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.

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.

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part II: applications.

BACKGROUND: The organoleptic quality of virgin olive oil depends on positive and negative sensory attributes. These attributes are related to volatile organic compounds and phenolic compounds that represent the aroma and taste (flavour) of the virgin olive oil. The flavour is the characteristic that can be measured by a taster panel. However, as for any analytical measuring device, the tasters, individually, and the panel, as a whole, should be harmonized and validated and proper olive oil standards are needed. RESULTS: In the present study, multivariate approaches are put into practice in addition to the rules to build a multivariate control chart from chromatographic volatile fingerprinting and chemometrics. Fingerprinting techniques provide analytical information without identify and quantify the analytes. This methodology is used to monitor the stability of sensory reference materials. CONCLUSION: The similarity indices have been calculated to build multivariate control chart with two olive oils certified reference materials that have been used as examples to monitor their stabilities. This methodology with chromatographic data could be applied in parallel with the 'panel test' sensory method to reduce the work of sensory analysis. © 2018 Society of Chemical Industry.

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part I: framework and fundamentals.

BACKGROUND: Virgin olive oil is the only food product for which sensory analysis is regulated to classify it in different quality categories. To harmonize the results of the sensorial method, the use of standards or reference materials is crucial. The stability of sensory reference materials is required to enable their suitable control, aiming to confirm that their specific target values are maintained on an ongoing basis. Currently, such stability is monitored by means of sensory analysis and the sensory panels are in the paradoxical situation of controlling the standards that are devoted to controlling the panels. RESULTS: In the present study, several approaches based on similarity analysis are exploited. For each approach, the specific methodology to build a proper multivariate control chart to monitor the stability of the sensory properties is explained and discussed. CONCLUSION: The normalized Euclidean and Mahalanobis distances, the so-called nearness and hardiness indices respectively, have been defined as new similarity indices to range the values from 0 to 1. Also, the squared mean from Hotelling's T2 -statistic and Q2 -statistic has been proposed as another similarity index. © 2018 Society of Chemical Industry.

Application of data mining methods for classification and prediction of olive oil blends with other vegetable oils.

The aim of this article is to study tree-based ensemble methods, new emerging modelling techniques, for authentication of samples of olive oil blends to check their suitability for classifying the samples according to the type of oil used for the blend as well as for predicting the amount of olive oil in the blend. The performance of these methods has been investigated in chromatographic fingerprint data of olive oil blends with other vegetable oils without needing either to identify or to quantify the chromatographic peaks. Different data mining methods-classification and regression trees, random forest and M5 rules-were tested for classification and prediction. In addition, these classification and regression tree approaches were also used for feature selection prior to modelling in order to reduce the number of attributes in the chromatogram. The good outcomes have shown that these methods allow one to obtain interpretable models with much more information than the traditional chemometric methods and provide valuable information for detecting which vegetable oil is mixed with olive oil and the percentage of oil used, with a single chromatogram.

Quantification of an intact monoclonal antibody, rituximab, by (RP)HPLC/DAD in compliance with ICH guidelines.

We studied the quantification of an intact therapeutic monoclonal antibody (mAb), rituximab (RTX), using (reversephase) high-performance liquid chromatography with diode array detection ((RP)HPLC/DAD). To this end, we developed a chromatographic method and validated it as stabilityindicating in accordance with the International Conference on Harmonization guidelines (ICH). A 300-Å C8 column (250 mm×4.6 mm, 5 µm) was used to perform the analysis, and the temperature was maintained at 70 °C. Although only one mAb was analyzed, it was necessary to apply a gradient to elute it with a complex organic mixture. Chromatograms were registered at several wavelengths, with λ =214 nm employed for quantification purposes. The method was developed to quantify marketed RTX under typical hospital administration conditions. Further dilution was avoided in order to prevent additional mAb modification, and in this way the method was shown to be linear from 60 to 5000 mg/L. The precision of the method (repeatability and intermediate precision, estimated as the relative standard deviation, RSD %), was less than 1.0 %. Accuracy, specificity, robustness, and system suitability were also evaluated as specified in the ICH guidelines.We conducted a comprehensive chromatographic analysis by submitting RTX to several informative stress conditions. These forced degradation studies were conducted for two reasons: to estimate the specificity of the method, and to evaluate the robustness of the mAb formulation against external stress factors when handling it in preparation for administration. Thus, we investigated the effects of acid, base, oxidation, ionic strength, temperature, and UV light. Although a slight modification to the intact mAb could not be distinguished chromatographically in the stress studies we conducted, the procedure proposed here to evaluate peak purity enabled us to detect it with a satisfactory level of confidence. The proposed method could therefore be considered stability-indicating for quantyfying the intact mAb since it is qualified to detect its degradation/modification. Finally, the method was used to evaluate RTX in a long-term stability study performed under hospital conditions of use.

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.

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.

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.

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.

Multivariate analysis of HT/GC-(IT)MS chromatographic profiles of triacylglycerol for classification of olive oil varieties.

The ability of multivariate analysis methods such as hierarchical cluster analysis, principal component analysis and partial least squares-discriminant analysis (PLS-DA) to achieve olive oil classification based on the olive fruit varieties from their triacylglycerols profile, have been investigated. The variations in the raw chromatographic data sets of 56 olive oil samples were studied by high-temperature gas chromatography with (ion trap) mass spectrometry detection. The olive oil samples were of four different categories ("extra-virgin olive oil", "virgin olive oil", "olive oil" and "olive-pomace" oil), and for the "extra-virgin" category, six different well-identified olive oil varieties ("hojiblanca", "manzanilla", "picual", "cornicabra", "arbequina" and "frantoio") and some blends of unidentified varieties. Moreover, by pre-processing methods of chemometric (to linearise the response of the variables) such as peak-shifting, baseline (weighted least squares) and mean centering, it was possible to improve the model and grouping between different varieties of olive oils. By using the first three principal components, it was possible to account for 79.50% of the information on the original data. The fitted PLS-DA model succeeded in classifying the samples. Correct classification rates were assessed by cross-validation.

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.

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.

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.

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.