Direct classification of olive oils by using two types of ion mobility spectrometers

Authenticity assessment of ground black pepper by combining headspace gas-chromatography ion mobility spectrometry and machine learning

Currently, the authentication of ground black pepper is a major concern, creating a need for a rapid, highly sensitive and specific detection tool to prevent the introduction of adulterated batches into the food chain. To this aim, head space gas-chromatography ion mobility spectrometry (HS-GC-IMS), combined with machine learning, is tested in this initial, proof-of-concept study. A broad variety of authentic samples originating from eight countries and three continents were collected and spiked with a range of adulterants, both endogenous sub-products and an assortment of exogenous materials. The method is characterized by no sample preparation and requires 20 min for chromatographic separation and ion mobility data acquisition. After an explorative analysis of the data, those were submitted to two different machine learning algorithms (partial least squared discriminant analysis-PLS-DA and support vector machine-SVM). While the PLS-DA model did not provide fully satisfactory performances, the combination of HS-GC-IMS and SVM successfully classified the samples as authentic, exogenously-adulterated or endogenously-adulterated with an overall accuracy of 90 % and 96 % on withheld test set 1 and withheld test set 2, respectively (at a 95 % confidence level). Some limitations, expected to be mitigated by further research, were encountered in the correct classification of endogenously adulterated ground black pepper. Correct categorization of the ground black pepper samples was not adversely affected by the operator or the time span of data collection (the method development and model challenge were carried out by two operators over 6 months of the study, using ground black pepper harvested between 2015 and 2019). Therefore, HS-GC-IMS, coupled to an intelligent tool, is proposed to: (i) aid in industrial decision-making before utilization of a new batch of ground black pepper in the production chain; (ii) reduce the use of time-consuming conventional analyses and; (iii) increase the number of ground black pepper samples analyzed within an industrial quality control frame.

Discrimination and Characterization of Volatile Flavor Compounds in Fresh Oriental Melon after Forchlorfenuron Application Using Electronic Nose (E-Nose) and Headspace-Gas Chromatography-Ion Mobility Spectrometry (HS-GC-IMS)

Aroma is a crucial factor determining the market value and consumer satisfaction of fresh oriental melon. However, few studies focus on the volatile flavor of fresh oriental melon, and the effect of forchlorfenuron application on the aroma profile is unclear. This study characterized the volatile profile of fresh oriental melon fruit after forchlorfenuron application by E-nose and HS-GC-IMS. The holistic variation of volatile compounds exhibited evident distinction based on linear discriminant analysis (LDA) with E-nose. Forty-eight volatile compounds were identified from fresh oriental melon via GC-IMS, mainly esters, alcohols, aldehydes, and ketones, along with smaller quantities of sulfides and terpenes. Compared to pollination melon fruits, 13 critical different volatile flavor compounds were screened out in forchlorfenuron application groups by the PLS-DA model, imparting sweet fruity flavor. The results of the current study provide a valuable basis for evaluating the flavor quality of oriental melon after forchlorfenuron treatment.

Identification of agricultural quarantine materials in passenger's luggage using ion mobility spectroscopy combined with a convolutional neural network

As economic globalization intensifies, the recent increase in agricultural products and travelers from abroad has led to an increase in the probability of invasive alien species. A major pathway for invasive alien species is agricultural quarantine materials (AQMs) in travelers' baggage. Thus, it is meaningful to develop efficient methods for early detection and prompt action against AQMs. In this study, a method based on the combination of odor detection of AQMs using ion mobility spectroscopy (IMS) and convolutional neural network (CNN) analysis for the identification of AQM species in luggage was developed. Two different ways were investigated to feed the IMS data of AQMs into the CNN, either as one-dimensional data (1D) (as a spectrum) or as two-dimensional data (2D) (as an IMS topographic map). The performances of CNN models were also compared to those of the commonly used classification algorithms: partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). By doing gradient-weighted class activation mapping (Grad-CAM), the essential IMS feature regions from the CNN models to predict different AQM species were also identified. The results of this research demonstrated that the application of the CNN to the IMS data of AQMs yielded superior classification performance compared to PLS-DA and SIMCA. Especially, the CNN-2D model which utilized the IMS topographic map as input achieved the best classification accuracy both on the calibration and validation sets. In addition, the Grad-CAM method had an ability to detect critical discriminating spectral regions for different types of AQM samples, and could provide explanation for the CNNs' decision-making. Despite the inherent limitations of the present analytical protocol, the results showed that the method of IMS in combination with a CNN has great potential to be a complement for sniffer dogs and X-ray imaging techniques to detect AQMs.

