傅里叶变换中红外光谱的牛奶品质无损检测分级

doi:10.3964/j.issn.1000-0593(2022)04-1243-07

Abstract
References
Related Citation (15)

Download: PDF (2452 KB)
Export: BibTeX | EndNote (RIS)

Abstract There are “high protein”, “high fat”, and other characteristics of milk in the market. In order to realize the nondestructive and rapid grading of super quality milk, high-protein characteristic milk, high-fat characteristic milk and ordinary milk, 5 121 milk samples from 10 pastures in Hebei Province in different months (January, March to October) were collected. Then the mid-infrared spectroscopy data were collected, the protein and fat content in milk were measured, the somatic cell number was measured, and the mid-infrared spectrum model of milk quality grading was established. Firstly, milk spectral analysis was carried out, and redundant bands were removed. Finally, the sensitive band combinations of 9 925～1 597 and 1 712～3 024 cm^-1 were selected as the full spectrum to establish the model. In order to improve the prediction accuracy and efficiency of the model, six spectral pre-processing methods were used to improve the signal-to-noise ratio of the original spectrum, including Standard normal variable transform (SNV), multiple scattering correction (MSC), the first derivative and second derivative, first difference and second-order difference. Comparing the effects of different pretreatment methods by establishing naive Bayes model (NB) and random forest model (RF), the second-order difference obtained the best prediction accuracy. The testing set accuracy was 92.11% and 96.87%, respectively. So second-order difference was identified as the best pretreatment method for further analysis. In order to simplify the models, UVE (Uninformative variable elimination), CARS (Competitive adaptive reweighed sampling), SCARS (Stability Competitive adaptive reweighted sampling) were utilized to extract the characteristic variables from the pre-processed spectrum by second-order difference method. Then, the NB and RF models were established based on the full spectral data and the selected characteristic variable data. The results showed that SCARS was the best feature extraction algorithm for the NB model, and the accuracy rates of the training set and the testing set were 94.45% and 93.94%, respectively.UVE-SCARS is the best feature extraction algorithm of the RF model, and the accuracy of the training set and test set are 99.86% and 96.48%, respectively. In conclusion, the second-order difference-UVE-CARS-RF model established based on Fourier transform the mid-infrared spectroscopy technology can realize the rapid and non-destructive prediction of classification of 4 kinds of milk. Through the establishment of mid-infrared spectrum model, the combination of milk protein, milk fat content and somatic cell number is the first time for direct classification and identification, which is unprecedented in previous studies. In applying the model, we only need to input the obtained milk mid-infrared spectral data into the model to output the prediction category, which has practical application value in the milk industry.

Key words：Mid-infrared spectrum; Milk; Quality grading; Nondestructive testing; Characteristics of the variable

Received: 2021-04-08 Accepted: 2021-05-30

ZTFLH:

S24

Corresponding Authors: WANG Qiao-hua, ZHANG Shu-jun E-mail: wqh@mail.hzau.edu.cn；sjxiaozhang@mail.hzau.edu.cn

Cite this article:

XIAO Shi-jie,WANG Qiao-hua,LI Chun-fang, et al. Nondestructive Testing and Grading of Milk Quality Based on Fourier Transform Mid-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1243-1249.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)04-1243-07 OR https://www.gpxygpfx.com/EN/Y2022/V42/I04/1243

[1] GUO Li-ya, WU Jian-xin, ZHANG Xiao-jian, et al（郭利亚, 吴建新, 张晓建，等）. China Dairy(中国乳业), 2020, (9): 66.
[2] CHEN Yan-sen, RUAN Jian, XIONG Jia-jun, et al（陈焱森, 阮健, 熊家军，等）. China Dairy Cattle(中国奶牛), 2018，(10): 66.
[3] CHEN Mei-jing, WANG Tong-tong, MENG Qing-yong（陈美静, 王铜铜, 孟庆勇）. China Dairy(中国乳业), 2020，(7): 9.
[4] LI Chun-yan, QIN Bao-liang(李春艳, 秦保亮). Graziery Veterinary Sciences·Electronic Version(畜牧兽医科学·电子版), 2019，(3): 16.
[5] De Marchi M, Bonfatti V, Cecchinato A, et al. Italian Journal of Animal Science, 2009, 8(2s): 399.
[6] Fleming A, Schenkel F S, Chen J, et al. Journal of Dairy Science, 2017, 100(6): 5073.
[7] Soyeurt H, Dehareng F, Gengler N, et al. Journal of Dairy Science, 2011, 94(4): 1657.
[8] DONG Li-feng, YAN Tian-hai, TU-yan, et al(董利锋, YAN Tianhai, 屠焰，等). Chinese Journal of Animal Nutrition(动物营养学报), 2016, 28(2): 326.
[9] YANG Yan-rong, YANG Ren-jie, DONG Gui-mei, et al(杨延荣, 杨仁杰, 董桂梅, 等). The Journal of Light Scattering(光散射学报), 2014, 26(2): 203.
[10] YANG Ren-jie, LIU Rong, XU Ke-xin(杨仁杰, 刘蓉, 徐可欣). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 31(9): 2383.
[11] CUI Chuan-jin, GU Shao-peng, ZUO Yue-ming(崔传金, 古少鹏, 左月明). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2011, 42(1): 193.
[12] WU Hai-yun, ZUO Yue-ming, CUI Chuan-jin, et al(吴海云, 左月明, 崔传金，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2012, 43(8): 164.
[13] SHAO Le, YU Hong, LIU Xi-jing, et al(邵乐, 于红, 刘溪婧, 等). Journal of Dalian Ocean University(大连水产学院学报), 2010, 25(1): 45.
[14] LIU Meng, SHEN Si, WANG Nan(刘猛, 申思, 王楠). Chinese Journal of Luminescence(发光学报), 2017, 38(5): 663.
[15] Bonfatti V, Di Martino G, Carnier P. J. Journal of Dairy Science, 2011, 94(12): 5776.
[16] Niero G, Penasa M, Gottardo P, et al. Journal of Dairy Science, 2016, 99(3): 1853.
[17] Etzion Y, Linker R, Cogan U, et al. Journal of Dairy Science, 2004, 87(9): 2779.
[18] FU Dan-dan, WANG Qiao-hua, GAO Sheng, et al(付丹丹, 王巧华, 高升, 等). Chinese Journal of Analytical Chemistry(分析化学), 2020, 48(2): 289.
[19] GAO Sheng, WANG Qiao-hua, LI Qing-xu, et al(高升, 王巧华, 李庆旭, 等). Chinese Journal of Analytical Chemistry(分析化学), 2019, 47(6): 941.