College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China.
College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Oct 5;278:121332. doi: 10.1016/j.saa.2022.121332. Epub 2022 May 4.
The discrimination approach of adulterated milk was proposed combined synchronous two-trace two-dimensional (2T2D) correlation slice spectra at the characteristic wavebands of adulterant in milk with multivariate method. Two common adulterants, melamine and urea, were analyzed to demonstrate useful by the method. 2T2D (near infrared) NIR slice spectra at characteristic wavebands of adulterant were extracted from the synchronous 2T2D correlation spectra, and were input to construct the N-way partial least squares discriminant analysis (NPLS-DA) models. One-dimensional (1D) spectroscopy featuring all the present components in the samples combined with partial least squares discriminant analysis (PLS-DA) was also evaluated for comparison. The results indicated that for one kind of adulterant in model, prediction accuracies of slice spectral models were both 100% for melamine-adulterated and urea-adulterated samples discrimination. Moreover, for two kinds of adulterants in model, prediction accuracies of slice spectral models were 90.57% and 100% for melamine-adulterated and urea-adulterated discrimination, respectively, which was better than those of 1D whole models based on PLS-DA (only 81.13% and 98.15%, respectively). The comparison informs that the 2T2D slice spectra extracted at the characteristic wavebands of adulterant highlighted the adulterant spectral features and was obviously advantage to improve the discrimination accuracy. Meanwhile, the complexity of slice spectra is significantly reduced compared with the whole matrix of synchronous 2T2D correlation spectra.
该方法结合多元方法,提出了在牛奶中特征波段的掺杂剂同步双迹二维(2T2D)相关谱的掺杂鉴别方法。该方法分析了两种常见的掺杂物,三聚氰胺和尿素,证明了其有用性。从同步 2T2D 相关谱中提取特征波段的 2T2D(近红外)NIR 切片谱,并将其输入到构建的 N 路偏最小二乘判别分析(NPLS-DA)模型中。还评估了一维(1D)光谱,其特点是样品中所有现有成分结合偏最小二乘判别分析(PLS-DA)。结果表明,对于模型中的一种掺杂物,三聚氰胺和尿素掺杂物的切片光谱模型的预测准确率均为 100%。此外,对于模型中的两种掺杂物,三聚氰胺和尿素掺杂物的切片光谱模型的预测准确率分别为 90.57%和 100%,优于基于 PLS-DA 的 1D 全模型(分别为 81.13%和 98.15%)。该比较表明,在掺杂物特征波段提取的 2T2D 切片谱突出了掺杂物的光谱特征,明显有利于提高判别准确率。同时,与同步 2T2D 相关谱的整个矩阵相比,切片谱的复杂性显著降低。