A metabolic fingerprinting approach based on selected ion flow tube mass spectrometry (SIFT-MS) and chemometrics: A reliable tool for Mediterranean origin-labeled olive oils authentication

Advanced analytical techniques for authenticity identification and quality evaluation in Essential oils: A review

Essential oils (EO), secondary metabolites of plants are fragrant oily liquids with antibacterial, antiviral, anti-inflammatory, anti-allergic, and antioxidant effects. They are widely applied in food, medicine, cosmetics, and other fields. However, the quality of EOs remain uncertain owing to their high volatility and susceptibility to oxidation, influenced by factors such as the harvesting season, extraction, and separation techniques. Additionally, the huge economic value of EOs has led to a market marked by widespread and varied adulteration, making the assessment of their quality challenging. Therefore, developing simple, quick, and effective identification techniques for EOs is essential. This review comprehensively summarizes the techniques for assessing EO quality and identifying adulteration. It covers sensory evaluation, physical and chemical property evaluation, and chemical composition analysis, which are widely used and of great significance for the quality evaluation and adulteration detection of EOs.

Analytical insights for ensuring authenticity of Greek agriculture products: Unveiling chemical marker applications

Food authentication, including the differentiation of geographical or botanical origin, the method of production i.e. organic vs. conventional farming as well as the detection of food fraud/adulteration, has been a rapidly growing field over the past two decades due to increasing public awareness regarding food quality and safety, nutrition, and health. Concerned parties include consumers, producers, and legislators. Thus, the development of rapid, accurate, sensitive, and reproducible analytical methods to guarantee the authenticity of foods is of primary interest to scientists and technologists. The aim of the present article is to summarize research work carried out on the authentication of Greek agricultural products using spectroscopic (NIR, FTIR, UV-Vis, Raman and fluorescence spectroscopy, NMR, IRMS, ICP-OES, ICP-MS) and chromatographic (GC, GC/MS, HPLC, HPLC/MS, etc.) methods of analysis in combination with chemometrics highlighting the chemical markers that enable product authentication. The review identified a large number of chemical markers including volatiles, phenolic substances, natural pigments, elements, isotopes, etc. which can be used for (i) the differentiation of botanical/geographical origin; conventional from organic farming; production procedure and vintage year, etc. and (ii) detection of adulteration of high quality plant and animal origin foods with lower value substitutes. Finally, the constant development of reliable analytical techniques in combination with law enforcement authorities will ensure authentic foods in terms of quality and safety for consumers.

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin

Over recent years, there has been an increase in the number of reported cases of food fraud incidents, whereas at the same time, consumers demand authentic products of high quality. The emerging volatilomics technology could be the key to the analysis and characterization of the quality of different foodstuffs. This field of omics has aroused the interest of scientists due to its noninvasive, rapid, and cost-profitable nature. This review aims to monitor the available scientific information on the use of volatilomics technology, correlate it to the relevant food categories, and demonstrate its importance in the food adulteration, authenticity, and origin areas. A comprehensive literature search was performed using various scientific search engines and "volatilomics," "volatiles," "food authenticity," "adulteration," "origin," "fingerprint," "chemometrics," and variations thereof as keywords, without chronological restriction. One hundred thirty-seven relevant publications were retrieved, covering 11 different food categories (meat and meat products, fruits and fruit products, honey, coffee, tea, herbal products, olive oil, dairy products, spices, cereals, and others), the majority of which focused on the food geographical origin. The findings show that volatilomics typically involves various methods responsible for the extraction and consequential identification of volatile compounds, whereas, with the aid of data analysis, it can handle large amounts of data, enabling the origin classification of samples or even the detection of adulteration practices. Nonetheless, a greater number of specific research studies are needed to unlock the full potential of volatilomics.

