Physical and chemical properties of edamame during bean development and application of spectroscopy-based machine learning methods to predict optimal harvest time

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 Climate Conditions before Harvest Date on Edamame Metabolome

Edamame is a green soybean that is rich in nutrients. Boiled edamame has been traditionally used for food in the East Asia region. It was known among farmers that conditions, such as temperature and climate on the day of harvest, affect the quality of edamame. Large-scale farmers harvest edamame on multiple days in the same year; however, the quality of edamame varies from day to day due to variations in climate conditions. In this study, we harvested edamame over several days between 2013 and 2018, obtained the climate conditions on the harvest date, and performed metabolome analysis using capillary electrophoresis mass spectrometry. To clarify the correlation between climate conditions before the harvest date and edamame components, comparative analyses of the obtained meteorological and metabolomic data were conducted. We found positive and negative correlations between the sunshine duration and average temperature, and the amounts of some edamame components. Furthermore, correlations were observed between the annual fluctuations in climate conditions and edamame components. Our findings suggest that the climate conditions before the date of harvesting are closely related to edamame quality.

Genome-wide association analysis of sucrose and alanine contents in edamame beans

The sucrose and Alanine (Ala) content in edamame beans significantly impacts the sweetness flavor of edamame-derived products as an important attribute to consumers' acceptance. Unlike grain-type soybeans, edamame beans are harvested as fresh beans at the R6 to R7 growth stages when beans are filled 80-90% of the pod capacity. The genetic basis of sucrose and Ala contents in fresh edamame beans may differ from those in dry seeds. To date, there is no report on the genetic basis of sucrose and Ala contents in the edamame beans. In this study, a genome-wide association study was conducted to identify single nucleotide polymorphisms (SNPs) related to sucrose and Ala levels in edamame beans using an association mapping panel of 189 edamame accessions genotyped with a SoySNP50K BeadChip. A total of 43 and 25 SNPs was associated with sucrose content and Ala content in the edamame beans, respectively. Four genes (Glyma.10g270800, Glyma.08g137500, Glyma.10g268500, and Glyma.18g193600) with known effects on the process of sucrose biosynthesis and 37 novel sucrose-related genes were characterized. Three genes (Gm17g070500, Glyma.14g201100 and Glyma.18g269600) with likely relevant effects in regulating Ala content and 22 novel Ala-related genes were identified. In addition, by summarizing the phenotypic data of edamame beans from three locations in two years, three PI accessions (PI 532469, PI 243551, and PI 407748) were selected as the high sucrose and high Ala parental lines for the perspective breeding of sweet edamame varieties. Thus, the beneficial alleles, candidate genes, and selected PI accessions identified in this study will be fundamental to develop edamame varieties with improved consumers' acceptance, and eventually promote edamame production as a specialty crop in the United States.

The nutritional composition of the vegetable soybean (maodou) and its potential in combatting malnutrition

Introduction

Global malnutrition continues to be a canker owing to poor eating habits and over-reliance on the major staple crops. Vegetable soybean (maodou) is gaining popularity globally as an affordable snack and vegetable.

Methods

In this study, we profiled the nutritional composition of 12 soybean cultivars at the vegetable (R6-R7) and mature (R8) stages. We also conducted an RNA-seq analysis during seed development, focusing on key biosynthesis enzymes for quality traits.

Results

The results showed that 100 g of maodou contained 66.54% moisture, 13.49% protein, 7.81% fatty acids, 2.47% soluble sugar, abundant content of minerals, and micronutrients, including folate (462.27 μg FW) and carotenoids (3,935.41 μg FW). Also, the isoflavone content of maodou ranged between 129.26 and 2,359.35 μg/g FW. With regard to the recommended daily allowance, 100 g fresh weight of maodou can contribute 26.98, 115.57, and 11.60% of protein, folate, and zinc, respectively, and significant proportions of other nutrients including linoleic acid (21.16%), linolenic acid (42.96%), zinc (11.60%), and iron (18.01%). On a dry weight basis, maodou has two to six folds higher contents of folate, tocopherol, and carotenoid than the mature soybean. Furthermore, RNA-seq analysis revealed that key biosynthesis enzymes of quality traits are differentially expressed during seed development and may contribute to variations in the content of quality traits at the vegetable and mature stages. Correlation analysis of quality traits at both stages revealed that protein only correlated positively with zinc at the vegetable stage but negatively correlated with total tocopherol and total fatty acid at the mature stage. Complex associations among folates, soluble sugar, and isoflavones were also identified.

Discussion

This study provides insight into the nutritional contents of vegetable soybean and demonstrates that maodou is essential for meeting the nutritional requirements of most countries.