Construction of Fourier Transform Near Infrared Spectroscopy Prediction Model for Main Components in Lycium ruthenicum Murr

Abstract

A quantitative method of main components (total sugar, reducing sugar, total acid, ammonia nitrogen, anthocyanins, procyanidins, total phenols, flavonoids and polysaccharides) in Lycium ruthenicum Murr was developed using Fourier transform near infrared spectroscopy (FT-NIR) combined with chemometric analysis. Firstly, 11 pretreatment methods were compared for the original spectra, and the optimal pretreatment method of each component was selected. Then, the model results established by partial least squares (PLS), interval partial least squares (iPLS) and synergistic interval partial least squares (siPLS) algorithms were compared, and finally siPLS was adopted for modeling. The results showed that the correlation coefficient of calibration (Rc) and prediction (RP) of total acids, ammonia nitrogen, anthocyanins, procyanidins, total phenols and flavonoids were all greater than 0.9818, and the relative analysis error (RPD) was more than 2.5, indicating an excellent model performance. The modeling effect of total sugar, reducing sugar, and polysaccharide was also good. The established calibration models could be used for actual detection. The predicted values of the verification samples did not significantly differ from the measured values, with a prediction error of only ±0.1%. Therefore, the prediction model had high reliability. The prediction model established in this study can enable nondestructive, rapid and accurate main components in dried and fresh fruits of Lycium ruthenicum Murr.

Keywords

• lycium ruthenicum murr
• fourier transform near infrared spectroscopy (ft-nir)
• spectral preprocessing
• model building
• nondestructive testing