse-spouted microwave vacuum drying (PSMVD) and freeze-drying (FD) on the drying characteristics, quality properties and microstructure of edamame. Six models were fitted the drying curves, and quality attributes were analyzed. Results indicated that Page model was the most suited model for edamame drying. Compared with HD, MRD and HMRD improved the quality of edamame and decreased the drying time by 45.59% and 36.03% respectively. The FD and PSMVD possessed higher rehydration ability, nutrient retention and antioxidant capacity compared with other methods. Moreover, PSMVD products showed a crunchy texture, the less color change and the shortest drying time (70 min). Microscopy images demonstrated a distinct porous structure in PSMVD, which facilitated the moisture transfer. Overall, PSMVD is a promising dehydration method for obtaining high value-added edamame products.