On-line headspace-multicapillary column-ion mobility spectrometry hyphenation as a tool for the determination of off-flavours in foods

Signal Preprocessing in Instrument-Based Electronic Noses Leads to Parsimonious Predictive Models: Application to Olive Oil Quality Control

Gas sensor-based electronic noses (e-noses) have gained considerable attention over the past thirty years, leading to the publication of numerous research studies focused on both the development of these instruments and their various applications. Nonetheless, the limited specificity of gas sensors, along with the common requirement for chemical identification, has led to the adaptation and incorporation of analytical chemistry instruments into the e-nose framework. Although instrument-based e-noses exhibit greater specificity to gasses than traditional ones, they still produce data that require correction in order to build reliable predictive models. In this work, we introduce the use of a multivariate signal processing workflow for datasets from a multi-capillary column ion mobility spectrometer-based e-nose. Adhering to the electronic nose philosophy, these workflows prioritized untargeted approaches, avoiding dependence on traditional peak integration techniques. A comprehensive validation process demonstrates that the application of this preprocessing strategy not only mitigates overfitting but also produces parsimonious models, where classification accuracy is maintained with simpler, more interpretable structures. This reduction in model complexity offers significant advantages, providing more efficient and robust models without compromising predictive performance. This strategy was successfully tested on an olive oil dataset, showcasing its capability to improve model parsimony and generalization performance.

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.

Antioxidative properties of fish roe peptides combined with polyphenol on the fish oil oleogel

BACKGROUND

This study aimed to investigate the effects of large yellow croaker (Larimichthys crocea) roe protein hydrolysate (LYCPH)-polyphenol (catechin (CA), gallic acid (GA), and tannic acid (TA)) conjugates on the oxidative stability of fish oil in an oleogel system.

RESULTS

Scanning electron microscopy and Fourier transform infrared spectroscopy suggested that the LYCPH-polyphenol conjugates were nearly spherical and non-covalent and that covalent effects could coexist between LYCPH and polyphenols. LYCPH-TA exhibited the highest ABTS scavenging, reducing capacities, and emulsifying stability. Raman spectra and chemometrics revealed that LYCPH-TA loaded with oleogels had the best oxidative stability. Additionally, 32 volatile compounds were identified in fish oil by headspace gas chromatography-ion mobility spectrometry.

CONCLUSION

Overall, this study demonstrated that fish oil oleogels loaded with LYCPH-polyphenol conjugates could inhibit fish oil oxidation. © 2022 Society of Chemical Industry.

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.

Full Workflows for the Analysis of Gas Chromatography-Ion Mobility Spectrometry in Foodomics: Application to the Analysis of Iberian Ham Aroma

Gas chromatography—ion mobility spectrometry (GC-IMS) allows the fast, reliable, and inexpensive chemical composition analysis of volatile mixtures. This sensing technology has been successfully employed in food science to determine food origin, freshness and preventing alimentary fraud. However, GC-IMS data is highly dimensional, complex, and suffers from strong non-linearities, baseline problems, misalignments, peak overlaps, long peak tails, etc., all of which must be corrected to properly extract the relevant features from samples. In this work, a pipeline for signal pre-processing, followed by four different approaches for feature extraction in GC-IMS data, is presented. More precisely, these approaches consist of extracting data features from: (1) the total area of the reactant ion peak chromatogram (RIC); (2) the full RIC response; (3) the unfolded sample matrix; and (4) the ion peak volumes. The resulting pipelines for data processing were applied to a dataset consisting of two different quality class Iberian ham samples, based on their feeding regime. The ability to infer chemical information from samples was tested by comparing the classification results obtained from partial least-squares discriminant analysis (PLS-DA) and the samples’ variable importance for projection (VIP) scores. The choice of a feature extraction strategy is a trade-off between the amount of chemical information that is preserved, and the computational effort required to generate the data models.

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.

