近红外特征光谱定量检测羊肉卷中猪肉掺假比例
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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CARSPLSR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。
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, SavitzkyGolay 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.
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, SavitzkyGolay 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.
引文
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[10]张德权,陈宵娜,孙素琴,等.羊肉嫩度傅里叶变换近红外光谱偏最小二乘法定量分析研究[J].光谱学与光谱分析,2008,28(11):2550-2553.DOI:10.3964/j.issn.1000-0593(2008)11-2550-04.
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[12]王文秀,彭彦昆,刘媛媛.基于近红外光谱的猪肉新鲜度无损检测方法的改进[J].食品安全质量检测学报,2015,6(8):3007-3013.
[13]姬瑞勤,黄岚,刘莉,等.鲜肉水分近红外漫反射方法及实验研究[J].光谱学与光谱分析,2008,28(8):1767-1771.DOI:10.3964/j.issn.1000-0593.2008.08.018.
[14]刘魁武,成芳,林宏建,等.可见/近红外光谱检测冷鲜猪肉中的脂肪、蛋白质和水分含量[J].光谱学与光谱分析,2009,29(1):102-105.DOI:10.3964/j.issn.1000-0593(2009)01-0102-04.
[15]张宁,张德权,李淑荣,等.近红外光谱结合SIMCA法溯源羊肉产地的初步研究[J].农业工程学报,2008,24(12):309-312.DOI:10.3321/j.issn:1002-6819.2008.12.063.
[16]成芳,樊玉霞,廖宜涛.应用近红外漫反射光谱对猪肉肉糜进行定性定量检测研究[J].光谱学与光谱分析,2012,32(2):354-359.DOI:10.3964/j.issn.1000-0593(2012)02-0354-06.
[17]ALAMPRESE C,CASALE M,SINELLI N,et al.Detection of minced beef adulteration with turkey meat by UV-vis,NIR and MIRspectroscopy[J].LWT-Food Science and Technology,2013,53(1):225-232.DOI:10.1016/j.lwt.2013.01.027.
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[19]SCHMUTZLER M,BEGANOVIC A,B?HLER G,et al.Methods for detection of pork adulteration in veal product based on FT-NIRspectroscopy for laboratory,industrial and on-site analysis[J].Food Control,2015,57(5):258-267.DOI:10.1016/j.foodcont.2015.04.019.
[20]牛晓颖,邵利敏,董芳,等.基于近红外光谱和化学计量学的驴肉鉴别方法研究[J].光谱学与光谱分析,2014,34(10):2737-2742.DOI:10.3964/j.issn.1000-0593(2014)10-2737-06.
[21]王丹竹.不同贮存温度和时间对冷冻猪肉品质的影响[D].长沙:湖南农业大学,2013:5-25.
[22]刘伟,赵众,袁洪福,等.光谱多元分析校正集和验证集样本分布优选方法研究[J].光谱学与光谱分析,2014,34(4):947-951.DOI:10.3964/j.issn.1000-0593(2014)04-0947-05.
[23]严衍禄,陈斌,朱大洲.近红外光谱分析的原理、技术与应用[M].北京:中国轻工业出版社,2013:172-173.
[24]覃礼堂,刘树深,肖乾芬,等.Q S A R模型内部和外部验证方法综述[J].环境化学,2013,32(7):1205-1211.DOI:10.7524/j.issn.0254-6108.2013.07.012.
[25]孔保华,刘骞,陈洪生.肉制品品质及质量控制[M].北京:科学出版社,2015:201-297.
[26]樊书祥,黄文倩,李江波,等.特征变量优选在苹果可溶性固形物近红外便携式检测中的应用[J].光谱学与光谱分析,2014,34(10):2707-2712.DOI:10.3964/j.issn.1000-0593(2014)10-2707-06.
[27]李江波,郭志明,黄文倩,等.应用CARS和SPA算法对草莓SSC含量NIR光谱预测模型中变量及样本筛选[J].光谱学与光谱分析,2015,35(2):372-378.DOI:10.3964/j.issn.1000-0593(2015)02-0372-07.
[28]郭志明.基于近红外光谱及成像的苹果品质无损检测方法和装置研究[D].北京:中国农业大学,2015:4-5.
[2]王彤,刘显军.混合羊肉卷的鉴别与管理[J].黑龙江畜牧兽医,2013(16):69-70.DOI:10.13881/j.cnki.hljxmsy.2013.16.033.
[3]周彤,李家鹏,李金春,等.一种基于多重实时荧光聚合酶链式反应熔解曲线分析的肉及肉制品掺假鉴别方法[J].食品科学,2017,38(12):217-222.DOI:10.7506/spkx1002-6630-201712033.
[4]杨志敏,丁武.近红外光谱技术快速鉴别原料肉掺假的可行性研究[J].肉类研究,2011,25(2):25-28.DOI:10.3969/j.issn.1001-8123.2011.02.007.
[5]李莹莹,张颖颖,丁小军,等.液相色谱-串联质谱法对羊肉中鸭肉掺假的鉴别[J].食品科学,2016,37(6):204-209.DOI:10.7506/spkx1002-6630-201606037.
[6]徐霞,成芳,应一斌.近红外光谱技术在肉品检测中的应用和研究进展[J].光谱学与光谱分析,2009,29(7):1876-1880.DOI:10.3964/j.issn.1000-0593(2009)07-1876-0.
