基于LVF-NIR的樱桃可溶性固形物含量无损速测模型的建立与优化

目的建立基于便携式近红外光谱仪的樱桃可溶性固形物含量无损快速定量检测模型,从而实现樱桃品质的无损快速检测。方法以北京通州产红灯樱桃、黄玉樱桃为研究对象,采用便携式线性渐变分光近红外光谱仪采集光谱数据,并采用折光仪测定其可溶性固形物含量;采用偏最小二乘回归结合全交互验证算法将光谱数据与可溶性固形物含量测定值建立定量校正模型,采用外部验证集对模型的预测性能做进一步测试。结果红灯樱桃可溶性固形物含量模型的R_C~2、RMSEC、R_(CV)~2、RMSECV、RPD分别为0.9194、0.79、0.8920、0.92、3.54,黄玉樱桃可溶性固形物含量模型的R_C~2、RMSEC、R_(CV)~2、RMSECV、RPD分别为0.8618、0.76、0.8246、0.86、2.70;两种樱桃可溶性固形物含量合并模型的R_C~2、RMSEC、R_(CV)~2、RMSECV、RPD分别为0.9125、0.81、0.8946、0.89、3.38。结论基于便携式线性渐变分光近红外光谱仪数据所建校正模型具有较好的准确度,可满足樱桃可溶性固形物含量的无损快速检测需求。

Development and optimization of the rapid and non-destructive quantitative models of soluble solid content of cherry based on LVF-NIR spectrometer

Objective To determine the cherry quality rapidly and nondestructively, the calibration models of the soluble solid content (SSC) of cherries based on the linear variable filter (LVF) near-infrared (NIR) spectrometer were developed. Methods Taking the Hongdeng and Huangyu cherries from Tongzhou District, City of Beijing as the research objects, the portable LVF-NIR spectrometer were applied to collect the LVF-NIR spectra data. Meanwhile, the values of SSC was determined by the refractometer. Partial least square (PLS) regression combined with full cross validation algorithm were applied to develop the quantitative analysis models of SSC, with the external validation sets being predicted to validate the prediction ability of the models developed above. Results For Hongdeng cherry, the R2C, RMSEC, R2CV, RMSECV and RPD of the calibration model were 0.9194, 0.79, 0.8920, 0.92, 3.54, respectively; for Huangyu cherry, the R2C, RMSEC, R2CV, RMSECV and RPD of the calibration model were 0.8618, 0.76, 0.8246, 0.86, 2.70, respectively; furthermore, for the both cherries, the R2C, RMSEC, R2CV, RMSECV and RPD of the calibration model were 0.9125, 0.81, 0.8946, 0.89, 3.38, respectively. Conclusion The calibration models based on LVF-NIR spectrometer are of good accuracy, which can be able to satisfy the requirements of rapid and non-destructive determination of SSC of cherries.