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Self-optimisation constitutes a very helpful tool for chemical process development, both in lab and in industrial applications. However, research on the application of model-free autonomous optimisation strategies (based on experimental investigation) for complex reactions of high industrial significance, which involve considerable intermediate and by-product formation, is still in an early stage. This article describes the development of an enhanced autonomous microfluidic reactor platform for organolithium and epoxide reactions that incorporates a successive combination of inline FT-IR spectrometer and online mass spectrometer. Experimental data is collected in real-time and used as feedback for the optimisation algorithms (modified Simplex algorithm and Design of Experiments) without time delay. An efficient approach to handle intricate optimisation problems is presented, where the inline FT-IR measurements are used to monitor the reaction’s main components, whereas the mass spectrometer’s high sensitivity permits insights into the formation of by-products. To demonstrate the platform’s flexibility, optimal reaction conditions of two organic syntheses are identified. Both pose several challenges, as complex reaction mechanisms are involved, leading to a large number of variable parameters, and a considerable amount of by-products is generated under non-ideal process conditions. Through multidimensional real-time optimisation, the platform supersedes labor- and cost-intensive work-up procedures, while diminishing waste generation, too. Thus, it renders production processes more efficient and contributes to their overall sustainability. Graphical abstract

The ion mobility spectra of the isomeric monoterpenes α-pinene, β-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. Graphical abstract

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.

Manganese (Mn) contained in cellulose is partially responsible for an increased consumption of paper bleaching chemicals (like O2, H2O2), consequently diminishing the efficiency in pulp processing, darkening the pulp and deteriorating pulp quality. Usually, Mn in the paper industry is removed employing the environmentally critical EDTA. A greener alternative constitutes, however, the use of siderophores, high-affinity metal-chelating organic compounds that are produced by microorganisms to acquire metals (Fe and Mn among others), like desferrioxamine B (DFOB) or desferrioxamine E (DFOE). The use of native Mn-transporter proteins, like PratA, constitutes another possibility for Mn removal. The evaluation of utilizing siderophores or PratA for Mn removal from cellulose in a circular economy scheme is therefore essential. Firstly, Mn removal from cellulose was performed by immobilizing siderophores or PratA on magnetic beads (M-PVA C22). Secondly, the beads were incubated overnight with a 2% cellulose suspension, allowing Mn-ligand complex formation. Finally, cellulose suspensions were submitted for Mn quantification, employing either the TCPP [Tetrakis(4-carboxyphenyl)porphyrin] method, the PAN [1-(2-pyridylazo)-2-naphthol] method or the Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). When non-immobilized ligands were employed, a 31% Mn removal was achieved; when using immobilized ligands, around 10% Mn removal was obtained. Treated and untreated cellulose was analyzed by SEM and the Mn distribution between the solid and liquid phase was parameterized using adsorption isotherm models. This novel greener method proved to be feasible and easy, leading to potential improvements in the paper industry. Next research steps are to optimize Mn removal and quantify Mn recovery after ligand decoupling before scaling-up.

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.

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.

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

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 Electronic supplementary material The online version of this article (10.1007/s00216-020-02842-y) contains supplementary material, which is available to authorized users.

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.

This work describes a simple approach for the untargeted profiling of volatile compounds for the authentication of the botanical origins of honey based on resolution-optimized HS-GC-IMS combined with optimized chemometric techniques, namely PCA, LDA, and kNN. A direct comparison of the PCA-LDA models between the HS-GC-IMS and 1H NMR data demonstrated that HS-GC-IMS profiling could be used as a complementary tool to NMR-based profiling of honey samples. Whereas NMR profiling still requires comparatively precise sample preparation, pH adjustment in particular, HS-GC-IMS fingerprinting may be considered an alternative approach for a truly fully automatable, cost-efficient, and in particular highly sensitive method. It was demonstrated that all tested honey samples could be distinguished on the basis of their botanical origins. Loading plots revealed the volatile compounds responsible for the differences among the monofloral honeys. The HS-GC-IMS-based PCA-LDA model was composed of two linear functions of discrimination and 10 selected PCs that discriminated canola, acacia, and honeydew honeys with a predictive accuracy of 98.6%. Application of the LDA model to an external test set of 10 authentic honeys clearly proved the high predictive ability of the model by correctly classifying them into three variety groups with 100% correct classifications. The constructed model presents a simple and efficient method of analysis and may serve as a basis for the authentication of other food types.

