Determination of Amino Acid Nitrogen in Soy Sauce Using Near Infrared Spectroscopy Combined with Characteristic Variables Selection and Extreme Learning Machine

Abstract

Amino acid nitrogen (AAN) is one of the most important indicators to assess the quality grade of soy sauce in China. Near infrared (NIR) spectroscopy technique combined with characteristic variable selection and extreme learning machine (ELM) was applied to detect AAN content in soy sauce in this work. First, the optimal spectral intervals were selected by synergy interval partial least square. Then, ELM model based on the optimal spectral intervals was established, called synergy interval extreme learning machine (Si-ELM) model. Support vector machine model based on the optimal intervals was established comparatively. These models were optimized by cross validation, and the performance of each final model was evaluated according to correlation coefficient ( $$ R_{\text{p}}^2 $$ ) and root mean square error of prediction (RMSEP) in prediction set. Si-ELM showed excellent performance. The best Si-ELM model was achieved with $$ R_{\text{p}}^2 = 0.9657 $$ and RMSEP = 0.0371 in the prediction set. It was concluded that NIR spectroscopy combined with Si-ELM was an appropriate method to detect AAN content in soy sauce.