Classification of Chemically Modified Celluloses Using a Near-Infrared Spectrometer and Soft Independent Modeling of Class Analogies

Abstract

A method for classification of eleven chemically modified celluloses has been developed with the use of near-infrared (NIR) spectroscopy and soft independent modeling of class analogies (SIMCA). The sample set consisted of 440 different batches from eleven different cellulose derivatives. A full factorial design in temperature and moisture was made for one sample from each class in order to introduce climate variations in the calibration sample set. Principal components analysis (PCA) models were made for each class, and samples not present in the calibration set were classified according to the SIMCA method. Only one type II error (acceptance of an unacceptable sample) was detected in the classification of the different celluloses. The number of type I errors (rejection of an acceptable sample) ranged from 0 to 14%. Subgroups, due to different manufacturers, viscosities, particle sizes, and degrees of substitution, were detected and correctly classified. The sample presentation, focus of the instrument, number of reference measurements, depth of penetration, and selection of training set samples are discussed.