A prototype gas chromatography-ion mobility spectrometry (GC-IMS) system, hyphenating temperature-ramped headspace GC to a modified drift time IMS cell, was evaluated and compared to a conventional, isothermal capillary column (CC)-IMS system on the example of the geographical differentiation of extra virgin olive oils (EVOO) from Spain and Italy. It allows orthogonal, 2D separation of complex samples and individual detection of compounds in robust and compact benchtop systems. The information from the high-resolution 3D fingerprints of volatile organic compound (VOC) fractions of EVOO samples were extracted by specifically developed chemometric MATLAB® routines to differentiate between the different olive oil provenances. A combination of unsupervised principal component analysis (PCA) with two supervised procedures, linear discriminant analysis (LDA) and k-nearest neighbors (kNN), was applied to the experimental data. The results showed very good discrimination between oils of different geographical origins, featuring 98 and 92% overall correct classification rate for PCA-LDA and kNN classifier, respectively. Furthermore, the results showed that the higher resolved 3D fingerprints obtained from the GC-IMS system provide superior resolving power for non-targeted profiling of VOC fractions from highly complex samples such as olive oil. Graphical abstract Principle of the determination of geographic origins of olive oils by chemometric analysis of three-dimensional HS-GC-IMS fingerprints.

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.

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.

Paper spray high-resolution accurate mass spectrometry is a fast and versatile analysis method. This ambient ionization technique enables the quantitation of xenobiotics in complex biological matrices without chromatography or conventional sample extraction. The simplicity, rapidity, and affordability of the paper spray mass spectrometry (PS-MS) method make the technique especially attractive for clinical investigations where fast and affordable sample analysis is crucial. A new PS-MS method for the quantitation of voriconazole in equine tears was developed and validated. For a concentration range of 10 to 1000 ng/mL, good linearity (R2 > 0.99), inter- and intra-run precision (coefficient of variation (CV) max. 11.9%), accuracy (bias of the nominal concentration ± 13.9%), and selectivity (signal areas of the double blanks represent 0.13 ± 0.05% of the lower limit of quantitation (LLOQ) signal in equine tears) were observed. The quantitation of voriconazole was based on three product ions and calculated relative to the isotope-labeled internal standard, voriconazole-d3, which had a final concentration of 250 ng/mL in the standards and samples. The matrix effect of the method showed an ionization suppression by reduction of the voriconazole response to 63.6%, 70.2%, and 81.9% for 30 ng/mL, 450 ng/mL, and 900 ng/mL in equine tears compared with voriconazole in solvent (methanol:water, 50:50, v:v). The method was used to analyze 126 study samples collected for a pharmacokinetic study investigating a novel approach for treatment of fungal keratitis in horses. Therefore, the integrity of the sample dilution (n = 6, CV 6.90%, and bias of nominal concentration + 8.40%) and the carryover effect (increase from 0.33 ± 0.21% to 1.33 ± 0.89% of the signal of the LLOQ) was further investigated. To our knowledge, this method is the first application of PS-MS for quantitation of drug concentrations in tears from any species.

For the first time, this study describes a HS-GC-IMS strategy for analyzing non-targeted volatile organic compounds (VOCs) profiles to distinguish between virgin olive oils of different classification. Correlations among measured flavor characteristics and sensory attributes evaluated by a test panel were determined by applying unsupervised (PCA, HCA) and supervised (LDA, kNN and SVM) chemometric techniques. PCA and HCA were applied for natural clustering of the samples and LDA, kNN, and SVM methods were used to create predictive models for olive oil classification. Identification of 26 target compounds revealed which compounds are responsible for discrimination, and how their distribution correlates with the sensory evaluation. In the investigated samples, LDA, kNN, and SVM models correctly classified 83.3%, 73.8%, and 88.1% of the samples, respectively. This suggests that mathematical correlations of HS-GC-IMS 3D fingerprints with the sensory analysis may be appropriate for calculating a good predictive value to classify virgin olive oils.

This work describes a simple approach for the untargeted profiling of volatile compounds for the authentication of the botanical origins of honey based on resolution-optimized HS-GC-IMS combined with optimized chemometric techniques, namely PCA, LDA, and kNN. A direct comparison of the PCA-LDA models between the HS-GC-IMS and

Author Summary: The globalization of trade of foods with its overwhelming variety has led to an increased demand for authentic products by all parts of the food value chain. In particular high-priced products are commonly adulterated, mislabeled or completely substituted. Honey is a typical example for such foods, which show an increasing number of adulterations, mostly related to the declaration of the botanical origin. A non-targeted 1H-NMR-based screening, combined with multivariate statistical analysis was applied as a fast and simple comprehensive approach to verify the botanical origin of honey samples. The NMR fingerprints of honey sample were processed by taylor-made chemometric tools, based on principal component analysis (PCA) and linear discriminant analysis (LDA) in custom MATLAB routines. The results obtained by PCA-LDA showed very good discrimination between the different honey types with 98.9% correct overall classification rate of the samples. Hence, this NMR based screening approach could be an effective alternative to traditional, laborious methods.

