Application of Various Algorithms for Spectral Variable Selection in NIRS Modeling of Red Ginseng Extraction

As an effective active component in red ginseng extraction, ginsenoside content has an important impact on the quality of follow-up products. Traditional chemical detection quality control methods have high costs and time-delay. Existing studies have shown that the fast and non-destructive near-infrared detection method is feasible for red ginseng extraction. However, the existing methods heavily rely on the data processing software algorithm of the instrument, which cannot meet the actual production accuracy and speed requirements. In order to monitor the extraction process rapidly and accurately, a variety of intelligent spectral selection algorithms are applied in the near-infrared spectral(NIRS) modeling, and the performance and robustness of different spectral selection algorithms are compared in this study. In order to detect the high content of ginsenoside Rgl and the low content Rc in the red ginseng extract, 128 samples of red ginseng extract in the first two times extracted were collected from three batches, 1 000-2 499 nm band NIRS data were obtained online, and the content of ginsenoside was determined by using the international standard high-performance liquid chromatography (HPLC). Firstly, the dimension of the input wavelength was reduced by using four wavelength selection algorithms, namely, competitive adaptive reweighting sampling (CARS), the uninformative variable elimination (UVE), random frog (RF) and successive projection algorithm (SPA). Then the selected wavelength was used for the linear partial least squares (PLS) quantitative model establishment. At last, the performance of the model was evaluated by the root mean square error (RMSE) coefficient of determination (R-2) and relative analysis error (RPD), etc. According to the PLS modeling results of four wavelength optimization algorithms, after RF optimization, the characteristic wavelength variable of the modeling decreased to 0. 67% of the original, R-2 of the ginsenoside Rgl and Rc content in red ginseng extract reached above 0. 94, the RMSE of the prediction was 0. 024 6 and 0. 013 5 respectively, and the RPD of prediction set reached above 4. 84, which reduced the difficulty of the modeling and improved the accuracy of modeling. From the comparison of RF and CARS modeling in the original spectrum, full-spectrum and SNV pretreated full spectrum, the overall performance of the RF wavelength selection algorithm model is better. Different spectral ranges and pretreatment methods have little impact on the performance and good robustness. In conclusion, RF is a relatively ideal wavelength selection algorithm for the modeling of red ginseng extract. PLS based on RF realizes the one-time modeling of two red ginseng extracts, which can be used to rapidly detect ginsenoside content in the extract. The study provides theoretical support for the online extraction control of medicine.