Detection of Mycobacterium tuberculosis complex field infections in cattle using fecal volatile organic compound analysis through gas chromatography-ion mobility spectrometry combined with chemometrics.

Bovine tuberculosis is considered a re-emerging disease caused by different species from the Mycobacterium tuberculosis complex (MTC), important not only for the livestock sector but also for public health due to its zoonotic character. Despite the numerous efforts that have been carried out to improve the performance of the current antemortem diagnostic procedures, nowadays, they still pose several drawbacks, such as moderate to low sensitivity, highlighting the necessity to develop alternative and innovative tools to complement control and surveillance frameworks. Volatilome analysis is considered an innovative approach which has been widely employed in animal science, including animal health field and diagnosis, due to the useful and interesting information provided by volatile metabolites. Therefore, this study assesses the potential of gas chromatography coupled to ion mobility spectrometry (GC-IMS) to discriminate cattle naturally infected (field infections) by MTC from non-infected animals. Volatile organic compounds (VOCs) produced from feces were analyzed, employing the subsequent information through chemometrics. After the evaluation of variable importance for the projection of compounds, the final discriminant models achieved a robust performance in cross-validation, as well as high percentages of correct classification (>90%) and optimal data of sensitivity (91.66%) and specificity (99.99%) in external validation. The tentative identification of some VOCs revealed some coincidences with previous studies, although potential new compounds associated with the discrimination of infected and non-infected subjects were also addressed. These results provide strong evidence that a volatilome analysis of feces through GC-IMS coupled to chemometrics could become a valuable methodology to discriminate the infection by MTC in cattle. IMPORTANCE Bovine tuberculosis is endemic in many countries worldwide and poses important concerns for public health because of their zoonotic condition. However, current diagnostic techniques present several hurdles, such as low sensitivity and complexity, among others. In this regard, the development of new approaches to improve the diagnosis and control of this disease is considered crucial. Volatile organic compounds are small molecular mass metabolites which compose volatilome, whose analysis has been widely employed with success in different areas of animal science including animal health. The present study seeks to evaluate the combination of fecal volatilome analysis with chemometrics to detect field infections by bovine tuberculosis (Mycobacterium tuberculosis complex) in cattle. The good robust performance of discriminant models as well as the optimal data of sensitivity and specificity achieved highlight volatilome analysis as an innovative approach with huge potential.

Comparing the potential of IR-spectroscopic techniques to gas chromatography coupled to ion mobility spectrometry for classifying virgin olive oil categories.

Virgin olive oil (OO) can be classified into three different categories: extra virgin, virgin and lampante. The official method for this classification, based on physicochemical analysis and sensory tasting, is considered useful and effective, although it is a costly and time-consuming process. The aim of this study was to assess the potential of some analytical techniques for classifying and predicting different OO categories to support official methods and to provide olive oil companies with a rapid tool to assess product quality. Thus, mid and near infrared spectroscopies (MIR and NIR) have been compared by using different instruments and with head-space gas chromatography coupled to an ion mobility spectrometer (HS-GC-IMS). High classification success rates in validation models were obtained using IR spectrometers (>70% and > 80% in average for ternary and binary classifications, respectively), although HS-GC-IMS showed greater classification potential (>85% and > 90%).

Review of the Methodologies for Measurement of Greenhouse Gas Emissions in Livestock Farming: Pig Farms as a Case of Study.

The global emission and accumulation of gases due to livestock farming is estimated to contribute to about 14.5% of the global warming effect due to greenhouse gases (GHG). Pig farming represents 9% of global livestock GHG emissions, without considering other activities of pork production process, such as feed production. Most of information about pig farms GHG emissions is based on theoretical calculations with not too much accuracy. Hence, there is a critical need to study the best sampling and analytical techniques (portable or not) that can be used to map their contribution to GHG emissions. The selection of the best analytical detection method becomes important for public policies on climate change, and in order to evaluate animal and manure handling practices to reduce GHG and to combat global warming. In this article, different techniques, which could be used to measure the emissions of GHG from livestock, are reviewed, showing the advantages and disadvantages of each technique, with special emphasis on those already used in studies about GHG from pig farms and those that allow the simultaneous determination of several species of gases. Open chambers equipped with photoacoustic multi-gas monitor have been the techniques most employed in intensive pig farms studies. Gas Chromatography coupled to different detectors has been only widely used in pig farms to monitor simultaneously several GHG species using previous sampling devices. However, there are no studies in the literature based on extensive pig farms. In these systems, micrometeorological techniques could be a promising strategy.

