基于近红外光谱技术快速检测椰汁品质
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摘要
本研究应用近红外光谱技术结合主成分分析法(PCA)对3个不同品种的椰子,3个不同品牌成品椰子饮料及椰子粉进行定性分析。结果表明,对椰子3种不同形式的加工产品(椰子原汁、椰子饮料、椰子粉)进行定性分析的准确判别率均达到100%。采用近红外光谱技术结合偏最小二乘法(PLS)对椰汁饮料中原汁含量进行定量分析。为保证所建模型的稳健性、准确性,消除干扰,采用6种不同的预处理方法对近红外光谱技术进行优化,结果表明经过中心化预处理可得最佳模型,其R~2_p、RMSEP、R~2_c、RMSEC分别为0.9942、0.0435、0.9932、0.0519。本研究表明近红外光谱技术可为市售椰汁及椰子加工制品品质的快速、无损检测提供一种新思路。
The NIRs combined with principal component analysis(PCA)were firstly employed to conduct the qualitative analysis on three different kinds of coconuts,three brands of coconut drinks,and three brands of coconut powders in this work. The results revealed that the different coconut products could be clearly identified,and the accuracy rates for all of the above qualitative analyses were up to 100%. Moreover,the NIR spectra combined with partial least squares(PLS)was used to quantitative analysis of the content of raw juice in coconut beverage. To ensure the robustness and accuracy of the used model and eliminate the noise and baseline drift,six kinds of pretreatment methods were employed to optimize the NIR spectra. The results showed that the model after centralized preprocessing exhibited the best performance,in which the determination coefficient of prediction(R~2_p),the root mean square prediction error(RMSEP),determination coefficient of calibration(R~2_c),and root mean squared error of calibration(RMSEC)were 0.9942,0.0435,0.9932,and 0.0519,respectively. This study showed that the NIR spectroscopy could provide a new idea for the rapid and nondestructive detection of the qualities of commercially available coconut products.
The NIRs combined with principal component analysis(PCA)were firstly employed to conduct the qualitative analysis on three different kinds of coconuts,three brands of coconut drinks,and three brands of coconut powders in this work. The results revealed that the different coconut products could be clearly identified,and the accuracy rates for all of the above qualitative analyses were up to 100%. Moreover,the NIR spectra combined with partial least squares(PLS)was used to quantitative analysis of the content of raw juice in coconut beverage. To ensure the robustness and accuracy of the used model and eliminate the noise and baseline drift,six kinds of pretreatment methods were employed to optimize the NIR spectra. The results showed that the model after centralized preprocessing exhibited the best performance,in which the determination coefficient of prediction(R~2_p),the root mean square prediction error(RMSEP),determination coefficient of calibration(R~2_c),and root mean squared error of calibration(RMSEC)were 0.9942,0.0435,0.9932,and 0.0519,respectively. This study showed that the NIR spectroscopy could provide a new idea for the rapid and nondestructive detection of the qualities of commercially available coconut products.
引文
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[2]陈朴森,张宸禹,张观飞,等.不同品牌椰子汁质量的研究[J].热带作物学报,2017,38(8):1549-1552.
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[6]Haughey S A,Graham S F,Cancou?t E,et al.The application of near-infrared reflectance spectroscopy(NIRS)to detect melamine adulteration of soya bean meal[J].Food Chemistry,2013,136(3):1557-1561.
[7]朱大洲,王坤,周光华,等.单粒大豆的近红外光谱特征及品种鉴别研究[J].光谱学与光谱分析,2010,30(12):3217-3221.
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[9]Ye M,Gao Z,Li Z,et al.Rapid detection of volatile compounds in apple wines using FT-NIR spectroscopy[J].Food Chemistry,2016,190:701-708.
[10]陈永明,林萍,何勇.基于遗传算法的近红外光谱橄榄油产地鉴别方法研究[J].光谱学与光谱分析,2009,29(3):671-674.
[11]Laroussimezghani S,Vanloot P,Molinet J,et al.Authentication of Tunisian virgin olive oils by chemometric analysis of fatty acid compositions and NIR spectra.Comparison with Maghrebian and French virgin olive oils[J].Food Chemistry,2014,173(173):122-152.
[12]庄新港,王丽丽,吴雪原,等.基于近红外的移动窗口 BP 神经网络实现山东绿茶产地溯源[J].红外与毫米波学报,2016,35(2):200-205.
[13]Teye E,Huang X,Sam L K,et al.Estimating cocoa bean parameters by FT-NIRS and chemometrics analysis[J].Food Chemistry,2015,176:403-410.
[14]Li Y,Guo Y,Liu C,et al.Food analytical methods[J].2017,10(6):1965-1971.
[15]李洋,吴志生,潘晓宁,等.在线近红外光谱在我国中药研究和生产中应用现状与展望[J].光谱学与光谱分析,2014,34(10):2632-2638.
[16]孙通,魏小梅,胡田,等.可见/近红外结合MIA变量优选和支持向量机判别山茶油的制取方式[J].食品工业科技,2014,35(20):62-65.
[17]Moser J K,Singh M,Rennick K A,et al.Detection of corn adulteration in Brazilian coffee(Coffea arabica)by tocopherol profiling and NIR spectroscopy[J].Journal of Agricultural & Food Chemistry,2015,63(49):10662-10668.
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[19]Ziegel E R.Chemometrics:Statistics and computer application in analytical chemistry[J].Technometrics,2001,43(2):240-270.
[20]高荣强,范世福.现代近红外光谱分析技术的原理及应用[J].分析仪器,2002(3):9-12.
[21]谷如祥,赵武奇,石珂心,等.近红外光谱测定苹果饮料中原果汁含量[J].食品工业科技,2013,34(20):75-77.
