基于可见-近红外漫反射光谱技术的葡萄贮藏期间可溶性固形物定量预测

Quantitative Prediction of Soluble Solids in Grapes during Storage Based on Visible and Near Infrared Diffuse Reflection Spectroscopy

利用可见-近红外漫反射光谱技术,建立不同品种葡萄贮藏期间可溶性固形物含量定量预测的通用模型。以10℃贮藏的玫瑰香葡萄、马奶葡萄、红提葡萄的混合光谱为定标材料,探讨不同化学计量学建模方法、不同光谱预处理方法、间隔点、平滑数以及不同特征波长区间选择对建模效果的影响及模型的品种适用性。结果显示,采用改进偏最小二乘法,16点平滑,间隔点数16点,结合二阶导数、去散射的处理方法,在波长范围408～1 092.8 nm内建立的模型效果最优,其交互验证误差和交互验证判定系数R2CV分别为0.308 7、0.980 2。由3种葡萄混合组成的预测集对模型进行评价,预测标准差0.354、预测判定系数Rp2为0.980 8、验证相对分析误差为6.22、预测残差平方和为7.993。模型对单一品种预测Rp2均达到0.94以上。因此,葡萄果实可溶性固形物含量的近红外预测模型具有可行性,可同时适用于多种葡萄品种。

This study aimed to establish a universal quantitative prediction model for soluble solids content（SSC） in different varieties of grapes during storage based on visible and near-infrared diffuse reflection spectra. The mixed spectra of Muscat, Manai and Red Globle grapes stored at 10 ℃ were taken as calibration materials, the influences of different stoichiometrical calibration methods, spectral pretreatment methods, gaps, smooth points, different effective wavelength intervals on the a pplicability of the established model for different grape varieties were examined. The results showed that the modified partial least squares combined with 16 smoothing points, second derivative within 16 gaps and the scattering method could produce the optimal model within the wavelength range of 408–1 092.8 nm with standard error of crossvalidation（SECV） and coefficient of determination of cross-validation（R2CV） of 0.308 7 and 0.980 2, respectively. The model was evaluated via the prediction set of the above three varieties of grapes. The standard error of prediction（SEP） was 0.354, the correlation coefficient Rp2 was 0.980 8, the relative prediction deviation（RPD） was 6.22, and the predicted residual sum of squares（PRESS） was 7.993. When being applied for predicting the single varieties, the Rp2 reached more than 0.94. Therefore, the near infrared detection model is useful to predict soluble solids content in grape and is suitable for different grape varieties at the same time.