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.

Assessment of extra virgin olive oil quality by miniaturized near infrared instruments in a rapid and non-destructive procedure

Food fraud in olive oil is a major concern for consumers and authorities due to the health risks and economic impacts. Common frauds include blending with other cheaper non-olive oils, or misleading labelling. The main issue is that legislation and methods presently used in routine laboratories are not always up to date with current fraudulent practices, making detection difficult, so new analytical methods development is required. This study focuses on developing an affordable and non-destructive analysis method based on NIR spectroscopy and chemometrics for EVOO quality assessment, specifically by monitoring 7 parameters of interest in EVOO measured by official methods and used to develop calibrations through NIR data. For this, two NIR low-cost portable instruments were employed, studied in-depth and compared with a NIR benchtop instrument. Calibration results enabled detection of atypical olive oils and excellent accuracy, especially for palmitic and oleic acid predictions, demonstrating the potential of the instruments.

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.

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.

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 (I_ERROR) and the index saving (I_SAVING) 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.

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.

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.

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.