Ion Mobility Spectrometry as a Potential Tool for Flavor Control in Chocolate Manufacture

Evaluation of IMS drift tube temperature on the peak shape of high boiling fragrance compounds towards allergen detection in complex cosmetic products and essential oils

Gas chromatography-ion mobility spectrometry (GC-IMS) has recently gained increasing attention for the analysis of volatile compounds due to its high sensitivity, selectivity, and robust design. Peak shape distortion, including peak tailing or broadening, are well known challenges in chromatographic analysis that result in peak asymmetry and decreased resolution. However, in IMS analysis peak tailing, which is independent on the column separation technique, have also been observed. As high boiling substances, such as monoterpenes, are mainly affected by enlarged peak tailing in GC-IMS, we propose that condensation or adsorption effects within the "cold" IMS cell, which is commonly operated at 45 °C-90 °C, are the root cause. To avoid condensation and to decrease peak tailing, we used a prototypic high temperature ion mobility spectrometry (HTIMS) in this work, which allows an increase of the IMS drift tube temperature up to 180 °C. This HTIMS was coupled to a GC column separation and used to analyse the peak shape of homologues series of ketones, alcohols, aldehydes, as well as high boiling fragrance compounds, such as monoterpenes and phenylpropanoids. While we were able to show that an increased IMS drift tube temperatures correlates well with improved peak shapes, the GC parameters of the HS-GC-HTIMS method, however, were found to have little effect on the peak shapes in IMS spectra. In particular monoterpenes, which display intense peak tailing at lower IMS drift tube temperatures, show significant improvement of the peak shape at higher IMS drift tube temperatures. This leads to the assumption that high boiling substances indeed undergo condensation effects within the IMS cell at low drift tube temperatures. For many separation tasks, such as the separation of the phenylpropanoids eugenol and isoeugenol, comparably low IMS temperatures of 120 °C are already sufficient to achieve a resolution above 1.5. However, the optimal drift tube temperature is dependent on the substance class. While the aspect ratio increases steadily for most monoterpenes, phenylpropanoids and aldehyde monomer peaks investigated, an optimal aspect ratio was found for ketones between 140 °C and 160 °C and alcohols between 120 °C and 140 °C. Lastly, the change of the reduced mobility K₀ with the increase of drift tube temperature was analysed. Compounds with similar chemical structure, such as the alcoholic monoterpenes citronellol and geraniol or the phenylpropanoids eugenol and isoeugenol show similar shifts of the K₀ value. Substances which differ in their chemical structure, such as the aldehyde monoterpenes citral and cinnamal have substantially different shifts of the K₀ value. With a future large substance database, the temperature dependant slope of the K₀ value of a substance could be used to identify the substance groups of unknown molecules. Furthermore, substances with the same drift time but different chemical composition could be separable through a change in drift tube temperature.

Volatilomics-Based Microbiome Evaluation of Fermented Dairy by Prototypic Headspace-Gas Chromatography–High-Temperature Ion Mobility Spectrometry (HS-GC-HTIMS) and Non-Negative Matrix Factorization (NNMF)

Fermented foods, such as yogurt and kefir, contain a versatile spectrum of volatile organic compounds (VOCs), including ethanol, acetic acid, ethyl acetate, and diacetyl. To overcome the challenge of overlapping peaks regarding these key compounds, the drift tube temperature was raised in a prototypic high-temperature ion mobility spectrometer (HTIMS). This HS-GC-HTIMS was used for the volatilomic profiling of 33 traditional kefir, 13 commercial kefir, and 15 commercial yogurt samples. Pattern recognition techniques, including principal component analysis (PCA) and NNMF, in combination with non-targeted screening, revealed distinct differences between traditional and commercial kefir while showing strong similarities between commercial kefir and yogurt. Classification of fermented dairy samples into commercial yogurt, commercial kefir, traditional mild kefir, and traditional tangy kefir was also possible for both PCA- and NNMF-based models, obtaining cross-validation (CV) error rates of 0% for PCA-LDA, PCA-kNN (k = 5), and NNMF-kNN (k = 5) and 3.3% for PCA-SVM and NNMF-LDA. Through back projection of NNMF loadings, characteristic substances were identified, indicating a mild flavor composition of commercial samples, with high concentrations of buttery-flavored diacetyl. In contrast, traditional kefir showed a diverse VOC profile with high amounts of flavorful alcohols (including ethanol and methyl-1-butanol), esters (including ethyl acetate and 3-methylbutyl acetate), and aldehydes. For validation of the results and deeper understanding, qPCR sequencing was used to evaluate the microbial consortia, confirming the microbial associations between commercial kefir and commercial yogurt and reinforcing the differences between traditional and commercial kefir. The diverse flavor profile of traditional kefir primarily results from the yeast consortium, while commercial kefir and yogurt is primarily, but not exclusively, produced through bacterial fermentation. The flavor profile of fermented dairy products may be used to directly evaluate the microbial consortium using HS-GC-HTIMS analysis.

Characterization of Volatile Component Changes in Peas under Different Treatments by GC-IMS and GC-MS

Volatile profiles of peas under 9 kinds of different treatments including native, washing, blanching, precooling, freezing, steaming, boiling, frying, and freeze-drying were characterized by GC-IMS and GC-MS. The differences of volatile compounds in different peas were observed from the characteristic fingerprints by GC-IMS. The Venn diagram found that the common flavor substances codetected by GC-IMS and GC-MS were n-hexanal, nonanal, 1-octene-3-ol, benzaldehyde, 6-methyl-5-hepten-2-one, trans-2-octenal, and 2-ethyl-3,5-dimethylpyrazine, which were speculated to be the key flavor substances of peas. The cluster analysis of the heat map conducted towards the differences of volatile components in peas under different treatments; the results indicated that peas could be mainly divided into four groups, which was consistent with the above conclusion of GC-IMS. Eight sensory descriptors were used to evaluate the aroma notes: sweet flowers, fat fragrance, waxy aldehydes, mushroom hay, roasted potato with nuts, vegetable-like bean, spicy dry tar, and bitter almond from the sensory analysis, and the sensory analysis also showed good agreement with the results of GC-IMS and GC-MS. The results indicated that the volatile compounds of peas under different treatments could be visualized and identified quickly via GC-IMS, and the samples could be clearly classified based on the difference of volatile compounds. Practical Application. In the study, fingerprints coupled with cluster analysis were a visualized method for the identification of volatile compounds. Meanwhile, a new method, the Venn diagram with OAV, was used to identify the key-aroma of products. Finally, a rapid method is established to classify products by GC-IMS. In future practical applications, GC-IMS can be used to classify products from different origins and different manufacturers. Similarly, it can identify fake and inferior products and whether the products have deteriorated. In addition, this research will provide a new strategy to find the relationship between flavor compounds and various processed technology towards different cereals.

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

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.

Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis

This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique's principle and focused on said technologies' applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample.