小波聚类方法和近红外光谱技术用于药片种类判别

对310个药片样品的近红外光谱数据进行了聚类分析。首先使用小波变换对光谱数据矩阵进行多尺度分解,在进行有效压缩之后,采用经典分类方法对合适选取的小波系数组合进行聚类分析,提出了小波聚类方法。该方法分别用于实验室药片、中试药片和规模生产药片样品的分析,按药片样品的组成得到4个类别。结果表明,对实验室药片和中试药片样品分类的精确度均达到100%;对于规模生产药片的分类,共120个样品中只有1个样品被错误划分,精确度也高达99.2%。近红外光谱技术结合小波聚类方法的聚类性能是令人满意的,相比经典聚类分析,更加快速、易于使用,对制药行业药片质量以及成本控制均有积极作用。

Discrimination of varieties of tablets using near-infrared spectroscopy by wavelet clustering

A dataset of 310 samples of tablet were obtained by using near infrared spectroscopy (NIR) technique, and then the NIR data were used to discriminate the four types of tablets with three scales. Wavelet clustering algorithm, a new unsupervised method, which applied a classical clustering strategy on the suitably chosen subset of wavelet coefficients, was introduced to improve the clustering performance. The optimal wavelet decomposition and wavelet coefficients partition were determined according to the index of discriminant accuracy. The total accuracy rates for laboratory-scale, pilot-scale and full-scale tablets samples were 100%, 100% and 99.2%, respectively, with only one sample misclassified. The overall results indicated that the wavelet clustering was an effective way for the discrimination analysis. NIR combined with wavelet clustering method is surely much more rapid and easier to use, and offers a feasible solution to the quality control of pharmaceutical tablet products.