同步荧光光谱结合CARS变量优选预测猪肉中四环素残留含量

肖海斌; 赵进辉; 袁海超; 洪茜; 刘木华

doi:10.3788/OPE.20132110.2513

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同步荧光光谱结合CARS变量优选预测猪肉中四环素残留含量

现代应用光学 | 更新时间：2020-08-12

- 同步荧光光谱结合CARS变量优选预测猪肉中四环素残留含量
- Determination of Tetracycline Content in Pork by Synchronous Fluorescence Spectroscopy with CARS Method
- 光学精密工程 2013年21卷第10期页码：2513-2519
- 作者机构：
  
  江西农业大学工学院/生物光电及应用重点实验室,江西南昌,330045
- 作者简介：
- 基金信息：
  
  国家科技支撑计划课题(2014BAB12B04)
- DOI：10.3788/OPE.20132110.2513
  中图分类号： O657.31
- 收稿日期：2013-03-13，
  
  修回日期：2013-05-29，
  
  网络出版日期：2012-10-19，
  
  纸质出版日期：2013-10-15
- 稿件说明：
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肖海斌赵进辉袁海超洪茜刘木华. 同步荧光光谱结合CARS变量优选预测猪肉中四环素残留含量[J]. 光学精密工程, 2013,21(10): 2513-2519

XIAO Hai-bin ZHAO Jin-hui YUAN Hai-chao HONG Qian LIU Mu-hua. Determination of Tetracycline Content in Pork by Synchronous Fluorescence Spectroscopy with CARS Method[J]. Editorial Office of Optics and Precision Engineering, 2013,21(10): 2513-2519
肖海斌赵进辉袁海超洪茜刘木华. 同步荧光光谱结合CARS变量优选预测猪肉中四环素残留含量[J]. 光学精密工程, 2013,21(10): 2513-2519 DOI： 10.3788/OPE.20132110.2513.

XIAO Hai-bin ZHAO Jin-hui YUAN Hai-chao HONG Qian LIU Mu-hua. Determination of Tetracycline Content in Pork by Synchronous Fluorescence Spectroscopy with CARS Method[J]. Editorial Office of Optics and Precision Engineering, 2013,21(10): 2513-2519 DOI： 10.3788/OPE.20132110.2513.

摘要

为实现快速检测猪肉中四环素残留含量，采用同步荧光法结合竞争适应重加权采样 (CARS)变量优选建立了猪肉中四环素残留含量的支持向量回归(SVR) 预测模型。从样本的三维同步荧光光谱中确定了最佳波长差为65nm，采用CARS方法从中挑选出与四环素相关的特征波长变量，并与连续投影算法(SPA)及遗传算法(GA)比较。最后，对优选出的16个波长变量应用SVR算法建立猪肉中四环素含量的预测模型。经过分析发现，多元散射校正(MSC)光谱预处理后的CARS方法优于SPA及GA变量选择方法，可以有效地筛选出全光谱中的特征波长变量。CARS-SVR建立的四环素预测模型优于原始光谱的SVR模型，其预测集的决定系数(R2)和预测均方根误差(RMSEP)分别为0.9612和10.94 mg/kg。研究结果表明，采用同步荧光法结合CARS-SVR模型可以用来预测猪肉中四环素残留含量，且CARS-SVR能有效地简化模型和提高预测精度。

Abstract

The support vector regression (SVR) prediction model was established for the rapid detection of tetracycline content in pork using synchronous fluorescence spectroscopy combined with competitive adaptive reweighted sampling (CARS) method

and CARS was used to select correlative variables from pork samples of spectra data of the optimum wavelength difference was 65nm. Then the performances of three variables selection methods including competitive adaptive reweighted sampling (CARS)

successive project algorithm (SPA) and genetic algorithm (GA) were compared. Finally

support vector regression (SVR) was used to establish prediction model for tetracycline content of pork using the 16 selected variables. The results showed that CARS was superior to SPA and GA

and can select features variables of full spectra efficiently. The prediction model of tetracycline by CARS-SVR was superior to that model established by original spectrum SVR

the determination coefficient (R2) and the root mean squared error of prediction (RMSEP) in the CARS-SVR model prediction sets were 0.9612 and 10.94 mg/kg

respectively. The results showed that synchronous fluorescence spectroscopy combined with CARS-SVR was feasible to predict tetracycline content of pork

and CARS-SVR method can simplify the model efficiently and improve prediction precision.

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references

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