Evidence map›Paper›PMID 41683022›Full record

ArticleFoods (Basel, Switzerland)2026

Adulteration Detection of Multi-Species Vegetable Oils in Camellia Oil Using SICRIT-HRMS and Machine Learning Methods.

Mei Wang, Ting Liu, Han Liao, Xian-Biao Liu, Qi Zou, Hao-Cheng Liu, Xiao-Yin Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Foods (Basel, Switzerland), 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
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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

7 authors.

Mei WangGanzhou General Inspection and Testing Institute, China National Quality and Inspection Center for Se-Rich and Camellia Oleifera Products (Jiangxi), Ganzhou 341000, China.
Ting LiuGanzhou General Inspection and Testing Institute, China National Quality and Inspection Center for Se-Rich and Camellia Oleifera Products (Jiangxi), Ganzhou 341000, China.
Han LiaoGanzhou General Inspection and Testing Institute, China National Quality and Inspection Center for Se-Rich and Camellia Oleifera Products (Jiangxi), Ganzhou 341000, China.
Xian-Biao LiuGanzhou General Inspection and Testing Institute, China National Quality and Inspection Center for Se-Rich and Camellia Oleifera Products (Jiangxi), Ganzhou 341000, China.
Qi ZouSchool of Public Health and Health Management, Gannan Medical University, Ganzhou 341000, China.ORCID 0000-0002-3148-4066
Hao-Cheng LiuSchool of Public Health and Health Management, Gannan Medical University, Ganzhou 341000, China.
Xiao-Yin WangSchool of Public Health and Health Management, Gannan Medical University, Ganzhou 341000, China.ORCID 0000-0001-9361-7365

Funding

Provincial Key R&D Program of Jiangxi 20233BBF64002Science and Technology Innovation Program from Forestry Administration of Jiangxi Province YCYJZX〔2023〕341Science and Technology Program Project of the State Administration for Market Regulation 2023MK073University-Level Scientific Research Projects of Gannan Medical University QD201913, QD202128, TD202406-2, TD202313, TD202313-1
6 · The paper itself

Abstract

We aimed to establish a rapid and precise method for identifying and quantifying multi-species vegetable oil (corn oil, olive oil (OLO), soybean oil, and sunflower oil (SUO)) adulterations in camellia oil (CAO), using soft ionization by chemical reaction in transfer-high-resolution mass spectrometry (SICRIT-HRMS) and machine learning methods. The results showed that SICRIT-HRMS could effectively characterize the volatile profiles of pure and adulterated CAO samples, including binary, ternary, quaternary, and quinary adulteration systems. The low

Indexed as

adulteration detectioncamellia oilmachine learningSICRIT-HRMS

Identifiers

PMID41683022
PMCPMC12896747

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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.