关键词: 傅里叶变换中红外光谱技术；β-乳球蛋白；牛乳；偏最小二乘回归法

Abstract: In this study, a prediction model based on Fourier transform mid-infrared spectroscopy was developed for the rapid detection of β-lactoglobulin in milk. The content of β-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 the β-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 (R2) of 0.932 for the calibration set, root mean square error of calibration (RMSEC) of 0.049%, R2 of 0.923 for the calibration set, and root mean square error of prediction (RMSEP) of 0.057%.

Key words: Fourier transform mid-infrared spectroscopy; β-lactoglobulin; milk; partial least squares regression