Innovative non-targeted liquid chromatography fingerprinting approach for authenticating tigernuts under Protected Designation of Origin quality seal.

BACKGROUND: Tigernut is a typical foodstuff from a specific region of Valencia (Spain) called 'L'Horta Nord', where it is commercialized under a Protected Designation of Origin (PDO) as Chufa de Valencia ('Valencia's tigernut'). PDO-recognized tigernuts present unique characteristics associated with their particular production region. Increasing demand and the associated expansion of its cultivation area has made necessary an exhaustive quality control to check the geographical origin and quality seal. RESULTS: In this work, a new multivariate analytical method capable of authenticating the PDO quality seal of tigernut samples was developed. Tigernut fat fraction was extracted under optimal conditions, applying the methodology of design of experiments. The analytical method combined fingerprinting methodology and chemometric tools to observe the natural grouping of samples using the exploratory analysis method and to develop classification models (partial least squares-discriminatory analysis; PLS-DA) to discriminate between two sample categories: (i) PDO tigernuts; and (ii) NON-PDO tigernuts. CONCLUSION: The built PLS-DA model demonstrated 100% accuracy, high sensitivity and specificity, revealing that the tigernut fat fraction can be applied to authenticate the PDO quality seal. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Recent Approaches for Analytical Characterization of Phospholipids in Food Matrices. Is the Phospholipid Fraction Exploited in the Authentication of Food Lipids?

Phospholipids (PhLs) are essential components of cell membranes, characterized by a hydrophobic tail and a hydrophilic headgroup. They play several roles in biological systems, including energy storage, protection, and antioxidant properties. PhLs are found naturally in foods such as egg yolks, milk, or vegetable oils. The composition and concentration of PhLs observed in these foods vary according to the analytical methodology applied, mainly in the extraction and sample treatment process. Analytical targeted approaches for characterized PhLs involve liquid chromatography and mass spectrometry techniques. These methods provide insights into the composition and content of PhLs in food matrices. However, there is limited research on using PhL profiles for food quality evaluation and authentication purposes. Untargeted approaches, such as fingerprinting, have the potential to assess the authenticity of food products by capturing analytical signals linked to the PhL fraction. This review focusses on recent analytical strategies used in characterizing PhLs in distinctive foodstuffs (eggs, milk, and vegetable oils). It discusses sample preparation, analytical separation, and detection techniques. The review also highlights the potential of multivariate approaches to incorporate information on PhL composition to assess the authenticity of food products, an area that has been largely overlooked in previous studies.

Critical evaluation of MS acquisition conditions and identification process in LC-Q-Orbitrap-MS for non-targeted analysis: pesticide residues as case of study.

In routine measurements, the length of analysis time and the number of samples analysed during a given time unit are crucial. Additionally, the analytical method used has to provide reliable results and be able to identify and quantify any compound present in the matrix. High-resolution equipment, including Orbitrap analysers, is commonly used for non-targeted determinations. However, researchers still rely on trial and error to achieve the best acquisition conditions on the mass spectrometer, which is a tedious and time-consuming process that can lead to errors. Moreover, tentative compound identification, particularly when using a non-targeted approach, heavily depends on commercial databases. All of these issues can ultimately result in incomplete identification of compounds in the study matrix. In this framework, the study presented here has a dual objective: to use the experimental design tool to optimise critical parameters in mass spectrometry using LC-Q-Orbitrap-MS equipment when working in a non-targeted approach and to compare the mzCloud™ and ChemSpider™ commercial databases included in Compound Discoverer software with TraceFinder home-made databases generated to evaluate the ability to identify compounds. The study's noteworthy findings reveal that employing an experimental design has facilitated rapid optimisation of the mass spectrometer's multiplexing and loop parameters. Furthermore, the study highlights that the lack of harmonisation in commercial databases poses a disadvantage in the identification of compounds, leading to superior results when using home-made databases. In the latter databases, around 80% of the compounds were identified, which is approximately twice the number identified in commercial databases (around 40% in the best case with ChemSpider™).

Instrument-Agnostizing Methodology for Liquid Chromatography-Mass Spectrometry Systems.

