Influence of low O2 and high CO2 environment on changes in metabolite concentrations in harvested vegetable soybeans

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.

Development of antifogging double-layer film using cellulose nanofibers and carboxymethyl chitosan for white Hypsizygus marmoreus preservation

Films with simultaneously excellent mechanical and anti-fog properties are of great importance for food packaging. A novel strategy is described here to prepare long-lasting anti-fog film with antibacterial and antioxidant capabilities via a simple, green approach. The CMC (carboxymethyl chitosan) gel was integrated with CNF/TA (cellulose nanofibers/tannic acid) composite solution based on layer-by-layer assembly to form a membrane with a bilayer structure. The anti-fog performance of the bilayer film could be adjusted by regulating the CNF/TA layer thickness. On the whole, the developed anti-fog film had high mechanical strength and excellent UV shielding properties, as well as good antibacterial and antioxidant properties, and could be non-fogging for a long time under water vapor (40 °C). The effect of double layer anti-fog film (3%CmFT-3) on the fresh-keeping effect of white Hypsizygus marmoreus was compared at room temperature (28 °C) with commercially available anti-fog PVC film. The results showed that the bilayer anti-fog film could effectively prevent the generation of fog, delay the Browning, inhibit mildew, improve the overall acceptability, and effectively extend the shelf life of white Hypsizygus marmoreus. This biomass-based anti-fog film offers great potential for the development of multifunctional green food packaging.

Nutritional quality of different potassium efficiency types of vegetable soybean as affected by potassium nutrition

Pot experiments were conducted in 2017, 2019, and 2020 to examine the effects of potassium nutrition on the nutritional components of vegetable soybeans with different K efficiency at immature and mature stages. Two vegetable soybean varieties with higher K efficiency and two varieties with lower K efficiency were studied in the low available K soil under the condition of no K and normal K fertilization. The results indicated that almost all nutritional components in vegetable soybean were affected by K, genotypes, inter-annual differences, and their interactions. In general, no K fertilization increased protein and amino acid concentrations but decreased oil, soluble sugar, sucrose, K, Mg, and Fe concentrations in immature and mature vegetable soybean. The sensitivity of nutritional components to K nutrition differed among varieties. For instance, K high-efficiency varieties generally exhibited higher protein and amino acid concentrations without K application. K high-efficiency vegetable soybeans are low-K tolerance varieties to isoflavones. The results of this study provide insights for high yield and quality vegetable soybean breeding against soil K deficiency.

Effect of electrolytes and surfactants on generation and longevity of carbon dioxide nanobubbles

The objective of this research was to investigate the effect of individual additions of mono- and divalent electrolytes (NaCl and CaCl₂), anionic (sodium dodecyl sulfate, SDS) and non-ionic surfactants (polysorbate 80, Tween 80) at varied concentrations on the generation and stability of bulk nanobubbles (NBs) from carbon dioxide (CO₂) gas in aqueous system. Overall, NBs generated in the small-amount salt fluids exhibited significantly (p ≤ 0.05) lower size range (150-350 nm). Smaller diameter and higher zeta potential magnitudes (18-24 mV) of the NBs in SDS medium were also observed and related to the higher CO₂ concentration (~1850 ppm) and lower surface tension (~64 mN/m) of the solution. However, the gas NBs were disappeared with the incorporation of Tween 80. The outcomes provide some more research-based details about the impact of potential nano-bubble stabilising agents on characteristics of NBs contributing to the green and sustainable NB-related applications in food sectors.

Metabolomics as a Tool to Study Underused Soy Parts: In Search of Bioactive Compounds

The valorization of agri-food by-products is essential from both economic and sustainability perspectives. The large quantity of such materials causes problems for the environment; however, they can also generate new valuable ingredients and products which promote beneficial effects on human health. It is estimated that soybean production, the major oilseed crop worldwide, will leave about 597 million metric tons of branches, leaves, pods, and roots on the ground post-harvesting in 2020/21. An alternative for the use of soy-related by-products arises from the several bioactive compounds found in this plant. Metabolomics studies have already identified isoflavonoids, saponins, and organic and fatty acids, among other metabolites, in all soy organs. The present review aims to show the application of metabolomics for identifying high-added-value compounds in underused parts of the soy plant, listing the main bioactive metabolites identified up to now, as well as the factors affecting their production.

