Evidence map›Paper›PMID 25636032›Full record

ArticleInternational journal of molecular sciences2015

Profiling of fatty acids composition in suet oil based on GC-EI-qMS and chemometrics analysis.

Jun Jiang, Xiaobin Jia

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 27% of its field
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

3 citing papers in PubMed, 10 citations in OpenAlex.

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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 at 1 institution in 1 country.

Jun JiangAffiliated Hospital on Integration of Chinese and Western Medicine, Nanjing University of Chinese Medicine, Xianlin Avenue 138#, Xianlin University City, Nanjing 210023, China. xuyan9323@126.com.
Xiaobin JiaAffiliated Hospital on Integration of Chinese and Western Medicine, Nanjing University of Chinese Medicine, Xianlin Avenue 138#, Xianlin University City, Nanjing 210023, China. xiaobinjia_nj@126.com.
Nanjing University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fatty acid (FA) composition of suet oil (SO) was measured by precolumn methylesterification (PME) optimized using a Box-Behnken design (BBD) and gas chromatography/electron ionization-quadrupole mass spectrometry (GC-EI-qMS). A spectral library (NIST 08) and standard compounds were used to identify FAs in SO representing 90.89% of the total peak area. The ten most abundant FAs were derivatized into FA methyl esters (FAMEs) and quantified by GC-EI-qMS; the correlation coefficient of each FAME was 0.999 and the lowest concentration quantified was 0.01 μg/mL. The range of recovery of the FAMEs was 82.1%-98.7% (relative standard deviation 2.2%-6.8%). The limits of quantification (LOQ) were 1.25-5.95 μg/L. The number of carbon atoms in the FAs identified ranged from 12 to 20; hexadecanoic and octadecanoic acids were the most abundant. Eighteen samples of SO purchased from Qinghai, Anhui and Jiangsu provinces of China were categorized into three groups by principal component analysis (PCA) according to the contents of the most abundant FAs. The results showed SOs samples were rich in FAs with significantly different profiles from different origins. The method described here can be used for quality control and SO differentiation on the basis of the FA profile.

Indexed as

Gas Chromatography-Mass SpectrometryAnimalsEsterificationFatty AcidsGoatsOilsPrincipal Component AnalysisSheepFatty AcidsOils

Identifiers

PMID25636032
PMCPMC4346870
OpenAlexW1987799927

What OpenQuestion 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 OpenQuestion graph.