近红外光谱技术用于复杂植物样品中无机离子测定的新方法

植物样品中的无机离子以一定形式与具有近红外吸收的有机基团结合, 因而可以借助近红外光谱技术测定其含量。对烟草样品近红外光谱数据及其小波变换处理后的小波系数，采用偏最小二乘法预测其中钾离子的含量，建立了小波变换与近红外光谱技术结合用于复杂植物样品中无机离子测定的新方法。结果表明：近红外光谱数据压缩为原始大小的3.3%，基本上没有光谱信息的丢失; 基于小波系数的模型优于传统的全谱模型，对于无机离子的测定可以取得较为准确的预测结果。

A Novel Method for the Determination of Inorganic Ions in Complex Plant Samples by Near Infrared Spectroscopy

Inorganic ions in plant samples can be determined by near-infrared (NIR) spectral technique, because they are combined with organic groups with NIR absorption. A method for the determination of inorganic ions in complex plant samples was established based on the combination of discrete wavelet transform (DWT) and NIR technique. In the proposed method, the raw NIR data and their wavelet coefficients are used for modeling and prediction of the contents of potassium in tobaccos by partial least square method (PLS). It is shown that there is almost no loss of spectral information with the NIR data compressed to 3.3% of its original size. The model based on wavelet coefficients is better than that based on the full NIR spectral range. With the improved method, accurate prediction can be achieved.