外文摘要：Soybean seeds are a key ingredient for producing quality tofu. Conventional methods for assessing soybean seed quality for tofu are time-consuming and labor-intensive. This study employs hyperspectral imaging (HSI) and machine learning to rapidly predict gypsum tofu quality from soybean seeds. Two hundred soybean seed varieties were classified into four categories based on tofu quality using hierarchical clustering. Hyperspectral scans of the soybean seeds were captured in the 900-1700 nm range. Using the Extreme Gradient Boost (XGBoost) algorithm, ten critical wavelengths were identified that correlate with protein, carbohydrate, and oil contents. A Convolutional Neural Network (CNN) model was subsequently developed, trained on HSI data from the soybean categories. For new soybean seeds, this CNN model successfully categorized them into distinct quality classes with 96-99 % accuracy. Further validation through tofu production demonstrated the model's robustness in predicting key tofu quality parameters like yield, firmness, and springiness. Overall, this pioneering research enabled rapid, non-destructive prediction of tofu quality from soybean seeds using HSI and CNN. With further refinements, this approach could revolutionize soybean seed quality assessment.
外文关键词：hyperspectral imaging;deep neural networks;Non-destructive inspection;XGBoost;Tofu quality
作者：Sun, Xin;Malik, Amanda;Ram, Billy;Arumugam, Dharanidharan;Jin, Zhao;Xu, Minwei
作者单位：North Dakota State Univ;Arizona State Univ
期刊名称：FOOD CONTROL
期刊影响因子：0.0
出版年份：2024
出版刊次：160
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台