Gas chromatography ion mobility spectrometry (GC-IMS) dataset of honey samples with different botanical origins.

Gas chromatography ion mobility spectrometry (GC-IMS) is a robust and sensitive benchtop technique commonly used for non-target screening of volatile organic compounds. It has been applied to authenticity analysis by generating characteristic "fingerprints" of food samples, well suited for chemometric data analysis. This dataset contains headspace GC-IMS spectra from 50 monofloral honey samples from three different botanical origins, 18 acacia honeys (Robinia pseudoacacia), 19 canola honeys (Brassica napus) and 18 honeydew honeys (forest flowers). Honeys were sourced from the beekeepers directly or obtained from governmental food inspectors from Baden-Wuerttemberg, Germany. Authenticity was confirmed by pollen analysis in the framework of the official control of foodstuffs. The data was acquired using a setup based on an Agilent 6890N gas chromatograph (Agilent Technologies, Palo Alto, CA) and an OEM Standalone IMS cell from G.A.S Sensorsysteme m. b. H. (Dortmund, Germany). All samples were recorded in duplicates and spectra are presented as raw data in the .mea file format. The dataset is available on Mendeley Data: https://data.mendeley.com/datasets/jxj2r45t2x.

Isolation, characterisation and description of the roseoflavin producer Streptomyces berlinensis sp. nov.

The Gram-positive bacteria Streptomyces davaonensis and Streptomyces cinnabarinus have been the only organisms known to produce roseoflavin, a riboflavin (vitamin B2) derived red antibiotic. Using a selective growth medium and a phenotypic screening, we were able to isolate a novel roseoflavin producer from a German soil sample. The isolation procedure was repeated twice, that is, the same strain could be isolated from the same location in Berlin 6 months and 12 months after its first isolation. Whole genome sequencing of the novel roseoflavin producer revealed an unusual chromosomal arrangement and the deposited genome sequence of the new isolate (G + C content of 71.47%) contains 897 genes per inverted terminal repeat, 6190 genes in the core and 107 genes located on an illegitimate terminal end. We identified the roseoflavin biosynthetic genes rosA, rosB and rosC and an unusually high number of riboflavin biosynthetic genes. Overexpression of rosA, rosB and rosC in Escherichia coli and enzyme assays confirmed their predicted functions in roseoflavin biosynthesis. A full taxonomic analysis revealed that the isolate represents a previously unknown Streptomyces species and we propose the name Streptomyces berlinensis sp. nov. for this roseoflavin producer.

Shaping the Future of Coffee: Climate Resilience, Liberica's Rise, and By-Product Innovation-Highlights from the International Coffee Convention 2023 (ICC2023).

The International Coffee Convention 2023 comprehensively addressed the contemporary challenges and advancements in the coffee industry, emphasizing sustainability, health, and innovation. This convention gathered experts and stakeholders to explore diverse aspects of coffee, ranging from the potential of underutilized species like Coffea liberica in terms of climate resilience to the innovative use of coffee by-products. The convention featured presentations and discussions, employing both empirical research and analytical reviews to explore various topics, including the health benefits of coffee, the advancements in traceability and authentication methods, and the impact of global regulatory changes on coffee production and trade. The key findings highlighted the importance of biodiversity in coffee production as a response to climate change, the significant health benefits and sustainability potential of coffee by-products, and the evolving landscape of coffee consumption patterns driven by technological innovations. The convention also stressed the need for alignment in global coffee trade regulations, particularly concerning deforestation and traceability. The 2023 convention underscored the complexity and interconnectivity of the coffee industry's challenges and opportunities. It concluded with a forward-looking perspective, emphasizing the need for continued research, sustainable practices, and collaborative efforts to shape the future of the coffee industry. The community is looking forward to furthering these discussions at the next International Coffee Convention in 2024.

Nontargeted Volatile Metabolite Screening and Microbial Contamination Detection in Fermentation Processes by Headspace GC-IMS.

