近红外光谱定性定量检测牛肉汉堡饼中猪肉掺假
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摘要
利用近红外光谱技术结合化学计量学方法,对不同肥肉占比的解冻牛肉汉堡饼中的猪肉掺假进行定性判别建模,并建立猪肉掺假比例的定量检测模型。结果表明:对不同掺假比例样品的判别,应用偏最小二乘判别分析方法效果优于主成分分析-支持向量机方法,最优模型校正集和验证集判别正确率均为100%。应用偏最小二乘方回归法定量检测不同肥瘦比解冻牛肉汉堡饼中的猪肉掺假比例,模型校正集和验证集的相关系数Rc和Rp、验证集均方根误差分别为0.968 9、0.861 1、7.221%。因此,应用近红外光谱技术可以实现对不同肥肉占比的解冻牛肉汉堡饼中的猪肉掺假进行定性判别和定量检测。
Both qualitative and quantitative detection models were established for thawed beef patties(with different proportions of fat) adulterated with pork using near infrared spectroscopy combined with chemometrics. The results showed that for the qualitative determination of samples with different proportions of adulteration, partial least squares discriminant analysis was better than principal component analysis(PCA) combined with support vector machine method, and the discriminant analysis gave an accuracy of 100% for both calibration and validation sets. Partial least squares regression method was applied to quantitative detection of thawed beef patties with different proportions of adulteration. The correlation coef?cient of calibration(R_c) and prediction(R_p) and the root mean square error of prediction(RMSECP) of the model were0.968 9, 0.861 1 and 7.221%, respectively. Therefore, near infrared spectroscopy(NIS) can be applied to both qualitative and quantitative detection of thawed beef patties with different proportions of fat adulterated with different amounts of pork.
Both qualitative and quantitative detection models were established for thawed beef patties(with different proportions of fat) adulterated with pork using near infrared spectroscopy combined with chemometrics. The results showed that for the qualitative determination of samples with different proportions of adulteration, partial least squares discriminant analysis was better than principal component analysis(PCA) combined with support vector machine method, and the discriminant analysis gave an accuracy of 100% for both calibration and validation sets. Partial least squares regression method was applied to quantitative detection of thawed beef patties with different proportions of adulteration. The correlation coef?cient of calibration(R_c) and prediction(R_p) and the root mean square error of prediction(RMSECP) of the model were0.968 9, 0.861 1 and 7.221%, respectively. Therefore, near infrared spectroscopy(NIS) can be applied to both qualitative and quantitative detection of thawed beef patties with different proportions of fat adulterated with different amounts of pork.
引文
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[15]徐霞,成芳,应义斌.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)07-1876-05.
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[17]ZHAO M,O’DONNELL C,DOWNEY G.Detection of offal adulteration in beefburgers using near infrared reflectance spectroscopy and multivariate modelling[J].Journal of Near Infrared Spectroscopy,2013,21(4):237.DOI:10.1255/jnirs.1057.
[18]MORSY N,SUN D W.Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beef[J].Meat Science,2013,93(2):292-302.DOI:10.1016/j.meatsci.2012.09.005.
[19]蔡先峰,郭波莉,魏益民,等.牛肉近红外光谱的地域及饲养期特征分析[J].中国农业科学,2011,44(20):4272-4278.DOI:10.3864/j.issn.0578-1752.2011.20.015.
[20]ZHAO M,DOWNEY G,O’DONNELL C P.Dispersive Raman spectroscopy and multivariate data analysis to detect offal adulteration of thawed beefburgers[J].Journal of Agricultural&Food Chemistry,2015,63(5):1433-1441.DOI:10.1021/jf5041959.
[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]朱毅宁,杨平,杨新艳,等.支持向量机结合主成分分析辅助激光诱导击穿光谱技术识别鲜肉品种[J].分析化学,2017,45(3):336-341.DOI:10.11895/j.issn.0253-3820.160570.
[23]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.
[24]褚小立,许育鹏,陆婉珍.偏最小二乘法方法在光谱定性分析中的应用研究[J].现代仪器与医疗,2007,13(5):13-15.DOI:10.3969/j.issn.1672-7916.2007.05.005.
[25]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,2013:171-174.
[26]庞滂.近红外定性定量模型的建立与应用[D].西安:西北大学,2008:10.DOI:10.7666/d.y1252774.
[27]赵杰文,呼怀平,邹小波.支持向量机在苹果分类的近红外光谱模型中的应用[J].农业工程学报,2007,23(4):149-152.DOI:10.3321/j.issn:1002-6819.2007.04.029.
