Identification of soy sauce using high-field asymmetric waveform ion mobility spectrometry combined with machine learning

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Soy sauce, an important condiment, varies greatly in the brand, geographical distribution, and production processes. We investigated the potential of volatile organic compounds (VOCs) serving as an indicator of soy sauce quality to detect three regions and two production technologies of Chinese soy sauce. An analytical method named high-field asymmetric waveform ion mobility spectrometry (FAIMS) was utilized for acquiring sample data. Wavelet packet decomposition (WPD) and principal component analysis (PCA) were used to extract the features of FAIMS data. 4 machine learning models were trained using these features, and the optimal parameters were obtained by a grid search. The scatter plots of the optimal two features we selected showed that the different regions and production technologies of soy sauce had obvious clustering trends. For the identification of different regions and production technologies, the training score, test score, and average cross-validation score of the optimal model were all 100%. Furthermore, the learning curves indicated that the optimal model obtained good performance and had low prediction errors. It was concluded that FAIMS combined with a suitable machine learning algorithm can successfully classify different regions and production technologies of Chinese soy sauce.