RESUMEN

Currently, the combination of fingerprinting methodology and environmentally friendly and economical analytical instrumentation is becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) obtained fingerprints of edible vegetable oils (sunflower and olive oils), and the capability of such fingerprints (obtained quickly, reliably and without any sample treatment) to discriminate/classify the analysed samples was evaluated. After data treatment, not only unsupervised pattern recognition techniques (as HCA and PCA), but also supervised pattern recognition techniques (such as SVM, kNN and SIMCA), showed that the main effect on discrimination/classification was associated with those regions of the Raman fingerprint related to free fatty acid content, especially oleic and linoleic acid. These facts allowed the discernment of the original raw material used in the oil's production. In all the models established, reliable qualimetric parameters were obtained.

RESUMEN

Oak wood is the main material used by coopers to manufacture casks for the aging of spirits or wines. Phenolic compounds are the main components extracted from the wood during spirit aging. In the present study, a chemometric approach based on unsupervised (PCA) and supervised (PLS-DA) pattern recognition techniques has been applied to the chromatographic instrumental fingerprints, obtained by ultra-high-performance liquid chromatography (UHPLC) at 280 nm, of the phenolic profiles of brandies aged in casks made of different oak wood species. The resulting natural data groupings and the PLS-DA models have revealed that the oak wood species, the toasting level, and the aging time are the most influential factors on the phenolic profile of the final products. Fingerprinting should be considered as a very useful feature, as it represents a considerable advantage, in terms of internal and quality control, for brandy producers.

Asunto(s)

RESUMEN

Brandies are spirits produced from wine spirit and wine distillates. The original wines selected to be distilled to produce the wine spirits as well as the distillation method used determine, to a large extent, the organoleptic characteristics of the final products. The young wine spirits evolve during their aging in oak casks, this being another key stage that affects the chemical and sensorial characteristics of the final brandy. In this work, seven different brandies have been studied. They were obtained from wine produced with and without the addition of sulfur dioxide, during their fermentation, using different distillation methods (single, double or serial distillation using pot stills and continuous column distillation) and aged for 14 or 28 months in three different types of oak wood (Quercus alba, Quercus robur and Quercus petraea) previously toasted to two different grades (medium or light). The use of unsupervised pattern recognition methods (HCA and FA) determined that the addition of sulfur dioxide during the fermentation of the base wine has a major influence on the aromatic and phenolic profile of the aged distillates. On the other hand, by means of supervised pattern recognition methods such as LDA and ANNs, the most significant variables that would allow to discriminate between the classes of brandies identified in the study were evaluated. Thus, the results obtained should cast some light on the most significant variables to be taken into account regarding Brandy production processes if a better control over these production processes is to be achieved, so that more exclusive and better quality products are obtained.

RESUMEN

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.

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

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.

RESUMEN

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.

Asunto(s)

RESUMEN

A single out-line HPLC-GC (FID) analytical method is applied to acquire the chromatographic fingerprint characteristic of the TMS-4,4'-desmetylsterol derivative fraction of several marketed edible vegetable oils in order to identify and discriminate the most valuable extra-virgin olive oils from the other vegetal oils (canola, corn, grape seed, linseed, olive pomace, peanut, rapeseed, soybean, sesame, seeds (non-specified composition but usually a blend of corn and sunflower) and sunflower). The natural structure of the preprocessed data undergoes a preliminary exploration using principal component analysis and heat map-based cluster analysis. A partial least squares-discriminant model is first trained from 53 oil samples (only 3 latent variables) and externally validated from 18 test oil samples. No classification errors are found and all the test samples are correctly classified. Additional classification models are also built in order to discriminate among vegetables-oil families and excellent results have been also achieved.

Asunto(s)

RESUMEN

This paper describes and discusses the application of trimethylsilyl (TMS)-4,4'-desmethylsterols derivatives chromatographic fingerprints (obtained from an off-line HPLC-GC-FID system) for the quantification of extra virgin olive oil in commercial vinaigrettes, dressing salad and in-house reference materials (i-HRM) using two different Partial Least Square-Regression (PLS-R) multivariate quantification methods. Different data pre-processing strategies were carried out being the whole one: (i) internal normalization; (ii) sampling based on The Nyquist Theorem; (iii) internal correlation optimized shifting, icoshift; (iv) baseline correction (v) mean centering and (vi) selecting zones. The first model corresponds to a matrix of dimensions 'n×911' variables and the second one to a matrix of dimensions 'n×431' variables. It has to be highlighted that the proposed two PLS-R models allow the quantification of extra virgin olive oil in binary blends, foodstuffs, etc., when the provided percentage is greater than 25%.

Asunto(s)

RESUMEN

Calibration is an operation whose main objective is to know the metrological status of a measurement system. Nevertheless, in analytical sciences, calibration has special connotations since it is the basis to do the quantification of the amount of one or more components (analytes) in a sample, or to obtain the value of one or more analytical parameters related with that quantity. Regarding this subject, the aim of analytical calibration is to find an empiric relationship, called measurement function, which permits subsequently to calculate the values of the amount (x-variable) of a substance in a sample, from the measured values on it of an analytical signal (y-variable). In this paper, the metrological bases of analytical calibration and quantification are established and, the different work schemes and calibration methodologies, which can be applied depending on the characteristic of the sample (analyte+matrix) to analyse, are distinguished and discussed. Likewise, the different terms and related names are clarified. A special attention has been paid to those analytical methods which use separation techniques, in relation with its effect on calibration operations and later analytical quantification.

Asunto(s)