Mass spectrometry is a powerful analytical technique used to identify unknown compounds, to quantify known compounds, and to elucidate the structure and chemical properties of molecules. Nevertheless, the transfer of data from one instrument to another is one of the main problems, and obtaining the same or similar information from an analogous instrument but from a different manufacturer or even with the same instrument after carrying out the analyses in different times spacing is not possible. Hence, a general methodology to provide a chromatographic signal (or chromatogram) independent of the instrument is needed. In this sense, this book chapter describes the standardization procedure of chromatographic signals obtained from mass spectrometry platforms to obtain instrument-agnostic chromatographic signals for the determination of standard retention scores. This parameter may be used for the quantification of compounds when different mass spectrometry platforms coupled to ultrahigh-performance liquid chromatography are employed.

The chromatographic similarity profile - An innovative methodology to detect fraudulent blends of virgin olive oils.

Extra virgin olive oil is a potentially vulnerable foodstuff that can be mixed with other vegetal edible oils including poorer quality olive oils in order to obtain illicit profits. These unauthorized operations may take place at any stage of the production process and radically affect the chemical composition. In this paper, the analysis of different virgin olive oil samples before and after blending with other lower-grade olive oils in different proportions were performed. The direct analysis of the samples by (NP)HPLC-DAD in a wavelength range between 190 and 700 nm allowed the simultaneous analysis of several compound families responsible of the colour including chlorophylls, pheophytins, carotenes and tocopherols, the first three responsible for the olive oil colour. Unsupervised pattern recognition techniques applied on the chromatography-agnostic fingerprints of unblended virgin olive oil samples clearly showed the occurrence of groupings according to the sample hue (green and yellow). Two strategies, based on revealing changes in the spectrum-chromatographic fingerprints, are tested in order to detect the occurrence of such fraudulent blends: two-input class classification methods (SIMCA) and similarity analysis. The SIMCA strategy was effective only for detecting blends carried out on virgin olive oils with a greenish hue (high chlorophyll/pheophytin content). Furthermore, the similarity profile, developed and applied for the first time in this study evidences the blending in all cases irrespective of the original olive oil hue.

Applying an instrument-agnostizing methodology for the standardization of pesticide quantitation using different liquid chromatography-mass spectrometry platforms: A case study.

Liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful technique commonly used for pesticide residue analysis in agri-food matrices. Despite the fact it has several advantages, one of the main problems is the transferability of the data from one analytical equipment to another for identification and quantitation purposes. In this study, instrument-agnostizing methodology was used to set standard retention scores (SRSs), which was utilized as a parameter for the identification of 74 targeted compounds when different instruments are used. The SRS variation was lower than 5% for most of the compounds included in this study, which is much lower than those obtained when retention times were compared, correcting the elution shift between LC instruments. Additionally, this methodology was also tested for quantitation purposes, and normalized areas were used as analytical responses, allowing for the determination of the concentrations of the targeted compounds in samples injected in one equipment using the analytical responses of standards from another one. The applicability of this approach was tested at two concentrations, 0.06 and 0.15 mg/kg, and less than 10 out of 74 compounds were quantified with an error higher than 40% at 0.06 mg/kg and 0.15 mg/kg, showing that this methodology could be useful to minimize differences between LC-MS systems.

Spatial-Temporal Event Analysis as a Prospective Approach for Signalling Emerging Food Fraud-Related Anomalies in Supply Chains.

One of the pillars on which food traceability systems are based is the unique identification and recording of products and batches along the supply chain. Patterns of these identification codes in time and place may provide useful information on emerging food frauds. The scanning of codes on food packaging by users results in interesting spatial-temporal datasets. The analysis of these data using artificial intelligence could advance current food fraud detection approaches. Spatial-temporal patterns of the scanned codes could reveal emerging anomalies in supply chains as a result of food fraud in the chain. These patterns have not been studied yet, but in other areas, such as biology, medicine, credit card fraud, etc., parallel approaches have been developed, and are discussed in this paper. This paper projects these approaches for transfer and implementation in food supply chains in view of future applications for early warning of emerging food frauds.

Chromatographic Fingerprinting and Food Identity/Quality: Potentials and Challenges.