[7]于红樱,祁慧娜,臧勇军.近红外分析技术在肉与肉制品分析中的应用[J].肉类工业,2006(11):8-9.DOI:10.3969/j.issn.1008-5467.2006.11.004.
[8]开晗,孔保华.近红外光谱检测技术在肉类工业中的应用[J].肉类研究,2011,25(9):35-37.DOI:10.3969/j.issn.1001-8123.2011.09.009.
[9]邹昊,田寒友,刘文营,等.应用便携式近红外仪检测生鲜羊通脊肉的嫩度[J].肉类研究,2014,28(10):15-19.
[10]张德权,陈宵娜,孙素琴,等.羊肉嫩度傅里叶变换近红外光谱偏最小二乘法定量分析研究[J].光谱学与光谱分析,2008,28(11):2550-2553.DOI:10.3964/j.issn.1000-0593(2008)11-2550-04.
[11]GEESINK G H,SCHREUTELKAMP F H,FRANKHUIZEN R,et al.Prediction of pork quality attributes from near infrared reflectance spectra[J].Meat Science,2003,65(1):661-668.DOI:10.1016/s0309-1740(02)00269-3.
[12]王文秀,彭彦昆,刘媛媛.基于近红外光谱的猪肉新鲜度无损检测方法的改进[J].食品安全质量检测学报,2015,6(8):3007-3013.
[13]姬瑞勤,黄岚,刘莉,等.鲜肉水分近红外漫反射方法及实验研究[J].光谱学与光谱分析,2008,28(8):1767-1771.DOI:10.3964/j.issn.1000-0593.2008.08.018.
[14]刘魁武,成芳,林宏建,等.可见/近红外光谱检测冷鲜猪肉中的脂肪、蛋白质和水分含量[J].光谱学与光谱分析,2009,29(1):102-105.DOI:10.3964/j.issn.1000-0593(2009)01-0102-04.
[15]张宁,张德权,李淑荣,等.近红外光谱结合SIMCA法溯源羊肉产地的初步研究[J].农业工程学报,2008,24(12):309-312.DOI:10.3321/j.issn:1002-6819.2008.12.063.
[16]成芳,樊玉霞,廖宜涛.应用近红外漫反射光谱对猪肉肉糜进行定性定量检测研究[J].光谱学与光谱分析,2012,32(2):354-359.DOI:10.3964/j.issn.1000-0593(2012)02-0354-06.
[17]ALAMPRESE C,CASALE M,SINELLI N,et al.Detection of minced beef adulteration with turkey meat by UV-vis,NIR and MIRspectroscopy[J].LWT-Food Science and Technology,2013,53(1):225-232.DOI:10.1016/j.lwt.2013.01.027.
[18]ROHMAN A,SISMINDAR I,ERWANTO Y,et al.Analysis of pork adulteration in beef meatball using Fourier transform infrared (FTIR) spectroscopy[J].Meat Science,2011,88(1):91-95.DOI:10.1016/j.meatsci.2010.12.007.
[19]SCHMUTZLER M,BEGANOVIC A,B?HLER G,et al.Methods for detection of pork adulteration in veal product based on FT-NIRspectroscopy for laboratory,industrial and on-site analysis[J].Food Control,2015,57(5):258-267.DOI:10.1016/j.foodcont.2015.04.019.
[20]牛晓颖,邵利敏,董芳,等.基于近红外光谱和化学计量学的驴肉鉴别方法研究[J].光谱学与光谱分析,2014,34(10):2737-2742.DOI:10.3964/j.issn.1000-0593(2014)10-2737-06.
[21]王丹竹.不同贮存温度和时间对冷冻猪肉品质的影响[D].长沙:湖南农业大学,2013:5-25.
[22]刘伟,赵众,袁洪福,等.光谱多元分析校正集和验证集样本分布优选方法研究[J].光谱学与光谱分析,2014,34(4):947-951.DOI:10.3964/j.issn.1000-0593(2014)04-0947-05.
[23]严衍禄,陈斌,朱大洲.近红外光谱分析的原理、技术与应用[M].北京:中国轻工业出版社,2013:172-173.
[24]覃礼堂,刘树深,肖乾芬,等.Q S A R模型内部和外部验证方法综述[J].环境化学,2013,32(7):1205-1211.DOI:10.7524/j.issn.0254-6108.2013.07.012.
[25]孔保华,刘骞,陈洪生.肉制品品质及质量控制[M].北京:科学出版社,2015:201-297.
[26]樊书祥,黄文倩,李江波,等.特征变量优选在苹果可溶性固形物近红外便携式检测中的应用[J].光谱学与光谱分析,2014,34(10):2707-2712.DOI:10.3964/j.issn.1000-0593(2014)10-2707-06.
[27]李江波,郭志明,黄文倩,等.应用CARS和SPA算法对草莓SSC含量NIR光谱预测模型中变量及样本筛选[J].光谱学与光谱分析,2015,35(2):372-378.DOI:10.3964/j.issn.1000-0593(2015)02-0372-07.
[28]郭志明.基于近红外光谱及成像的苹果品质无损检测方法和装置研究[D].北京:中国农业大学,2015:4-5.