A data fusion system based on attenuated total reflectance mid-infrared spectroscopy and colorimetry combined with chemometrics for monitoring the fermentation process of Candida utilis.

The fermentation of Candida utilis has become a cost-effective and high-yield process, characterized by its high nutritional value, high productivity, and short fermentation time. To efficiently and comprehensively monitor the fermentation process of Candida utilis, this study employed an integrated data fusion system, based on attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy and colorimetry, combined with chemometrics to achieve efficient and comprehensive monitoring of the Candida utilis fermentation process. By analyzing the trends in key physicochemical indicators during fermentation, the fermentation process could be divided into four stages: 0-3 h, 3-9 h, 9-24 h, and 24-33 h. Principal component analysis (PCA) was employed to reduce the dimensionality of the highly collinear infrared spectral data to 1-10 principal components. Through the evaluation of four distinct machine learning algorithms, the optimal data fusion method and the best-performing machine learning model (random forest, with a classification accuracy of 95.30 %) were identified. PCA and linear discriminant analysis (LDA) revealed that samples from different fermentation times exhibited distinct clustering trends, although full differentiation was not achieved. To enhance classification accuracy, supervised learning models based on class labels were introduced. A comparison of classification accuracy between single signal and fusion signal approaches revealed that the optimal model achieved superior performance on the fusion dataset, with a 4-stage classification accuracy of 0.978, which was higher than that of the 11-stage classification. Quantitative prediction of key physicochemical parameters during the fermentation process was conducted using partial least squares regression (PLSR) and support vector regression (SVR) based on colorimeter dataset, ATR-MIR dataset, and fusion dataset. The data fusion strategy demonstrated excellent predictive performance for physicochemical indicators, particularly in predicting pH (R2p = 0.940, RMSEP = 0.210) and cell concentration (R2p = 0.946, RMSEP = 0.236). The color difference dataset exhibited the highest accuracy in predicting reducing sugar (R2p = 0.987, RMSEP = 0.988). The results demonstrated that, compared to PLSR, SVR demonstrated superior performance in these quantitative analysis tasks, enabling more accurate prediction of the physicochemical parameters in the Candida utilis fermentation process.