Chromatograms are a valuable source of information about the chemical composition of the food being analyzed. Sometimes, this information is not explicit and appears in a hidden or not obvious way. Thus, the use of chemometric tools and data-mining methods to extract it is required. The fingerprint provided by a chromatogram offers the possibility to perform both identity and quality testing of foodstuffs. This perspective is aimed at providing an updated opinion of chromatographic fingerprinting methodology in the field of food authentication. Furthermore, the limitations, its absence in official analytical methods, and the future directions of this methodology are discussed.

Chromatographic Fingerprinting Enables Effective Discrimination and Identitation of High-Quality Italian Extra-Virgin Olive Oils.

The challenging process of high-quality food authentication takes advantage of highly informative chromatographic fingerprinting and its identitation potential. In this study, the unique chemical traits of the complex volatile fraction of extra-virgin olive oils from Italian production are captured by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and explored by pattern recognition algorithms. The consistent realignment of untargeted and targeted features of over 73 samples, including oils obtained by different olive cultivars (n = 24), harvest years (n = 3), and processing technologies, provides a solid foundation for sample identification and discrimination based on production region (n = 6). Through a dedicated multivariate statistics workflow, identitation is achieved by two-level partial least-square (PLS) regression, which highlights region diagnostic patterns accounting between 58 and 82 of untargeted and targeted compounds, while sample classification is performed by sequential application of soft independent modeling for class analogy (SIMCA) models, one for each production region. Samples are correctly classified in five of the six single-class models, and quality parameters [i.e., sensitivity, specificity, precision, efficiency, and area under the receiver operating characteristic curve (AUC)] are equal to 1.00.

Multivariate approach for the authentication of vanilla using infrared and Raman spectroscopy.

Many different versions of vanilla extracts exist in the market in a variety of origins, purity levels and composition with little effective regulation. In this study, vanilla is authenticated both in terms of purity and geographical origin applying a multivariate approach using near infrared (NIR), mid infrared (MIR) and Raman spectroscopy following a complex experimental design. Partial least squares-discriminant analysis (PLS-DA) was applied to the spectral data to produce qualitative models. The prediction accuracy of the models was externally validated from the specific success/error contingencies. The results showed that MIR and Raman are reliable for authenticating vanilla in terms of purity, obtaining sensitivity, specificity, precision, and efficiency values equal to 1.00, and Raman is especially suitable for indicating the geographical origin of vanilla extracts, achieving performance metrics around 0.9.

Standardization of chromatographic signals - Part II: Expanding instrument-agnostic fingerprints to reverse phase liquid chromatography.

There is a large amount of literature relating to multivariate analytical methods using liquid chromatography together with multivariate chemometric/data mining methods in the food science field. Nevertheless, dating the obtained results cannot be compared as they are based on data acquired by a particular analytical instrument, thus they are instrument-dependant. Therefore, this creates difficulties in generating a database large enough to gather together all the variability of the samples. The solution to this problem is to obtain an instrument-agnostic chromatographic signal that is independent of the chromatographic state, i.e., measuring instrument or particular condition of the same instrument from which it was acquired. This paper describes the methodology to be followed to obtain standardized instrumental fingerprints when liquid chromatography is used for prior separation. For this purpose both internal and external chemical standards series are used as references. As an application example, we have applied this methodology for the determination of biophenols in olive oil by liquid chromatography coupled to ultraviolet-visible detector (LC-UV), using three different LC-UV instruments. The instrument-agnostic fingerprints obtained show a high grade of similarity, regardless of the state of the chromatographic system or the time of acquisition.

Standardization of chromatographic signals - Part I: Towards obtaining instrument-agnostic fingerprints in gas chromatography.

One of the main causes for the sparse use of multivariate analytical methods in routine laboratory work is the dependency on the measuring instrument from which the analytical signal is acquired. This issue is especially critical in chromatographic equipment and results in limitations of their applicability. The solution to this problem is to obtain a standardized instrument-independent signal -or instrument-agnostic signal- regardless of the measuring instrument or of the state of the same instrument from which it has been acquired. The combined use of both internal and external standard series, allows us to have external and transferable references for the normalization of both the intensity and the position of each element of the data vector being arranged from the raw signal. From this information, a simple mathematical data treatment process is applied and instrument-agnostic signals can be secured. This paper describes and applies the proposed methodology to be followed for obtaining standardized instrumental fingerprints from two significant fractions of virgin olive oil (volatile organic compounds and triacylglycerols), obtained by gas chromatography coupled to mass spectrometry (GC-MS) and analysed with two temperature conditions (conventional and high-temperature, respectively). The results of both case studies show how the instrument-agnostic fingerprints obtained are coincidental, regardless of the state of the chromatographic system or the time of acquisition.

