摘要
采用化学方法测定78个小麦样品中的淀粉含量,运用近红外光谱仪采集小麦样品近红外光谱.用光学处理和数学处理等手段对模型的影响进行了探讨.结果显示,反相离散多元校正(Inverse MSC)与二阶导数结合对光谱数据进行预处理后,运用偏最小二乘法(PLS)所建分析模型的预测效果最优,其相关系数为0.951 7.以17个验证集样品进行外部检验,得小麦籽粒中淀粉含量的模型预测值与化学值之间的相关系数为0.978 6.
The starch contents in 78 wheat samples were determined by a chemical method, and the near infrared spectra of the wheat samples were collected by a near infrared spectrometer. 60 samples were selected as the calibration samples to build a near infrared spectroscopic model for rapid determination of the starch content in the wheat, and the influences of the model were discussed by using optical treatment, mathematical treatment and so on. The results showed that the model built by using partial least square （PLS） after treating the spectra data by inverse MSC and second derivative method had the optimal prediction effect, and the correlation coefficient was 0.951 7.17 samples were used as validation sets for external inspection, and the correlation coefficient between the prediction value and the chemical value of the starch content in the wheat was 0.978 6.
出处
《河南工业大学学报（自然科学版）》
CAS
北大核心
2011年第6期24-27,共4页
Journal of Henan University of Technology：Natural Science Edition
基金
河南省产业技术研究与开发项目(102109000007)
关键词
近红外光谱
淀粉含量
小麦
检测模型
near-infrared spectroscopy
starch content
wheat
calibration model