Towards superior plant-based foods using metabolomics

Metabolomics is proving a useful approach for many of the main future goals in agronomy and food production such as sustainability/crop resilience, food quality, safety, storage, and nutrition. Targeted and/or untargeted small-molecule analysis, coupled to chemometric analysis, has already unveiled a great deal of the complexity of plant-based foods, but there is still 'dark matter' to be discovered. Moreover, state-of-the-art food metabolomics offers insights into the molecular mechanisms underlying sensorial and nutritional characteristics of foods and thus enables higher precision and speed. This review describes recent applications of food metabolomics from fork to farm and focuses on the opportunities these bring to continue food innovation and support the shift to plant-based foods.

Efficient preservation of sprouting vegetables under simulated microgravity conditions

The effectiveness of a simulated microgravity environment as a novel method for preserving the freshness of vegetables was investigated. Three types of vegetables were selected: vegetable soybean, mung bean sprouts, and white radish sprouts. These selected vegetables were fixed on a three-dimensional rotary gravity controller, rotated slowly. The selected vegetables were stored at 25°C and 66% of relative humidity for 9, 6, or 5 d while undergoing this process. The simulated microgravity was controlled utilizing a gravity controller around 0 m s-2. The mung bean sprouts stored for 6 d under simulated microgravity conditions maintained higher thickness levels than the vegetable samples stored under normal gravity conditions (9.8 m s-2) for the same duration. The mass of all three items decreased with time without regard to the gravity environment, though the samples stored within the simulated microgravity environment displayed significant mass retention on and after 3 d for mung bean sprout samples and 1 d for white radish sprout samples. In contrast, the mass retention effect was not observed in the vegetable soybean samples. Hence, it was confirmed that the mass retention effect of microgravity was limited to sprout vegetables. As a result of analysis harnessing a mathematical model, assuming that the majority of the mass loss is due to moisture loss, a significant difference in mass reduction coefficient occurs among mung bean sprouts and white radish sprouts due to the microgravity environment, and the mass retention effect of simulated microgravity is quantitatively evaluated utilizing mathematical models. Simulated microgravity, which varies significantly from conventional refrigeration, ethylene control, and modified atmosphere, was demonstrated effective as a novel method for preserving and maintaining the freshness of sprout vegetables. This founding will support long-term space flight missions by prolonging shelf life of sprout vegetables.

Digitization of Broccoli Freshness Integrating External Color and Mass Loss

Yellowing of green vegetables due to chlorophyll decomposition is a phenomenon indicating serious deterioration of freshness, and it is evaluated by measuring color space values. In contrast, mass reduction due to water loss is a deterioration of freshness observed in all horticultural crops. Therefore, in this study, we propose a novel freshness evaluation index for green vegetables that combines the degree of greenness and mass loss. The green color retention rate was measured using a computer vision system, and the mass retention rate was measured by weighing. Linear discriminant analysis (LDA) was performed using both variables (greenness and mass) as covariates to obtain a single freshness evaluation value (first canonical variable). The correct classification of storage period length by LDA was 96%. Green color retention alone allowed for classification of storage durations between 0 day and 10 days, whereas LDA could classify storage durations between 0 day and 12 days. The novel freshness evaluation index proposed by this research, which integrates greenness and mass, has been shown to be more accurate than the conventional evaluation index that uses only greenness.

Hyperspectral Imaging Coupled with Multivariate Analysis and Image Processing for Detection and Visualisation of Colour in Cooked Sausages Stuffed in Different Modified Casings

A hyperspectral imaging system was for the first time exploited to estimate the core colour of sausages stuffed in natural hog casings or in two hog casings treated with solutions containing surfactants and lactic acid in slush salt. Yellowness of sausages stuffed in natural hog casings (control group, 20.26 ± 4.81) was significantly higher than that of sausages stuffed in casings modified by submersion for 90 min in a solution containing 1:30 (w/w) soy lecithin:distilled water, 2.5% wt. soy oil, and 21 mL lactic acid per kg NaCl (17.66 ± 2.89) (p < 0.05). When predicting the lightness and redness of the sausage core, a partial least squares regression model developed from spectra pre-treated with a second derivative showed calibration coefficients of determination (R_c²) of 0.73 and 0.76, respectively. Ten, ten, and seven wavelengths were selected as the important optimal wavelengths for lightness, redness, and yellowness, respectively. Those wavelengths provide meaningful information for developing a simple, cost-effective multispectral system to rapidly differentiate sausages based on their core colour. According to the canonical discriminant analysis, lightness possessed the highest discriminant power with which to differentiate sausages stuffed in different casings.