摘要
通过建立基于傅里叶变换中红外光谱预测模型实现牛乳中β-乳球蛋白的快速检测。将收集到的260个不同批次的牛乳样品使用高效液相色谱定量检测β-乳球蛋白含量作为参比值;通过乳成分分析仪采集牛乳样品的中红外光谱,选择有效波段,并将原始中红外光谱先经过Savitsky-Golay(SG)平滑、一阶导数或二阶导数等预处理方法消除背景噪音,再利用偏最小二乘回归法构建β-乳球蛋白预测模型。通过比较不同中红外光谱预处理方法,最终选择SG 5点平滑二阶导数的偏最小二乘回归模型为最优模型,获得校正集相关性系数R^(2)为0.932,校正均方根误差为0.049%,预测集相关性系数R^(2)为0.923,预测均方根误差为0.057%,模型具有良好的准确性。
In this study,a prediction model based on Fourier transform mid-infrared spectroscopy was developed for the rapid detection ofβ-lactoglobulin in milk.The content ofβ-lactoglobulin in 260 different batches of milk samples was determined as reference by high performance liquid chromatography(HPLC),and the mid-infrared spectra of raw milk were collected using an Fourier transform mid-infrared spectrometer.The effective wavebands were selected,and the background noise was eliminated by Savitsky-Golay(SG)smoothing,first-order derivative or second-order derivative pretreatment before the establishment of theβ-lactoglobulin prediction model by using partial least squares regression(PLSR).The results showed that SG five-point smoothing second-order derivative was the optimum preprocessing method.The PLSR model exhibited good prediction accuracy with correlation coefficient(R^(2))of 0.932 for the calibration set,root mean square error of calibration(RMSEC)of 0.049%,R^(2)of 0.923 for the calibration set,and root mean square error of prediction(RMSEP)of 0.057%.
作者
黄建辉
张彦辉
张耀广
赵翠琴
HUANG Jianhui;ZHANG Yanhui;ZHANG Yaoguang;ZHAO Cuiqin(Hebei Food Inspection and Research Institute,Shijiazhuang 050021,China;Key Laboratory of Dairy Quality and Safety Control,Ministry of Agriculture and Rural Affairs,Junlebao Dairy Group Co.Ltd.,Shijiazhuang 050021,China)
出处
《乳业科学与技术》
2025年第1期14-19,共6页
JOURNAL OF DAIRY SCIENCE AND TECHNOLOGY