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.

Usage considerations for headspace-gas chromatography-ion mobility spectrometry as a suitable technique for qualitative analysis in a routine lab.

The potential of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) to perform non-targeted qualitative analysis of complex samples has led to an unprecedented increase in its popularity in recent years. The operating principle of IMS makes quality control essential to ensure adequate results. Besides this, the suitability of GC-IMS is determined by multiple phenomena that take place before and during IMS detection. The present work discusses a novel GC-IMS quality control protocol for both beginners and experienced users. Likewise, it describes factors that must be taken into account in order to develop a robust GC-IMS qualitative analysis method and, if needed, to achieve the identification of VOCs present in real samples. The developed quality control protocol was successfully employed in our laboratory for the routine analysis of >500 real samples (olive oil and Iberian ham) for 6 months, thus it is recommended for the analysis of a great number of complex samples. Furthermore, the behaviour of the ions produced in the ionisation chamber and the possible reactions between them in GC-IMS qualitative analysis were assessed.

Headspace Gas Chromatography Coupled to Mass Spectrometry and Ion Mobility Spectrometry: Classification of Virgin Olive Oils as a Study Case.

Due to its multiple advantages, ion mobility spectrometry (IMS) is being considered as a complementary technique to mass spectrometry (MS). The goal of this work is to investigate and compare the capacity of IMS and MS in the classification of olive oil according to its quality. For this purpose, two analytical methods based on headspace gas chromatography (HS-GC) coupled with MS or with IMS have been optimized and characterized for the determination of volatile organic compounds from olive oil samples. Both detectors were compared in terms of sensitivity and selectivity, demonstrating that complementary data were obtained and both detectors have proven to be complementary. MS and IMS showed similar selectivity (10 out of 38 compounds were detected by HS-GC-IMS, whereas twelve compounds were detected by HS-GC-MS). However, IMS presented slightly better sensitivity (Limits of quantification (LOQ) ranged between 0.08 and 0.8 µg g-1 for HS-GC-IMS, and between 0.2 and 2.1 µg g-1 for HS-GC-MS). Finally, the potential of both detectors coupled with HS-GC for classification of olive oil samples depending on its quality was investigated. In this case, similar results were obtained when using both HS-GC-MS and HS-GC-IMS equipment (85.71 % of samples of the external validation set were classified correctly (validation rate)) and, although both techniques were shown to be complementary, data fusion did not improve validation results (80.95% validation rate).

A robustness study of calibration models for olive oil classification: Targeted and non-targeted fingerprint approaches based on GC-IMS.

The dual separation in gas chromatography-ion mobility spectrometry generates complex multi-dimensional data, whose interpretation is a challenge. In this work, two chemometric approaches for olive oil classification are compared to get the most robust model over time: i) an non-targeted fingerprinting analysis, in which the overall GC-IMS data was processed and ii) a targeted approach based on peak-region features (markers). A total of 701 olive samples from two harvests (2014-2015 and 2015-2016) were analysed and processed by both approaches. The models built with data samples of 2014-2015 showed that both approaches were suitable for samples classification (success >74%). However, when these models were applied for classifying samples from 2015-2016, better values were obtained using markers. The combination of data from the two harvests to build the chemometric models improved the percentages of success (>90%). These results confirm the potential of GC-IMS based approaches for olive oil classification.

Use of whole electrophoretic profile and chemometric tools for the differentiation of three olive oil qualities.