Gas chromatography combined with ion mobility spectrometry (GC-IMS) is a powerful separation and detection technique for volatile organic compounds (VOC). This combination is characterized by exceptionally low detection limits in the low ppbv range, high 2-dimensional selectivity, and robust operation. These qualities make it an ideal tool for nontarget screening approaches. Fermentation broths contain a substantial number of VOC, either from the medium or produced by microbial metabolism, that are currently not regularly measured for process monitoring. In this study, Escherichia coli, Saccharomyces cerevisiae, Levilactobacillus brevis, and Pseudomonas fluorescens were exemplarily used as model organisms and cultivated, and the headspace was analyzed by GC-IMS. Additionally, mixed cultures for every combination of two of the microorganisms were also characterized. Multivariate data analysis of the GC-IMS data revealed that it is possible to differentiate between the microorganisms using PLS-DA with a prediction accuracy of 0.92. The mixed cultures could be separated from the pure cultures with accuracies between 0.87 and 1.00 depending on the organism. GC-IMS data correlate with the optical density and can be used to follow and model growth curves. The root mean squared errors ranged between 10 and 20% of the maximum value, depending on the organism. Peak identification with reference compounds did not reveal specific marker compounds, rather the pattern was found to be responsible for the model performance.

Benchtop volatilomics supercharged: How machine learning based design of experiment helps optimizing untargeted GC-IMS gas phase metabolomics.

Gas chromatography-ion mobility spectrometry (GC-IMS) plays a significant role in both targeted and non-targeted analyses. However, the non-linear behavior of IMS and its complex ion chemistry pose challenges for finding optimal experimental conditions using existing methodologies. To address these issues, integrating machine learning (ML) strategies offers a promising approach. In this study, we propose a hybrid strategy, combining design of experiment (DOE) and machine learning (ML) for optimizing GC-IMS conditions in non-targeted volatilomic/flavoromic analysis, with saffron volatiles as a case study. To begin, a rotatable circumscribed central composite design (CCD) is used to define five influential GC-IMS factors of sample amount, headspace temperature, incubation time, injection volume, and split ratio. Subsequently, two ML models are utilized: multiple linear regression (MLR) as a linear model and Bayesian regularized-artificial neural network (BR-ANN) as a nonlinear model. These models are employed to predict the response variables of total peak areas (PAs) and the number of detected peaks (PNs) in GC-IMS. The findings show that there is a direct correlation between the factors in GC-IMS and the PNs, suggesting that MLR is a suitable approach for building a model in this scenario. However, the PAs exhibit nonlinear behavior, suggesting that BR-ANN is better suitable to capture this complexity. Notably, Derringer's desirability function is utilized to integrate the PAs and PNs, and in this scenario, MLR demonstrates satisfactory performance in modeling the GC-IMS factors.

Automated 2D peak detection in gas chromatography-ion mobility spectrometry through persistent homology.

BACKGROUND: Gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful analytical technique which has gained widespread use in a variety of fields. Detecting peaks in GC-IMS data is essential for chemical identification. Topological data analysis (TDA) has the ability to record alterations in topology throughout the entire spectrum of GC-IMS data and retain this data in diagrams known as persistence diagrams. To put it differently, TDA naturally identifies characteristics such as mountains, volcanoes, and their higher-dimensional equivalents within the original data and measures their significance. RESULTS: In the present contribution, a novel approach based on persistent homology (a flagship technique of TDA) is suggested for automatic 2D peak detection in GC-IMS. For this purpose, two different GC-IMS data examples (urine and olive oil) are used to show the performance of the proposed method. The outputs of the algorithm are GC-IMS chromatogram with detected peaks, persistence plot showing the importance (intensity) of the detected peaks and a table with retention times (RT), drift times (DT), and persistence scores of detected peaks. The RT and DT can be used for identification of the peaks and persistence scores for quantitation. Additionally, watershed segmentation is applied to the GC-IMS images to index individual peaks and segment overlapping compounds allowing for a more accurate identification and quantification of individual peaks. SIGNIFICANCE: Inspection of the results for GC-IMS datasets showed the accurate and reliable performance of the proposed strategy based on persistent homology for automatic 2D GC-IMS peak detection for qualitative and quantitative analysis. In addition, this approach can be easily extended to other types of hyphenated chromatographic and/or spectroscopic data.

A geographical origin assessment of Italian hazelnuts: Gas chromatography-ion mobility spectrometry coupled with multivariate statistical analysis and data fusion approach.