Robust Automated SIFT-MS Quantitation of Volatile Compounds in Air Using a Multicomponent Gas Standard

Selected ion flow tube mass spectrometry, SIFT-MS, has been widely used in industry and research since its introduction in the mid-1990s. Previously described quantitation methods have been advanced to include a gas standard for a more robust and repeatable analytical performance. The details of this approach to calculate the concentrations from ion-molecule reaction kinetics based on reaction times and instrument calibration functions determined from known concentrations in the standard mix are discussed. Important practical issues such as the overlap of product ions are outlined, and best-practice approaches are presented to enable them to be addressed during method development. This review provides a fundamental basis for a plethora of studies in broad application areas that are possible with SIFT-MS instruments.

Increasing the Robustness of SIFT-MS Volatilome Fingerprinting by Introducing Notional Analyte Concentrations

Selected ion flow tube-mass spectrometry (SIFT-MS) is an analytical technique for volatile detection and quantification. SIFT-MS can be applied in a "white box" approach, measuring concentrations of target compounds, or as a "black box" fingerprinting technique, scanning all product ions during a full scan. Combining SIFT-MS full scan data acquired from multibatches or large-scale experiments remains problematic due to signal fluctuation over time. The standard approach of normalizing full scan data to the total signal intensity was insufficient. This study proposes a new approach to correct SIFT-MS fingerprinting data. In this concept, all of the product ions from a full scan are considered individual compounds for which notional concentrations can be calculated. Converting ion count rates into notional analyte concentrations accounts for any changes in the instrument parameters. The benefits of the proposed approach are demonstrated on three years of data from both multibatches and long-term experiments showing a significant reduction of system-induced fluctuations providing a better focus on the changes of interest.

Authentication of extra virgin Argan oil by selected-ion flow-tube mass-spectrometry fingerprinting and chemometrics

Recognized for its nutritional and therapeutic use, extra-virgin Argan Oil (EVAO) is frequently adulterated. Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) spectra were applied to quantify adulterants (i.e., Argan oil of lower quality (LQAO), olive oil (OO), and sunflower oil (SO)) in EVAO. Four data sets, i.e., using H₃O⁺, NO⁺, O₂⁺ reagent ions, and the combined data were considered. Soft independent modelling of class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA) were assessed to distinguish adulterated- from pure EVAO. The effectiveness of SIFT-MS associated with PLS and support vector machine (SVM) regression to quantify trace adulterants in EVAO was evaluated. Variable Importance in Projection (VIP), and interval-PLS (iPLS) were also investigated to extract useful features. Different models were built to predict the EVAO authenticity and the degree of adulteration. High accuracy was achieved. SIFT-MS spectra handled with the appropriate chemometric tools were found suitable for the quality evaluation of EVAO.

Rapid Identification of Adulteration in Extra Virgin Olive Oil via Dynamic Headspace Sampling and High-Pressure Photoionization Time-of-Flight Mass Spectrometry

High-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS) combined with dynamic headspace sampling was developed for rapid identification of adulteration in extra virgin olive oil (EVOO). The volatile organic compound (VOC) fingerprints of EVOO, refined rapeseed oil (r-RO), peanut oil (PO), corn oil (CO), fragrant rapeseed oil (f-RO), and sunflower oil (SO) were obtained in just 1.5 min, which enabled satisfactory classification of different edible oils. 1,4-Bis(methylene)cyclohexane and dimethyl disulfide were unique VOCs in r-RO and f-RO, respectively, while 2,5-dimethylpyrazine and 2-methylpyrazine were distinctive VOCs in PO. Percentages as low as 3% r-RO, 1% PO, and 1% f-RO in r-RO-EVOO, PO-EVOO, and f-RO-EVOO mixtures, respectively, were successfully identified based on the characteristic VOCs. Linear regression equations of these VOCs were established and utilized for predicting the adulteration proportions. The good agreements between the actual adulteration proportions and the predicted ones demonstrated that HPPI-TOFMS was reliable for the quantification of EVOO adulteration.

