Summary
This paper presents the first reported near-infrared spectroscopy model for quantifying water content in human teeth, using partial least squares regression with second derivative pretreatment. The model achieved strong predictive performance (R² 0.91, RMSECV 0.4% w/w) with spectral weightings consistent with O-H absorption, suggesting that clinical caries detection via NIR imaging operates primarily through water content measurement rather than scattering phenomena. This foundational work clarifies the physical basis of an emerging diagnostic technology entering clinical practice.
Regional applicability
The methodological contribution is internationally applicable to improving non-invasive caries detection in clinical dentistry. UK NHS dental services could benefit from validated NIR-based screening tools if the model is further refined for clinical implementation.
Key measures
Cross-validation R² = 0.91; RMSECV = 0.4% w/w; model based on second derivative pretreatment with five factors; O-H overtone and combination feature reliance
Outcomes reported
The study developed a partial least squares regression model to estimate water content in human teeth using near-infrared spectroscopy, achieving a cross-validation R² of 0.91 and root mean square error of cross-validation of 0.4% w/w. The model weightings indicated that caries detection via NIR imaging relies primarily on water absorption features rather than light scattering.
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