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

Hindawi International Journal of Analytical Chemistry Volume 2018, Article ID 3160265, 8 pages https://doi.org/10.1155/2018/3160265 Research Article Detection of Adulteration in Canola Oil by Using GC-IMS and Chemometric Analysis Tong Chen , Xinyu Chen, Daoli Lu, and Bin Chen School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China Correspondence should be addressed to Bin Chen; Received 14 March 2018; Revised 2 July 2018; Accepted 2 September 2018; Published 23 September 2018 Academic Editor: David Touboul Copyright © 2018 Tong Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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). 1. Introduction Canola oil is a vegetable oil derived from rapeseed which has low erucic acid content [1]. In the 1980s, in order to decrease the health concerns about erucic acid, rapeseed varieties free from erucic acid were developed by using selective breeding [2, 3]. And then, these varieties were called canola. Nowadays, China has become one of the major consumers of canola oil, and it is also popular with consumers in Canada, Europe, and South America. Canola oil can provide consumers with many health benefits that others cannot provide. For example, canola oil is low in saturated fat and high in polyunsaturated fats, with a good ratio of omega-6 to omega-3, which make it very suitable for cooking [4–6]. Since canola oil has its specific function to human body, it has become one of the most susceptible food materials adulterated with other vegetable oils of lower quality, which is a serious threat to the health of consumers. Therefore, it requires reliable tools and methods for analyzing the purity of edible vegetable oil. Many techniques have been developed and used to detect adulteration in oil. These techniques include physicalchemical analysis, spectral analysis, gas chromatography (GC), gas chromatography-mass spectrometer (GC-MS), and electronic nose. Physical and chemical analysis includes sensory evaluation, colorimetry, centrifugation, and freezing. These traditional methods are simple and convenient and suitable for local monitoring. However, physical and chemical analysis methods are not accurate, require high degree technical expertise, and can only determine whether the sample is adulterated without finding out which specific component is adulterated. Spectral methods, e.g., Nuclear Magnetic Resonance Spectroscopy (NMR) [7], Raman [8], Fourier Transform Infrared (FTIR) [9], and Fluorescence [10], were shown to be useful for detection and quantification of adulteration in oil. However, their data analysis requires specialized software and complex algorithms which are difficult for common users to master. Chromatographic methods, such as GC-FID (flame ionization detector) [11], GC-MS [12], and high performance liquid chromatography (HPLC) [13], have been proven to be effective in detecting adulteration in oil. Nevertheless, the requirement for standard samples and high input of time and labor make them unsuitable for onsite analysis, thus limiting the wide use of them. Electronic nose [14] also has been used to evaluate the quality of oil. However, it needs electrode activation process during which 2 sensor poisoning may occur depending on operation and ambient conditions. Ion mobility spectrometry (IMS) is an analytical technique and was initially developed for the detection of explosives and chemical warfare agents [15]. At present, it has been widely used in novel application in the field of agricultural products and foods [16]. IMS is used to separate and identify ionized molecules in the gas phase based on their mobility in a carrier gas, which is considered as a screening technique due to its ability to identify the properties of samples at considerable low cost and short analysis time without pretreatment. On the other hand, IMS has limitations in detecting complex sample (e.g., food) for having low resolution and the risk of mutual interferences between analytes [17]. Yet, if combined with GC, the capability of IMS in separating various components is strengthened. Chromatographic elution of each target compound can be automatically analyzed and the obtained data information is richer because both retention time and drift time information are included [18]. GC-IMS has been shown to be able to characterize and discriminate adulteration in oil, wines, honey, and meat [19–23]. Successful applications have been reported on the determination of aldehydes in oil, adulteration in extra virgin olive oils by using UV-IMS [24], and determination of volatile compounds [25]. Most of the previous reports on canola oil analysis mainly focused on the adulteration detection and main component quantification of oil species [8, 26], with few studies performed on aroma differentiation. Odour is an important quality criterion for edible vegetable oil. The previous relevant studies often transformed the matrix to vector (like peaks selected manually as variables) for chemometric analysis by UV-IMS or GC-IMS, which may result in losing information of certain analytes. In addition, to the best of our knowledge, no recent work has been conducted using chemometrics for feature extraction of the two-dimensional data produced from GC-IMS instrument. Therefore, the potential use of GCIMS for detection of canola oil adulteration was investigated. The aims of this study were (1) to investigate the use of GCIMS combined with pattern recognition methods to detect the presence of adulterant in canola oil, (2) to apply a new method to extract information for two-dimensional data, (3) to build a model for content prediction of adulterated oil in canola oil, and (4) to develop a rapid method for adulteration detection in canola oil. 2. Materials and Methods 2.1. Preparation of Oil (...truncated)