大米蛋白质、脂肪、总糖、水分近红外检测模型研究  

NIR Spectra Detection Model of Protein,Fat,Total Sugar and Moisture in Rice

作　　者：李路[1] 黄汉英[1] 赵思明[2] 胡月来[1] 杨素仙[1] 

机构地区：[1]华中农业大学工学院,武汉430070 [2]华中农业大学食品科技学院,武汉430070

出　　处：《中国粮油学报》2017年第7期121-126,共6页Journal of the Chinese Cereals and Oils Association

基　　金：湖北省重大科技创新计划(2014ABC009);中央高校基本科研业务费专项资金资助(2662015PY078;2662015QC020)

摘　　要：采用近红外光谱技术对大米蛋白质、脂肪、总糖、含水量进行检测。运用经典Kennard-Stone法选取校正集及预测集样本,运用分段小波消噪对光谱进行预处理,通过竞争性自适应重加权采样筛选出与样本性质相关的特征波长,比较偏最小二乘法和BP神经网络法所建立的大米蛋白质、脂肪、总糖、含水量的检测模型。对于大米蛋白质、总糖和水分的检测,2种方法所建立模型的决定系数均大于0.9,相对标准差均小于2.6%,具有良好的精度和稳定性。对于大米脂肪的检测,偏最小二乘模型的性能相对稍好,其决定系数为0.949 5,相对标准差为13.69%。The near- infrared （NIR） spectrum was used to detect the protein, fat, total sugar and moisture content in rice. Kennard- Stone method was used to select the calibration set and prediction set samples. Then the segmented wavelet de -noising algorithm was used to preprocess the spectrum before screening the characteristic wavelength applied the competitive adaptive reweighted sampling （CARS）. The detection models of the rice protein, fat, total sugar and moisture content based on the partial least squares （PLS） and the BP neural network were com- pared to determine an appropriate detecting method. The coefficient of determination （R2 ） of the models for the rice protein, total sugar and moisture content detection were 〉 0.9, and the relative standard deviation （RSD） were 〈 2.6%. Therefore, all models established by the two methods for the rice protein, total sugar and moisture content de- tection were accurate and stable. For detecting the rice fat content, the performance of the PLS model was better whose R2 and RSD value were 0.949 5 and 13.69%.

关 键 词：大米 近红外光谱 PLS BP神经网络 CARS 

分 类 号：TS201.2[轻工技术与工程—食品科学]