Hazelnut is a commodity that has gained interest in the food science community concerning its authenticity. The quality of the Italian hazelnuts is guaranteed by Protected Designation of Origin and Protected Geographical Indication certificates. However, due to their modest availability and the high price, fraudulent producers/suppliers blend, or even substitute, Italian hazelnuts with others from different countries, having a lower price, and often a lower quality. To contrast or prevent these illegal activities, the present work investigated the application of the Gas Chromatography-Ion mobility spectrometry (GC-IMS) technique on the hazelnut chain (fresh, roasted, and paste of hazelnuts). The raw data obtained were handled and elaborated using two different ways, software for statistical analysis, and a programming language. In both cases, Principal Component Analysis and Partial Least Squares-Discriminant Analysis models were exploited, to study how the Volatile Organic Profiles of Italian, Turkish, Georgian, and Azerbaijani products differ. A prediction set was extrapolated from the training set, for a preliminary models' evaluation, then an external validation set, containing blended samples, was analysed. Both approaches highlighted an interesting class separation and good model parameters (accuracy, precision, sensitivity, specificity, F1-score). Moreover, a data fusion approach with a complementary methodology, sensory analysis, was achieved, to estimate the performance enhancement of the statistical models, considering more discriminant variables and integrating at the same time further information correlated to quality aspects. GC-IMS could be a key player as a rapid, direct, cost-effective strategy to face authenticity issues regarding the hazelnut chain.

A methodological approach to preprocessing FTIR spectra of adulterated sesame oil.

Fourier transform infrared (FTIR) spectroscopy is established as an effective and fast method for the confirmation of the authenticity of food and among other, edible oils. However, no standard procedure is available for applying preprocessing as a vital step in obtaining accurate results from spectra. This study proposes a methodological approach to preprocessing FTIR spectra of sesame oil adulterated with vegetable oils (canola oil, corn oil, and sunflower oil). The primary preprocessing methods investigated are orthogonal signal correction (OSC), standard normal variate transformation (SNV), and extended multiplicative scatter correction (EMSC). Other preprocessing methods are used both as standalone methods and in combination with the primary preprocessing methods. The preprocessing results are compared using partial least squares regression (PLSR). OSC alone or with detrending were the most accurate in predicting the adulteration level of sesame oil, with a maximum coefficient of prediction (R2p) range of 0.910 to 0.971 for different adulterants.

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.

GC-IMS data on the discrimination between geographic origins of olive oils.

Gas chromatography hyphenated with ion mobility spectrometry (GC-IMS) is an emerging benchtop technique for sensitive and selective detection of volatile organic compounds. It is commonly used for non-target screening (NTS) of complex sample materials, such as food products. Resulting spectra are used as "fingerprints" for multivariate chemometric data analysis to extract information. This has been successfully applied in the field of food fraud detection in several published studies. The presented dataset contains GC-IMS measurements of extra virgin olive oil samples from Spain, Italy, and Greece. It allows classification and class modelling to differentiate geographic origins and was used in the associated publication gc-ims-tools, a new Python package for chemometric analysis of GC-IMS data (https://doi.org/10.1016/j.foodchem.2022.133476) as an example to demonstrate the functionality.

Finding features - variable extraction strategies for dimensionality reduction and marker compounds identification in GC-IMS data.

Gas chromatography hyphenated to ion mobility spectrometry (GC-IMS) is a powerful, two-dimensional separation and detection technique for volatile organic compounds (VOC). Low detection limits, high selectivity and robust operation characterize it as an ideal tool for non-target screening (NTS) approaches. Combined with multivariate data analysis, it has been successfully applied to several areas in food science, such as authenticity control and flavor profiling. The recorded raw data feature high numbers of variables due to the high scan speeds of the instrument. Additionally, NTS approaches - by design - record more data than required. Therefore, reducing the number of variables is a key step in any machine learning pipeline to reduce overfitting, overlong training times and model complexity. The aim of the study is a comparison between the two most used dimensionality reduction techniques, PCA and PLS, regarding interpretability, as a tool to find marker compounds, and performance as a preprocessing step for supervised learning. Both feature per variable visualizations, which allows easy interpretation of results and retains a connection to the input data, which can lead to the discovery of marker compounds. A GC-IMS dataset about the botanical origin of honey is used, and all formatting steps necessary to apply PCA and PLS to higher dimensional data and obtain intuitive figures are explained. To evaluate effectiveness as a preprocessing step in a supervised pipeline four supervised algorithms were fitted with PCA or PLS variable reduction. PLS proved to be a more effective step in a supervised workflow in terms of accuracy, while PCA is highly effective for revealing preprocessing weaknesses such as misalignments.

gc-ims-tools - A new Python package for chemometric analysis of GC-IMS data.

