Abstract
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 MicroNIRTM1700 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 (R2), and the root mean square error prediction (RMSEP) were used as indicators for the predictability of the PLS models. In MicroNIRTM1700 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.
Highlights
Edible oil has been widely used for making dishes such as salad or fried food
CARS achieved a good prediction with the least variables, we can see that from both Figs 4 and 5. the reason may be that variables are heavily collinear and the model’s variance could be reduced with fewer variables
It indicated that the predictions of MicroNIR1700, MEMS-FTIR and i-Raman-785s were comparable to their corresponding reference methods for Iodine value (IV) determination and the three portable devices based on edible oil analysis is suitable for on-site measurement of IV for edible oil or other biodiesel production
Summary
Edible oil has been widely used for making dishes such as salad or fried food. It can provide essential nutrients and energy. First is to investigate the feasibility of using MEMS-FTIR, MicroNIRTM1700 and i-Raman Plus-785S to quantify IV of edible oil based on PLS regression models. Second is to investigate the influence of variable selection methods especially BOSS and VCPA on the robustness and predictability of calibration models developed by PLS.
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