Gas Chromatography-Ion Mobility Spectrometry Detection of Odor Fingerprint as Markers of Rapeseed Oil Refined Grade

Abstract

In this work, gas chromatography-ion mobility spectrometry (GC-IMS) was used to analyze the volatile organic compound changes of rapeseed oil with different refined grades, the odor fingerprints of refined rapeseed oil were constructed, and a nonlinear model was built to realize rapid and accurate discrimination of rapeseed oil with different refined grades. 124 rapeseed oil samples with different refined grades were collected and analyzed by GC-IMS and chemometric tools, and 34 characteristic peaks were selected by the colorized difference method as variables to characterize the internal quality in rapeseed oil of different refined grades. The principal component analysis algorithm was used to further reduce dimensionality and extract the most relevant information. The k-nearest neighbor algorithm was applied to build a discriminant model. All the samples were recognized accurately without errors, and the results show the potential of this method to discriminate different refined grades of vegetable oil.

Characterization and Biological Activities of Seed Oil Extracted from Berberis dasystachya Maxim. by the Supercritical Carbon Dioxide Extraction Method

“…These compounds included 7 esters, 17 aldehydes, four ketones, 5 alcohols, 1 terpene, and 1 organic acid. The highest number of compounds (33) was found in BDSO extracted by the PEE method, whereas the lowest number of compounds (26) was found in BDSO extracted by the SC-CO 2 method. This result is inconsistent with other reports focused on the volatile compounds of rattan pepper oil obtained by PEE and SC-CO 2 [40].…”

“…Thus, several spectra at a given retention time can be simultaneously processed to obtain information on both retention time and drift time, and larger amounts of analytical information from each sample can be obtained [26,27]. GC-IMS can not only detect compounds from different plant origins, but can also be used for evaluating and characterizing various products and vegetable oils [24,26,28]. At present, however, information on the changes of volatile components in oils of preserved B. dasystachya seeds during extraction processing is unavailable.…”

Application of gas chromatography-ion mobility spectrometry in the analysis of food volatile components

“…Olive oil Compared with some signal peaks, the classification prediction rate of full spectrum is improved, CC-IMS can better distinguish, MCC has higher efficiency. Geographical sources and varieties are successfully distinguished [26][27][28][29] Refined sunflower oil and rapeseed oil Established the volatile fingerprints, it can accurately identify the refining grade [30,31] Meat products Beef, lamb, chicken Characteristic flavor compounds were effectively characterized [32] Cordyceps militaris chicken soup Enzymolysis promoted the volatilization of typical flavor compounds, cordyceps militaris inhibited some flavor substances [33] Iberian ham Identified the feeding methods and classified the samples [34] Fruit and vegetable products Matsutake Established the volatile fingerprints, hot air drying affects the formation of carbon volatile substances [35] Kumquat, strawberry Explored their flavor substances [36,37] Wine Brandy Established a brandy age identification model [40] Raspberry wine Sequential inoculation can impart "fruity" and "sweetness" [41] Aquatic products Silver carp Found the dominant bacterial groups for the putrefaction of cold silver carp [42] Dried salted fish The difference of volatile components at different temperatures, the main markers of microbial deterioration were determined.…”

“…In addition, Chen Tong et al established the volatile fingerprints of refined sunflower oil [30] and refined rapeseed oil [31], using the two-dimensional difference spectrum method and the color difference method to screen out 22 and 34 characteristic peaks respectively as a variable that characterizes the difference in the degree of refining, combined with the k-proximity algorithm, it can accurately identify vegetable oils of different refining grades. The former's successful discrimination rate for samples can reach 97.3%.…”

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

“…Using targeted approaches and applying PCA- k NN, the same authors reported a successful classification of rapeseed oils according to their quality (grade 1–4) and a successful determination of vegetable oil according to its botanical origin (sesame oil, rapeseed oil, and camellia oil). For the classification of the rapeseed oil quality, the colorized differences method was applied to CC-IMS data, resulting in 34 peaks of interest and a predictive accuracy of 100% [ 138 ]. Furthermore, Otsu’s method and colorized differences method was used for automatic peak detection, resulting in 88 peaks of interest and a predictive accuracy of 98.3% for the classification of vegetable oil using MCC-IMS data [ 139 ].…”