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| Rapid Determination of αs1-Casein and κ-Casein in Milk Based on Fourier Transform Infrared Spectroscopy |
| XIAO Shi-jie1, WANG Qiao-hua1, 2*, FAN Yi-kai3, LIU Rui3, RUAN Jian3, WEN Wan4, LI Ji-qi4, SHAO Huai-feng4, LIU Wei-hua5, ZHANG Shu-jun3* |
1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
2. Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River; Ministry of Agriculture and Rural Agriculture, Wuhan 430070, China
3. Key Laboratory of Animal Breeding and Reproduction of Minstry of Education, Huazhong Agricultural University, Wuhan 430070, China
4. Ningxia Hui Autonomous Region Animal Husbandary Workstation, Yinchuan 750002, China
5. Ningxia Hui Autonomous Region Veterinary Medicine and Feed Suqervision Institute, Yinchuan 750011, China |
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Abstract In order to find a rapid detection method for the content of two main allergens (αs1 and κ-casein) in milk, 211 Chinese Holstein milk samples from four provinces of Henan, Hubei, Ningxia and Inner Mongolia were selected as the research objects, and a non-destructive and rapid detection model of αs1 and κ-casein in milk was established based on Fourier transform mid infrared spectroscopy. Firstly, the original spectrum of milk was pre-analyzed, and it was found that strongly influenced the spectral absorption of milk. The two main absorption regions of water (1 597～1 712 and 3 024～3 680 cm-1) were analyzed. It was found that the absorption region of water (1 597～1 712 cm-1) overlapped with that of protein (1 558～1 705 cm-1)(amide Ⅰ). By comparing the effect of removing 1 597～1 712 cm-1, the spectral region of 925.92～3 005.382 cm-1 was selected as the sensitive band for subsequent analysis. The dimension of the selected full spectrum was reduced manually, and MCCV eliminated the abnormal samples. The support vector machine regression model (SVR) was established by using eight preprocessing algorithms, such as Savitzky-Golay convolution smoothing (S-G), standard normal variable (SNV). Meanwhile, three feature selection algorithms were combined, such as competitive adaptive reweighting algorithm (CARS) and information-free variable elimination algorithm (UVE). The results showed that for αs1- casein, the SVR model established by the combination of the first derivative and CARS algorithm was the best, the training set correlation coefficient (RC) and test set correlation coefficient (RP) were 0.882 7 and 0.899 8, respectively, and the training set root mean square error (RMSEC) and test set root mean square error (RMSEP) were 1.136 3 and 1.372 6, respectively. For κ-casein, the SVR model established by the combination of second-order difference and UVE algorithm was the best. The training set correlation coefficient (RC) and test set correlation coefficient (RP) were 0.914 7 and 0.887 7, respectively, and the training set root mean square error (RMSEC) and test set root mean square error (RMSEP) were 0.473 5 and 0.558 1, respectively. The results showed that the SVR model based on Fourier transform mid-infrared spectroscopy can be used to detect the content of allergens αs1 and κ-casein in milk, and the prediction effect was good. This study can make up for the blank of rapid and non-destructive detection of casein in milk by spectral technology in China.
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Received: 2020-11-23
Accepted: 2021-02-18
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Corresponding Authors:
WANG Qiao-hua, ZHANG Shu-jun
E-mail: wqh@mail.hzau.edu.cn；sjxiaozhang@mail.hzau.edu.cn
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