Capturing and Explaining Sensory Differences among Organically Grown Vegetable-Soybean Varieties Grown in Northern California

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.

Understanding the Role of Overall Appearance and Color in Consumers' Acceptability of Edamame

Appearance properties of vegetables can affect consumers' acceptance toward them as well as purchase intent. Hence, appearance is highly associated with quality of edamame (Glycine max (L.) Merr.), a protein-rich vegetable that is experiencing increased sales in the USA. Edamame is a high-value specialty crop and its production has been promoted in Virginia and other states in the USA where the tobacco production has decreased in the last decade. To support current efforts to develop the edamame industry in the USA, consumer and color data of 10 edamame genotypes grown in Virginia were analyzed in this follow-up study to understand the role of overall appearance and color characteristics in consumers' acceptability of edamame beans. In two consecutive years, untrained adult volunteers used 9-point hedonic scales (1 = “dislike extremely”, 9 = “like extremely”) to evaluate appearance and overall liking of edamame samples (cooked and shelled edamame beans) and our researchers measured the reflective color of the samples with a chroma meter. In the first year, sensory panelists also completed a choice-based conjoint analysis to determine their willingness-to-pay (WTP) for dark vs. light green edamame beans in a salad. Edamame genotypes were significantly different in appearance and overall liking (p < 0.05) and the genotype R14-16195 was the most liked overall. Hedonic scores and color were significantly affected by “year” (p < 0.05), so intentional changes between years (e.g., sample preparation) should be avoided in future studies. Consumers showed higher WTP for dark green edamame beans. Additionally, green intensity (color index) and a* color coordinate were correlated to appearance liking scores (p < 0.05), which suggests color data can support breeding selection criteria and possibly predict consumer acceptability. Employing color measurement as quality control method can help improve harvest procedures, post-harvest handling, and define edamame quality standards for the USA market.

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.

Assessing Consumer Preferences and Intentions to Buy Edamame Produced in the U.S

Due to the growing consumer demand for edamame (vegetable soybean) in the U.S., the domestic production of this specialty crop has been promoted in several Mid-Atlantic and Southeast states as an economically attractive alternative to replace the decreasing tobacco production. For the edamame agrobusiness to be successful in the U.S., consumer studies are as needed as new commercial cultivars that are developed for the U.S. environment. Thus, in this exploratory study, we investigated consumers' preferences and intentions to buy edamame products in the U.S., especially domestic products. Data was collected through a web-based survey distributed through QualtricsXM and a convenience sampling method was chosen. Volunteers who completed the survey (N = 309) were 82% female, 57% residents of the South Atlantic area, and 79% daily consumers of vegetables. Survey respondents had a positive attitude toward domestically produced vegetables and valued supporting U.S. producers. Overall, domestically grown, in-shell edamame products were preferred compared to shelled edamame or imported products. Regarding future purchasing, respondents exhibited higher intention to buy fresh edamame relative to frozen edamame. Additionally, respondents considered price, availability, and familiarity with the vegetable brand, respectively, as the most important factors in their decision-making process to buy edamame products. Our study confirmed there is a market potential for domestically produced edamame and it also provides valuable information to support future studies, production decisions, and the growth of the edamame agrobusiness in the U.S.

Utilizing Consumer Perception of Edamame to Guide New Variety Development

Consumption of edamame (vegetable soybeans) has increased significantly in the U.S. over the last 20 years. Although market demand has been increasing, most edamame is still imported from Asian countries. A team of multistate plant-breeding programs in the mid-Atlantic and Southeast U.S. has focused on developing new breeding lines that grow well in the U.S. and deliver what domestic growers, processors and consumers need and expect from their edamame. In our study, sensory evaluation was used to identify edamame genotypes and sensory attributes preferred by consumers to support breeding selection criteria. In the first year (reported as our “screening study”), 20 edamame genotypes were grown in three locations: Newport, AR, and Blacksburg and Painter, VA. In the second year (reported as our “validation study”), 10 edamame genotypes selected after our screening study were grown in Blacksburg and Painter, VA, Portageville, MO, and Stoneville, MS. In both years of research, untrained participants (adults; vegetable consumers not allergic to soy; N ≥ 50) used a traditional 9-point acceptability (hedonic) scale (1 = “dislike extremely”; 9 = “like extremely”) to evaluate overall-liking, aroma, appearance, taste, and texture, and a 5-point scale (1 = “not sweet,” 5 = “extremely sweet”) to evaluate sweetness intensity. Next, participants used a check-all-that-apply (CATA) list of selected sensory terms to describe the sensory characteristics of each edamame sample. Overall acceptability of edamame genotypes was significantly different among all genotypes (p &lt; 0.05). Samples described as “bitter,” “sour” (flavor) or “starchy” (texture) were associated with lower acceptability scores while “salty” and “sweet” (flavor) were correlated with higher acceptability. Sensory data from the screening study were used to select the best genotypes by use of a defined decision process based on the consumer data. The validation study tested the selection decisions and further supported the genotype choices. Sensory evaluation is a powerful tool to direct breeders to improve market acceptability and develop new edamame genotypes. Both screening and validation studies illustrate the significant role of consumer sensory data in support of genotypes targeted for domestic (U.S.) production.