利用可见近红外反射光谱无损检测小米的粘度
目的 建立适用于小米粘度无损检测的可见/近红外反射光谱法。方法 使用光谱仪获取小米在367~1020 nm波段范围内的漫反射光谱, 采用多元散射校正法(multiple scattering correction, MSC)和一阶导 数法(first derivation, 1st-D)对原始反射光谱进行处理, 并且使用主成分分析确定最佳主成分数, 建立小米粘度判别模型, 使用全交叉验证法进行模型验证。结果 使用原始反射光谱、MSC处理光谱和1st-D处理光谱, 分别提取了6、12和12个主成分, 建立的峰值粘度模型Rcv在0.86以上, 对验证集的预测结果Rp在0.82~0.86之间; 而使用1st-D处理光谱提取12个最优主成分, 建立的模型可较好地预测小米粘度的破损值, Rcv为0.8573, 对验证集的预测结果Rp为0.8309。结论 该方法适用于小米粘度的无损检测, 为小米加工品质的快速检测提供一定的理论支持。
Objective To establish a method for nondestructive determination of the viscosity of millet by the visual/near infrared reflectance (VIS/NIR) spectroscopy. Methods The VIS/NIR reflectance spectrum of millet in 367~1020 nm was collected by spectrography. The spectrum was pretreated using multiplicative scattering correction (MSC) and first derivative (1st-D). The best number of principal components were determined by principal component analysis. Then the calibration models of millet viscosity were established and the full cross validation were used to examine the effect of model. Results Six, 12 and 12 principal components were extracted by using the original reflectance spectra, MSC spectra and 1st-D spectra. The established peak viscosity model Rcv were more than 0.86, and the predicted result of the verification set RP were between 0.82 and 0.86. The 1st-D spectrum was used to extract the 12 optimal principal components. The established model could predict the damage value of millet viscosity better, Rcv was 0.8573, and the prediction result of the verification set RP was 0.8309. Conclusion This method is suitable for nondestructive testing of millet viscosity, and provides some theoretical support for the rapid detection of millet processing quality.
标题：利用可见近红外反射光谱无损检测小米的粘度
英文标题：Nondestructive detection of viscosity of millet by visual/near infrared reflectance spectroscopy
作者：
田晓琳 山西师范大学食品科学学院
吴建虎 山西师范大学食品科学学院
兰雷珍 山西师范大学食品科学学院
楼琰 山西师范大学食品科学学院
李越 山西师范大学食品科学学院
中文关键词:小米,快速粘度测定仪,粘度,可见/近红外反射光谱,主成分分析,
英文关键词:millet,rapid visco analyser,viscosity,visual/near infrared reflectance spectroscopy,principle component analysis,
发表日期：2018-02-26
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