A comparative study on classification of edible vegetable oils by infrared, near infrared and fluorescence spectroscopy combined with chemometrics

化学计量学 主成分分析 线性判别分析 偏最小二乘回归 植物油 傅里叶变换红外光谱 化学 光谱学 模式识别(心理学) 近红外光谱 分析化学(期刊) 红外光谱学 荧光光谱法 数学 人工智能 生物系统 食品科学 色谱法 荧光 计算机科学 统计 生物 有机化学 物理 量子力学 神经科学
作者
Libo Yuan,Xiangru Meng,Kehui Xin,Ying Ju,Yan Zhang,Chunling Yin,Leqian Hu
出处
期刊:Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy [Elsevier BV]
卷期号:288: 122120-122120 被引量:35
标识
DOI:10.1016/j.saa.2022.122120
摘要

Driven by economic benefits like any other foods, vegetable oil has long been plagued by mislabeling and adulteration. Many studies have addressed the field of classification and identification of vegetable oils by various analysis techniques, especially spectral analysis. A comparative study was performed using Fourier transform infrared spectroscopy (FTIR), visible near-infrared spectroscopy (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics to distinguish different types of edible vegetable oils. FTIR, Vis-NIR and EEMs datasets of 147 samples of five vegetable oils from different brands were analyzed. Two types of pattern recognition methods, principal component analysis (PCA)/multi-way principal component analysis (M-PCA) and partial least squares discriminant analysis (PLS-DA)/multilinear partial least squares discriminant analysis (N-PLS-DA), were used to resolve these data and distinguish vegetable oil types, respectively. PCA/M-PCA analysis exhibited that three spectral data of five vegetable oils showed a clustering trend. The total correct recognition rate of the training set and prediction set of FTIR spectra of vegetable oil based on PLS-DA method are 100%. The total recognition rate of Vis-NIR based on PLS-DA are 100% and 97.96%. However, the total correct recognition rate of training set and prediction set of EEMs data based on N-PLS-DA method is 69.39% and 75.51%, respectively. The comparative study showed that FTIR and Vis-NIR combined with chemometrics were more suitable for vegetable oil species identification than EEMs technique. The reason may be concluded that almost all chemical components in vegetable oil can produce FTIR and NIR absorption, while only a small amount of fluorophores can produce fluorescence. That is, FTIR and NIR can provide more spectral information than EEMs. Analysis of EEMs data using self-weighted alternating trilinear decomposition (SWATLD) also showed that fluorophores were a few and irregularly distributed in vegetable oils.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
龚圣博完成签到,获得积分10
刚刚
望舒完成签到,获得积分10
刚刚
慈善家完成签到,获得积分10
1秒前
Akim的应助被Wendy采纳,获得100
2秒前
2秒前
2秒前
胡平发布了新的文献求助10
2秒前
Lee_Ding_95发布了新的文献求助10
2秒前
FashionBoy的应助被王不见王采纳,获得30
2秒前
3秒前
4秒前
外星人发布了新的文献求助10
4秒前
ddtmjj66完成签到 ,获得积分20
5秒前
liyu发布了新的文献求助20
6秒前
DW的应助被wdsdfkl采纳,获得10
7秒前
8秒前
多乐完成签到 ,获得积分10
8秒前
凛冬发布了新的文献求助10
8秒前
脑袋尖尖的完成签到,获得积分10
8秒前
abu完成签到,获得积分10
9秒前
一蓑烟雨完成签到 ,获得积分10
9秒前
zz发布了新的文献求助10
9秒前
科研通AI6.4的应助被奇逆采纳,获得10
9秒前
迈0312发布了新的文献求助10
10秒前
苏七本柒完成签到 ,获得积分10
10秒前
好奇大宝贝完成签到 ,获得积分10
10秒前
动听的小懒虫完成签到,获得积分10
10秒前
阿智完成签到,获得积分10
11秒前
12秒前
淙淙完成签到,获得积分10
12秒前
Hermy完成签到,获得积分10
12秒前
李健的小迷弟的应助被凛冬采纳,获得10
14秒前
可爱的函函的应助被Esther采纳,获得10
14秒前
羊羊羊完成签到,获得积分10
15秒前
赘婿的应助被牛123采纳,获得10
15秒前
16秒前
CiCi完成签到 ,获得积分10
18秒前
万能图书馆的应助被外星人采纳,获得10
18秒前
Bonnie发布了新的文献求助10
18秒前
SkeMent完成签到,获得积分10
19秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Les Mantodea De Guyane 2000
Energie-Contracting 1500
Klimaschutzrecht durch den Energiesektor im Mehrebenensystem – Ein Rechtsvergleich 1000
Stochastic Models in Operations Research,Vol. 2 600
Guidelines for fracture-safe and fatigue-reliable design of steel structures 600
Problems and Solutions for Integer and Combinatorial Optimization: Building Skills in Discrete Optimization 600
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 工程类 有机化学 化学工程 生物化学 物理 复合材料 内科学 催化作用 光电子学 人工智能 细胞生物学 无机化学 物理化学 数学
热门帖子
关注 科研通微信公众号,转发送积分 8026099
求助须知:如何正确求助?哪些是违规求助? 9467576
关于积分的说明 21226760
捐赠科研通 7597083
什么是DOI,文献DOI怎么找? 3387550
关于科研通互助平台的介绍 2520133
邀请新用户注册赠送积分活动 2423913