Feasibility study on prediction of the grain mixtures for black sesame paste recipe with different chemometric methods. (1st December 2022) - Cheng, Yuhan; Wang, Yu; Leng, Tuo; Zhu, Liwen; Jing, Ying; Xie, Jianhua; Yu, Qiang; Chen, Yi
Abstract: An attractive strategy for quantitative analysis of the grain mixtures for black sesame paste was proposed in this research by combined application of FT-IR spectroscopy and chemometrics. The FT-IR spectra of three grain (black sesame, black rice, and black bean) mixtures, which are used for producing black sesame paste were collected. Based on the FT-IR spectra, different regression methods were utilized and optimized to estimate each mixture component content. The PCA-Class results revealed that each mixture class was highly distinct and allowed discrimination from others. PLSR, siPLS, and PCR were comparatively performed to calibrate regression models. The optimal model was achieved with the R 2 higher than 0.98 and the RMSE less than 4. The identification results of hold-out validation samples proved the accuracy of the model and indicated that PCR was the most suitable regression model. The results indicate that FT-IR spectroscopy combined with chemometrics could be potentially applied in the industry for quantitative analysis of mixed foods in the future. Highlights: FT-IR and chemometrics were employed to detect the proportion of black sesame paste. PCA-Class can analyze and detect the kind of grains in black sesame paste. PCR can successfully predict the proportion of the various grains in black sesame paste. Out of sample validation of the calibration models results confirmed this method was accurate and effective.