Geographical authentication of virgin olive oil by GC-MS sesquiterpene hydrocarbon fingerprint: Verifying EU and single country label-declaration

According to the last report from the European Union (EU) Food Fraud Network, olive oil tops the list of the most notified products. Current EU regulation states geographical origin as mandatory for virgin olive oils, even though an official analytical method is still lacking. Verifying the compliance of label-declared EU oils should be addressed with the highest priority level. Hence, the present work tackles this issue by developing a classification model (PLS-DA) based on the sesquiterpene hydrocarbon fingerprint of 400 samples obtained by HS-SPME-GC-MS to discriminate between EU and non-EU olive oils, obtaining an 89.6% of correct classification for the external validation (three iterations), with a sensitivity of 0.81 and a specificity of 0.95. Subsequently, multi-class discrimination models for EU and non-EU countries were developed and externally validated (with three different validation sets) with successful results (average of 92.2% of correct classification for EU and 96.0% for non-EU countries).

Untargeted selected ion flow tube mass spectrometry headspace analysis: High-throughput differentiation of virgin and recycled polyethylene pellets

RATIONALE

Recycled plastics are increasingly used for packaging of fast-moving consumer goods (FMCG). Compared with packaging made from virgin polymers, there is greater risk of taints entering products due to prior use of the polymers and incomplete cleaning. Increased quality assurance testing of polymer feedstock is required for recycled packaging. Selected ion flow tube mass spectrometry (SIFT-MS) analysis coupled with multivariate statistical data processing can provide high-throughput untargeted screening of recycled polymers at low cost per sample.

METHODS

SIFT-MS is a direct-injection MS technique that provides high-throughput automated headspace analysis of polymer samples when coupled with a syringe-injection autosampler (12 incubated samples per hour). Full-scan SIFT-MS data were processed using multivariate statistical analysis (specifically, the soft independent modeling by class analogy (SIMCA) algorithm).

RESULTS

SIFT-MS full-scan data were acquired for ten replicates each of ten recycled and four virgin high-density polyethylene (HDPE) pellet products from multiple manufacturers. The samples varied approximately 20-fold in terms of total volatile residue, while showing very high repeatability across replicates. SIFT-MS scan data were dominated by aliphatic and monoterpene hydrocarbon residues, and - to a lesser extent - alcohols. Application of the SIMCA algorithm to the data resulted in successful classification by both individual samples and manufacturers.

CONCLUSIONS

Automated, untargeted SIFT-MS analysis coupled with multivariate statistical data analysis has the potential to provide rapid, effective screening of recycled polymer products, which would provide increased quality assurance of recycled polymers used for FMCG.

Volatile fingerprint of food products with untargeted SIFT-MS data coupled with mixOmics methods for profile discrimination: Application case on cheese

Volatile organic compounds (VOCs) emitted by food products are decisive for the perception of aroma and taste. The analysis of gaseous matrices is traditionally done by detection and quantification of few dozens of characteristic markers. Emerging direct injection mass spectrometry technologies offer rapid analysis based on a soft ionisation of VOCs without previous separation. The recent increase of selectivity offered by the use of several precursor ions coupled with untargeted analysis increases the potential power of these instruments. However, the analysis of complex gaseous matrix results in a large number of ion conflicts, making the quantification of markers difficult, and in a large volume of data. In this work, we present the exploitation of untargeted SIFT-MS volatile fingerprints of ewe PDO cheeses in a real farm model, using mixOmics methods allowing us to illustrate the typicality, the manufacturing processes reproducibility and the impact of the animals' diet on the final product.

The use of analytical techniques coupled with chemometrics for tracing the geographical origin of oils: A systematic review (2013-2020)

The global market for imported, high-quality priced foods has grown dramatically in the last decade, as consumers become more conscious of food originating from around the world. Many countries require the origin label of food to protect consumers need about true characteristics and origin. Regulatory authorities are looking for an extended and updated list of the analytical techniques for verification of authentic oils and to support law implementation. This review aims to introduce the efforts made using various analytical tools in combination with the multivariate analysis for the verification of the geographical origin of oils. The popular analytical tools have been discussed, and scientometric assessment that underlines research trends in geographical authentication and preferred journals used for dissemination has been indicated. Overall, we believe this article will be a good guideline for food industries and food quality control authority to assist in the selection of appropriate methods to authenticate oils.