PLS-DA vs sparse PLS-DA in food traceability. A case study: Authentication of avocado samples.

Conventional and sparse partial least squares-discriminant analysis (PLS-DA and sPLS-DA) have been successfully tested in order to authenticate avocado samples in terms of three different geographical origins and six kinds of cultivar. For this, lipid chromatographic fingerprints of different avocado fruits have been acquired using gas chromatography coupled with flame ionization detector (GC-FID) and employed for building classification models. In addition, classification models concatenating strategy has been applied, which has proved to be successful to resolve multiclass problems in food authentication. Finally, fine performance metrics around of 0.95 were obtained for both multivariate classification methods.

ROC curves for the optimization of one-class model parameters. A case study: Authenticating extra virgin olive oil from a Catalan protected designation of origin.

This paper proposes a ROC curve-based methodology to find optimal classification model parameters. ROC curves are implemented to set the optimal number of PCs to build a one-class SIMCA model and to set the threshold class value that optimizes both the sensitivity and specificity of the model. The authentication of the geographical origin of extra-virgin olive oils of Arbequina botanical variety is presented. The model was developed for samples from Les Garrigues, target class, Samples from Siurana were used as the non-target class. Samples were measured by FT-Raman with no pretreatment. PCA was used as exploratory technique. Spectra underwent pre-treatment and variables were selected based on their VIP score values. ROC curve and others already known criteria were applied to set the threshold class value. The results were better when the ROC curve was used, obtaining performance values higher than 82%, 75% and 77% for sensitivity, specificity and efficiency, respectively.

The occurrence: A meaningful parameter to be considered in the validation of multivariate classification-based screening methods - Application for authenticating virgin olive oil.

The development of multivariate screening analytical methods in the analytical chemistry field focused particularly on food authentication is growing in recent years, which is evidenced by the increase of scientific publications. Currently there are several guides and technical reports about how -univariate qualitative methods should be properly validated to produce reliable and accurate (fitted-for-purpose) results. Nevertheless, this is not the case when multivariate methods are considered. Aimed at redressing this untenable disadvantage, this paper proposes some guidelines for the validation of multivariate classification-based screening methods. As an application example, the detection of adulteration of virgin olive oil with any other edible vegetal oils is showed. The analytical techniques employed are liquid chromatography coupled to diode array detector (LC-DAD) and gas chromatography coupled to flame ionization detector (GC-FID). For the correct validation of the multivariate screening method a new parameter which never considered before, named occurrence, is accounted. Also, it has been developed two new applicability indicators of the multivariate screening methods: the assignation error index (IERROR) and the index saving (ISAVING) to establish the validation requirements. Then the validation parameters of the methods: precision (or target predictive value), sensitivity, non-target predictive value, specificity and accuracy were estimated. The main conclusion of the work has been the need to take accounts the occurrence value to establish the specific validation requirements to apply the multivariate screening method in a particular scenario.

Authentication of the Geographical Origin of Margarines and Fat-Spread Products from Liquid Chromatographic UV-Absorption Fingerprints and Chemometrics.

Fat-spread products are a stabilized emulsion of water and vegetable oils. The whole fat content can vary from 10 to 90% (w/w). There are different kinds, which are differently named, and their composition depends on the country in which they are produced or marketed. Thus, having analytical solutions to determine geographical origin is required. In this study, some multivariate classification methods are developed and optimised to differentiate fat-spread-related products from different geographical origins (Spain and Morocco), using as an analytical informative signal the instrumental fingerprints, acquired by liquid chromatography coupled with a diode array detector (HPLC-DAD) in both normal and reverse phase modes. No sample treatment was applied, and, prior to chromatographic analysis, only the samples were dissolved in n­hexane. Soft independent modelling of class analogy (SIMCA) and partial least squares-discriminant analysis (PLS-DA) were used as classification methods. In addition, several classification strategies were applied, and performance of the classifications was evaluated applying proper classification metrics. Finally, 100% of samples were correctly classified applying PLS-DA with data collected in reverse phase.