Due to its high sensitivity and resolving power, gas chromatography ion mobility spectrometry (GC-IMS) is an emerging benchtop technique for non-target screening of complex sample materials. Given the wide range of applications, such as food authenticity, custom data analysis workflows are needed. As a common basis, they necessarily share many functionalities such as file input/output, preprocessing methods, exploratory or supervised analysis and visualizations. This study introduces a new open source, fully customizable Python package for handling and analysis of GC-IMS data. A workflow to classify olive oils by geographical origin exemplarily demonstrates functionality and ease of use. Key preprocessing steps, exploratory - and supervised data analysis and feature selections are visualized. All code and detailed documentation are freely available as open source under the BSD 3-clause license at https://github.com/Charisma-Mannheim/gc-ims-tools.

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.

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.

MIR spectroscopy versus MALDI-ToF-MS for authenticity control of honeys from different botanical origins based on soft independent modelling by class analogy (SIMCA) - A clash of techniques?

In this study, highly reproducible MIR spectroscopy and highly sensitive MALDI-ToF-MS data were directly compared for the metabolomic profiling of monofloral and multifloral honey samples from three different botanical origins canola, acacia, and honeydew. Subsequently, three different classification models were applied to the data of both techniques, PCA-LDA, PCA- kNN, and soft independent modelling by class analogy (SIMCA) as class modelling technique. All monofloral external test set samples were classified correctly by PCA-LDA and SIMCA with both data sets, while multifloral test set samples could only be identified as outliers by the SIMCA technique, which is a crucial aspect in the authenticity control of honey. The comparison of the two used analytical techniques resulted in better overall classification results for the monofloral external test set samples with the MIR spectroscopic data. Additionally, clearly more multifloral external samples were identified as outliers by MIR spectroscopy (91.3%) as compared to MALDI-ToF-MS (78.3%). The results indicate that the high reproducibility of the used MIR technique leads to a generally better ability of separating monofloral honeys and in particular, identifying multifloral honeys. This demonstrates that benchtop-based techniques may operate on an eye-level with high-end laboratory-based equipment, when paired with an optimal data analysis strategy.

Nitrogen monoxide as dopant for enhanced selectivity of isomeric monoterpenes in drift tube ion mobility spectrometry with 3H ionization.

The ion mobility spectra of the isomeric monoterpenes α-pinene, ß-pinene, myrcene, and limonene in drift tube ion mobility spectrometry (IMS) with 3H radioactive ionization are highly similar and difficult to distinguish. The aim of this work was to enhance the selectivity of IMS by the addition of nitrogen monoxide (NO) as dopant and to investigate the underlying changes in ion formation responsible for the modified ion signals observed in the ion mobility spectra. Even though 3H-based-IMS systems have been used in hyphenation with gas chromatography (GC) for profiling of volatile organic compounds (VOCs), the investigation of ion formation still remains challenging and was exemplified by the investigated monoterpenes. Nonetheless, the formation of monomeric, dimeric, and trimeric ion clusters could be tentatively confirmed by a mass-to-mobility correlation and the highly similar pattern of ion signals in the monomer region was attributed to isomerization mechanisms potentially occurring after proton transfer reactions. The addition of NO as dopant could finally lead to the formation of additional product ions and increased the selectivity of IMS for the investigated monoterpenes as confirmed by principal component analysis (PCA). The discrimination of monoterpenes in the volatile profile is highly relevant in the quality control of hops and was given as the example for application. The results indicate that additional product ions were obtained by the formation of NO+ adduct ions, next to hydride abstraction, charge transfer, or fragmentation reactions. This approach can potentially leverage selectivity issues in VOC profiling of complex matrices, such as food matrices or raw materials in combination with chemometric pattern recognition techniques.

Volatilomic Profiling of Citrus Juices by Dual-Detection HS-GC-MS-IMS and Machine Learning-An Alternative Authentication Approach.

