Towards improvement in prediction of iodine value in edible oil system based on chemometric analysis of portable vibrational spectroscopic data

Iodine value (IV) is a significant parameter to illustrate the quality of edible oil. In this study, three portable spectroscopy devices were employed to determine IV in mixed edible oil system, a new Micro-Electro-Mechanical-System (MEMS) Fourier Transform Infrared Spectrometer (MEMS-FTIR), a Micro...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2018-10, Vol.8 (1), p.14729-9, Article 14729
Main Authors: Yan, Hong, Zhang, Jixiong, Gao, Jingxian, Huang, Yangming, Xiong, Yanmei, Min, Shungeng
Format: Article
Language:English
Subjects:
119/118
140/133
639/638/11/2257
639/705/531
692/700/784
Datasets
Edible oils
Fourier analysis
Fourier transforms
Humanities and Social Sciences
Iodine
Microelectromechanical systems
multidisciplinary
Science
Science (multidisciplinary)
Spectroscopy
Vegetable oils
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Iodine value (IV) is a significant parameter to illustrate the quality of edible oil. In this study, three portable spectroscopy devices were employed to determine IV in mixed edible oil system, a new Micro-Electro-Mechanical-System (MEMS) Fourier Transform Infrared Spectrometer (MEMS-FTIR), a MicroNIR TM 1700 and an i-Raman Plus-785S. Quantitative model was built by Partial least squares (PLS) regression model and four variable selection methods were applied before PLS model, which are Monte Carlo uninformative variables elimination (MCUVE), competitive reweighted sampling (CARS), bootstrapping soft shrinkage approach (BOSS) and variable combination population analysis (VCPA). The coefficient of determination (R 2 ), and the root mean square error prediction (RMSEP) were used as indicators for the predictability of the PLS models. In MicroNIR TM 1700 dataset, MCUVE gave the lowest RMSEP (2.3440), in MEMS-FTIR dataset, CARS showed the best performance with RMSEP (2.2185), in i-Raman Plus-785S dataset, BOSS gave the lowest RMSEP (2.5058). They all had great improvements than full spectrum PLS model. Four variable selection methods take a smaller number of variables and perform significant superiority in prediction accuracy. It was demonstrated that three new portable instruments would be suitable for the on-site determination of edible oil quality in infrared and Raman field.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-33022-9