Authentication of the geographical origin of extra-virgin olive oil of the Arbequina cultivar by chromatographic fingerprinting and chemometrics.

This paper proposes to use chromatographic fingerprints coupled to multivariate techniques to authenticate the geographical origin of extra-virgin olive oils (EVOO) of the Arbequina botanical variety. This methodology uses the whole or part of the chromatogram as input data for the classification models but does not identify or quantify the chemicals constituents. Arbequina monovarietal EVOOs from three geographical origins were studied: two from adjacent European Protected Designation of Origin areas, Siurana and Les Garrigues, in Catalonia in the northeast of Spain; and the third from the south of Spain (Andalucia and Murcia). Three chromatographic fingerprints of each sample were obtained by both reverse and normal phase liquid chromatography coupled to charged aerosol detector (HPLC-CAD), and high temperature gas chromatography coupled to flame ionization detector [(HT)GC-FID]. Principal component analysis (PCA) was used as exploratory technique and soft independent modelling of class analogy (SIMCA) and partial least square-discriminant analysis (PLS-DA) were used as classification methods. High and low-level data fusion strategies were also applied to improve the classification results obtained when the data acquired from each analytical technique were separately used. The results were best for the PLS-DA model with low-level fusion of two techniques (HT)GC-FID with HPLC-CAD, independently of the phase mode. Sensitivity and specificity were 100% in almost all classes, error was 0% for all classes and an inconclusive ratio of just 4% was obtained for the Les Garrigues class due to double assignations.

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review.

In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.

Differentiation of avocados according to their botanical variety using liquid chromatographic fingerprinting and multivariate classification tree.

BACKGROUND: The oil content, composition and marketing threshold value of an avocado depends on the cultivar hence, identifying the cultivar of the avocado fruit is desirable. However, analytical methods have not been reported with this aim. RESULTS: A multivariate classification tree method was proposed to discriminate three commercial botanical varieties of avocado: Hass, Fuerte and Bacon, using high-performance liquid chromatography coupled to a charged aerosol detector (HPLC-CAD). Prior to the chromatographic analysis the avocados were lyophilized and then the oil fraction was extracted using a pressurized liquid extraction system. Normal and reverse phase liquid chromatography were applied in order to obtain the chromatographic fingerprint for each sample. Soft independent modelling of class analogies (SIMCA) and partial least-squares discriminant analysis (PLS-DA) were applied. Classification quality metrics were determined to evaluate the performance of the classification. Several strategies to develop the classification models were employed. Finally, the useful application of 'classification trees' methodology, which has been scarcely applied in the field of analytical food control, was evaluated to perform a multiclass classification. CONCLUSION: Discrimination of the three botanical varieties was achieved. The best classification was obtained when the PLS-DA is applied on the normal-phase chromatographic fingerprints. Classification trees are showed to be useful tools that provide complementary information to single concatenated models showing different results from the same prediction sample set. © 2019 Society of Chemical Industry.

Classification of olive oils according to their cultivars based on second-order data using LC-DAD.

Second-order data acquired using liquid chromatography coupled to a diode array detector were used to classify extra virgin olive oils samples according to their cultivars. The chromatographic fingerprints from the epoxidised fraction were obtained using normal-phase liquid chromatography. To reduce the data matrices two strategies were employed: (1) multivariate curve resolution-alternating least squares (MCR-ALS) and (2) a new strategy proposed in this work based on the fusion of the mean data profiles in both spectral and time domains. Several conventional chemometric tools were then applied to both raw and reduced data: principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), soft independent modelling of class analogies (SIMCA) and n-way partial least-squares-discriminant analysis (NPLS-DA). Furthermore, an emergent multivariate classification method known as random forest (RF) has been first applied to second-order data. It was shown that RF is more efficient than conventional tools. Indeed, the obtained sensibility, specificity and accuracy are 1.00, 0.92 and 0.95 respectively; these performance metrics are significantly better than the values found for the other methods.