A prototype dual-detection headspace-gas chromatography-mass spectrometry-ion mobility spectrometry (HS-GC-MS-IMS) system was used for the analysis of the volatile profile of 47 Citrus juices including grapefruit, blood orange, and common sweet orange juices without requiring any sample pretreatment. Next to reduced measurement times, substance identification could be improved substantially in case of co-elution by considering the characteristic drift times and m/z ratios obtained by IMS and MS. To discriminate the volatile profiles of the different juice types, extensive data analysis was performed with both datasets, respectively. By principal component analysis (PCA), a distinct separation between grapefruit and orange juices was observed. While in the IMS data grapefruit juices not from fruit juice concentrate could be separated from grapefruit juices reconstituted from fruit juice concentrate, in the MS data, the blood orange juices could be differentiated from the orange juices. This observation leads to the assumption that the IMS and MS data contain different information about the composition of the volatile profile. Subsequently, linear discriminant analysis (LDA), support vector machines (SVM), and the k-nearest-neighbor (kNN) algorithm were applied to the PCA data as supervised classification methods. Best results were obtained by LDA after repeated cross-validation for both datasets, with an overall classification and prediction ability of 96.9 and 91.5% for the IMS data and 94.5 and 87.9% for the MS data, respectively, which confirms the results obtained by PCA. Additional data fusion could not generally improve the model prediction ability compared to the single data, but rather for certain juice classes. Consequently, depending on the juice class, the most suitable dataset should be considered for the prediction of the class membership. This volatilomic approach based on the dual detection by HS-GC-MS-IMS and machine learning tools represent a simple and promising alternative for future authenticity control of Citrus juices.

A paper-based colorimetric sensor array for discrimination of monofloral European honeys based on gold nanoparticles and chemometrics data analysis.

A sensitive and simple nanomaterial based colorimetric sensor array (NBCSA) was developed for discrimination of monofloral honey from various European countries based on three botanical origins as Acacia, Canola and Honeydew. The NBCSA was designed by spotting gold (AuNPs) and silver (AgNPs) nanoparticles synthesized using six different reducing and/or capping agents. The colour intensity of AuNPs represented differential changes when interacting with volatile organic compounds appeared in the headspace of the honey samples. The color difference maps, which are calculated as the difference between color intensity of the sensor before and after exposing to the sample vapors, were used as a fingerprint to discriminate the honey samples based on botanical origin. Classification was achieved utilizing data pre-processing and chemometrics data analysis. Fitting accuracies of 88% and 86% were obtained by partial least squares discriminant analysis and linear discriminant analysis whereas 100% was achieved using support vector machine.

Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning.

For the first time, a prototype HS-GC-MS-IMS dual-detection system is presented for the analysis of volatile organic compounds (VOCs) in fields of quality control of brewing hop. With a soft ionization and drift time-based ion separation in IMS and a hard ionization and m/z-based separation in MS, substance identification in the case of co-elution was improved, substantially. Machine learning tools were used for a non-targeted screening of the complex VOC profiles of 65 different hop samples for similarity search by principal component analysis (PCA) followed by hierarchical cluster analysis (HCA). Partial least square regression (PLSR) was applied to investigate the observed correlation between the volatile profile and the α-acid content of hops and resulted in a standard error of prediction of only 1.04% α-acid. This promising volatilomic approach shows clearly the potential of HS-GC-MS-IMS in combination with machine learning for the enhancement of future quality assurance of hops. Graphical abstract.

Data fusion of GC-IMS data and FT-MIR spectra for the authentication of olive oils and honeys-is it worth to go the extra mile?

The potential benefit of data fusion based on different complementary analytical techniques was investigated for two different classification tasks in the field of foodstuff authentication. Sixty-four honey samples from three different botanical origins and 53 extra virgin olive oil samples from three different geographical areas were analyzed by attenuated total reflection IR spectroscopy (ATR/FT-IR) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS). The obtained datasets were combined in a low-level data fusion approach with a subsequent multivariate classification by principal component analysis-linear discriminant analysis (PCA-LDA) or partial least squares-discriminant analysis (PLS-DA). Performing a back projection of PCA loadings, the influence of variables in the FT-IR spectra (one-dimensional) and the GC-IMS profiles (two-dimensional) on the discrimination was visualized within the original axis of the two data sources. Validation results of the classification models were compared to the results that could be obtained by using the individual data blocks separately. For both the honey and olive oil samples, a decreased cross-validation error rate and more robust model was obtained due to the low-level data fusion. The results show that data fusion is an effective strategy for improving the classification performance, particularly for challenging classification tasks such as the discrimination of olive oils with different geographical origin. Graphical abstract.