[28]吴丽君,殷沛沛,殷艳飞,等.近红外光谱结合偏最小二乘判别分析区分造纸法再造烟叶产品[J].分析科学学报,2015,31(6):809-814.DOI:10.13526/j.issn.1006-6144.2015.06.015.
[29]周学秋.近红外光谱定量分析方法的常见问题[C]//全国第二届近红外光谱学术会议论文集.长沙:全国第二届近红外光谱学术会议,2008.
[2]李婷婷,张桂兰,赵杰,等.肉及肉制品掺假鉴别技术研究进展[J].食品安全质量检测学报,2018,9(2):409-415.
[3]MONTOWSKA M,FORNAL E.Label-free quantification of meat proteins for evaluation of species composition of processed meat products[J].Food Chemistry,2017,42:1092-1100.DOI:10.1016/j.foodchem.2017.06.059.
[4]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.
[5]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.
[6]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.
[7]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.
[8]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.
[9]王靖,丁佳兴,郭中华,等.基于近红外高光谱成像技术的宁夏羊肉产地鉴别[J].食品工业科技,2018,39(2):250-254;260.DOI:10.13386/j.issn1002-0306.2018.02.047.
[10]SCHMUTZLER M,BEGANOVIC A,B諬LER 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.
[11]JOSELL?MARTINSSON L,BORGGAARD C,et al.Determination of RN-phenotype in pigs at slaughter-line using visual and near infrared spectroscopy[J].Meat Science,2000,55(3):273-278.DOI:10.1016/S0309-1740(99)00151-5.
[12]MAMANI-LINARES L W,GALLO C,ALOMAR D.Identification of cattle,llama and horse meat by near infrared reflectance or transflectance spectroscopy[J].Meat Science,2012,90(2):378-385.DOI:10.1016/j.meatsci.2011.08.002.
[13]赵红波,谭红,史会兵,等.近红外光谱技术鉴别猪肉和牛肉的研究[J].中国农学通报,2011,27(26):151-155.
[14]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.
[15]徐霞,成芳,应义斌.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)07-1876-05.
[16]SCHMUTZLER M,BEGANOVIC A,B諬LER 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.
[17]ZHAO M,O’DONNELL C,DOWNEY G.Detection of offal adulteration in beefburgers using near infrared reflectance spectroscopy and multivariate modelling[J].Journal of Near Infrared Spectroscopy,2013,21(4):237.DOI:10.1255/jnirs.1057.
[18]MORSY N,SUN D W.Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beef[J].Meat Science,2013,93(2):292-302.DOI:10.1016/j.meatsci.2012.09.005.
[19]蔡先峰,郭波莉,魏益民,等.牛肉近红外光谱的地域及饲养期特征分析[J].中国农业科学,2011,44(20):4272-4278.DOI:10.3864/j.issn.0578-1752.2011.20.015.
[20]ZHAO M,DOWNEY G,O’DONNELL C P.Dispersive Raman spectroscopy and multivariate data analysis to detect offal adulteration of thawed beefburgers[J].Journal of Agricultural&Food Chemistry,2015,63(5):1433-1441.DOI:10.1021/jf5041959.
[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]朱毅宁,杨平,杨新艳,等.支持向量机结合主成分分析辅助激光诱导击穿光谱技术识别鲜肉品种[J].分析化学,2017,45(3):336-341.DOI:10.11895/j.issn.0253-3820.160570.
[23]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.
[24]褚小立,许育鹏,陆婉珍.偏最小二乘法方法在光谱定性分析中的应用研究[J].现代仪器与医疗,2007,13(5):13-15.DOI:10.3969/j.issn.1672-7916.2007.05.005.
[25]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,2013:171-174.
[26]庞滂.近红外定性定量模型的建立与应用[D].西安:西北大学,2008:10.DOI:10.7666/d.y1252774.
[27]赵杰文,呼怀平,邹小波.支持向量机在苹果分类的近红外光谱模型中的应用[J].农业工程学报,2007,23(4):149-152.DOI:10.3321/j.issn:1002-6819.2007.04.029.
[28]吴丽君,殷沛沛,殷艳飞,等.近红外光谱结合偏最小二乘判别分析区分造纸法再造烟叶产品[J].分析科学学报,2015,31(6):809-814.DOI:10.13526/j.issn.1006-6144.2015.06.015.
[29]周学秋.近红外光谱定量分析方法的常见问题[C]//全国第二届近红外光谱学术会议论文集.长沙:全国第二届近红外光谱学术会议,2008.
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