Abstract
This work applied the FT-NIR spectroscopy technique with the aid of chemometrics algorithms to determine the adulteration content of extra virgin olive oil (EVOO). Informative spectral wavenumbers were obtained by the use of a novel variable selection algorithm of bootstrapping soft shrinkage (BOSS) during partial least-squares (PLS) modeling. Then, a PLS model was finally constructed using the best variable subset obtained by the BOSS algorithm to quantitative determine doping concentrations in EVOO. The results showed that the optimal variable subset including 15 wavenumbers was selected by the BOSS algorithm in the full-spectrum region according to the first local lowest value of the root-mean-square error of cross validation (RMSECV), which was 1.4487 % v/v. Compared with the optimal models of full-spectrum PLS, competitive adaptive reweighted sampling PLS (CARS–PLS), Monte Carlo uninformative variable elimination PLS (MCUVE–PLS), and iteratively retaining informative variables PLS (IRIV–PLS), the BOSS–PLS model achieved better results, with the coefficient of determination (R2) of prediction being 0.9922, and the root-mean-square error of prediction (RMSEP) being 1.4889 % v/v in the prediction process. The results obtained indicated that the FT-NIR spectroscopy technique has the potential to perform a rapid quantitative analysis of the adulteration content of EVOO, and the BOSS algorithm showed its superiority in informative wavenumbers selection.
Highlights
With the rising prices of cooking oil, greedy traders and suppliers may resort to unethical practices, such as mixing low-value cooking oil with high-value cooking oil [1]
The results obtained indicated that the Fourier transform near-infrared (FT-NIR) spectroscopy technique has the potential to perform a rapid quantitative analysis of the adulteration content of Extra virgin olive oil (EVOO), and the bootstrapping soft shrinkage (BOSS) algorithm showed its superiority in informative wavenumbers selection
A five-fold cross validation was used for the optimization of relevant parameters, and the optimal variables were determined according to the first local lowest root-mean-square error of cross validation (RMSECV) value
Summary
With the rising prices of cooking oil, greedy traders and suppliers may resort to unethical practices, such as mixing low-value cooking oil with high-value cooking oil [1]. The consumers cannot detect these low-value, inexpensive ingredients in cooking oils, so they pay more for them. Extra virgin olive oil (EVOO) is native to the Mediterranean area, is known as “the gold of liquids”, “the queen of plant oils”, and “the Mediterranean nectar”, and is an established Chinese consumer favorite [2]. The consumption of the EVOO has increased in recent years. EVOO adulteration has spread in the Chinese market. Adulteration causes confusion in the edible oil market and violates the rights of consumers. A fast and effective analytical method of EVOO adulteration is required to assist government’s regulations
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