Adulteration detection of essence in sesame oil based on headspace gas chromatography-ion mobility spectrometry

Sesame oil is a traditional and delicious edible oil in China and Southeast Asia with a high price. However, sesame oil essence was often illegally added to cheaper edible oils to counterfeit sesame oil. In this study, a rapid and accurate headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) method was proposed to detect the counterfeit sesame oil where the other cheap oils were adulterated with essence. Combined with chemometric methods including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF), authentic and counterfeit sesame oils adulterated with sesame essence (0.5%, w/w) were easily separated into two groups. More importantly, 2-methylbutanoic acid, 2-furfurylthiol, methylpyrazine, methional, and 2,5-dimethylpyrazine were found to be markers of sesame essence, which were used to directly identify the sesame essence. The determination of volatile compounds based on HS-GC-IMS was proven to be an effective method for adulteration detection of essence in sesame oil.

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

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

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.

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.

Fast discrimination of avocado oil for different extracted methods using headspace-gas chromatography-ion mobility spectroscopy with PCA based on volatile organic compounds

To establish a method for fast discrimination of avocado oil for different extracted methods, the headspace-gas chromatography-ion mobility spectroscopy (HS-GC-IMS) combined with principal component analysis (PCA) was used to analyze non-target volatile organic compounds (VOCs). The results showed that 40 VOCs were identified, and the VOCs of the extraction method had a significant difference and had been well distinguished in PCA. The species and content of avocado oil obtained by squeeze method were more than the aqueous methods and supercritical carbon dioxide extraction methods (SC CO2). In addition, the different avocado oil had their characteristic compounds: the 2-acetylthiazole and ethyl propionate were the unique compounds in the avocado oil obtained by SC CO2. A rapid method for the determination of avocado oil obtained by different extraction methods based on HS-GC-IMS had been established, and the method was fast and simple and had a good application prospect in the prediction of avocado oil processing methods.

Limitations of GC-QTOF-MS Technique in Identification of Odorous Compounds from Wastewater: The Application of GC-IMS as Supplement for Odor Profiling

Odorous emissions from wastewater treatment plants (WWTPs) cause negative impacts on the surrounding areas and possible health risks on nearby residents. However, the efficient and reliable identification of WWTPs’ odorants is still challenging. In this study, odorous volatile organic compounds (VOCs) from domestic wastewater at different processing units were profiled and identified using gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography quadrupole-time-of-flight mass spectrometry (GC-QTOF-MS). The GC-QTOF-MS results confirmed the odor contribution of sulfur organic compounds in wastewater before primary sedimentation and ruled out the significance of most of the hydrocarbons in wastewater odor. The problems in odorous compounds analysis using GC-QTOF-MS were discussed. GC-IMS was developed for visualized analysis on composition characteristics of odorants. Varied volatile compounds were detected by GC-IMS, mainly oxygen-containing VOCs including alcohols, fatty acids, aldehydes and ketones with low odor threshold values. The fingerprint plot of IMS spectra showed the variation in VOCs’ composition, indicating the changes of wastewater quality during treatment process. The GC-IMS technique may provide an efficient profiling method for the changes of inlet water and performance of treatment process at WWTPs.

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

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

A simple and quick method to detect adulterated sesame oil using 3D fluorescence spectra

Adulterated sesame oil seriously damages the interests of consumers and the health of market. In this paper, a simple, fast and real-time model for identifying adulterated sesame oil (ASO) was proposed by combining 3D fluorescence spectra with wavelet moments (WMs). First, noise and data volume of the experimental data were reduced by wavelet multiresolution decomposition (WMRSD), which improved the stability and real-time of the model. Next, WMs were used to extract the features of the 3D fluorescence spectra and proved to be effective by hierarchical clustering results. Then, the qualitative quality of WMs of the same orders, different orders and the combinations were evaluated by Dunn's validity index (DVI), and the rules were given, respectively. Finally, the target WMs for identifying ASO were determined. This model is simple and fast, and expandable to online measurement, providing a reference for identification and adulteration of vegetable oils.

