食品科学 ›› 2019, Vol. 40 ›› Issue (2): 287-292.doi: 10.7506/spkx1002-6630-20180531-452

• 安全检测 • 上一篇    下一篇

近红外特征光谱定量检测羊肉卷中猪肉掺假比例

白京,李家鹏*,邹昊,田寒友,刘飞,王辉,李文采,张振琪,王守伟*   

  1. (中国肉类食品综合研究中心,北京食品科学研究院,肉类加工技术北京市重点实验室,北京 100068)
  • 出版日期:2019-01-25 发布日期:2019-01-22
  • 基金资助:
    “十三五”国家重点研发计划重点专项(2017YFE0110800);欧盟地平线2020计划项目(H2020-SFS-45-2016);北京市优秀人才培养资助项目(2017754154700G099);丰台区科技新星计划项目(KJXX201710)

Quantitative Detection of Pork in Adulterated Mutton Rolls Based on Near Infrared Spectroscopy

BAI Jing, LI Jiapeng*, ZOU Hao, TIAN Hanyou, LIU Fei, WANG Hui, LI Wencai, ZHANG Zhenqi, WANG Shouwei*   

  1. (Beijing Key Laboratory of Meat Processing Technology, China Meat Research Center, Beijing Academy of Food Sciences, Beijing 100068, China)
  • Online:2019-01-25 Published:2019-01-22

摘要: 利用近红外漫反射光谱技术结合化学计量学方法对解冻掺假羊肉卷,进行猪肉掺假比例的定量检测研究。按照不同肥肉占比和不同猪肉掺假比例,制备324?个样品,并利用近红外光谱仪采集其光谱数据。对原始数据进行SG(Savitzky-Golay)平滑、SG一阶导、SG二阶导、多元散射校正、中心化、标准正态变量校正等预处理,并利用偏最小二乘回归(partial least square regression,PLSR)进行建模分析,其中SG平滑结合一阶求导预处理的模型预测效果最优。针对最佳预处理光谱采用竞争性自适应加权采样(competitive adaptive reweighted sampling,CARS)算法进行波长筛选,并建立特征波长PLSR模型,模型预测效果得到提高。其中,校正集和验证集决定系数分别为0.983?6和0.972?5,校正集和验证集的均方根误差分别为0.043?7和0.057?7,范围误差比为7.62。应用该CARS-PLSR模型对检验集进行预测,真实值与预测值的相关系数为0.913?8,结果表明采用近红外光谱分析技术可以实现不同肥肉占比羊肉卷中猪肉掺假比例的定量检测。

关键词: 近红外, 羊肉卷, 猪肉掺假, 定量检测, 特征光谱

Abstract: The study aimed to develop an approach to quantify pork in adulterated mutton rolls with different fat ratios based on near infrared spectroscopy combined with chemometrics. A total of 324 samples were prepared with different proportions of fat and pork. Spectra were collected by using a near infrared spectrometer. Savitzky-Golay (SG) smoothing, Savitzky-Golay smoothing-first derivation (SG-1st), Savitzky-Golay smoothing-second derivative (SG-2st), multiplicative scatter correction (MSC), centralized correction (Center), and standard normal variate (SNV) were used to preprocess the original spectral data. A prediction model was established by using partial least squares regression (PLSR) method. Based on the prediction parameters, the optimal pretreatment method was SG-1st. Competitive adaptive weighted sampling (CARS) was used to select the optimal wavebands in order to further enhance the prediction capability of the model. The determination coefficients for calibration and validation sets were 0.983 6 and 0.972 5, respectively. The root mean square errors of calibration and validation (RMSEC and RMSEP) were 0.043 7 and 0.057 7, the ratio of performance to standard deviate (RPD) was 7.62, and the correlation coefficient between the CARS model prediction and the experimental data for test set was 0.913 8. The results showed that by using near infrared spectral analysis, the proportion of pork in adulterated mutton rolls with different fat ratios could be rapidly quantified.

Key words: near infrared spectroscopy, mutton rolls, pork adulteration, quantitative detection, characteristic spectra

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