Application of compressed sensing for selecting relevant variables for a model to predict the quality of Japanese fermented soy sauce. (January 2020)

• Record Type: Journal Article
• Authors: Wang, Shuo; Liu, Beiyi; Xu, Li; Tamura, Takehiro; Kyouno, Nobuyuki; Liu, Xiaofang; Zhang, Han; Akiyama, Yoshinobu; Chen, Jie Yu
• Is Part Of: Innovative food science & emerging technologies. Volume 59(2019)
• Publication Date: 2020-01
• Subjects: Soy sauce -- Predictive quality -- Variable selection approach -- Compressed sensing -- Partial least squares regression; Food -- Biotechnology -- Periodicals; Food industry and trade -- Technological innovations -- Periodicals; Aliments -- Biotechnologie -- Périodiques; Food -- Biotechnology; Periodicals; Electronic journals; 664.005
• DOI: 10.1016/j.ifset.2019.102241
• Languages: English
• ISSNs: 1466-8564

Abstract

Abstract: In order to predict the quality of Japanese fermented soy sauces, this study focuses on selecting relevant variables for developing a flexible and objective model. There were 74 parameters with the potential to influence the overall acceptability of soy sauce being measured and regarded as potential variables for predicting the sensory scores of soy sauce samples. The variable selection approach was inspired by Compressed Sensing (CS) theory and has been used for the first time on the calibration set (soy sauce samples were collected directly from the Akita Prefectural Soy Sauce Competitions in 2016 and 2017) to evaluate the contribution of each predictive variable to the sensory score. Consequently, 30 predictive variables which make a great contribution to the quality for predicting soy sauce were successfully selected by CS-based method. The selected variables covered the important variables of sensory evaluation such as color, taste, and fragrance. Subsequently, the model for predicting soy sauce quality was established using partial least squares regression, based on the selected variables. The validity of the model was evaluated using soy sauce samples produced in 2018 leading to values of r 2 and RMSEP for the validation samples of 0.80 and 11.47, respectively. Therefore, the model was considered to be suitable for predicting the sensory quality of soy sauce. The results also confirmed that the CS-based method provided a new approach to selecting variables of practical importance for developing a predictive model. Highlights: The compress sensing theory (CS) can be used in a variable selection approach for assessing the quality of food products 30 parameters were automatically selected by the CS-based method among 74 measured parameters related to the quality The predictive model provides a nice quantitative evaluation and detail reflection for the quality of soy sauce