Determination of total acid content and moisture content during solid-state fermentation processes using hyperspectral imaging

• Hyperspectral imaging technology was used to rapidly predict the TAC and MC in the vinegar culture of SSF.
• Dependent on the 3-D datacube, models for prediction TAC and MC were developed with chemometrics methods.
• Efficient spectral variables were selected by siPLS and GA-PLS base on the HIS.
• Optimum spatial features were extracted by PCA and GLCM.
• HSI with chemometrics produces detailed TAC and MC distribution maps in vinegar culture during SSF process.

Total acid content (TAC) and moisture content (MC) are very important parameters during Solid-State Fermentation (SSF) processes. The feasibility of using hyperspectral imaging (HSI) technology for predicting TAC and MC in vinegar cultures during SSF processes was investigated. Prediction models were constructed using variables selected from spectral and spatial data from associated 3-D hyperspectral datacubes to predict the relative content of TAC and MC for each pixel in the hyperspectral image. Models were developed using genetic algorithm (GA) optimization combined with partial least squares regression (PLS) dependent on the spectral variables yielded good prediction results for both TAC and MC. The determination coefficients (Rp2Rp2) for TAC and MC were 0.8565 and 0.8162, respectively. Finally, the distribution maps of TAC and MC for a vinegar culture sample were obtained. These distribution maps could be implemented to estimate the uniformity of fermentation products during SSF.

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