ArticleScientific reports2026
Species-level identification and differentiation of deer antlers using ATR-FTIR spectroscopy and chemometrics.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Antlers are the fastest-growing osseous tissue in mammals, undergoing periodic regeneration associated with the mating season. Their high strength and resistance to fracture have made them a valuable material for the manufacture of hunting tools, artifacts, furniture, and traditional medicines. The growing demand for antlers and derived products has fueled illegal trade, contributing to a significant decline in deer populations. Therefore, the present study aims to identify and differentiate antler samples from three deer species (spotted deer, sambar deer, and swamp deer) using ATR-FTIR spectroscopy coupled with chemometrics. Initial visual comparison of antler spectra failed to distinguish species due to their similar chemical compositions. However, the PCA model achieved partial separation with an accuracy of 93.33%, limited by some spectral misclustering. To improve differentiation, a PLS-DA model was developed, which distinguished antler spectra with 100% accuracy. External validation and a blind test further confirmed the model's predictive performance, yielding 100% accuracy. The findings demonstrate that ATR-FTIR spectroscopy combined with PLS-DA provides a reliable and efficient approach for species-level identification of antler samples. This approach will offer a valuable tool for wildlife forensic investigations and assist law enforcement agencies in monitoring and curbing the illegal antler trade.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.