Analysis of the Solid-state Fermentation Process in Zhenjiang Vinegar by Using Hyperspectral Imaging
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Abstract:
The objective of this study was to investigate the imaging and spectral characteristics of fermented vinegar grains by using the hyperspectral imaging technique (HPIT), to rapidly predict the fermentation status. Initially, a primary component analysis (PCA) was performed. The pre-treated spectral information was then combined with the partial least squares (PLS), interval PLS (iPLS), and synergy interval PLS (siPLS) of the full spectrum to establish rapid prediction models for total acid content, pH, and non-volatile acid content, in order to select the best prediction model. Three characteristic images were chosen based on the different main components represented by the imaging data. We extracted four characteristic variables (contrast, correlation, angular second moment, and homogeneity) by texture analysis, based on gray level co-occurrence matrix. The K-nearest neighbor (KNN) method was used to establish a recognition model for fermented vinegar grains, with a predicted recognition rate of 90.04%, which would enable a good prediction of the vinegar grain fermentation status. The synergy interval partial least squares (siPLS) model displayed the best performance, predicting the total acid content, pH value, and root mean squared error of prediction (RMSEP) of non-volatile acids to be 0.75, 0.05, and 0.3, respectively. This finding indicated that the model could rapidly predict important physical and chemical parameters. Therefore, it would be feasible to use HPIT for the rapid prediction of fermentation quality of vinegar grains. This study provides an effective and rapid means of detection to improve the process operation and quality of fermentation.
