State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, People's Republic of China.
Appl Spectrosc. 2014;68(2):245-54. doi: 10.1366/13-07091.
A novel near-infrared spectroscopy (NIRS) method has been researched and developed for the simultaneous analyses of the chemical components and associated properties of mint (Mentha haplocalyx Briq.) tea samples. The common analytes were: total polysaccharide content, total flavonoid content, total phenolic content, and total antioxidant activity. To resolve the NIRS data matrix for such analyses, least squares support vector machines was found to be the best chemometrics method for prediction, although it was closely followed by the radial basis function/partial least squares model. Interestingly, the commonly used partial least squares was unsatisfactory in this case. Additionally, principal component analysis and hierarchical cluster analysis were able to distinguish the mint samples according to their four geographical provinces of origin, and this was further facilitated with the use of the chemometrics classification methods-K-nearest neighbors, linear discriminant analysis, and partial least squares discriminant analysis. In general, given the potential savings with sampling and analysis time as well as with the costs of special analytical reagents required for the standard individual methods, NIRS offered a very attractive alternative for the simultaneous analysis of mint samples.
一种新的近红外光谱（NIRS）方法已经被研究和开发出来，用于同时分析薄荷（Mentha haplocalyx Briq.）茶样品的化学成分和相关性质。常见的分析物有：总多糖含量、总黄酮含量、总酚含量和总抗氧化活性。为了解决这样的分析中的 NIRS 数据矩阵，最小二乘支持向量机被发现是预测的最佳化学计量学方法，尽管它与径向基函数/偏最小二乘模型非常接近。有趣的是，在这种情况下，常用的偏最小二乘法并不令人满意。此外，主成分分析和层次聚类分析能够根据薄荷样品的四个产地省份进行区分，并且使用化学计量学分类方法——K-最近邻、线性判别分析和偏最小二乘判别分析，这一过程更加便捷。总的来说，考虑到采样和分析时间的节省以及标准个别方法所需的特殊分析试剂的成本，NIRS 为薄荷样品的同时分析提供了一个非常有吸引力的替代方案。
