FTIR-ATR指纹图谱的主成分分析-马氏距离法应用于烟用香精质量控制

Quality Assessment of Tobacco Flavor by Classification of Principal Component Analysis-Mahalanobis Distance Combined with FTIR-ATR Fingerprint

在配制过程正常的条件下,分批采集150个三种正常烟用香精的傅里叶变换衰减全反射红外指纹图谱,并对原始数据进行均值中心化处理,同时对其光谱信号进行二阶导数基线校正和Karl Norris降噪处理,应用化学计量学中的主成分分析-马氏距离法分类建模,然后对烟用香精配制质量进行多元统计过程控制(MSPC),结果表明,将马氏距离上限的控制范围(UCL)设定在该类平均马氏距离的+3σ范围之内,均获得了正确的监控预报。

In normal condition, 150 Fourier transform infrared-attenuated total reflection spectra of three kinds of tobacco flavor were measured. Mean centering and Karl Norris second derivative filter were employed for treating the spectra data. Whereafter, class models of tobacco flavor were established with classification of principal component analysis-Mahalanobis distance. Class models have been applied to multivariate statistical process control （ MSPC） for the quality assessment of tobacco flavor in site. The upper control limit（UCL） was based on this kind of average Mahalanobis distance within ＋3σ. The results of monitoring are correct.