Confocal Raman microspectroscopy combined with spectral screening algorithms for quantitative analysis of starch in rice

Starch is one of the main components of rice. The feasibility of confocal Raman microspectroscopy (CRM) combined with spectral screening algorithms for quantitative detection of starch in rice was investigated in this paper. Firstly, the Raman spectra of the samples were processed by pre-processing and spectral screening algorithm of the data. Secondly, the quantitative starch detection models were respectively established by correction set by partial least squares regression, particle swarm optimization-support vector regression, gray wolf optimizer-support vector regression (GWO-SVR) as well as back propagation neural network. After that, the quantitative starch detection models were validated using the prediction set. Eventually, the related coefficient of the prediction set, root mean square error of the prediction set, and mean relative error of GWO-SVR starch quantification detection model combined with normalization and IPLS screening were respectively 0.8915, 1.03%, and 1.08%. The experimental results manifested that the swift and precise detection of starch content in rice by CRM was workable after proper pre-processing and spectral screening algorithm.