Even more than 50 years after the ban of DDT in Germany, farmers are still affected by its persistence in contaminated soils. Depending on the crop cultivated on such soils, this often leads to low-level residues of DDT and its metabolites DDE and DDD ("DDX"), which are perceived as a risk by the food value chain. Pesticide formulations used in modern agriculture commonly contain high levels of surfactants, but so far no open-field studies have evaluated the effects of these treatments on the mobility of lipophilic contaminants, such as DDX. In this field trial, a 1.03 ha section was cultivated with Cucurbita maxima under realistic conditions to monitor the mobility of DDX in low-level contaminated agricultural soils in dependence of common pesticide applications. A typical organic treatment was compared to a conventional protocol. Soil samples were taken before and after each application. Samples from the organic section featured significantly higher extractable DDX contents in soil and water compared to the conventional section. The results show that modern pesticide treatments can have an unforeseen, yet significant influence on the mobilization and, subsequently, on the plant bioavailability of incurred DDX residues depending on the formulation composition.

It has been suggested that lutein and zeaxanthin may decrease the risk for age-related macular degeneration. Surprisingly, oleoresins rich in zeaxanthin are not yet available on the market. Several authors have reported enhanced stability of esterified xanthophylls, so plants containing zeaxanthin esters were investigated to establish valuable sources for the production of durable oleoresins. Liquid chromatography-atmospheric pressure chemical ionization mass spectrometry [LC-(APCI)MS] was used to unequivocally identify zeaxanthin esters of a standard mixture and in several plant extracts. Zeaxanthin esters were quantified on the basis of their respective molecular masses using zeaxanthin for calibration; total zeaxanthin was determined after saponification of aliquots of the extracts. Thus, dried wolfberries (Lycium barbarum), Chinese lanterns (Physalis alkekengi), orange pepper (Capsicum annuum), and sea buckthorn (Hippophae rhamnoides) proved to be valuable zeaxanthin ester sources. The present LC-MS method allows for an even more detailed analysis of zeaxanthin esters than reported previously.

Temperature and pH are important parameters in food processing. Their effect on degradation of plasmid and plant DNA was investigated. Application of HPLC permitted us to quantitatively distinguish between covalently closed circular and nicked plasmid DNA. Nicking of plasmid DNA in buffer increased strongly at decreased pH (99% within 90 min at 65 °C/pH 4.0). A virtually identical degradation rate was observed in tomato serum. Temperature exerted only minor effects (15% within 90 min at 85 °C/pH 8.4). The combined effect of the parameters was additive. Degradation of plant DNA was monitored by determining the maximal detectable fragment length using polymerase chain reaction. With transgenic maize DNA in buffer, fragments up to 957 bp remained detectable for at least 90 min upon application of 65 °C at pH 4.0. Soymilk and tofu were produced from genetically modified soybeans. After boiling (10 min) of raw soymilk, the detectable fragment length decreased from 1,714 to 1,339 bp. The study of the final tofu product did not reveal any further DNA degradation. In commercial soy protein isolate, a rather highly processed food, DNA fragments of at least 714 bp were detected.

Isotopic fingerprinting was evaluated for its potential to generate characteristic fingerprints of crop protection products in an extensive survey, using the insecticide Fipronil. One hundred and twenty batches of Fipronil from the BASF production site in France were analyzed for the isotope ratios of δ(13)C, δ(15)N, and δ(34)S. Samples spanned a production time of four years and were analyzed by elemental analysis, coupled to isotope ratio mass spectrometry (EA/IRMS). A number of Fipronil samples from other sources were analyzed in the same manner and were compared to the samples from BASF by means of multivariate data analysis. The isotopic fingerprint was sufficiently specific to differentiate between Fipronil from BASF production and Fipronil from other producers. This suggests that isotopic fingerprinting is suitable for the authenticity control of active compounds in crop protection products. It is anticipated that this technique will deliver great benefit in the defense against counterfeits and illegal parallel imports.