Evidence map›Paper›PMID 42540346›Full record

ArticleGlobal challenges (Hoboken, NJ)2026

Validated Near-Infrared Spectroscopy and Chemometric Modelling for Rapid Quantification of Essential Oil Yield and α/β-Santalol in

Muhammad Hassnain, Muhammad Rizwan Azhar

Abstract read
In one paragraph

Article in Global challenges (Hoboken, NJ), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Muhammad HassnainSchool of Engineering Edith Cowan University (ECU) Joondalup Western Australia Australia.
Muhammad Rizwan AzharSchool of Engineering Edith Cowan University (ECU) Joondalup Western Australia Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

Indexed as

artificial intelligencechemometric modellingessential oil yieldmachine Learningnear‐infrared spectroscopyportable spectroscopysandalwood processingwood classification

Identifiers

PMID42540346
PMCPMC13425625

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.