采后猕猴桃可溶性固形物含量的高光谱无损检测  

Nondestructive Detection of Soluble Solid Content of Postharvest Kiwifruits Based on Hyperspectral Imaging Technology

作　　者：董金磊[1] 郭文川[1] 

机构地区：[1]西北农林科技大学机械与电子工程学院,陕西杨凌712100

出　　处：《食品科学》2015年第16期101-106,共6页Food Science

基　　金：国家自然科学基金面上项目(31171720)

摘　　要：为探讨基于高光谱成像技术无损检测采后猕猴桃可溶性固形物含量（soluble solids content,SSC）的可行性,基于猕猴桃900~1 700 nm波长范围的反射高光谱,建立了预测SSC的偏最小二乘、支持向量机及误差反向传播（error back propagation,BP）网络模型,并综合比较了分别以全光谱的226个波长,利用连续投影算法提取的12个有效波长和采用无信息变量消除法提取的128个有效波长作为模型的输入变量对各模型预测效果的影响。结果表明,连续投影算法能有效地提取有效波长,其在简化模型方面优势明显;BP网络与连续投影算法相结合具有最好的预测性能（预测相关系数为0.924,预测均方根误差为0.766）。研究表明,高光谱成像技术可无损检测猕猴桃的SSC,该技术将使猕猴桃内部品质的工业化分级成为可能。To investigate the feasibility of using hyperspectral imaging technique to detect the soluble solid content（SSC） of postharvest kiwifruits based on the obtained reflectance spectra over the range of 900–1 700 nm, SSC prediction models were established using partial least squares, support victor machine and back propagation artificial neural networks. The effects of different input variables on model performance were compared comprehensively at 226 wavelengths in full spectra. The input variables investigated included 12 and 128 effective wavelengths selected by successive projection algorithm and uninformative variable elimination, respectively. The results showed that successive projection algorithm could extract the effective wavelengths efficiently, and it had obvious predominance in simplifying SSC prediction model. BP neural network had better SSC predication performance. BP network combined with successive projection algorithm had the best SSC prediction performance with correlation coefficient of 0.924 and root-mean-square error of 0.766 for prediction set. The present study indicated that hyperspectral imaging technique could be used to detect SSC of postharvest kiwifruits nondestructively, and the technique is feasible for industrial grading of kiwifruits based on internal quality.

关 键 词：猕猴桃 高光谱 可溶性固形物含量 BP网络 连续投影算法 

分 类 号：O433.5[机械工程—光学工程] S663.4[理学—光学]