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作 者:陈美林 杜芬妮 陈业 程超 单长海 杨迪 莫开菊 Chen Meilin;Du Fenni;Chen Ye;Cheng Chao;Shan Changhai;Yang Di;Mo Kaiju(College of Biology Science and Technology,Hubei Minzu University,Enshi 445000;National Selenium - rich Product Quality Testing Center,Enshi 445000;Hubei Key Laboratory of Biological Resources Conservation and Utilization,Enshi 445000)
机构地区:[1]湖北民族大学生物科学与技术学院,恩施445000 [2]恩施土家族苗族自治州食品药品检验检测中心,恩施445000 [3]生物资源保护与利用湖北省重点实验室,恩施445000
出 处:《中国粮油学报》2019年第10期133-139,共7页Journal of the Chinese Cereals and Oils Association
基 金:湖北省科技厅民族专项(2016AKB057)
摘 要:利用傅里叶变换衰减全反射红外光谱(ATR-FTIR)法测定米粉中硒代胱氨酸的硒含量,以期探寻一种快捷、绿色环保且准确的硒含量测定方法。采用添加法制备样本,衰减全反射中红外光谱仪采集红外光谱,TQAnalyst分析软件作偏最小二乘法建模并验证,选择1600~650cm^-1作为样品的红外光谱分析波段,用导数对原始红外光谱处理,结果表明二阶导数处理后的模型,噪音小、失真少且预测效果更好,模型的决定系数(R2C)都大于0.995、因子数为5~8、校正均方差(RMSEC)为1.17~2.68,验证均方差(RMSEP)为4.0530~6.2346验证集预测值相对偏差在20%以内的占87.95%,含量在18μg/100g以上更是占了96.75%,含量在50μg/100g(不含)以上的样品93.33%偏差在10%以内,预测效果较准确。The Selenium content of selenocystine in rice flour was determined by Fourier transform attenuated total reflection infrared spectroscopy ( ATR - FTIR) method in order to find a fast,green and accurate approach to determine selenium content.Samples were prepared by additive method,infrared spectrum was acquired by attenuated total reflection mid - infrared spectrometer,and TQ Analyst analysis software was used to model and verify by partial least squares method.Moreover,the 1 600 ~ 650 cm^- 1 was selected as the infrared spectrum analysis band of the sample,and the derivative was used to Infrared spectroscopy.The results showed that the model after second derivative processing has less noise,less distortion and better prediction effect.The coefficient of determination ( RC 2 ) of the model was greater than 0.995,the factor number was 5 ~ 8,and the mean square error was corrected.( Root Mean Square Error of Calibration,RMSEC) was 1.17 ~ 2.68,and the Root Mean Square Error of Prediction ( RMSEP) was 4.053 0 ~ 6.234 6,the relative deviation of the predicted value of the verification set was within 85%, which is 87.95%.The sample was 96.75% above 18 μg /100g,and the 93.33% deviation of the sample above 50 μg /100 g ( excluding) is within 10%,and the prediction effect was more accurate.
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