Studying dynamic aroma release by headspace-solid phase microextraction-gas chromatography-ion mobility spectrometry (HS-SPME-GC-IMS): method optimization, validation, and application

To understand aroma perception from complex food matrices' determination of dynamic aroma release during simulated oral processing is necessary. In this study optimization, validation and application of a novel method coupling headspace-solid phase microextraction (HS-SPME) with gas chromatography-ion mobility spectrometry (GC-IMS) is presented. Thirteen character impact compounds imparting different chemical properties are studied to understand capabilities and limitations of the method. It was shown for the first time that the temperature of the IMS sample inlet can be increased up to 200 °C without instrumental constraints. Linear calibration was possible for eleven of the thirteen compounds with one decade dynamic range. The limit of detection and quantitation were 2.1-63.0 ppb and 7.2-210.1 ppb, respectively. Diacetyl could be detected in negative polarity mode of IMS, however with lower precision compared to the compounds detected in positive mode. Limitations of the method were short HS-SPME extraction time, which in the case of caproic acid was not sufficient for reliable quantification. Additionally, δ-decalactone could not be detected due to maximum GC temperature of 200 °C. Application of the method to determine dynamic aroma release from a dairy matrix was successfully shown for nine compounds. Analysis of complex food matrix was performed with similar precision compared to analysis in aqueous solution, thus proving high robustness of the method towards matrix effects.

Food safety and quality assessment: comprehensive review and recent trends in the applications of ion mobility spectrometry (IMS)

Ion mobility spectrometry (IMS) is an analytical separation and diagnostic technique that is simple and sensitive and a rapid response and low-priced technique for detecting trace levels of chemical compounds in different matrices. Chemical agents and environmental contaminants are successfully detected by IMS and have been recently considered to employ in food safety. In addition, IMS uses stand-alone or coupled analytical diagnostic tools with chromatographic and spectroscopic methods. Scientific publications show that IMS has been applied 21% in the pharmaceutical industry, 9% in environmental studies and 13% in quality control and food safety. Nevertheless, applications of IMS in food safety and quality analysis have not been adequately explored. This review presents the IMS-related analysis and focuses on the application of IMS in food safety and quality. This review presents the important topics including detection of traces of chemicals, rate of food spoilage and freshness, food adulteration and authenticity as well as natural toxins, pesticides, herbicides, fungicides, veterinary, and growth promoter drug residues. Further, persistent organic pollutants (POPs), acrylamide, polycyclic aromatic hydrocarbon (PAH), biogenic amines, nitrosamine, furfural, phenolic compounds, heavy metals, food packaging materials, melamine, and food additives were also examined for the first time. Therefore, it is logical to predict that the application of the IMS technique in food safety, food quality, and contaminant analysis will be impressively increased in the future. HighlightsCurrent status of IMS for residues and contaminant detection in food safety.To assess all the detected contaminants in food safety, for the first time.Identified IMS-related parameters and chemical compounds in food safety control.

On-Line Coupling of Chip-Electrochromatography and Ion Mobility Spectrometry

We report the first hyphenation of chip-electrochromatography (ChEC) with ion mobility spectrometry (IMS). This approach combines the separation power of two electrokinetically driven separation techniques, the first in liquid phase and the second in gas phase, with a label-free detection of the analytes. For achieving this, a microfluidic glass chip incorporating a monolithic separation column, a nanofluidic liquid junction for providing post-column electrical contact, and a monolithically integrated electrospray emitter was developed. This device was successfully coupled to a custom-built high-resolution drift tube IMS with shifted potentials. After proof-of-concept studies in which a mixture of five model compounds was analyzed in less than 80 s, this first ChEC-IMS system was applied to a more complex sample, the analysis of herbicides spiked in the wine matrix. The use of ChEC before IMS detection not only facilitated the peak allocation and increased the peak capacity but also enabled analyte quantification. As both, ChEC and IMS work at ambient conditions and are driven by high voltages, no bulky pumping systems are needed, neither for the hydrodynamic pumping of the mobile phase as in high-performance liquid chromatography nor for generating a vacuum system as in mass spectrometry. Accordingly, the approach has great potential as a portable analytical system for field analysis of complex mixtures.

