提出了一种以样品光谱类间相关系数之和最小为准则进行光谱波长逐步筛选的方法（stepwise selection basing on minimum sum of correlation coefficients, SMCC）, 以类间距离与类内距离和的比值最大化（符合分析者主观预期目标）作为定性分析中特征波长筛选效果的评价依据, 并使用红塔集团提供的2012年17种不同类型工业分级烟叶作为试验样品, 以验证筛选方法的有效性。 研究表明, 采用CO1分级烟叶光谱作为参照类别, 筛选出10个特征波长点: 采用特征波长计算得到的类内欧氏距离的平均值为采用全部波长计算得到的平均值的1.69倍, 采用特征波长计算得到的类间欧氏距离的平均值为采用全部波长计算得到的平均值的3.70倍, 采用特征波长计算得到的类间欧氏距离与类内欧氏距离和的比值的平均值为采用全部波长计算得到的平均值的2.21倍。 特征波长的类间与类内欧氏距离和的比值增大, 说明筛选出来的特征波长能更加有效的表达不同类间的远近关系以及同一类内的离散度, SMCC算法是一种有效的、 可应用于近红外光谱定性分析中的特征波长筛选方法。

The present article proposed a method of stepwise selecting characteristic wavelengths based on minimum sum of correlation coefficients (SMCC). The maximization of the ratio of inter-class Euclidean distance to the sum of inner-class Euclidean distances was used as evaluation basis in qualitative analysis of near infrared spectroscopy. Seventeen kinds of grading tobacco leaf in 2012, provided by Hongta Group, were used as experimental samples to verify the effectiveness of this new method. CO1 was selected as the reference category and ten points were selected as characteristic wavelengths. The results indicated that the average value of inner-class Euclidean distance, calculated by characteristic wavelengths, was 1.69 times as large as that calculated by all wavelengths. The average value of inter-class Euclidean distance, calculated by characteristic wavelengths, was 3.70 times as large as that calculated by all wavelengths. The average value of the ratio of inter-class Euclidean distance to the sum of inner-class Euclidean distances, calculated by characteristic wavelength, was 2.21 times as large as that calculated by all wavelengths. The ratio of characteristic wavelengths was increased. The characteristic wavelengths can express the classical differences. It was showed that SMCC was an effective way to select characteristic wavelengths in qualitative analyses of near infrared spectroscopy.