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

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

Sustainable Palm Oil-The Role of Screening and Advanced Analytical Techniques for Geographical Traceability and Authenticity Verification

Palm oil production from oil palm (Elaeis guineensis Jacq.) is vital for the economy of Malaysia. As of late, sustainable production of palm oil has been a key focus due to demand by consumer groups, and important progress has been made in establishing standards that promote good agricultural practices that minimize impact on the environment. In line with the industrial goal to build a traceable supply chain, several measures have been implemented to ensure that traceability can be monitored. Although the palm oil supply chain can be highly complex, and achieving full traceability is not an easy task, the industry has to be proactive in developing improved systems that support the existing methods, which rely on recorded information in the supply chain. The Malaysian Palm Oil Board (MPOB) as the custodian of the palm oil industry in Malaysia has taken the initiative to assess and develop technologies that can ensure authenticity and traceability of palm oil in the major supply chains from the point of harvesting all the way to key downstream applications. This review describes the underlying framework related to palm oil geographical traceability using various state-of-the-art analytical techniques, which are also being explored to address adulteration in the global palm oil supply chain.

Discrimination of Chinese yellow wine from different origins based on flavor fingerprint

In this study, discrimination of Chinese yellow wines from Shaoxing, Shandong, and Hubei in China has been carried out according to volatile flavor components. A total of 122 yellow wine samples were characterized by gas chromatography–ion mobility spectrometry (GC–IMS). A simple color mixing method was visually used to select characteristic peaks based on the RGB color model. Then, the volatile organic compounds corresponding to the selected characteristic peaks were identified via library searching, and the height values of those peaks were arranged for further chemometric pretreatment. Principal component analysis was employed to reveal significant differences and potential patterns between samples. Finally, quadratic discriminant analysis was applied to develop a classification model and achieved a correct classified rate of 95.35% for the prediction set. The results prove that the aroma composition combined with chemometric tools can be used as a fingerprinting technique to protect the product of origin and enable the authenticity of Chinese yellow wine.

Characterization of Arabica and Robusta Coffees by Ion Mobility Sum Spectrum

Aroma is one of the main characteristics of coffee specimens. Different mixtures of Arabica and Robusta coffees are usually found in the market to offer specific aroma or flavor profiles to consumers. However, the mixed samples or their proportions are not always identified in the product labels. Since the price of Arabica is much higher than that of Robusta, this lack of information is not only an economical issue but a possible fraud to consumers, besides the potential allergic reaction that these mixtures may trigger in some individuals. In this paper, two sample preparation techniques were compared before the analysis of the total volatile organic compounds (VOCs) found in Robusta, Arabica, and in the mixture from both coffee types. The comparison of the signals obtained from the analyses showed that the VOCs concentration levels obtained from the headspace (HS) analyses were clearly higher than those obtained from the pre-concentration step where an adsorbent, an active charcoal strip (ACS + HS), was used. In the second part of this study, the possibility of using the headspace gas-chromatography ion mobility spectrometry (HS-GC-IMS) for the discrimination between Arabica, Robusta, and mixed coffee samples (n = 30) was evaluated. The ion mobility sum spectrum (IMSS) obtained from the analysis of the HS was used in combination with pattern recognition techniques, namely linear discrimination analysis (LDA), as an electronic nose. The identification of individual compounds was not carried out since chromatographic information was not used. This novel approach allowed the correct discrimination (100%) of all of the samples. A characteristic fingerprint for each type of coffee for a fast and easy identification was also developed. In addition, the developed method is ecofriendly, so it is a good alternative to traditional approaches.

