紫外/可见光谱技术无损检测李子可溶性固形物含量

Nondestructive Detection of Soluble Solids Content of Plums Based on UV/Vis Spectroscopy Technology

利用光谱技术结合化学计量学对李子可溶性固形物含量检测进行研究,为李子品质无损检测提供科学方法。通过反射式光谱采集系统获取了"红"李子和"青"李子的平均光谱,并对原始光谱数据进行预处理;应用连续投影算法(SPA)和竞争性自适应重加权算法(CARS)对预处理后的光谱数据提取特征波长;分别建立基于全光谱和特征波长的预测李子可溶性固形物含量的误差反向传播(BP)网络模型。结果表明:利用SPA和CARS算法分别从全光谱的1024个波长中选取出31和104个特征波长;而基于特征波长建立的CARS-BP网络模型效果最优,其相关系数rc为0.998,rp为0.887,均方根误差RMSEC为0.026,RMSEP为1.767。这表明光谱技术结合化学计量学进行李子可溶性固形物含量的无损检测具有可行性。

The spectroscopy technology combined with chemometrics was used to measure soluble solids content of plums and provide a scientific method for nondestructive measurement of plums quality. The spectra acquisition system was used to collect the average spectral reflectance of ‘Red’ and ‘Green’ plums. And the standard normal variation(SNV)was used to preprocess original spectral reflectance. Then the successive projection algorithm(SPA)and competitive adaptive reweighted sampling(CARS)method were used to select characteristic wavelengths. And an error back propagation(BP)network model was established based on full spectra and selected characteristic wavelengths for predicting soluble solids content of plums. The results showed that 31 and 104 characteristic wavelengths were selected from 1024 wavelengths by SPA and CARS,respectively. CARS-BP model had the best calibration ability of soluble solids content of plums(rc=0.998,RMSEC=0.026)and the best prediction ability of soluble solids content of plums(rp=0.887,RMSEP=1.767). This study indicates that spectroscopy technology combined with chemometrics is effective for detection on soluble solids content of plums.