Impacte
Exportar
Enllaç permanent
Matèria
Nota
Museum scientific collections preserve invaluable biological
archives that provide insights into historical biodiversity and environmental
change. Determining the age of specimens often relies on destructive,
labor-intensive, and costly methods, limiting their use on rare or valuable
materials. In this study, we present a fully nondestructive and rapid
approach for classifying the temporal origin of zoological skeletal
specimens using portable near-infrared spectroscopy combined with an
advanced chemometric framework, exemplified by red squirrel (Sciurus
vulgaris) bones. Two compact NIR instruments, covering distinct
wavelength ranges, were employed to analyze bone samples collected
from two temporal groups: “historical” (1916−1923) and “modern”
(2005−2021). To extract chemically meaningful information while
accounting for instrumental and physical variability, we implemented a maximum likelihood principal component analysis−logistic
regression (MLPCA-LR) strategy that explicitly incorporates the measurement error covariance structure. The resulting models
achieved perfect or near-perfect discrimination, validated through cross-validation, independent test sets, and bootstrap analysis.
Compared to the widely used partial least-squares discriminant analysis (PLS-DA), the MLPCA-LR framework demonstrated
superior robustness and interpretability. This study suggests that NIR spectroscopy with portable sensors, combined with MLPCALR,
offers a nondestructive and accessible approach for temporal classification of skeletal specimens, enabling practical in situ
screening in museums without invasive sampling or expert operators.
Condicions d’ús
Document relacionat
Analytical Chemistry, vol. 98, núm. 13 (2016), p. 9658–9671