An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test

Sensory evaluation, carried out by panel tests, is essential for quality classification of virgin olive oils (VOOs), but is time consuming and costly when many samples need to be assessed; sensory evaluation could be assisted by the application of screening methods. Rapid instrumental methods based on the analysis of volatile molecules might be considered interesting to assist the panel test through fast pre-classification of samples with a known level of probability, thus increasing the efficiency of quality control. With this objective, a headspace gas chromatography-ion mobility spectrometer (HS-GC-IMS) was used to analyze 198 commercial VOOs (extra virgin, virgin and lampante) by a semi-targeted approach. Different partial least squares-discriminant analysis (PLS-DA) chemometric models were then built by data matrices composed of 15 volatile compounds, which were previously selected as markers: a first approach was proposed to classify samples according to their quality grade and a second based on the presence of sensory defects. The performance (intra-day and inter-day repeatability, linearity) of the method was evaluated. The average percentages of correctly classified samples obtained from the two models were satisfactory, namely 77% (prediction of the quality grades) and 64% (prediction of the presence of three defects) in external validation, thus demonstrating that this easy-to-use screening instrumental approach is promising to support the work carried out by panel tests.

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

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

Mechanism of aroma compounds changes from sea cucumber peptide powders (SCPPs) under different storage conditions

A rapid and sensitive measurement technique was used to investigate the mechanism of aroma compounds changes in SCPPs under the storage conditions with hygroscopicity and no-microorganism (HNM), nonhygroscopicity and no-microorganism (NHNM) and hygroscopicity and microorganism (HM) by HS-GC-IMS. The types and signal of aroma compounds increased obviously at the 5th day under the storage condition (HNM and HM). The signal of majority of aroma compounds decreased gradually since the 5th day. However, during the storage of SCPPs for 15 days, the total signals of aldehydes, ketones and alcohols gradually increased and reached a maximum. Thereinto, the saturated aldehydes such as hexanal had been produced as an off-flavor. These off-flavor compounds principally including aldehydes and ketones could be generated through Maillard reaction, while alcohols could be generated by microbial fermentation. The study discovered moisture adsorption and microorganism during storage could affect aroma compounds of SCPPs and the effect of moisture absorption was greater than microorganisms.

Gas Chromatography-Ion Mobility Spectrometry Detection of Odor Fingerprint as Markers of Rapeseed Oil Refined Grade

In this work, gas chromatography-ion mobility spectrometry (GC-IMS) was used to analyze the volatile organic compound changes of rapeseed oil with different refined grades, the odor fingerprints of refined rapeseed oil were constructed, and a nonlinear model was built to realize rapid and accurate discrimination of rapeseed oil with different refined grades. 124 rapeseed oil samples with different refined grades were collected and analyzed by GC-IMS and chemometric tools, and 34 characteristic peaks were selected by the colorized difference method as variables to characterize the internal quality in rapeseed oil of different refined grades. The principal component analysis algorithm was used to further reduce dimensionality and extract the most relevant information. The k-nearest neighbor algorithm was applied to build a discriminant model. All the samples were recognized accurately without errors, and the results show the potential of this method to discriminate different refined grades of vegetable oil.

Ion Mobility Spectrometry in Food Analysis: Principles, Current Applications and Future Trends

In the last decade, ion mobility spectrometry (IMS) has reemerged as an analytical separation technique, especially due to the commercialization of ion mobility mass spectrometers. Its applicability has been extended beyond classical applications such as the determination of chemical warfare agents and nowadays it is widely used for the characterization of biomolecules (e.g., proteins, glycans, lipids, etc.) and, more recently, of small molecules (e.g., metabolites, xenobiotics, etc.). Following this trend, the interest in this technique is growing among researchers from different fields including food science. Several advantages are attributed to IMS when integrated in traditional liquid chromatography (LC) and gas chromatography (GC) mass spectrometry (MS) workflows: (1) it improves method selectivity by providing an additional separation dimension that allows the separation of isobaric and isomeric compounds; (2) it increases method sensitivity by isolating the compounds of interest from background noise; (3) and it provides complementary information to mass spectra and retention time, the so-called collision cross section (CCS), so compounds can be identified with more confidence, either in targeted or non-targeted approaches. In this context, the number of applications focused on food analysis has increased exponentially in the last few years. This review provides an overview of the current status of IMS technology and its applicability in different areas of food analysis (i.e., food composition, process control, authentication, adulteration and safety).