Characterization of volatile metabolites formed by molds on barley by mass and ion mobility spectrometry

The contamination of barley by molds on the field or in storage leads to the spoilage of grain and the production of mycotoxins, which causes major economic losses in malting facilities and breweries. Therefore, on-site detection of hidden fungus contaminations in grain storages based on the detection of volatile marker compounds is of high interest. In this work, the volatile metabolites of 10 different fungus species are identified by gas chromatography (GC) combined with two complementary mass spectrometric methods, namely, electron impact (EI) and chemical ionization at atmospheric pressure (APCI)-mass spectrometry (MS). The APCI source utilizes soft X-radiation, which enables the selective protonation of the volatile metabolites largely without side reactions. Nearly 80 volatile or semivolatile compounds from different substance classes, namely, alcohols, aldehydes, ketones, carboxylic acids, esters, substituted aromatic compounds, alkenes, terpenes, oxidized terpenes, sesquiterpenes, and oxidized sesquiterpenes, could be identified. The profiles of volatile and semivolatile metabolites of the different fungus species are characteristic of them and allow their safe differentiation. The application of the same GC parameters and APCI source allows a simple method transfer from MS to ion mobility spectrometry (IMS), which permits on-site analyses of grain stores. Characterization of IMS yields limits of detection very similar to those of APCI-MS. Accordingly, more than 90% of the volatile metabolites found by APCI-MS were also detected in IMS. In addition to different fungus genera, different species of one fungus genus could also be differentiated by GC-IMS.

Modular and reconfigurable gas chromatography / differential mobility spectrometry (GC/DMS) package for detection of volatile organic compounds (VOCs)

Due to the versatility of present day microcontroller boards and open source development environments, new analytical chemistry devices can now be built outside of large industry and instead within smaller individual groups. While there are a wide range of commercial devices available for detecting and identifying volatile organic compounds (VOCs), most of these devices use their own proprietary software and complex custom electronics, making modifications or reconfiguration of the systems challenging. The development of microprocessors for general use, such as the Arduino prototyping platform, now enables custom chemical analysis instrumentation. We have created an example system using commercially available parts, centered around on differential mobility spectrometer (DMS) device. The Modular Reconfigurable Gas Chromatography - Differential Mobility Spectrometry package (MR-GC-DMS) has swappable components allowing it to be quickly reconfigured for specific application purposes as well as broad, generic use. The MR-GC-DMS has a custom user-friendly graphical user interface (GUI) and precisely tuned proportional-integral-derivative controller (PID) feedback control system managing individual temperature-sensitive components. Accurate temperature control programmed into the microcontroller greatly increases repeatability and system performance. Together, this open-source platform enables researchers to quickly combine DMS devices in customized configurations for new chemical sensing applications.

Trace analysis by ion mobility spectrometry: From conventional to smart sample preconcentration methods. A review

Ion mobility spectrometry (IMS) is a rapid and high sensitive technique widely used in security and forensic areas. However, a lack of selectivity is usually observed in the analysis of complex samples due to the scarce resolution of the technique. The literature concerning the use of conventional and novel smart materials in the pretreatment and preconcentration of samples previous to IMS determinations has been critically reviewed. The most relevant strategies to enhance selectivity and sensitivity of IMS determinations have been widely discussed, based in the use of smart materials, as immunosorbents, aptamers, molecularly imprinted polymers (MIPs), ionic liquids (ILs) and nanomaterial. The observed trend is focused on the development of IMS analytical methods in combination of selective sample treatments in order to achieve quick, reliable, sensitive, and selective methods for the analysis of complex samples such as biological fluids, food, or environmental samples.