| 光谱学与光谱分析 |
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| On-Site Evaluation of Raw Milk Qualities by Portable Vis/NIR Transmittance Technique |
| WANG Jia-hua1, ZHANG Xiao-wei1, WANG Jun1, HAN Dong-hai2* |
1. College of Food and Biological Engineering, Xuchang University, Xuchang 461000, China
2. College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China |
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Abstract To ensure the material safety of dairy products, visible (Vis)/near infrared (NIR) spectroscopy combined with chemometrics methods was used to develop models for fat, protein, dry matter (DM) and lactose on-site evaluation. A total of 88 raw milk samples were collected from individual livestocks in different years. The spectral of raw milk were measured by a portable Vis/NIR spectrometer with diffused transmittance accessory. To remove the scatter effect and baseline drift, the diffused transmittance spectra were preprocessed by 2nd order derivative with Savitsky-Golay (polynomial order 2, data point 25). Changeable size moving window partial least squares (CSMWPLS) and genetic algorithms partial least squares (GAPLS) methods were suggested to select informative regions for PLS calibration. The PLS and multiple linear regression (MLR) methods were used to develop models for predicting quality index of raw milk. The prediction performance of CSMWPLS models were similar to GAPLS models for fat, protein, DM and lactose evaluation, the root mean standard errors of prediction (RMSEP) were 0.115 6/0.103 3, 0.096 2/0.113 7, 0.201 3/0.123 7 and 0.077 4/0.066 8, and the relative standard deviations of prediction (RPD) were 8.99/10.06, 3.53/2.99, 5.76/9.38 and 1.81/2.10, respectively. Meanwhile, the MLR models were also calibrated with 8, 10, 9 and 7 variables for fat, protein, DM and lactose, respectively. The prediction performance of MLR models was better than or close to PLS models. The MLR models to predict fat, protein, DM and lactose yielded the RMSEP of 0.107 0, 0.093 0, 0.136 0 and 0.065 8, and the RPD of 9.72, 3.66, 8.53 and 2.13, respectively. The results demonstrated the usefulness of Vis/NIR spectra combined with multivariate calibration methods as an objective and rapid method for the quality evaluation of complicated raw milks. And the results obtained also highlight the potential of portable Vis/NIR instruments for on-site assessing quality indexes of raw milk.
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Received: 2014-05-20
Accepted: 2014-07-27
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Corresponding Authors:
HAN Dong-hai
E-mail: handh@cau.edu.cn
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[1] aic′ S, Ozaki Y. Analytical Chemistry, 2001, 73(1): 64.
[2] Chen J Y, Iyo C, Terada F, et al. Journal of Near Infrared Spectroscopy, 2002, 10(4): 301.
[3] PI Fu-wei, WANG Yan-ling, LU Chao, et al(皮付伟, 王燕岭, 鲁 超, 等). Modern Scientific Instruments(现代科学仪器), 2006，16(4): 34.
[4] Artime C E C, de la Fuente J A B, Garcia M A P, et al. Conference Record-IEEE Instrumentation and Measurement Technology Conference Proceedings, (Victoria B C) 2008. IMTC, 2008. IEEE, 1471.
[5] Wu Di, He Yong, Feng Shuijuan. Analytica Chimica Acta, 2008, 610(2): 232.
[6] Tsenkova R, Meilina H, Kuroki S, et al. Journal of Near Infrared Spectroscopy, 2009, 17(6): 345.
[7] Kawasaki M, Kawamura S, Tsukahara M, et al. Computers and Electronics in Agriculture, 2008, 63(1): 22.
[8] Kawamura S, Kawasaki M, Nakatsuji H, et al. Sensing and Instrumentation for Food Quality and Safety, 2007, 1(1): 37.
[9] Al-Qadiri H M, Lin M, Al-Holy M A, et al. Journal of Dairy Science, 2008, 91(3): 950.
[10] Aernouts B, Polshin E, Lammertyn J, et al. Journal of Dairy Science, 2011, 94(11): 5315.
[11] AOAC international. 972. 16. AOAC official method- Fat, Lactose, Protein, and Solids in milk using Mid-infrared Spectroscopy Method. AOAC, 1996-03.
[12] Du Y P, Liang Y Z, Jiang J H, et al. Analytica Chimica Acta, 2004, 501(2): 183.
[13] Jiang J H, Berry R J, Siesler H W, et al. Analytical Chemistry, 2002, 74(14): 3555.
[14] Leardi R. Journal of Chemometrics, 2000, 14(5—6): 643.
[15] Bogomolov A, Melenteva A. Chemometrics and Intelligent Laboratory Systems, 2013, 126(7): 129. |
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