桂花酒中山梨酸钾拉曼表面增强预测模型在其他果酒中的传递

自行搭建的拉曼光谱点扫描系统，以柠檬酸钠还原法配制的SC银溶胶为表面增强剂，建立了桂花酒中山梨酸钾的定量预测模型，模型校正集决定系数(R2_C)和均方根误差(RMSEC)分别为0.978 9和0.070 3 g·kg-1，验证集决定系数(R2_P)和均方根误差(RMSEP)分别为0.934和0.165 7 g·kg-1。桂花酒中山梨酸钾的定量预测模型为主光谱模型，结合K/S算法，探讨了基于DS算法和PDS算法将桂花酒主光谱模型向杨梅酒的修正传递方法。结果显示，用K/S算法选取4个杨梅酒样品，基于DS算法传递桂花酒主光谱模型验证结果R_P和RMSEP值分别为0.906 1和0.215 0 g·kg-1。K/S算法选取3个杨梅酒样品(窗口宽度为5)，基于PDS算法传递桂花酒主光谱模型验证结果R_P和RMSEP值分别为0.905 5和0.225 0 g·kg-1。DS算法和PDS算法均可以用少量样品将桂花酒中山梨酸钾的主光谱预测模型有效传递给杨梅酒，实现了一种被测物预测模型在同类物种间的传递，具有重要实用意义。

Calibration Transfer of Surface-Enhanced Raman Spectroscopy Quantitative Prediction Model of Potassium Sorbate in Osmanthus Wine to Other Wine

Based on a self-built Raman scanning system and the SERS substrate named SC silver sol which was prepared with sodium citrate reduction method, a quantitative prediction model of potassium sorbate in osmanthus wine was established. The 34 osmanthus wine samples were divided into calibration set and validation set, and the potassium concentration prediction model was established by multiple linear regression method. The determination coefficient and the root mean square error of the calibration set were 0.978 9 and 0.070 3 g·kg-1 respectively, the determination coefficient and the root mean square error of the validation set were 0.934 and 0.165 7 g·kg-1 respectively. The quantitative prediction model of potassium sorbate in osmanthus wine as the main spectrum model and by using the DS algorithm and the PDS algorithm, discussed the model transfer method of potassium sorbateintwo different wines. The K/S algorithm was used to sort the Raman spectra of bayberry wine. The main spectrum correction matrix was composed of osmanthus wine samples which had the same concentration ascalibration matrix. 15 bayberry wine samples were prepared to verify the effect of the transfer model. The results of the DS algorithm showed that R_P and RMSEP were 0.906 1 and 0.215 0 g·kg-1 respectively. The results of the PDS algorithm showed that R_P and RMSEP were 0.905 5 and 0.225 g·kg-1 respectively. DS algorithm and PDS algorithm can be achieved with a small number of samples for effective model transfer, and the best samples of the two methods were 4 and 3 respectively. In addition, window width of 5 was the best choice of PDS algorithm. Prediction model of potassium sorbate in osmanthus wine was suitable for the quantitative prediction of potassium sorbate in red bayberry wine by DS algorithm or PDS algorithm. The DS algorithm and PDS algorithm can achieve the transfer of prediction model for potassium sorbate in different wines. Potassium sorbatein red bayberry wine can be predicted by the prediction model of osmanthus wine.