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.

A Comparison of the Stability of Refined Edible Vegetable Oils under Frying Conditions: Multivariate Fingerprinting Approach.

The stability of highly consumed vegetable refined oils after discontinuous frying of potatoes was compared. Both those vegetable oils containing additives and those that did not were considered. Vegetable oil samples were evaluated using refractive index, anisidine and peroxide values, UV absorbance and dielectric constant-based determination of the content of total polar compounds. Chemical changes caused over the frying time were monitored and multivariate modelling of the data was carried out. A new gas chromatographic-mass spectroscopy method was intended to record a fingerprint of both polar and non-polar compound fractions. Multivariate models of chromatographic fingerprints were also developed, and the results obtained from both approaches were verified to be statistically similar. In addition, multivariate modelling also allows to differentiate among vegetable oils according to oxidation performance. Indeed, it was initially observed that olive oils presented the highest natural thermo-oxidative stability compared to other seed oils, although it should be noted that these differences were not significant when regarding olive pomace oils and seed oils containing synthetic additives.

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.

Monitoring the Shelf Life of Refined Vegetable Oils under Market Storage Conditions-A Kinetic Chemofoodmetric Approach.

Most physicochemical and sensory properties of edible vegetable oils are not stable over time. One of the main causes of quality depletion of vegetable oils is oxidation, which influences sensory acceptability and nutritional value, and could even lead to toxic compounds. That negative influence affects international refined oil prices and the variety of its culinary applications. Modelling quality depletion of vegetable oils and establishing the shelf life, generally accepted as the time until rancidity becomes evident, already remains a challenge for the industry. Hence, this paper will show a promising chemofoodmetric methodology, as an easy and straightforward tool to estimate the current shelf-life of refined vegetable oils, based on a comprehensive characterisation of quality depletion-related changes over storage time under real market conditions. The methodology for building a multivariate kinetic ageing-based model is described, taking into account all time-related physicochemical parameters and chemometric processing tools. From a particular ageing state, multiparametric models are able to reliably infer the expected storage time for each vegetable oil so that it remains consistent with acceptability requirements. The results of the study pointed out the accuracy of multivariate shelf-life modelling with regard to univariate modelling. Discrepancies were found in the oxidation rates of oils extracted from different plant seeds.

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.

Innovative and thorough practice to certify reference materials for sensory defects of olive oil.

An important problem in the olive sector is the occasional mismatch of results obtained by different tasting panels when the same olive oil sample is analysed. These discrepancies could be minimised by using reference materials (RM) for taster training. A comprehensive protocol based on the combined use of sensory and instrumental analysis for the certification of olive oil batches as RMs, developed within the framework of the project 'Operational Group INTERPANEL', is proposed. Similarity indices (R2, cosθ and NEAR) applied on GC-MS fingerprints, allow a successful homogeneity and stability assessment of produced batches. Furthermore, the use of robust statistics combined with a set of instructions developed to remove outliers were applied with excellent results on sensory data set provided by supra-panel composed by more than 100 qualified tasters. This work is the first to provide a comprehensive protocol for certification of real olive oil samples as RM for sensory analysis.

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.

The Relevance of Monoclonal Antibodies in the Treatment of COVID-19.

Major efforts have been made in the search for effective treatments since the outbreak of the COVID-19 infection in December 2019. Extensive research has been conducted on drugs that are already available and new treatments are also under development. Within this context, therapeutic monoclonal antibodies (mAbs) have been the subject of widespread investigation focusing on two target-based groups, i.e., non-SARS-CoV-2 specific mAbs, that target immune system responses, and SARS-CoV-2 specific mAbs, designed to neutralize the virus protein structure. Here we review the latest literature about the use of mAbs in order to describe the state of the art of the clinical trials and the benefits of using these biotherapeutics in the treatment of COVID-19. The clinical trials considered in the present review include both observational and randomized studies. We begin by presenting the studies conducted using non-SARS-CoV-2 specific mAbs for treating different immune disorders that were already on the market. Within this group of mAbs, we focus particularly on anti-IL-6/IL-6R. This is followed by a discussion of the studies on SARS-CoV-2 specific mAbs. Our findings indicate that SARS-CoV-2 specific mAbs are significantly more effective than non-specific ones.

