Abstract
This study aimed to apply near infrared spectroscopy with chemometrics to differentiate various stages of teeth, as well as to predict the enamel surface changes and the crystallite size of the carious specimens. Qualitative analysis based on principal component analysis revealed that spectral data pretreated with Savitzky-Golay derivative followed by standard normal variate could differentiate sound teeth, artificial carious teeth, and natural carious teeth with sufficient accuracy. The first principal component described 98.82 % of the variability in the spectral data. With respect to quantitative analysis, the carious specimens were divided to five groups; non-treatment and treatment with various pharmaceutical preparations. The calibration model was constructed from the spectral data training set by a partial least square regression. As a result, the model based on spectral data pretreated by the first derivative method could predict the enamel surface changes and the crystallite size accurately. The model had the lowest root mean square error of prediction, the highest correlation coefficient and acceptable bias value. This study demonstrated that near infrared spectroscopy was a convenient and reliable tool for dental caries assessment.
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