A Screening Method Based on Headspace-Ion Mobility Spectrometry to Identify Adulterated Honey

Nowadays, adulteration of honey is a frequent fraud that is sometimes motivated by the high price of this product in comparison with other sweeteners. Food adulteration is considered a deception to consumers that may have an important impact on people's health. For this reason, it is important to develop fast, cheap, reliable and easy to use analytical methods for food control. In the present research, a novel method based on headspace-ion mobility spectrometry (HS-IMS) for the detection of adulterated honey by adding high fructose corn syrup (HFCS) has been developed. A Box-Behnken design combined with a response surface method have been used to optimize a procedure to detect adulterated honey. Intermediate precision and repeatability studies have been carried out and coefficients of variance of 4.90% and 4.27%, respectively, have been obtained. The developed method was then tested to detect adulterated honey. For that purpose, pure honey samples were adulterated with HFCS at different percentages (10-50%). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed a tendency of the honey samples to be classified according to the level of adulteration. Nevertheless, a perfect classification was not achieved. On the contrary, a full classification (100%) of all the honey samples was performed by linear discriminant analysis (LDA). This is the first time the technique of HS-IMS has been applied for the determination of adulterated honey with HFCS in an automatic way.

Application of Headspace Gas Chromatography-Ion Mobility Spectrometry for the Determination of Ignitable Liquids from Fire Debris

A fast and correct identification of ignitable liquid residues in fire debris investigation is of high importance in forensic research. Advanced fast analytical methods combined with chemometric tools are usually applied for these purposes. In the present study, the Headspace Gas Chromatography-Ion Mobility Spectrometry (HS-GC-IMS) combined with chemometrics is proposed as a promising technique for the identification of ignitable liquid residues in fire debris samples. Fire debris samples were created in the laboratory, according to the Destructive Distillation Method for Burning that is provided by the Bureau of Forensic Fire and Explosives. Four different substrates (pine wood, cork, paper, and cotton sheet) and four ignitable liquids of dissimilar composition (gasoline, diesel, ethanol, and paraffin) were used to create the fire debris. The Total Ion Current (TIC) Chromatogram combined with different chemometric tools (hierarchical cluster analysis and linear discriminant analysis) allowed for a full discrimination between samples that were burned with and without ignitable liquids. Additionally, a good identification (95% correct discrimination) for the specific ignitable liquid residues in the samples was achieved. Based on these results, the chromatographic data from HS-GC-IMS have been demonstrated to be very useful for the identification and discrimination of ignitable liquids residues. The main advantages of this approach vs. traditional methodology are that no sample manipulation or solvent is required; it is also faster, cheaper, and easy to use for routine analyses.

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.

Chemometric applications to assess quality and critical parameters of virgin and extra-virgin olive oil. A review

Today virgin and extra-virgin olive oil (VOO and EVOO) are food with a large number of analytical tests planned to ensure its quality and genuineness. Almost all official methods demand high use of reagents and manpower. Because of that, analytical development in this area is continuously evolving. Therefore, this review focuses on analytical methods for EVOO/VOO which use fast and smart approaches based on chemometric techniques in order to reduce time of analysis, reagent consumption, high cost equipment and manpower. Experimental approaches of chemometrics coupled with fast analytical techniques such as UV-Vis spectroscopy, fluorescence, vibrational spectroscopies (NIR, MIR and Raman fluorescence), NMR spectroscopy, and other more complex techniques like chromatography, calorimetry and electrochemical techniques applied to EVOO/VOO production and analysis have been discussed throughout this work. The advantages and drawbacks of this association have also been highlighted. Chemometrics has been evidenced as a powerful tool for the oil industry. In fact, it has been shown how chemometrics can be implemented all along the different steps of EVOO/VOO production: raw material input control, monitoring during process and quality control of final product.

Chemometrics for ion mobility spectrometry data: recent advances and future prospects

This is the first comprehensive review on chemometric techniques used in ion mobility spectrometry data analysis.

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.

Optimal Design of an Olive Oil Mill by Means of the Simulation of a Petri Net Model

Global concurrence is a topic that affects many companies in most sectors of the economy. In particular, the improvement in the manufacturing, packing, storage, and transportation of food has allowed farming companies from all over the world to compete for customers of a global market. In order to achieve success in this complex environment, it is convenient for the companies to be efficient even before the beginning of their business activity. This paper presents a decision support methodology for improving the design and management of an olive oil manufacturing facility based on the development of a Petri net model of the system, the simulation of its behaviour under a selected set of alternative configurations, and the choice of the most promising one by means of an optimization algorithm.