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基于可见/近红外光谱技术快速测定果汁中二氧化钛含量

[Quickly determination of titanium dioxide content in juice based on Vis/NIR spectroscopy technique].

作者信息

Duan Min, Bao Yi-dan, He Yong

机构信息

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.

出版信息

Guang Pu Xue Yu Guang Pu Fen Xi. 2010 Jan;30(1):74-7.

Abstract

In order to quickly and accurately detect the content of titanium dioxide in the juice, a method combining chemometrics and Vis/NIR spectroscopy technique was used in the present study. First, the content of titanium dioxide in the juice sample was determined by using spectrophotometer and standard curve of titanium dioxide. Then, different amount of pure titanium dioxide was adulterated into the juice collected from the market to prepare eight different content samples. A total of 320 juice samples were studied. Two hundred samples (25 samples for each content) were randomly selected from the 320 samples to be the calibration set while the other 120 samples (15 samples for each content) were selected as the validation set. The spectra of juice were within near infrared (NIR) and mid-infrared (MIR). First six different preprocessing methods were compared, such as standard normal variate (SNV), moving average, derivative and multivariate scatter correction (MSC). The optimal partial least squares(PLS)was built after the performance comparison of different preprocessing methods. Another algorithm, principal component-artificial neural network (PC-ANN), was also used: first, the original spectral date was processed using principal component analysis, the best number of principal components (PCs) was selected, and the scores of these PCs would be taken as the input of the artificial neural network (ANN). The PC-ANN was trained with samples in the calibration collection and the samples in prediction set were predicted. After comparison, MSC was found to be the most appropriate spectral preprocessing method and the best number of PCs is 7. The correlation coefficients (R2) between the real values and predicted ones by discriminant analysis model were 0.9008 (PLS) and 0.8684 (PC-ANN) respectively. The root mean standard errors of prediction (RMSEP) by PLS and PC-ANN were 0.05 (PLS) and 0.04 (PC-ANN) respectively. The result indicated that the content of titanium dioxide in the juice powder to be quickly detected by nondestructive determination method was very feasible and laid a solid foundation for setting up the titanium dioxide content forecasting model of juice powder.

摘要

为了快速、准确地检测果汁中二氧化钛的含量,本研究采用了化学计量学与可见/近红外光谱技术相结合的方法。首先,利用分光光度计和二氧化钛标准曲线测定果汁样品中二氧化钛的含量。然后,将不同量的纯二氧化钛掺入从市场收集的果汁中,制备八个不同含量的样品。共研究了320个果汁样品。从320个样品中随机选取200个样品(每种含量25个样品)作为校正集,其余120个样品(每种含量15个样品)作为验证集。果汁的光谱范围在近红外(NIR)和中红外(MIR)之间。首先比较了六种不同的预处理方法,如标准正态变量变换(SNV)、移动平均、导数和多元散射校正(MSC)。在比较不同预处理方法的性能后,建立了最优的偏最小二乘法(PLS)。还使用了另一种算法,主成分-人工神经网络(PC-ANN):首先,利用主成分分析对原始光谱数据进行处理,选择最佳主成分数(PCs),并将这些主成分的得分作为人工神经网络(ANN)的输入。用校正集中的样品对PC-ANN进行训练,并对预测集中的样品进行预测。比较后发现,MSC是最合适的光谱预处理方法,最佳主成分数为7。判别分析模型预测值与真实值之间的相关系数(R2)分别为0.9008(PLS)和0.8684(PC-ANN)。PLS和PC-ANN的预测均方根误差(RMSEP)分别为0.05(PLS)和0.04(PC-ANN)。结果表明,采用无损测定法快速检测果汁粉中二氧化钛含量是非常可行的,为建立果汁粉中二氧化钛含量预测模型奠定了坚实的基础。

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