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%).

Feed Supplementation Detection during the Last Productive Stage of the Acorn-Fed Iberian Pig through a Faecal Volatilome Analysis.

The acorn-fed Iberian pig is known worldwide due to the quality of the resulting products commercialized after a natural and free grazing period of fattening in the dehesa agroforestry ecosystem. The quality regulation of the pig breed reserves "acorn" denomination for only those products obtained from animals exclusively fed grazing acorns and other natural resources; however, sometimes, feed supplementation of the pig's diet is fraudulently employed to reach an earlier slaughtering weight and to increase pig stocking rate, a strategy called postre (meaning "feed supplement"). In this sense, although many studies focused on Iberian pig diet have been published, the field detection of feed use for acorn-fed pig during the last finishing stage foraging in the dehesa, a practice which clashes with the official regulation, has not been explored yet. The present study employs a volatilome analysis (gas chromatography coupled to ion mobility spectrometry) of a non-invasive biological sample (faeces) to discriminate the grazing diet of only natural resources, that acorn-fed Iberian pigs are supposed to have, from those pigs that are also supplemented with feed. The results obtained show the suitability of the methodology used and the usefulness of the information obtained from faeces samples to discriminate and detect the fraudulent use of feed for acorn-fed Iberian pig fattening: a classification success ranging between 86.4% and 100% was obtained for the two chemometric approaches evaluated. These, together with the results of discriminant models, are discussed, in addition to the importance that the methodology optimized implies for the Iberian pig sector and market, which is also introduced. This methodology could be adapted to control organic farming animals or other upstanding livestock production systems which are supposed to be fully dependent on a natural grazing diet.

Relationship between Volatile Organic Compounds and Microorganisms Isolated from Raw Sheep Milk Cheeses Determined by Sanger Sequencing and GC-IMS.

Recently, the interest of consumers regarding artisan cheeses worldwide has increased. The ability of different autochthonous and characterized lactic acid bacteria (LAB) to produce aromas and the identification of the volatile organic compounds (VOCs) responsible for flavor in cheeses are important aspects to consider when selecting strains with optimal aromatic properties, resulting in the diversification of cheese products. The objective of this work is to determine the relationship between VOCs and microorganisms isolated (Lacticaseibacillus paracasei, Lactiplantibacillus plantarum, Leuconostoc mesenteroides and Lactococcus lactis subsp. hordniae) from raw sheep milk cheeses (matured and creamy natural) using accuracy and alternative methods. On combining Sanger sequencing for LAB identification with Gas Chromatography coupled to Ion Mobility Spectrometry (GC−IMS) to determinate VOCs, we describe cheeses and differentiate the potential role of each microorganism in their volatilome. The contribution of each LAB can be described according to their different VOC profile. Differences between LAB behavior in each cheese are shown, especially between LAB involved in creamy cheeses. Only L. lactis subsp. hordniae and L. mesenteroides show the same VOC profile in de Man Rogosa and Sharpe (MRS) cultures, but for different cheeses, and show two differences in VOC production in skim milk cultures. The occurrence of Lactococcus lactis subsp. hordniae from cheese is reported for first time.

Instrumental Techniques to Classify Olive Oils according to Their Quality.

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Geographical origin, curing plant and commercial category discrimination of cured Iberian hams through volatilome analysis at industry level.

Cured Iberian ham is a worldwide known product due to its high quality. Nowadays, there is a lack of official analytical methods to differentiate geographical origin (Protected Designation of Origin (PDO)), the curing plant where hams are processed, as well as the commercial categories in each industry. In this work, volatile organic compounds (VOCs) extracted from 998 Iberian hams were analyzed by Gas Chromatography coupled to Ion Mobility Spectrometry (GC-IMS), using the subsequent information to design discriminant models. High classification percentages were obtained for the three objectives of the study: 98,5% for geographical origin, 93,5% and 100% for curing plant discrimination, and an average rate of 84,5% for commercial category discrimination in the seven curing plants subject to study. Differences obtained in discriminant models are probably due to the complexity of Iberian ham manufacturing process. In this sense, the results obtained in the present study suggest slight differences between geographical areas and industries evaluated, even covered by the same PDO. Also, those differences may be related to the existing variability in terms of breed purity and feeding regime of Iberian pigs, which are two of the main determining factors of ham aroma.