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.

Leachables from plastic materials in contact with drugs. State of the art and review of current analytical approaches.

The plastics used in drug packaging systems and medical devices are composed of homologous polymers and generally contain additives such as antioxidants, plasticizers and others, to improve their physicochemical properties. However, these additives have potential drawbacks due to possible migration or leaching towards the drug product. Leaching can cause a change in the chemical composition of the drug which, in turn, could modify its therapeutic action and, in some cases, its organoleptic properties. Leachables may also be considered a health hazard due to their inherent toxicological properties. The analytical characterization (detection, identification, typification/qualification and quantification) of leachable substances is mandatory and this information must be included in the application dossier for the drug before it can receive regulatory approval. The main aim of this paper is to collect and contextualise the reported analytical approaches for characterising and/or controlling organic leachables from plastic materials in contact with drugs. We also describe the state of the art of leachables in conjunction with a valuable, broad-based compilation of directives and guidelines. We end by presenting an updated collection of leachables both gas and liquid chromatography studies as separation techniques over the last eight years. We decided to focus our review exclusively on organic leachables as there is already a wide body of research on inorganic impurities.

Homogeneity assessment of reference materials for sensory analysis of liquid foodstuffs. The virgin olive oil as case study.

Sensory properties are critical characteristics that determine quality and can be evaluated by trained tasting panels. The panels function as multi-sensor measuring instrument and need the use of reference materials (RMs) for training. The homogeneity between units packaged from a batch of RM can be evaluated by gas chromatography coupled to flame ionization detection (GC-FID), using this instrumental technique as an alternative to sensory analysis. For this purpose, the fingerprint methodology is applied, taking into account that the homogeneity assessment will be based on evaluating the similarity between the fingerprints of the fraction of volatile organic compounds acquired from samples representative of the batch. The proposed methodology is applied with good results to evaluate the homogeneity of several RMs for sensory analysis of virgin olive oil samples, using similarity indices, control charts and exploratory analysis of multivariate data to observe the grouping RM and fingerprint regions representative of each defect.

Intact charge variant analysis of ziv-aflibercept by cationic exchange liquid chromatography as a proof of concept: Comparison between volatile and non-volatile salts in the mobile phase.

Ziv-aflibercept (ziv-AFL) is a complex fusion protein which is widely used in hospitals for the treatment of colorectal metastatic cancer. Charge variants are critical attributes for assessing post-transitional modifications (PMTs) that have to be controlled during the development and manufacture of these proteins and until their administration to patients. Cation exchange (CEX) chromatography is a charge-sensitive analytical method that is well suited for analysing charge variants in proteins. The aim of this paper is to analyse the charge variants of ziv-AFL in the medicine (Zaltrap®) when fresh and when degraded. Two CEX chromatographic methods were compared for this purpose. The former was an adaptation of the method used in the first published study in which charge variants were analysed via pH gradient elution using volatile, low ionic strength buffers with direct coupling to high-resolution Orbitrap mass spectrometry. The second method was developed and optimized during our research using the salt-mediated pH gradient mode and classical non-volatile, high ionic strength buffers which were incompatible with direct coupling with mass detection. Fresh and controlled degraded samples of ziv-AFL were used to evaluate the capacity of both CEX chromatographic strategies for detecting charge variants in ziv-AFL. In the controlled degradation study the samples of the medicine were subjected to three stress factors: temperature of 60 °C for three hours, freeze/thaw process -two cycles-, and exposure to light for twelve hours. The CEX chromatographic method with non-volatile salts in the mobile phase enabled better detection of charge variants degraded ziv-AFL samples than the method using volatile salts with lower ionic strength. In addition, the complexity of the mass spectra data generated made it impossible to identify the multicharge variant species of ziv-AFL. Although charge variants were not separated in ziv-AFL fresh sample, our results indicate that the method with non-volatile salts in the mobile phase could be used to characterize and track changes in the charge variant UV chromatographic profile of ziv-AFL in fresh and degraded samples, even though it cannot be coupled to a mass detector and there is therefore no information about mass. The increase of basic protein degraded compounds were the most important degradation pattern detected in ziv-AFL (Zaltrap®).

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.