Detection of Adulteration in Canola Oil by Using GC-IMS and Chemometric Analysis

Abstract

The aim of the present study was to detect adulteration of canola oil with other vegetable oils such as sunflower, soybean, and peanut oils and to build models for predicting the content of adulterant oil in canola oil. In this work, 147 adulterated samples were detected by gas chromatography-ion mobility spectrometry (GC-IMS) and chemometric analysis, and two methods of feature extraction, histogram of oriented gradient (HOG) and multiway principal component analysis (MPCA), were combined to pretreat the data set. The results evaluated by canonical discriminant analysis (CDA) algorithm indicated that the HOG-MPCA-CDA model was feasible to discriminate the canola oil adulterated with other oils and to precisely classify different levels of each adulterant oil. Partial least square analysis (PLS) was used to build prediction models for adulterant oil level in canola oil. The model built by PLS was proven to be effective and precise for predicting adulteration with good regression (R2>0.95) and low errors (RMSE ≤ 3.23).

Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR

“…The strategy 2 (sixteen selected wavenumbers) on PLS-DA lead to a slight diminishment of the performance, obtaining, in the worst scenario, a 3% of classification error. Performance obtained in the present study showed a slight improvement respect to the results reached by Chen et al (2018), where adulterants were detected up to a 5% proportion using PLS regression on GC analysis with errors of about 2-3%. It can be stated that SIMCA-MC gave a better description of the data.…”

Sustainable Palm Oil—The Role of Screening and Advanced Analytical Techniques for Geographical Traceability and Authenticity Verification

“…Similar to this study, Hamdan et al [79] detected SPO at low concentrations using a different approach, which exploits the dielectric properties of CPO at different temperatures and sludge contamination levels. The applicability of GC-IMS in different areas of food analysis, authentication, adulteration, and safety have been demonstrated in many different studies [84][85][86][87]. The advantages attributed to GC-IMS can be further enhanced if integrated with additional separation technologies such as traditional liquid chromatography (LC) and gas chromatography (GC) mass spectrometry (MS) workflows to identify specific compounds with added confidence, either in a targeted or non-targeted approach.…”

Ion Mobility Spectrometry in Food Analysis: Principles, Current Applications and Future Trends

“…Harms and wild Eurya spp. (Theaceae)) with cheaper Sapium honey [85], canola oil with other vegetable oils (i.e., sunflower, soybean, and peanut oils) [86], and crude palm oil with process byproducts of lower quality (i.e., palm fiber oil and sludge palm oil) [87]. As previously mentioned, GC–IMS methods should mainly be used as screening approaches because MS results essential for confirmation.…”