基于近红外光谱的海藻糖浆组分快速定量检测

发布时间：2018-06-09 21:23

本文选题：海藻糖浆 + 定量　；参考：《食品与机械》2017年10期

【摘要】：研究采用近红外光谱(near infrared spectroscopy,NIRS)快速检测单酶法生产海藻糖浆(海藻糖、麦芽糖及葡萄糖)组成的方法。取65个海藻糖浆作为样本,扫描得到近红外光谱图,分为48个样本校正集,17个样本预测集,计算分析结果表明:一阶微分(first derivative,1D)与S-G平滑(savitzky-golay filter)组合处理为最优预处理方法;运用TQ analyst建模软件中主成分回归(principal component regression,PCR)算法和偏最小二乘法(partial least squares,PLS)算法分别对海藻糖浆建模,发现采用偏最小二乘法(partial least squares,PLS)的海藻糖浆组分模型稳定性和预测能力更好;运用PLS、1D、S-G平滑组合预处理海藻糖浆组分模型,不仅降低光谱的背景噪声,同时还提高模型的稳定性。海藻糖浆各组分模型的交叉验证均方差(RMSEC)、交叉验证决定系数(Rc)、预测均方差(RMSEP)、预测决定系数(RP)依次为:海藻糖模型分别为0.188,0.995,0.089,0.989;麦芽糖模型分别为0.143,0.997,0.131,0.969;葡萄糖模型分别为0.147,0.997,0.094,0.999。NIRS检测快速、无损便捷,可用于检测单酶法生产海藻糖浆的组分。
[Abstract]:A method for rapid detection of trehalose, maltose and glucose in trehalose, maltose and glucose by near infrared spectroscopy was studied. Taking 65 seaweed syrup as samples, the NIR spectra were divided into 48 sample correction sets and 17 sample prediction sets. The results showed that the combination of first order differential first derivation 1D) and S-G smoothing savitzky-golay filterwas the best pretreatment method. The principal component regression (PCA) algorithm and partial least squares (partial least squares) algorithm in TQ analyst modeling software are used to model trehalose syrup, respectively. The results show that the partial least squared analyst model has better stability and prediction ability. The pretreatment of algal syrup component model with PLS1DX S-G smoothing not only reduces the background noise of the spectrum, but also improves the stability of the model. The cross validation mean square deviation (RMSEC), cross validation decision coefficient (RcN), predictive mean square error (RMSEPN), predictive decision coefficient (RPP) of each component model of seaweed syrup were 0.1880.995U 0.0890.89, 0.1430.9970.131g / 0.96999.The glucose model was 0.1470.9970.0940.999.NIRS, respectively. Nondestructive and convenient, it can be used to detect the components of trehaline syrup produced by single enzyme method.
【作者单位】：长沙理工大学化学与生物工程学院;
【基金】：农业部财政部项目(编号:农办财函[2016]6号) 湖南省自然科学基金(编号:2015JJ2010)
【分类号】：O657.33

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