Edible Oils Differentiation Based on the Determination of Fatty Acids Profile and Raman Spectroscopy—A Case Study

Abstract

This study proposes a comparison between two analytical techniques for edible oil classification, namely gas-chromatography equipped with a flame ionization detector (GC-FID), which is an acknowledged technique for fatty acid analysis, and Raman spectroscopy, as a real time noninvasive technique. Due to the complexity of the investigated matrix, we used both methods in connection with chemometrics processing for a quick and valuable evaluation of oils. In addition to this, the possible adulteration of investigated oil varieties (sesame, hemp, walnut, linseed, sea buckthorn) with sunflower oil was also tested. In order to extract the meaningful information from the experimental data set, a supervised chemometric technique, namely linear discriminant analysis (LDA), was applied. Moreover, for possible adulteration detection, an artificial neural network (ANN) was also employed. Based on the results provided by ANN, it was possible to detect the mixture between sea buckthorn and sunflower oil.

Application of fuzzy algorithms in conjunction with 1H‐NMR spectroscopy to differentiate alcoholic beverages

“…10,11,23 Nowadays, increasing attention is given to the association between the spectroscopic methods and artificial intelligence for the development of food and beverages recognition models. Artificial neural networks (ANNs) 18,24 and machine learning (ML) algorithms 15,16 or fuzzy algorithms 17 are among the most promising tools for this purpose. In this light, the present study discusses the potential of the association between the 1 H-NMR spectroscopy and fuzzy algorithms for the discrimination of wines and fruit distillates.…”

Rapid identification of flaxseed oil based on portable fiber optic Raman spectroscopy combined with an oil microscopy method

“…Owing to the excessive amount of information collected by Raman spectroscopy, redundant data might be generated, which may hinder the extraction of information. Thus, PCA has been applied to simplify the data while retaining the information from the original data to the maximum (Covaciu et al., 2020). Hence, Raman spectral data of the flaxseed and adulterated oils were subjected to dimensionality reduction analysis (700–1800 nm), and the first three principal components (with a cumulative contribution of 95.2%) were selected for modeling analysis.…”

In Search of Authenticity Biomarkers in Food Supplements Containing Sea Buckthorn: A Metabolomics Approach

“…Hurkova et al [122] used direct analysis in real-time coupled with high-resolution mass spectrometry (DART-HRMS), ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS), and highperformance liquid chromatography coupled with diode array detector (HPLC-DAD) to authenticate one SB food supplement (oil-based capsule) purchased at a hypermarket in the Czech Republic. Covaciu et al [123] applied Raman spectroscopy, and gas-chromatography equipped with a flame ionization detector (GC-FID), combined with the supervised chemometric technique for oil differentiation, and found this suitable approach to detect possible adulteration of SB oil with sunflower oil. A multilayer perceptron-artificial neural network (MLP-ANN) was also tested in the same study [123].…”

“…Covaciu et al [123] applied Raman spectroscopy, and gas-chromatography equipped with a flame ionization detector (GC-FID), combined with the supervised chemometric technique for oil differentiation, and found this suitable approach to detect possible adulteration of SB oil with sunflower oil. A multilayer perceptron-artificial neural network (MLP-ANN) was also tested in the same study [123]. Berghian-Grosan and Magdas [124] proposed a new, cost-effective approach for the control and authentication of edible oils, based on the rapid processing of Raman spectra using machine learning algorithms.…”