近红外光谱法快速测定榨菜中亚硝酸盐含量
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摘要
为研究利用傅立叶近红外光谱分析仪(NIRS)快速测定市售榨菜中亚硝酸盐的含量,先取榨菜样品按GB5009.33-2016测定其亚硝酸盐含量,再向榨菜样品中添加亚硝酸钠,制成亚硝酸钠浓度范围为0.122～39.0875 mg/kg,浓度梯度为0.66 mg/kg的60个样本校正集;与10个样本预测集采集对应的傅立叶近红外光谱曲线,将光谱信息与实际测量值相关联,利用TQ analyst建模软件进行计算分析。结果表明:建模最优预处理方法为一阶微分(1D)与Savitzky-Golay filter滤波平滑的组合预处理;比较分析偏最小二乘法(PLS)与主成分回归法(PCR)的亚硝酸盐样品建立的光谱模型,数据结果显示采用偏最小二乘法(PLS)的亚硝酸盐组分模型稳定性和预测能力更好;内部交叉验正均方差(RSMECV)、交叉验证决定系数(Rc)、外部预测均方根误差(RMSEP)、预测决定系数(RP)相关系数(r)分别为0.0310、0.9925、0.0141、0.9720、0.9378。经F检验与t检验,与国标所测结果无显著性差异。NIRS检测快速,无损便捷,可用于市售榨菜中亚硝酸盐残留量的定量检测。
In order to detect the contents of nitrite in commercial mustard by Fourier-Near infrared spectroscopy (NIRS),the nitrite contents of the mustard sample was determined according to GB5009.33-2016,and then sodium nitrite was added to the mustard sample to prepare a 60-sample calibration set with a sodium nitrite concentration ranging from 0.122 to 39.0875 with a concentration gradient of 0.66.A Fourier NIR spectrum curve corresponding to the 10 sample prediction set acquisitions was used to match the spectral information with actual measurement values,and the calculation analysis was performed by TQ analyst modeling software.The calculation and analysis results showed that the optimal modeling method was the combined preprocessing of the first order differential (1 D) and Savitzky-Golay filter smoothing.A comparative analysis of spectral models established by partial least squares (PLS) and principal component regression (PCR) nitrite samples.The data showed that the partial least squares (PLS) nitrite component model had better stability and prediction ability,internal cross-test positive mean square error (RSMECV),cross-validation coefficient (Rc),external prediction root mean square error (RMSEP),prediction coefficient (RP) correlation coefficient (r) were 0.0310,0.9925,0.0141,0.9720 and 0.9378.After the F test and the t-test,there was no significant difference between the results measured by the national standard and the national standard.The NIRS test was rapid,non-destructive and convenient,and could be used for the quantitative detection of nitrite residues in commercial mustard.
In order to detect the contents of nitrite in commercial mustard by Fourier-Near infrared spectroscopy (NIRS),the nitrite contents of the mustard sample was determined according to GB5009.33-2016,and then sodium nitrite was added to the mustard sample to prepare a 60-sample calibration set with a sodium nitrite concentration ranging from 0.122 to 39.0875 with a concentration gradient of 0.66.A Fourier NIR spectrum curve corresponding to the 10 sample prediction set acquisitions was used to match the spectral information with actual measurement values,and the calculation analysis was performed by TQ analyst modeling software.The calculation and analysis results showed that the optimal modeling method was the combined preprocessing of the first order differential (1 D) and Savitzky-Golay filter smoothing.A comparative analysis of spectral models established by partial least squares (PLS) and principal component regression (PCR) nitrite samples.The data showed that the partial least squares (PLS) nitrite component model had better stability and prediction ability,internal cross-test positive mean square error (RSMECV),cross-validation coefficient (Rc),external prediction root mean square error (RMSEP),prediction coefficient (RP) correlation coefficient (r) were 0.0310,0.9925,0.0141,0.9720 and 0.9378.After the F test and the t-test,there was no significant difference between the results measured by the national standard and the national standard.The NIRS test was rapid,non-destructive and convenient,and could be used for the quantitative detection of nitrite residues in commercial mustard.
引文
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[15]鲁超,韩东海,温朝晖,等.基于近红外光谱检测腐乳盐坯中盐分含量的研究[J].中国酿造,2006(4):9-13.
[16]刘冰.近红外光谱法在涪陵榨菜品质检测方面应用研究[D].重庆:西南大学,2011:53-59.
[17]朱向荣,李高阳.基于近红外光谱与组合间隔偏最小二乘法的稻米镉含量快速检测[J].食品与机械,2015,3(4):43-50.
[18]Chen J,Zhu S P.Rapid determination of total protein and wet gluten in commercial wheat flour using si SVR-NIR[J]. Food Chemistry,2017,221:1939-1946.
[19]陈大伟,闫昭,刘昊岩.SVD系列算法在评分预测中的过拟合现象[J].山东大学学报:工学版,2014,44(3):15-21.
