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

Bajoub, Aadil; Medina‐Rodríguez, Santiago; Ajal, El Amine; Cuadros-Rodrı́guez, Luis; Monasterio, Romina Paula; Vercammen, Joeri; Fernández‐Gutiérrez, Alberto

doi:10.1016/j.foodres.2017.12.027

Cited by 38 publications

(34 citation statements)

References 26 publications

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“…[15] Having in mind the complexity of VOO matrix, the use of different metabolomic approaches giving comprehensive information about the sample under study (mainly, but not exclusively, based on MS or NMR and chemometrics) has been proposed as a way to address the problem of origin mislabeling in the olive oil sector. [21] It is worthy to mention some other interesting examples illustrating the differentiation of VOO samples according to their geographical provenance based on the determination of specific chemical classes (triacylglycerols, [22] fatty acids, [23] esterified sterols, [24] or volatile compounds [25,26] ). [17,18] Phenolic compounds profiling combined with PCA and LDA has been pointed out as a very promising approach in origin authentication studies too.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Capability of LC‐MS and GC‐MS Multi‐Class Methods to Discriminate Virgin Olive Oils from Different Geographical Indications and to Identify Potential Origin Markers

Olmo-García

Wendt

Kessler

et al. 2019

Euro J Lipid Sci & Tech

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Looking for a strategy to authenticate the declared origin of commercial extra virgin olive oils (EVOOs), 126 samples from six different Mediterranean geographical indications (GIs) are analyzed by means of two different platforms [LC‐ESI‐QTOF MS (in positive and negative polarity) and GC‐APCI‐QTOF MS (in positive mode)] combined to chemometrics. The sample treatment and chromatographic/detection conditions (in both platforms) are chosen to enable the comprehensive characterization of the complete minor fraction of the oils within a single run. Noticeable discrimination among the six evaluated GIs [Priego de Córdoba and Baena (Spain), Kalamata (Greece), Toscano (Italy), and Ouazzane and Meknès (Morocco)] is achieved building two‐class PLS‐DA models which consider the data coming from both platforms. The contribution of a few thousand molecular features to the statistical models is evaluated in depth and several compounds are pointed out as possible origin markers, describing characteristic compositional patterns for each GI in the evaluated crop year. The complementarity of the different analytical approaches is discussed and diverse strategies are used to identify the classifiers. Practical Applications: Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI) labels are important tools to promote high quality EVOOs, assuring the connection to a particular territory and the characteristic combination of natural and human factors which make possible to obtain unique oils. In this context, it is imperative to furnish the control labs with innovative tools and methods which are able to provide extensive information about the EVOOs’ minor fraction (of unquestionable importance regarding its overall quality) in just one run and to give the chance to find and identify (and validate) origin markers. The utility of validated classifiers to authenticate the belonging (or not) of an EVOO to a particular GI is clear. The consumers’ confidence can be perceptibly undermined if the geographical name is used on products not having the expected qualities or if the production specifications are sometimes not followed by producers. The capability of LC‐MS and GC‐MS multi‐class methods to discriminate virgin olive oils from different geographical indications (crop season 2016‐2017) and to identify potential origin markers is demonstrated.

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Section: Introductionmentioning

confidence: 99%

Exploring the Capability of LC‐MS and GC‐MS Multi‐Class Methods to Discriminate Virgin Olive Oils from Different Geographical Indications and to Identify Potential Origin Markers

Olmo-García

Wendt

Kessler

et al. 2019

Euro J Lipid Sci & Tech

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“…In addition, the combination of different ionization chemistries generates specific fragmentation that make SIFT-MS a powerful tool for targeted analysis 10 . This specificity has also a big added value for untargeted pattern based method, allowing classification model development based on specific ion channels acquired with specific precursors 9 . Due to the technology progress on the precursor's chemistry and the development of untargeted SIFT-MS analysis, there is a growing need for optimization and processing methods allowing the selection of the best precursors and informative ion channels.…”

Section: Discussionmentioning

confidence: 99%

“…On one side, separation science based on multidimensional methods such as comprehensive two-dimensional gas chromatography (GC × GC) appeared as one of the methods of choice for the characterization of complex mixtures of (semi)volatile chemicals, especially when coupled to high-resolution time-of-flight mass spectrometry (HRTOFMS) 8 . On another side, direct introduction instruments such as selected ion flow tube MS (SIFT-MS) offers now the capacity to perform both targeted and non-targeted analyses within a few minutes ( [9][10][11]. At the price of high cost equipment and limited adaptability to routine medical usage, GC × GC-HRTOFMS offers the possibility to almost completely characterize the VOC sample composition.…”

mentioning

confidence: 99%

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

Stefanuto

Zanella

Vercammen

et al. 2020

Sci Rep

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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.

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“… Increased use of SIFT‐MS data (whether targeted or SCAN mode) combined with multivariate statistics to provide rapid origin and quality screening. Examples presented here include the very recent publications on high‐throughput origin determination of Argan and olive oils. SIFT‐MS has potential to provide on‐line or high‐throughput flavour analysis correlations with sensory analysis, as illustrated by the work on Parmesan and beef On‐line monitoring to optimize product flavour during processing and avoid defects, as demonstrated by Barringer and co‐workers for tomato, cocoa, swiss cheese and coffee …”

Section: Concluding Remarks and Future Directionsmentioning

confidence: 99%

“…47 More recently, SIFT-MS combined with chemometrics has been used to authenticate the geographic origin labelling of 130 virgin olive oils from six different olive-growing regions around the Mediterranean. 48 Another study also examined the effectiveness of SIFT-MS vs chemical profiling to classify 95 extra-virgin Argan oils originating from five Moroccan Argan forest locations. 49 In both studies, the authors found the full scan option of SIFT-MS to be a sensitive and effective differentiator of geographic origin.…”

Section: Edible Oilsmentioning

confidence: 99%

Comprehensive odorant analysis for on‐line applications using selected ion flow tube mass spectrometry (SIFT‐MS)

Langford¹,

Padayachee²,

McEwan

et al. 2019

Flavour & Fragrance J

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Selected ion flow tube mass spectrometry (SIFT‐MS) is a direct mass spectrometry technique developed for volatile organic compound (VOC) analysis. In this comprehensive review of the SIFT‐MS flavour literature, its flexibility and utility are evident. SIFT‐MS has found wide application in the analysis of formation and release of odorants in fruits, vegetables, nuts, and seeds – including flavour and sensory optimization during cooking. Dairy products and poultry, red meat, and seafood also comprise a significant number of studies across processing, sensory, and shelf‐life studies. Edible oils, initially investigated for flavour and oxidative status, have recently been very successfully classified according to origin using a combined chemometrics and SIFT‐MS approach. Other recent developments – across different food groups – have been growth in applications to packaging (including meats in modified atmosphere packaging), and correlations of SIFT‐MS data with various types of sensory testing. The continuing growth of food odorant publications indicates that the SIFT‐MS technique has a significant role to play in understanding and managing favourable and unfavourable aroma formation in both academia and industry.

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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

Cited by 38 publications

References 26 publications

Exploring the Capability of LC‐MS and GC‐MS Multi‐Class Methods to Discriminate Virgin Olive Oils from Different Geographical Indications and to Identify Potential Origin Markers

Exploring the Capability of LC‐MS and GC‐MS Multi‐Class Methods to Discriminate Virgin Olive Oils from Different Geographical Indications and to Identify Potential Origin Markers

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

Comprehensive odorant analysis for on‐line applications using selected ion flow tube mass spectrometry (SIFT‐MS)

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