Application of GC-IMS coupled with chemometric analysis for the classification and authentication of geographical indication agricultural products and food

Analysis and application of volatile metabolic profiles of Escherichia coli: a preliminary GC-IMS-based study

Nosocomial infections caused by Escherichia coli (E. coli) may pose serious risks to patients, and early identification of pathogenic bacteria and drug sensitivity results can improve patient prognosis. In this study, we clarified the composition and relative content of volatile organic compounds (VOCs) generated by E. coli in tryptic soy broth (TSB) using gas chromatography-ion mobility spectrometry (GC-IMS). We explored whether imipenem (IPM) could be utilized to differentiate between carbapenem-sensitive E. coli (CSEC) and carbapenem-resistant E. coli (CREC). The results revealed that 36 VOCs (alcohols, aldehydes, acids, esters, ketones, pyrazines, heterocyclic compounds, and unknown compounds) were detected using GC-IMS. Besides, the results indicated that changes in the relative content of VOCs as well as changes in the signal intensity of fingerprints were able to assess the growth state of bacteria during bacterial growth and help identify E. coli. Lastly, under selective pressure of IPM, volatile fingerprints of E. coli could be employed as a model to distinguish CSEC from CREC strains.

Gas chromatography-ion mobility spectrometry (GC-IMS) technique and its recent applications in grain research

Grains are the primary source of food for most people worldwide and constitute a major source of carbohydrates. Many novel technologies are being employed to ensure the safety and reliability of grain supply and production. Gas chromatography-ion mobility spectrometry (GC-IMS) can effectively separate and sensitively detect volatile organic compounds. It possesses advantages such as speed, convenience, high sensitivity, no pretreatment, and wide applicability. In recent years, many studies have shown that the application of GC-IMS technology for grain flavor analysis can play a crucial role in grains. This article elucidates the working principle of GC-IMS technology, reviews the application of GC-IMS in grains in the past 5 years. GC-IMS technology is mainly applied in four aspects in grains. In grain classification, it distinguishes varieties, quality, origin, production year, and processing methods based on the trace differences in volatile organic compounds, thereby fulfilling various grain classification requirements such as origin tracing, geographical indication product recognition, variety identification, production year identification, and detection of counterfeit and inferior grain samples. In optimizing the processing technology of grains and their products, it can improve food flavor, reduce undesirable flavors, and identify better processing parameters. In grain storage, it can determine the storage time, detect spoilage phenomena such as mold and discoloration during storage, eliminate pests affecting storage, and predict the vitality of seeds after storage. In aroma evaluation of grains and their processed products, it can assess the impact of new raw materials, new technologies, fermentation processes, and even oral processing on the quality of grain products. This article also summarizes the characteristics of GC-IMS technology, compiles typical grain flavor compounds, and provides prospects for the future application of GC-IMS. © 2024 Society of Chemical Industry.

Explorative Study on Volatile Organic Compounds of Cinnamon Based on GC-IMS

Cinnamon is one of the most popular spices worldwide, and volatile organic compounds (VOCs) are its main metabolic products. The misuse or mixing of cinnamon on the market is quite serious. This study used gas chromatography-ion migration spectroscopy (GC-IMS) technology to analyze the VOCs of cinnamon samples. The measurement results showed that 66 VOCs were detected in cinnamon, with terpenes being the main component accounting for 45.45%, followed by aldehydes accounting for 21.21%. The content of esters and aldehydes was higher in RG-01, RG-02, and RG-04; the content of alcohols was higher in RG-01; and the content of ketones was higher in RG-02. Principal component analysis, cluster analysis, and partial least squares regression analysis can be performed on the obtained data to clearly distinguish cinnamon. According to the VIP results of PLS-DA, 1-Hexanol, 2-heptanone, ethanol, and other substances are the main volatile substances that distinguish cinnamon. This study combined GC-IMS technology with chemometrics to accurately identify cinnamon samples, providing scientific guidance for the efficient utilization of cinnamon. At the same time, this study is of great significance for improving the relevant quality standards of spices and guiding the safe use of spices.

Characterization of Volatile Compounds in Donkey Meat by Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) Combined with Chemometrics

Volatile compounds (VOCs) are an important factor affecting meat quality. However, the characteristic VOCs in different parts of donkey meat remain unknown. Accordingly, this study represents a preliminary investigation of VOCs to differentiate between different cuts of donkey meat by using headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) combined with chemometrics analysis. The results showed that the 31 VOCs identified in donkey meat, ketones, alcohols, aldehydes, and esters were the predominant categories. A total of 10 VOCs with relative odor activity values ≥1 were found to be characteristic of donkey meat, including pentanone, hexanal, nonanal, octanal, and 3-methylbutanal. The VOC profiles in different parts of donkey meat were well differentiated using three- and two-dimensional fingerprint maps. Nine differential VOCs that represent potential markers to discriminate different parts of donkey meat were identified by chemometrics analysis. These include 2-butanone, 2-pentanone, and 2-heptanone. Thus, the VOC profiles in donkey meat and specific VOCs in different parts of donkey meat were revealed by HS-GC-IMS combined with chemometrics, whcih provided a basis and method of investigating the characteristic VOCs and quality control of donkey meat.

Discriminant Analysis of Aroma Differences between Cow Milk Powder and Special Milk Powder (Donkey, Camel, and Horse Milk Powder) in Xinjiang Based on GC-IMS and Multivariate Statistical Methods

In order to explore the aromatic differences between Xinjiang cow milk powder and specialty milk powder (donkey, camel, and horse milk powder), Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) analysis was employed to investigate the volatile compounds in these four types of milk powders. A total of 61 volatile substances were detected, with ketones, aldehydes, and alcohols being the primary flavor components in the milk powders. While the aromatic components of the different milk powders showed similarities in terms of types, there were significant differences in their concentrations, exhibiting distinct characteristics for each type. The Partial Least Squares Discriminant Analysis (PLS-DA) showed that there were 15, 14, and 23 volatile compounds that could be used for discrimination of cow milk powder against specialty milk powders, respectively. And it was validated by Receiver Operating Characteristic (ROC) analysis, and finally, 8, 6, and 19 volatile compounds were identified as valid differential marker substances. To facilitate visual discrimination between the different milk powders, we established GC-IMS fingerprint spectra based on the final discriminant markers. These studies provide theoretical guidance for the application of volatile compounds to discriminate adulteration of milk powder marketed in Xinjiang.