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.

Authentication of the Montanera Period on Carcasses of Iberian Pigs by Using Analytical Techniques and Chemometric Analyses.

The potential of two complementary analytical techniques (near infrared spectroscopy, NIRS and gas chromatography-ion mobility spectrometry, GC-IMS) was used to establish the time that Iberian pigs have been fed on acorns and pasture and to verify their genetic purity. For both techniques it was neither necessary to carry out any chemical treatment in advance nor to identify individual compounds. The results showed that both the NIR spectrum and the spectral fingerprint obtained by GC-IMS were affected by the time that the Iberian pig feeds on natural resources. High percentages of correct classification were achieved in the calibration for both techniques: >98% for the days of montanera and >96% for the breed by NIRS and >99% for the days of montanera and >98% for the breed by GC-IMS. The results obtained showed that NIR spectra taken from intact samples is a quick classification method according to the time of montanera and breed.

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.

Portable Raman Spectrometer as a Screening Tool for Characterization of Iberian Dry-Cured Ham.

Dry-cured Iberian ham is officially classified into different commercial categories according to the pig's breed and feeding regime. These reach very different prices, thus promoting labelling fraud and causing great damage to the food sector. In this work, a method based on Raman spectroscopy was explored as a rapid in situ screening tool for Iberian ham samples. A total of 110 samples were analyzed to assess the potential of this technique to differentiate purebred, crossbred, acorn-fed and feed-fed dry-cured Iberian ham. A continuous signal probably due to sample fluorescence was obtained, which hid the Raman scattering signal. Therefore, chemometric treatment was applied in order to extract non-apparent information. High validated classification rates were obtained for feeding regime (83.3%) and breed (86.7%). In addition, an interlaboratory study was carried out to confirm the applicability of the method with 52 samples, obtaining a validated rate above 80%.

Application of Volatilome Analysis to the Diagnosis of Mycobacteria Infection in Livestock.

Volatile organic compounds (VOCs) are small molecular mass metabolites which compose the volatilome, whose analysis has been widely employed in different areas. This innovative approach has emerged in research as a diagnostic alternative to different diseases in human and veterinary medicine, which still present constraints regarding analytical and diagnostic sensitivity. Such is the case of the infection by mycobacteria responsible for tuberculosis and paratuberculosis in livestock. Although eradication and control programs have been partly managed with success in many countries worldwide, the often low sensitivity of the current diagnostic techniques against Mycobacterium bovis (as well as other mycobacteria from Mycobacterium tuberculosis complex) and Mycobacterium avium subsp. paratuberculosis together with other hurdles such as low mycobacteria loads in samples, a tedious process of microbiological culture, inhibition by many variables, or intermittent shedding of the mycobacteria highlight the importance of evaluating new techniques that open different options and complement the diagnostic paradigm. In this sense, volatilome analysis stands as a potential option because it fulfills part of the mycobacterial diagnosis requirements. The aim of the present review is to compile the information related to the diagnosis of tuberculosis and paratuberculosis in livestock through the analysis of VOCs by using different biological matrices. The analytical techniques used for the evaluation of VOCs are discussed focusing on the advantages and drawbacks offered compared with the routine diagnostic tools. In addition, the differences described in the literature among in vivo and in vitro assays, natural and experimental infections, and the use of specific VOCs (targeted analysis) and complete VOC pattern (non-targeted analysis) are highlighted. This review emphasizes how this methodology could be useful in the problematic diagnosis of tuberculosis and paratuberculosis in livestock and poses challenges to be addressed in future research.