[20]公丽艳,孟宪军.基于主成分与聚类分析的苹果加工品质评价[J].农业工程学报,2004,30(13):276-283.
[21]褚小立,邓勇,杜一平.近红外光谱分析技术实用手册[M].北京:机械工业出版社2016:1-4,114-160.
[22]蒋霞,张晓,白铁成,等.近红外光谱技术结合PLS和SPA检测鲜冬枣表面农药残留量的方法[J].江苏农业科学,2018,46(2):146-149.
[23]靳庭良,张宝青.回归分析中t检验与F检验关系的进一步探讨[J].统计与决策,2009,297(21):7-9.
[24]李云雁,胡传荣.试验设计与数据处理[M].北京:化学工业出版社.2008,23-41.
[25]刘闽碧.用Excel做t检验和F检验[J].海峡预防医学杂志,2002,8(5):67-68.
[2]李轶欣,王玉田,顾英.灌肠制品中亚硝酸钠残留量及肉品发色率预测模型的建立[J].食品工业科技,2010,31(2):131-133.
[3]汪菊,付大友,徐晨曦.食品中亚硝酸盐快速检测方法的研究[J].食品工业科技,2015,36(9):278-280.
[4]黄韬睿,王鑫,孟甜.酱腌菜中亚硝酸盐检测方法的研究进展[J].食品研究与开发,2016,37(6):204-206.
[5]王君,刘蓉.近红外光谱技术在液态食品掺假检测中的应用[J].食品工业科技,2016,37(7):374-386.
[6]杰克.沃克曼,洛伊斯.文依编著,诸小立,等译.近红外光谱解析实用指南[M].北京:化学工业出版社,2009:12-13.
[7]Hirschfhfeld T.Salinity determination using NIRA[J].Appled spectroscopy,1985,39(4):740-741.
[8]Hans Buning-Pfaue.Analysis of water in food by near infrared spectroscopy[J].Food Chemistry,2003,82:107-115.
[9]Lin M,Cavinato A G,Huang Y,et al.Predicting sodium chloride content in commercial king(Oncorhynchus tshawytscha)and chum(O.keta)hot smoked salmon fillet portions by shortwavelength near-infrared(SW-NIR)spectroscopy[J]. Food Research International,2003,36(8):761-766.
[10]De M M,Manuelian C L,Ton S,et al.Prediction of sodium content in commercial processed meat products using near infrared spectroscopy[J].Meat Science,2017,125:61-65.
[11]中华人民共和国卫生部.GB 5009.33-2016食品安全国家标准食品中亚硝酸盐与硝酸盐的测定[S].北京:中国标准出版社,2016:149-153.
[12]刘翠玲,吴景珠,孙晓荣.近红外光谱技术在食品品质检测方法中的研究[M].北京:机械工业出版社,2015:24-47.
[13]Ferreira D S,Galo O F,Pallone J A L,et al.Comparison and application of near-infrared(NIR)and mid-infrared(MIR)spectroscopy for determination of quality parameters in soybean samples[J].Food Control,2014,35(1):227-232.
[14]严衍禄,赵龙莲,韩东海.近红外光谱分析基础与应用[M].北京:中国轻工业出版社,2005:29-81.
[15]鲁超,韩东海,温朝晖,等.基于近红外光谱检测腐乳盐坯中盐分含量的研究[J].中国酿造,2006(4):9-13.
[16]刘冰.近红外光谱法在涪陵榨菜品质检测方面应用研究[D].重庆:西南大学,2011:53-59.
[17]朱向荣,李高阳.基于近红外光谱与组合间隔偏最小二乘法的稻米镉含量快速检测[J].食品与机械,2015,3(4):43-50.
[18]Chen J,Zhu S P.Rapid determination of total protein and wet gluten in commercial wheat flour using si SVR-NIR[J]. Food Chemistry,2017,221:1939-1946.
[19]陈大伟,闫昭,刘昊岩.SVD系列算法在评分预测中的过拟合现象[J].山东大学学报:工学版,2014,44(3):15-21.
[20]公丽艳,孟宪军.基于主成分与聚类分析的苹果加工品质评价[J].农业工程学报,2004,30(13):276-283.
[21]褚小立,邓勇,杜一平.近红外光谱分析技术实用手册[M].北京:机械工业出版社2016:1-4,114-160.
[22]蒋霞,张晓,白铁成,等.近红外光谱技术结合PLS和SPA检测鲜冬枣表面农药残留量的方法[J].江苏农业科学,2018,46(2):146-149.
[23]靳庭良,张宝青.回归分析中t检验与F检验关系的进一步探讨[J].统计与决策,2009,297(21):7-9.
[24]李云雁,胡传荣.试验设计与数据处理[M].北京:化学工业出版社.2008,23-41.
[25]刘闽碧.用Excel做t检验和F检验[J].海峡预防医学杂志,2002,8(5):67-68.