应用可见/近红外光谱技术快速检测果珍中二氧化钛的含量

Quickly Determination of Titanium Dioxide Content in Juice Based on Vis/NIR Spectroscopy Technique

为了实现快速检测果珍中的二氧化钛含量,提出了应用近红外光谱技术结合化学计量学的快速检测方法。研究采用了320份果珍样本进行光谱特性的检测,其中200个样本用来建模,120个样本进行预测。首先比较了标准正态变量校正(SNV)、变量标准化(Normalize)、多元散射校正(MSC)等6种不同的数据预处理方法对偏最小二乘法(PLS)建模预测效果的影响。然后将PLS模型与应用主成分(PC)建立的主成分-神经网络校正(PC-ANN)模型进行比较。结果表明,MSC预处理的效果最好,PLS模型的最佳主成分数为7,预测值与标准值的相关系数R^2达0.900 8,预测标准误差RMSEP为0.05。PC-ANN模型预测值与标准值的R^2为0.868 4,RMSEP为0.04。说明PLS模型比PC-ANN模型的预测效果好。同时本研究也说明能够应用可见/近红外技术对二氧化钛进行快速定量测定。

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 sampleswere 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 preproeessing 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 （R^2 ） between the real values and predicted ones by discriminantanalysis model were 0. 900 8 （PLS） and 0. 868 4 （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 diox ide 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.