[22]李祥辉,连媛媛,杨木莎,等.采用近红外光谱技术快速检测桃汁原汁含量的方法研究[J].福州大学学报:自然科学版,2019(1):124-128.
[23]付才力,李颖,陈荔凡,等.近红外光谱技术快速检测莲子粉[J].光谱学与光谱分析,2018,38(2):424-429.
[24]丁家欣,张秋海,李树莉,等.应用近红外光谱法快速测定蜂蜜中葡萄糖和果糖含量[J].光谱学与光谱分析,2016(s1):197-198.
[25]杰尔·沃克曼,洛伊斯·文依.近红外光谱解析实用指南[M].北京:化学工业出版社,2009.
[26]朱亨银,傅霞萍,游贵荣,等.近红外光谱定性与定量分析技术在莲子无损检测中的应用[J].光谱学与光谱分析,2015,35(10):2752-2756.
[27]陈兰珍.蜂蜜品质近红外光谱评价技术研究[D].北京:中国农业科学院,2010.
[2]陈朴森,张宸禹,张观飞,等.不同品牌椰子汁质量的研究[J].热带作物学报,2017,38(8):1549-1552.
[3]卢琨.2015～2016年我国椰子产业的生产与贸易发展形势分析[J].世界热带农业信息,2017(Z1):1-6.
[4]Chang C W.Near infrared reflectance spectroscopy-principal components regression analyses of soil properties[J].Soil Science Society of America Journal,2001,65(2):480-490.
[5]Gemperline P J,Webber L D,Cox F O.Raw materials testing using soft independent modeling of class analogy analysis of near-infrared reflectance spectra[J].Analytical Chemistry,1989,61(2):138-144.
[6]Haughey S A,Graham S F,Cancou?t E,et al.The application of near-infrared reflectance spectroscopy(NIRS)to detect melamine adulteration of soya bean meal[J].Food Chemistry,2013,136(3):1557-1561.
[7]朱大洲,王坤,周光华,等.单粒大豆的近红外光谱特征及品种鉴别研究[J].光谱学与光谱分析,2010,30(12):3217-3221.
[8]Shao Y,Cen Y,He Y,et al.Infrared spectroscopy and chemometrics for the starch and protein prediction in irradiated rice[J].Food Chemistry,2011,126(4):1856-1861.
[9]Ye M,Gao Z,Li Z,et al.Rapid detection of volatile compounds in apple wines using FT-NIR spectroscopy[J].Food Chemistry,2016,190:701-708.
[10]陈永明,林萍,何勇.基于遗传算法的近红外光谱橄榄油产地鉴别方法研究[J].光谱学与光谱分析,2009,29(3):671-674.
[11]Laroussimezghani S,Vanloot P,Molinet J,et al.Authentication of Tunisian virgin olive oils by chemometric analysis of fatty acid compositions and NIR spectra.Comparison with Maghrebian and French virgin olive oils[J].Food Chemistry,2014,173(173):122-152.
[12]庄新港,王丽丽,吴雪原,等.基于近红外的移动窗口 BP 神经网络实现山东绿茶产地溯源[J].红外与毫米波学报,2016,35(2):200-205.
[13]Teye E,Huang X,Sam L K,et al.Estimating cocoa bean parameters by FT-NIRS and chemometrics analysis[J].Food Chemistry,2015,176:403-410.
[14]Li Y,Guo Y,Liu C,et al.Food analytical methods[J].2017,10(6):1965-1971.
[15]李洋,吴志生,潘晓宁,等.在线近红外光谱在我国中药研究和生产中应用现状与展望[J].光谱学与光谱分析,2014,34(10):2632-2638.
[16]孙通,魏小梅,胡田,等.可见/近红外结合MIA变量优选和支持向量机判别山茶油的制取方式[J].食品工业科技,2014,35(20):62-65.
[17]Moser J K,Singh M,Rennick K A,et al.Detection of corn adulteration in Brazilian coffee(Coffea arabica)by tocopherol profiling and NIR spectroscopy[J].Journal of Agricultural & Food Chemistry,2015,63(49):10662-10668.
[18]Frank I E,Friedman J H.A statistical view of some chemometrics regression tools[J].Technometrics,1993,35(2):109-135.
[19]Ziegel E R.Chemometrics:Statistics and computer application in analytical chemistry[J].Technometrics,2001,43(2):240-270.
[20]高荣强,范世福.现代近红外光谱分析技术的原理及应用[J].分析仪器,2002(3):9-12.
[21]谷如祥,赵武奇,石珂心,等.近红外光谱测定苹果饮料中原果汁含量[J].食品工业科技,2013,34(20):75-77.
[22]李祥辉,连媛媛,杨木莎,等.采用近红外光谱技术快速检测桃汁原汁含量的方法研究[J].福州大学学报:自然科学版,2019(1):124-128.
[23]付才力,李颖,陈荔凡,等.近红外光谱技术快速检测莲子粉[J].光谱学与光谱分析,2018,38(2):424-429.
[24]丁家欣,张秋海,李树莉,等.应用近红外光谱法快速测定蜂蜜中葡萄糖和果糖含量[J].光谱学与光谱分析,2016(s1):197-198.
[25]杰尔·沃克曼,洛伊斯·文依.近红外光谱解析实用指南[M].北京:化学工业出版社,2009.
[26]朱亨银,傅霞萍,游贵荣,等.近红外光谱定性与定量分析技术在莲子无损检测中的应用[J].光谱学与光谱分析,2015,35(10):2752-2756.
[27]陈兰珍.蜂蜜品质近红外光谱评价技术研究[D].北京:中国农业科学院,2010.