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中国精品科技期刊2020
彭海洋,巫忠东,林涛,等. 基于近红外光谱技术的牦牛奶粉掺假检测与产地识别研究[J]. 食品工业科技,2024,45(22):1−9. doi: 10.13386/j.issn1002-0306.2024020297.
引用本文: 彭海洋,巫忠东,林涛,等. 基于近红外光谱技术的牦牛奶粉掺假检测与产地识别研究[J]. 食品工业科技,2024,45(22):1−9. doi: 10.13386/j.issn1002-0306.2024020297.
PENG Haiyang, WU Zhongdong, LIN Tao, et al. Study on Adulteration Detection and Origin Identification of Yak Milk Powder Based on Near-infrared Spectroscopy Technology[J]. Science and Technology of Food Industry, 2024, 45(22): 1−9. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2024020297.
Citation: PENG Haiyang, WU Zhongdong, LIN Tao, et al. Study on Adulteration Detection and Origin Identification of Yak Milk Powder Based on Near-infrared Spectroscopy Technology[J]. Science and Technology of Food Industry, 2024, 45(22): 1−9. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2024020297.

基于近红外光谱技术的牦牛奶粉掺假检测与产地识别研究

Study on Adulteration Detection and Origin Identification of Yak Milk Powder Based on Near-infrared Spectroscopy Technology

  • 摘要: 牦牛奶粉的掺假检测和产地识别有助于保障食品安全、维护消费者权益,是促进乳制品市场健康发展的重要举措。传统的DNA检测方法和稳定同位素分析技术检测周期长,难以满足快速、低成本现场分析的需求。针对以上问题,本研究建立了一种基于近红外光谱技术(Near-infrared Spectroscopy, NIRS)快速辨别牦牛奶粉掺假及产地的方法。收集了来自四川、甘肃、云南及青海的9个品牌的牦牛奶粉。在制备掺假样品之前,采用聚合酶链式反应(Polymerase Chain Reaction,PCR)技术和DNA凝胶电泳验证所收集的牦牛奶粉中是否掺杂了牛奶粉。完成验证后,进行掺假样品的制备以及近红外光谱数据的采集。采用K最邻近法(K-Nearest Neighbors,KNN)建立分类模型,偏最小二乘回归(Partial Least Squares Regression, PLSR)建立定量预测模型。通过优化光谱预处理方法和变量筛选方法进一步提升定量预测模型的预测能力。结果表明,KNN对牦牛奶粉掺假检测(纯牛奶粉、纯牦牛奶粉、掺杂着牛奶粉的牦牛奶粉)及产地识别(四川、甘肃、云南、青海)实现了100%的正确分类。掺假定量预测模型的校正集相关系数(Rc)为0.9975,预测集相关系数(Rp)为0.9913,预测集均方根误差(Root Mean Square Error of Prediction, RMSEP)为1.9823%,性能偏差比(Ratio of Performance to Deviation, RPD)为7.2522。本方法可快速、准确地预测牦牛奶粉中牛奶粉的掺杂以及牦牛奶粉产地的辨别,为牦牛奶粉的质量控制提供技术支持。

     

    Abstract: Adulteration detection and origin identification of yak milk powder are essential to ensure food safety and safeguard consumer rights interests., thereby promoting the healthy development of the dairy product market. Traditional DNA detection methods and isotope analysis show long detection time, which are inapplicable to rapid, low-cost on-site analysis. To address these issues, a rapid adulteration detection and identification of the origin of yak milk powder based on Near-infrared Spectroscopy (NIRS) technology was established in this study. Yak milk powder samples from nine brands from Sichuan, Gansu, Yunnan, and Qinghai were collected. Before preparing adulterated samples, Polymerase Chain Reaction (PCR) technology and DNA gel electrophoresis were used to verify whether the collected yak milk powder were adulterated with cow milk powder. Then adulterated samples were prepared and NIRS data were collected. The K-Nearest Neighbors (KNN) method was employed to establish a classification model. Partial Least Squares Regression (PLSR) was used to establish a quantitative prediction model. The predictive ability of quantitative prediction model was improved by optimizing spectral preprocessing methods and variable selection methods. Results showed that KNN achieved 100% correct classification for adulteration detection (pure cow milk powder, pure yak milk powder, yak milk powder adulterated with cow milk powder) and origin identification (Sichuan, Gansu, Yunnan, Qinghai). The calibration set correlation coefficient (Rc), the prediction set correlation coefficient (Rp), the root mean square error of prediction (RMSEP), and the ratio of performance to deviation (RPD) of the adulteration quantitative prediction model were 0.9975, 0.9913, 1.9823%, and 7.2522, respectively. This method enables rapid and accurate prediction of cow milk powder adulteration in yak milk powder and identification of the origin of yak milk powder, offering technical support for the quality control of yak milk powder.

     

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