Plastic breeze: Volatile organic compounds (VOCs) emitted by degrading macro- and microplastics analyzed by selected ion flow-tube mass spectrometry

Pollution from microplastics (MPs) has become one of the most relevant topics in environmental chemistry. The risks related to MPs include their capability to adsorb toxic and harmful molecular species, and to release additives and degradation products into ecosystems. Their role as a primary source of a broad range of harmful volatile organic compounds (VOCs) has also been recently reported. In this work, we applied a non-destructive approach based on selected-ion flow tube mass spectrometry (SIFT-MS) for the characterization of VOCs released from a set of plastic debris collected from a sandy beach in northern Tuscany. The interpretation of the individual SIFT-MS spectra, aided by principal component data analysis, allowed us to relate the aged polymeric materials that make up the plastic debris (polyethylene, polypropylene, and polyethylene terephthalate) to their VOC emission profile, degradation level, and sampling site. The study proves the potential of SIFT-MS application in the field, as a major advance to obtain fast and reliable information on the VOCs emitted from microplastics. The possibility to obtain qualitative and quantitative data on plastic debris in less than 2 min also makes SIFT-MS a useful and innovative tool for future monitoring campaigns involving statistically significant sets of environmental samples.

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Olive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) assisted by machine learning is used for the classification of 139 virgin olive oils in terms of their geographical origin. The LIBS spectra of these olive oil samples were used to train different machine learning algorithms, namely LDA, ERTC, RFC, XGBoost, and to assess their classification performance. In addition, the variable importance of the spectral features was calculated, for the identification of the most important ones for the classification performance and to reduce their number for the algorithmic training. The algorithmic training was evaluated and tested by means of classification reports, confusion matrices and by external validation procedure as well. The present results demonstrate that machine learning aided LIBS can be a powerful and efficient tool for the rapid authentication of the geographic origin of virgin olive oil.

Virgin Olive Oil Volatile Compounds: Composition, Sensory Characteristics, Analytical Approaches, Quality Control, and Authentication

Volatile organic compounds strongly contribute to both the positive and negative sensory attributes of virgin olive oil, and more and more studies have been published in recent years focusing on several aspects regarding these molecules. This Review is aimed at giving an overview on the state of the art about the virgin olive oil volatile compounds. Particular emphasis was given to the composition of the volatile fraction, the analytical issues and approaches for analysis, the sensory characteristics and interaction with phenolic compounds, and the approaches for supporting the Panel Test in virgin olive oil classification and in authentication of the botanical and geographic origin based on volatile compounds. A pair of detailed tables with a total of approximately 700 volatiles identified or tentatively identified to date and tables dealing with analytical procedures, sensory characteristics of volatiles, and specific chemometric approaches for quality assessment are also provided.

Characterization of virgin olive oils from Spanish olive varieties introduced in Mendoza, Argentina, and their comparison with the autochthonous variety

BACKGROUND

The aim of this work was to evaluate and compare oil production and its quality in three Spanish olive varieties (Genovesa, Villalonga, and Nevadillo blanco) growing outside the Mediterranean basin with the Argentine autochthonous variety (Arauco). Fruit parameters and oil characteristics were evaluated using samples collected from the germplasm collection of Mendoza province and elaborated in the same place.

RESULTS

The levels of phenolic compounds and the fatty acid composition of the samples were comparable with those previously published for these Spanish varieties, grown in the Mediterranean basin, showing the adaptability of olive trees. Observing the levels of phenolic compounds and oxidative stability, a strong correlation between oxidative stability and oleocanthal was observed.

