Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm

The feasibility of rapid determination of the process variables (i.e. pH and moisture content) in solid-state fermentation (SSF) of wheat straw using Fourier transform near infrared (FT-NIR) spectroscopy was studied. Synergy interval partial least squares (siPLS) algorithm was implemented to calibrate regression model. The number of PLS factors and the number of subintervals were optimized simultaneously by cross-validation. The performance of the prediction model was evaluated according to the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The measurement results of the optimal model were obtained as follows: RMSECV = 0.0776, Rc = 0.9777, RMSEP = 0.0963, and Rp = 0.9686 for pH model; RMSECV = 1.3544% w/w, Rc = 0.8871, RMSEP = 1.4946% w/w, and Rp = 0.8684 for moisture content model. Finally, compared with classic PLS and iPLS models, the siPLS model revealed its superior performance. The overall results demonstrate that FT-NIR spectroscopy combined with siPLS algorithm can be used to measure process variables in solid-state fermentation of wheat straw, and NIR spectroscopy technique has a potential to be utilized in SSF industry.

Graphical abstractThe figure below shows the optimal combinations of subintervals selected are [7 8 11 18], which are corresponding to 6005.25–5723.69, 6290.66–6009.10, 7146.90–6865.34 and 9144.79–8863.23 cm−1 in the full spectral region. There are 296 variables in the combinations of spectral subintervals selected by siPLS. It shows that these spectral regions chosen by siPLS are correlated to moisture content in solid-state fermented product.Figure optionsDownload full-size imageDownload as PowerPoint slideHighlights► FT-NIR spectroscopy with siPLS to predict process variable in SSF of wheat straw. ► Predictive models were successfully established for pH and moisture content using wet samples. ► Optimal model was achieved by siPLS with selected spectral subintervals. ► NIRs technique with chemometrics methods could be used as an effective tool for condition monitoring of SFF process.