Olive oil is a liquid fat obtained from olives (the fruit of Olea europea). It is one of the most important ingredients of the Mediterranean diet, due to its health benefits. Depending on its quality, olive oil can be classified as extra virgin (EVOO), virgin (VOO) and lampante (LOO). Currently, an official method defines the quality parameters of the different categories of olive oil using different analytical techniques and a sensory analysis through a Panel Test. However, the evaluation of olive oil quality by tasting panels has some drawbacks, such as the subjectivity of the analysis and the lack of panels accredited outside Spain. For this reason, fast, simple and reliable analytical methods, which can differentiate the categories of olive oil are needed. In this work, the potential of a method using capillary electrophoresis (CE) with ultraviolet (UV) detection as an additional method to the ones already included in the official method has been investigated. The separations were performed using a 45 mM sodium tetraborate buffer (pH 9), and the analytes were measured at 200 nm. For chemometric model construction, the whole electrophoretic profile was processed. It required a correction of migration time shift, which was solved using two internal standards (naphthol and benzoic acid), and a correction of the drift baseline. The results obtained after applying the method to 130 olive oil samples are very promising, achieving success rates above 91%. Finally, the use of all information found in the electropherogram was compared with that based on the selection and integration of only some peaks.

Target vs spectral fingerprint data analysis of Iberian ham samples for avoiding labelling fraud using headspace - gas chromatography-ion mobility spectrometry.

The data obtained with a polar or non-polar gas chromatography (GC) column coupled to ion mobility spectrometry (IMS) has been explored to classify Iberian ham, to detect possible frauds in their labelling. GC-IMS was used to detect the volatile compound profile of dry-cured Iberian ham from pigs fattened on acorn and pasture or on feed. Due to the two-dimensional nature of GC-IMS measurements, great quantities of data are obtained and an exhaustive chemometric processing is required. A first approach was based on the processing of the complete spectral fingerprint, while the second consisted of the selection of individual markers that appeared throughout the spectra. A classification rate of 90% was obtained with the first strategy, and the second approach correctly classified all Iberian ham samples according to the pigs' diet (classification rate of 100%). No significant differences were found between the GC columns tested in terms of classification rate.

Target identification of volatile metabolites to allow the differentiation of lactic acid bacteria by gas chromatography-ion mobility spectrometry.

The purpose of this work was to study the potential of gas chromatography-ion mobility spectrometry (GC-IMS) to differentiate lactic acid bacteria (LAB) through target identification and fingerprints of volatile metabolites. The LAB selected were used as reference strains for their influence in the flavour of cheese. The four strains of LAB can be distinguished by the fingerprints generated by the volatile organic compounds (VOCs) emitted. 2-butanone, 2-pentanone, 2-heptanone and 3-methyl-1-butanol were identified as relevant VOCs for Lactobacillus casei and Lactobacillus paracasei subsp. paracasei. 2-Butanone and 3-methyl-1-butanol were identified in Lactococcus lactis subsp. lactis and Lactococcus cremoris subsp. cremoris. The IMS signals monitoring during a 24-30h period showed the growth of the LAB in vitro. The results demonstrated that GC-IMS is a useful technology for bacteria recognition and also for screening the aromatic potential of new isolates of LAB.

Extraction of toxic compounds from saliva by magnetic-stirring-assisted micro-solid-phase extraction step followed by headspace-gas chromatography-ion mobility spectrometry.

A new sample extraction procedure based on micro-solid-phase extraction (µSPE) using a mixture of sorbents of different polarities (polymeric reversed-phase sorbent HLB, silica-based sorbent C18, and multiwalled carbon nanotubes) was applied to extract benzene, toluene, butyraldehyde, benzaldehyde, and tolualdehyde present in saliva to avoid interference from moisture and matrix components and enhance sensitivity and selectivity of the ion mobility spectrometry (IMS) methodology proposed. The extraction of target analytes from saliva samples by using µSPE were followed by the desorption step carried out in the headspace vials placed in the autosampler of the IMS device. Then, 200 µL of headspace was injected into the GC column coupled to the IMS for its analysis. The method was fully validated in terms of sensitivity, precision, and recovery. The LODs and LOQs obtained, when analytes were dissolved in saliva samples to consider the matrix effect, were within the range of 0.38-0.49 and 1.26-1.66 µg mL(-1), respectively. The relative standard deviations were <3.5 % for retention time and drift time values, which indicate that the method proposed can be applied to determine toxic compounds in saliva samples. Graphical abstract Summary of steps followed in the experimental set up of this work.

