淀粉液化液葡萄糖当量值的近红外快速检测
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摘要
为解决淀粉工业中淀粉液化液葡萄糖当量(Dextrose Equivalent,DE)值的化学检测方法繁琐复杂问题,研究近红外光谱(near infrared spectroscopy,NIRS)快速检测淀粉液化液DE值的方法。取88个淀粉液化液作为样本,扫描得到近红外光谱图,分为72个初始样本校正集,10个初始样本预测集,剔除6个问题样品,研究了运用多元散射校正(Multiplicative signal correction,MSC)、标准正态变量变化(Standard normal variate,SNV)、平滑(smoothing)、多项式平滑(Savitzky-Golay卷积平滑,SG)、一阶导数(1st derivative,1D)、二阶导数(2nd derivative,2D)等不同预处理方法和偏最小二乘法(partial least squares,PLS)、主成分回归法(principal component regression,PCR)组合的建模效果。计算分析结果表明:多元散射校正结合一阶导数与偏最小二乘法算法建模的初始模型稳定性和预测能力较好;近红外DE值模型的主成分因子数、交叉验证均方差(RMSEC)、交叉验证决定系数(R_C)、预测均方差(RMSEP)、预测决定系数(R_P)依次为:3、1.53、0.9723、1.44、0.9746。因此该NIRS检测方法快速简便,可用于淀粉液化液DE值的快速检测。
The chemical detection methods of starch liquefaction liquid's Dextrose Equivalent value are complicated. In order to solve this problem,fast detection of starch liquefying solution's DE value by near infrared spectroscopy (NIRS) has been researched. The 88 samples of starch liquidfaction liquid and their NIRS were divided into a calibration set of 72 samples and a prediction set of 10 samples beside 6 suspicious samples. The modeling effects of different pretreatment methods such as Multiplicative signal correction,Standard normal variate,smoothing,Savitzky-Golay,1 st derivative,2 nd derivative,partial least squares and principal component regression combination were studied. Calculation results showed that the model stability and prediction ability by the first derivative (1 D) and partial least squares (PLS) algorithm was a suit method. The Factor,RMSEC,R_C,RMSEP,R_P of NIRS DE model were:3,1.53,0.9723,1.44,0.9746. Therefore,the NIRS detection method can be used for rapid detection of the DE of starch liquefaction liquid.
The chemical detection methods of starch liquefaction liquid's Dextrose Equivalent value are complicated. In order to solve this problem,fast detection of starch liquefying solution's DE value by near infrared spectroscopy (NIRS) has been researched. The 88 samples of starch liquidfaction liquid and their NIRS were divided into a calibration set of 72 samples and a prediction set of 10 samples beside 6 suspicious samples. The modeling effects of different pretreatment methods such as Multiplicative signal correction,Standard normal variate,smoothing,Savitzky-Golay,1 st derivative,2 nd derivative,partial least squares and principal component regression combination were studied. Calculation results showed that the model stability and prediction ability by the first derivative (1 D) and partial least squares (PLS) algorithm was a suit method. The Factor,RMSEC,R_C,RMSEP,R_P of NIRS DE model were:3,1.53,0.9723,1.44,0.9746. Therefore,the NIRS detection method can be used for rapid detection of the DE of starch liquefaction liquid.
引文
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[3]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.
[4]Wang lu,Sun D W,Pu H B,et al.Quality analysis,classification,and authentication of liquid foods by near-infrared spectroscopy:A review of recent research developments[J].Critical Reviews in Food Science and Nutrition,2017:1524-1538.
[5]Silva V H D,Reboucas M V,Salles A R,et al.Determination of naphtha composition by near infrared spectroscopy and multivariate regression to control steam cracker processes[J].Fuel Processing Technology,2015,131:230-237.
[6]Nie P C,Dong Tao,He Leon,et al.The effects of dryingtemperature on nitrogen concentration detection in calcium soil studied by NIR spectroscopy[J].Applied Sciences,2018,8(2):269.
[7]陈辰,鲁晓翔,张鹏,等.玫瑰香葡萄贮藏期间糖酸品质的近红外检测[J].食品与发酵工业,2015,41(6):175-180.
[8]冯晓元,王宝刚,李文生,等.近红外漫反射光谱预测久保桃查溶性固形物初步实验研究[J].食品工业科技,2008,29(3):273-275.
[9]Munawar A A,Horsten D V,Wegener J K,et al.Rapid and non-destructive prediction of mango quality attributes using Fourier transform near infrared spectroscopy and chemometrics[J].Engineering in Agriculture Environment & Food,2016,9(3):208-215.
[10]李海燕,周海军,郭峰,等.液体近红外分析仪在葡萄糖浆检测中应用的优势[J].食品工程,2017(1):61-64.
[11]Hao J W,Chen N D,Chen C W,et al.Rapid quantification of polysaccharide and the main onosaccharides in Dendrobium huoshanense by near-infrared attenuated total reflectance spectroscopy[J].J Pharm Biomed Anal,2018:331-338.
[12]Carames E T S,Alamar P D,Poppi R J,et al.Quality control of cashew apple and guava nectar by near infrared spectroscopy[J].Journal of Food Composition & Analysis,2017,56:41-46.
[13]闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化[J].光谱学与光谱分析,2004,24(10):1205-1209.
[14]严衍录近红外光谱分析基础与应用[M].北京:中国轻工业出版社,2005:32-39.
[15]陆婉珍.近红外光谱仪器[M].北京:化学工业出版社,2010.
[16]郑瑞娜,谢定,杨倩圆.基于近红外光谱的海藻糖浆组分快速定量检测[J].食品与机械,2017(10):60-63
[17]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.