近红外光谱技术无损检测大米中蛋白质
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摘要
为建立一种无损的大米蛋白质近红外检测方法,以138份大米作为样本,分别对蛋白质和蛋白质(干基)进行近红外建模。经优化得到相应的最佳模型,两模型的内部校正决定系数R_内~2分别为0.950 6和0.965 2,内部相对分析误差RPD_内分别为4.50和5.36,内部交叉验证标准差RMSECV分别为0.197和0.231,外部校正决定系数R_外~2分别为0.956 5和0.974 1,外部相对分析误差RPD_外分别为4.79和6.21,外部交叉验证标准差RMSEP分别为0.147和0.215。对比两模型,蛋白质(干基)的近红外模型具有更好的线性和预测能力,但RMSECV较蛋白质大,精密度不如蛋白质近红外模型。
In order to establish a nondestructive detection method of the protein in rice by near infrared spectroscopy, 138 kinds of rice as the research samples, the models of protein and protein(dry matter basis) were been built respectively. By the corresponding optimization, the internal model correction coefficients R~2 of the best models were 0.950 6 and 0.965 2, the relative errors of RPD in internal analysis were 4.50 and 5.36, root mean square errors of cross validation RMSECV were 0.197 and 0.231, the external correction coefficient R~2 were 0.956 5 and 0.974 1, relative errors of external RPD analysis were 4.79 and 6.21, and root mean square errors of prediction RMSEP were 0.147 and 0.215. Compared with the two model, the near infrared model of protein(dry matter basis) had better linearity and prediction ability, but the RMSECV was bigger than protein model, and the precision was not as good as protein near infrared model.
In order to establish a nondestructive detection method of the protein in rice by near infrared spectroscopy, 138 kinds of rice as the research samples, the models of protein and protein(dry matter basis) were been built respectively. By the corresponding optimization, the internal model correction coefficients R~2 of the best models were 0.950 6 and 0.965 2, the relative errors of RPD in internal analysis were 4.50 and 5.36, root mean square errors of cross validation RMSECV were 0.197 and 0.231, the external correction coefficient R~2 were 0.956 5 and 0.974 1, relative errors of external RPD analysis were 4.79 and 6.21, and root mean square errors of prediction RMSEP were 0.147 and 0.215. Compared with the two model, the near infrared model of protein(dry matter basis) had better linearity and prediction ability, but the RMSECV was bigger than protein model, and the precision was not as good as protein near infrared model.
引文
[1]刘明博,唐延林,李晓利,等.大米蛋白质含量近红外光谱检测模型研究[J].中国农学通报,2013,29(12):212-216.
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[11]袁佰华.稻米蛋白质研究概述[J].现代食品,2016,29(5):64-67.
[12]DELWICHE S R,PITT NORRIS K H.Sensitivity of near infrared absorption tomoisture content versus water activity instarch and cellulose[J].Cereal Chem,1992,69(1):107-109.
[13]GAINES C S,WINDHAM W R.Effect of wheat moisture content on meal apparent particle size and hardness scores determined by near-infrared reflectance spectroscopy[J].Cereal Chem,1998,75(3):386-391.
[14]张灵帅,王卫东,谷运红,等.水分含量对近红外测定小麦蛋白质含量的影响[J].安徽农业科学,2010,38(1):96-97.
[15]WILLIAMS P C,SOBERING D.How do we do it:a brief summary of the methods we use in developing near infrared calibrations[M].UK:Chichester,1996:185-188.
[2]XIE L H,TANG S Q,CHEN N.Optimisation of nearinfrared reflectance model in measuring protein and amylose content of rice flour[J].Food Chemistry,2014(142):92-100.
[3]李勇,魏益民,王锋.影响近红外光谱分析结果准确性的因素[J].核农学报,2005,19(3):236-240.
[4]CEN H Y,HE Y.Theory and application of near infrared reflectance spectroscopy in determination of food quality[J].Trends in Food Science Technology,2007,18(2):72-83.
[5]张欣,单杨.国外近红外光谱分析技术在食品安全问题中的应用研究进展[J].食品工业科技,2010,31(9):398-401.
[6]褚小立,陆婉珍.近五年我国近红外光谱分析技术研究与应用进展[J].光谱学与光谱分析,2014,34(10):2595-2605.
[7]SUN C X,YU Y H,DUAN B W.Rapid prediction of rice quality characteristics by near-infrared reflectance spectroscopy for breeding programs[J].Cereal Chem,2014,91(3):270-275.
[8]毕京翠,张文伟,肖应辉,等.应用近红外光谱技术分析稻米蛋白质含量[J].作物学报,2006,32(5):709-715.
[9]俞法明,陆艳婷,严文潮,等.用近红外光谱技术快速测定籼稻品种的蛋白质含量[J].中国粮油学报,2009,24(5):134-138.
[10]李勇,魏益民,王锋.影响近红外光谱分析结果准确性的因素[J].核农学报,2005,19(3):236-240.
[11]袁佰华.稻米蛋白质研究概述[J].现代食品,2016,29(5):64-67.
[12]DELWICHE S R,PITT NORRIS K H.Sensitivity of near infrared absorption tomoisture content versus water activity instarch and cellulose[J].Cereal Chem,1992,69(1):107-109.
[13]GAINES C S,WINDHAM W R.Effect of wheat moisture content on meal apparent particle size and hardness scores determined by near-infrared reflectance spectroscopy[J].Cereal Chem,1998,75(3):386-391.
[14]张灵帅,王卫东,谷运红,等.水分含量对近红外测定小麦蛋白质含量的影响[J].安徽农业科学,2010,38(1):96-97.
[15]WILLIAMS P C,SOBERING D.How do we do it:a brief summary of the methods we use in developing near infrared calibrations[M].UK:Chichester,1996:185-188.