CONCLUSION

The characteristics of the fruit and oil differed according to variety and season. The inter-harvest stability was different depending on the variety. Genovesa was observed to be the most stable variety according to its fruit and oil characteristics - even more stable than the autochthonous variety, Arauco. However, in terms of the composition of phenolic compounds, Arauco was the most stable between harvests, this characteristic being more important for the taste and uniformity of the product. © 2020 Society of Chemical Industry.

Multimodal combination of GC × GC-HRTOFMS and SIFT-MS for asthma phenotyping using exhaled breath

Chronic inflammatory lung diseases impact more than 300 million of people worldwide. Because they are not curable, these diseases have a high impact on both the quality of life of patients and the healthcare budget. The stability of patient condition relies mostly on constant treatment adaptation and lung function monitoring. However, due to the variety of inflammation phenotypes, almost one third of the patients receive an ineffective treatment. To improve phenotyping, we evaluated the complementarity of two techniques for exhaled breath analysis: full resolving comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOFMS) and rapid screening selected ion flow tube MS (SIFT-MS). GC × GC-HRTOFMS has a high resolving power and offers a full overview of sample composition, providing deep insights on the ongoing biology. SIFT-MS is usually used for targeted analyses, allowing rapid classification of samples in defined groups. In this study, we used SIFT-MS in a possible untargeted full-scan mode, where it provides pattern-based classification capacity. We analyzed the exhaled breath of 50 asthmatic patients. Both techniques provided good classification accuracy (around 75%), similar to the efficiency of other clinical tools routinely used for asthma phenotyping. Moreover, our study provides useful information regarding the complementarity of the two techniques.

Validation Study of Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) in Heritage Science: Characterization of Natural and Synthetic Paint Varnishes by Portable Mass Spectrometry

The identification at molecular level of organic materials in heritage objects as paintings requires in most cases the collection of micro-samples followed by micro-destructive analysis. In this study, we explore the possibility to characterize natural and synthetic resins used as paint varnishes by mean of non-invasive analysis of released volatile organic compounds (VOCs) through selected ion flow tube-mass spectrometry (SIFT-MS). SIFT-MS is a portable direct mass spectrometric technique that achieves the analysis of VOCs at trace levels in real time, by controlled ultra-soft chemical ionization using eight different chemical ionization agents. We tested the portable instrumentation on different reference resins used as paint varnishes, both natural (mastic, dammar, and colophony) and synthetic (Paraloid B67, MS2A, Regalrez 1094, and polyvinyl acetate), to evaluate the possibility to acquire qualitative data for the identification of these materials in heritage objects avoiding any sampling. This new analytical approach was validated by comparison with the traditional approach for VOCs analysis based on solid phase micro extraction-gas chromatography/mass spectrometry (SPME-GC/MS) analysis. The results demonstrate the use of SIFT-MS as an in situ non-invasive and non-destructive mass spectrometric technique to identify organic materials, such as paint varnishes.

Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils

Flavour is a special way to discriminate extra virgin olive oils (EVOOs) from other aroma plant oils. In this study, different ratios (5, 10, 15, 20, 30, 50, 70 and 100%) of peanut oil (PO), corn oil (CO) and sunflower seed oil (SO) were discriminated from raw EVOO using flavour fingerprint, electronic nose and multivariate analysis. Fifteen different samples of EVOO were selected to establish the flavour fingerprint based on eight common peaks in solid-phase microextraction-gas chromatography-mass spectrometry corresponding to 4-methyl-2-pentanol, (E)-2-hexenal, 1-tridecene, hexyl acetate, (Z)-3-hexenyl acetate, (E)-2-heptenal, nonanal and α-farnesene. Partial least square discrimination analysis (PLS-DA) was used to differentiate EVOOs and mixed oils containing more than 20% of PO, CO and SO. Furthermore, better discrimination efficiency was observed in PLS-DA than PCA (70% of CO and SO), which was equivalent to the correlation coefficient method of the fingerprint (20% of PO, CO and SO). The electronic nose was able to differentiate oil samples from samples containing 5% mixture. The discrimination method was selected based on the actual requirements of quality control.