Determination of volatile compounds by GC-IMS to assign the quality of virgin olive oil.

The characterisation of different olive oil categories (extra virgin, virgin and lampante) using Ion Mobility Spectrometry (IMS) was improved by replacing the multicapillary column (MCC) with a capillary column (CC). The data obtained with MCC-IMS and CC-IMS were evaluated, studying both the global and the specific information obtained after the analysis of the volatile fraction of olive oils. A better differentiation of the oil categories was obtained employing CC vs MCC, since the classification percentage obtained with the CC-IMS was 92% as opposed to 87% obtained with MCC-IMS; although in productivity analytical terms, MCC offer a faster analysis than GC. The specific information obtained was also used to build a database, with a view to facilitating the characterization of specific attributes of olive oils. A total of 26 volatile metabolites (aldehydes, ketones, alcohols and esters) were identified. Finally, as revealed by an ANOVA test, some volatiles differed markedly in content among the different categories of oil. The data obtained confirms the potential of IMS as a reliable analytical screening technique, which can be used to assign the correct category to an olive oil sample.

Ion mobility spectrometry versus classical physico-chemical analysis for assessing the shelf life of extra virgin olive oil according to container type and storage conditions.

An experimental study was conducted to assess the stability of a single-variety (Arbequina) extra virgin olive oil (EVOO) as a function of container type and storage conditions over a period of 11 months. EVOO quality was assessed by using ion mobility spectrometry (IMS), which provides increased simplicity, expeditiousness, and relative economy. The results were compared with the ones obtained by using the official method based on classical physico-chemical analysis. Bag-in-box, metal, dark glass, clear glass, and polyethylene terephthalate containers holding EVOO were opened on a periodic basis for sampling to simulate domestic use; in parallel, other containers were kept closed until analysis to simulate the storage conditions on market shelves. The results of the physico-chemical and instrumental analyses led to similar conclusions. Thus, samples packaged in bag-in-box containers preserved oil quality for 11 months, better than other container types. The HS-GC-IMS results confirm that 2-heptenal and 1-penten-3-one are two accurate markers of EVOO quality.

Enhancing sensitivity and selectivity in the determination of aldehydes in olive oil by use of a Tenax TA trap coupled to a UV-ion mobility spectrometer.

A Tenax TA trap was coupled to an ion mobility spectrometer in order to improve basic analytical properties such as sensitivity and selectivity. The analytical performance of this combination was assessed in the determination of volatile aldehydes between 3 and 6 carbon atoms present in olive oil. The aldehydes were extracted and adsorbed into the trap, from which they were thermally desorbed for analysis by UV-Ion Mobility Spectrometry (UV-IMS). Sensitivity was increased by the preconcentration step and selectivity by the combination of temperature programmed thermal desorption and the ability of the ion mobility spectrometer to monitor the desorbed analytes in real time. The limits of detection obtained were lower than 0.3 mg kg(-1) and the relative standard deviation lower than 10%. A one-way analysis of variance (ANOVA) was used to identify significant differences between olive oil grades in terms of peak heights for the target aldehydes.

Determination of mandelic acid enantiomers in urine by derivatization in supercritical carbon dioxide prior to their determination by gas chromatography.

The use of a supercritical carbon dioxide reaction medium for the determination of the (R)- and (S)-enantiomers of mandelic acid (MA) is proposed. The process involves a previous derivatization step under supercritical conditions by which the carboxyl group is esterified with methanol, then followed by acylation of the hydroxyl group in methyl MA with pentafluoropropionic anhydride in the absence of a catalyst. These derivatization steps cause no enantiomeric inversion. The derivatized enantiomers are extracted and quantified by gas chromatography. A BETA DEX 225 capillary column allows the separation of (R)-MA and (S)-MA as pentafluoropropionyl methyl esters with good resolution and precision. The overall method was used to determine both enantiomers in urine samples.