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Validated Near-Infrared Spectroscopy and Chemometric Modelling for Rapid Quantification of Essential Oil Yield and α/β-Santalol in Santalum Album L.
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DOI：10.1002/gch2.70137.png)
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Abstract
En 中文
The sandalwood industry remains constrained by destructive, time-intensive assays for essential oil (EO) yield, composition, moisture content, and wood fraction, which limit real-time decision-making. We report a unified near-infrared spectroscopy-artificial intelligence (NIRS-AI) platform for non-destructive analytics across the Santalum album L. value chain. Reflectance and transmittance spectra from solid matrices (disks, logs, chips, and powders), oils, ethanol extracts, and CID-derived emulsions were acquired using benchtop (400–2500 nm) and portable (900–1700 nm) spectrometers and calibrated against hydrodistillation, gas chromatography, extraction, and moisture assays. Advanced chemometric modelling using AI-based machine learning techniques, including regularised regression, ensemble learning, boosting algorithms, and neural networks, was used to capture nonlinear spectral–property relationships and benchmark application-specific predictive performance. Independent external validation yielded R2 values of 0.97 for EO yield, 0.96 and 0.94 for α- and β-santalol, 0.99 for the heartwood-sapwood ratio, 0.86 for oil moisture, 0.98 for ethanol extract yield, and 0.94 for portable emulsion-yield prediction. NIRS also outperformed visible spectra for classifying heartwood, sapwood, inner and outer bark, and transition wood. ΔR2 analysis showed that ensemble models were comparatively robust to preprocessing variation, whereas linear models were more sensitive to changes in the variance structure.
Keywords:
artificial intelligence
chemometric modelling
essential oil yield
machine Learning
near-infrared spectroscopy
portable spectroscopy
sandalwood processing
wood classification
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