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To understand the effects of volatile compounds on the evaluation of soy sauces so their quality could be classified, samples collected over 3 consecutive years from 2015 to 2017 were evaluated then ranked. The volatile compounds of these evaluated samples were directly extracted by headspace analysis to reduce technical interference and to identify the most common compounds in soy sauce. Sixty-four volatile compounds were identified in the evaluated soy sauces, of which 35 were found in all samples. The correlation coefficients between the levels of volatile compounds detected and the sensory rankings of the evaluated soy sauces were calculated. This enabled those volatile compounds having a greater measure of influence, both negatively and positively, on the quality of the soy sauce to be identified. It was noteworthy that the quality of Japanese fermented soy sauces was negatively correlated with their levels of 4-ethyl-2methoxyphenol and pyrazines. • Volatile compounds were correlated directly to the sensory evaluation. • 19 compounds were found to be positively correlated with the quality increased. • 25 compounds showed a negative correlation to sensory quality of soy sauce. • The obtained results provide additional data for flavor optimization of soy sauce. [ABSTRACT FROM AUTHOR]

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. • 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 [ABSTRACT FROM AUTHOR]