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).

Quality authentication of virgin olive oils using orthogonal techniques and chemometrics based on individual and high-level data fusion information.

Currently, extra virgin olive oil, virgin olive oil and lampante olive oil are classified using physical-chemical analyses and a sensory analysis of fruitiness and defects, which is carried out by expert panels. This manual analysis is nowadays considered to be controversial and therefore analytical methodologies, which may be automated to classify these samples, are needed. In this work, we propose using an analytical platform based on two orthogonal techniques to determine the flavour components perceived in the mouth and the components contributing to the olive oils (OOs) aroma, respectively. For the former, capillary electrophoresis with ultraviolet detector (CE-UV) and high-performance liquid chromatography with UV or fluorescence detection were explored. The CE-UV analysis provided better results with the developed chemometric models (principal component analysis, linear discriminant analysis and k-nearest neighbors method). While for the latter, headspace (HS) - gas chromatography coupling with ion mobility spectrometry (GC-IMS) was selected due to the easy applicability of this technique to classify OOs. Then both techniques, CE-UV and GC-IMS, were selected to be integrated into one analytical platform. The potential of using both complementary/orthogonal techniques was demonstrated using high-level data fusion of CE-UV and GC-IMS data.

Chemical Fingerprinting of Olive Oils by Electrospray Ionization-Differential Mobility Analysis-Mass Spectrometry: A New Alternative to Food Authenticity Testing.

Recently, the olive oil industry has been the subject of harsh criticism for false labeling and even adulterating olive oils. This situation in which both the industry and the population are affected leads to an urgent need to increase controls to avoid fraudulent activities around this precious product. The aim of this work is to propose a new analytical platform by coupling electrospray ionization (ESI), differential mobility analysis (DMA), and mass spectrometry (MS) for the analysis of olive oils based on the information obtained from the chemical fingerprint (nontargeted analyses). Regarding the sample preparation, two approaches were proposed: (i) sample dilution and (ii) liquid-liquid extraction (LLE). To demonstrate the feasibility of the method, 30 olive oil samples in 3 different categories were analyzed, using 21 of them to elaborate the classification model and the remaining 9 to test it (blind samples). To develop the prediction model, principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used. The overall success rate of the classification to differentiate among extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO) was 89% for the LLE samples and 67% for the diluted samples. However, combining both methods, the ability to differentiate EVOO from lower-quality oils (VOO and LOO) and the edible oils (EVOO and VOO) from nonedible oil (LOO) was 100%. The results show that ESI-DMA-MS can become an effective tool for the olive oil sector.

Use of a non-destructive sampling method for characterization of Iberian cured ham breed and feeding regime using GC-IMS.

Iberian cured ham from purebred pigs fattened on grazing acorns is a highly appreciated product. There are several analytical methods to avoid its labelling fraud; however, these require opening the ham. The aim of this work is testing a non-destructive sampling method which does not spoil the physical integrity of the ham. It consists of a puncture with a needle imitating a traditional olfactory system. After removing the needle impregnated with fat, its volatiles are analyzed with a Gas Chromatography (GC) coupled to Ion Mobility Spectrometry (IMS). The potential of this methodology was studied analyzing 156 Iberian hams from pigs under two different feeding regimes (acorns versus feed) and from different breed (Iberian versus Duroc crossed). Intensity of GC-IMS plot features was extracted and chemometric differentiation models were obtained; one for feeding regime and another for breed, providing validated classification rates of 91.7% and 100%, respectively. In addition, 29 features used for construction of both models were tentatively identified using chemical standards. The suitability of the method for quality control analysis was characterized by means of a precision study. As a conclusion, GC-IMS becomes a useful tool to guarantee dry-cured Iberian ham authenticity and detect labelling fraud.

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.

Quality assessment of olive oils based on temperature-ramped HS-GC-IMS and sensory evaluation: Comparison of different processing approaches by LDA, kNN, and SVM.

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.