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

The research was supported by Progetto Ager.Fondazioni in rete per la ricerca agroalimentare. Project acronym Violin. Valorization of Italian olive products through innovative analytical tools; years 2016-2021.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.Progetto Ager.Fondazioni in rete per la ricerca agroalimentar

Evaluating the whiteness of spectroscopy-based non-destructive analytical methods – Application to food analytical control

Recent advancements in analytical chemistry in the food quality field have emphasized ecofriendly analytical techniques eschewing chemicals and solvents. Various methodologies exist for assessing the sustainability of analytical methods, however none has provided guidance for appraising non-destructive methods, especially predevelopment. Among these, the RGB approach stands out, evaluating method colour via three main criteria: analytical performance, environmental impact, and practical efficiency. This framework offers a comprehensive evaluation, aiming for a "white" colour denoting excellence across all three categories. This article introduces an adapted RGB method for ex-ante evaluation of new non-destructive analytical methods pre-development. It outlines key steps for evaluating method "whiteness". As a guiding example, the approach was applied to three analytical methods focussed on quality and authenticity control of edible vege- table oils utilizing solvent-free spectroscopic techniques. Results underscored a priori feasibility assessment value, aligning evaluative objectives with intended method goals.Project (ref.: CPP2021-008672), funded by MCIN/AEI/501100011033 (Spanish Ministry of Science and Innovation)European Union NextGenerationEU/PRTR"Funding for open access charge: Universidad de Granada / CBU

A Sensor-Based Methodology to Differentiate Pure and Mixed White Tequilas Based on Fused Infrared Spectra and Multivariate Data Treatment

G.P.-C. acknowledges a research grant from UNAM, Universidad Nacional Autónoma de México (Grants: PIAPI 2042 and PAPIIT IT200918).C.H.P.-B. acknowledges Universidad Autónoma de Sinaloa (México) for a PhD scholarship and further support.Mexican Tequila is one of the most demanded import spirits in Europe. Its fast-raising worldwide request makes counterfeiting a profitable activity affecting both consumers and legal distillers. In this paper, a sensor-based methodology based on a combination of infrared measurements (IR) and multivariate data analysis (MVA) is presented. The case study is about differentiating two categories of white Tequila: pure Tequila (or '100% agave') and mixed Tequila (or simply, Tequila). The IR spectra were treated and fused with a low-level approach. Exploratory data analysis was performed using PCA and partial least squares (PLS), whilst the authentication analyses were carried out with PLS-discriminant analysis (DA) and soft independent modeling for class analogy (SIMCA) models. Results demonstrated that data fusion of IR spectra enhanced the outcomes of the authentication models capable of differentiating pure from mixed Tequilas. In fact, PLS-DA presented the best results which correctly classified all fifteen commercial validation samples. The methodology thus presented is fast, cheap, and of simple application in the Tequila industry.Universidad Nacional Autonoma de Mexico PIAPI 2042 PAPIIT IT20091

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

The authors are deeply grateful to the ‘Valencia’ PDO Manage- ment Body for providing the samples for this study. In addition, AMJC acknowledges the grant (RYC2021-031993-I) funded by MCIN/AEI/501100011033 and ‘European Union NextGeneration EU/PRTR’. Funding for open access charge: Universidad de Gra- nada / CBUA.BACKGROUND: Tigernut is a typical foodstuff from a specific region of Valencia (Spain) called ‘L'Horta Nord’, where it is com- mercialized under a Protected Designation of Origin (PDO) as Chufa de Valencia (‘Valencia's tigernut’). PDO-recognized tiger- nuts 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 experi- ments. 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.Grant (RYC2021-031993-I) funded by MCIN/AEI/501100011033 and ‘European Union NextGeneration EU/PRTR’Funding for open access charge: Universidad de Granada / CBU

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 lowcost 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 instrument

A Sensor-Based Methodology to Differentiate Pure and Mixed White Tequilas Based on Fused Infrared Spectra and Multivariate Data Treatment

[Abstract]: Mexican Tequila is one of the most demanded import spirits in Europe. Its fast-raising worldwide request makes counterfeiting a profitable activity affecting both consumers and legal distillers. In this paper, a sensor-based methodology based on a combination of infrared measurements (IR) and multivariate data analysis (MVA) is presented. The case study is about differentiating two categories of white Tequila: pure Tequila (or ‘100% agave’) and mixed Tequila (or simply, Tequila). The IR spectra were treated and fused with a low-level approach. Exploratory data analysis was performed using PCA and partial least squares (PLS), whilst the authentication analyses were carried out with PLS-discriminant analysis (DA) and soft independent modeling for class analogy (SIMCA) models. Results demonstrated that data fusion of IR spectra enhanced the outcomes of the authentication models capable of differentiating pure from mixed Tequilas. In fact, PLS-DA presented the best results which correctly classified all fifteen commercial validation samples. The methodology thus presented is fast, cheap, and of simple application in the Tequila industry.Universidad Nacional Autónoma de México; PIAPI 2042Universidad Nacional Autónoma de México; PAPIIT IT20091

Estudio analítico de la fracción transesterificada del aceite de oliva. aplicación en problemas de autentificación de aceite de oliva

Esta Tesis Doctoral describe, desarrolla y aplica la metodología de huellas dactilares cromatográficas y espectroscópicas en el ámbito de la autentificación de aceite de oliva. Las aplicaciones se centran en la obtención de datos derivados de la medida instrumental de la fracción metil-transesterificada de muestras de aceite de oliva de diferentes categorías comerciales (virgen extra, virgen y oliva), aceite de orujo de oliva y muestras de otros quince tipos de aceites vegetales comestibles en los que se incluyen aceites de (por orden alfabético): avellana, cacahuete, canola, cártamo, colza, girasol, girasol alto oleico, lino, maíz, palma, semillas, sésamo, soja, trigo (germen), y uva (pepita). El objetivo principal es el desarrollo de métodos analíticos rápidos que conlleven el empleo de diferentes técnicas analíticas como la cromatografía de líquidos acoplada a diferentes detectores o las espectroscopias vibracionales (infrarrojo y Raman), aplicando herramientas quimiométricas para extraer la información analítica relevante. A través de la aplicación conjunta de las técnicas analíticas y herramientas quimiométricas es posible aplicar diferentes procesos relacionados con la autentificación de aceite de oliva, como: discriminar entre aceite de oliva y otros aceites vegetales, detectar adulteraciones de aceite de oliva con otros aceites vegetales y cuantificar la proporción del mismo en mezclas con otros aceites vegetales. Se aplican diversas técnicas analíticas, como la cromatografía de líquidos acoplada a un detector de aerosol en corona cargado (HPLC-CAD) y a un detector de fila de diodos (HPLC-DAD), la espectroscopia de infrarrojo cercano (FT-NIR) y medio (FT-MIR) y la espectroscopia Raman, para obtener diferentes huellas dactilares de la fracción metil-transesterificada de los aceites vegetales. Las matrices de datos correspondientes a estas huellas dactilares instrumentales serán tratadas aplicando una batería de técnicas y métodos propios de la quimiometría, como el análisis exploratorio de componentes principales (PCA), métodos de clasificación mediante análisis discriminante (DA) y de modelado de clases (CM), métodos de calibración multivariable. Además se aplica la cromatografía de gases acoplada a espectrometría de masas para caracterizar los componentes presentes en dicha fracción.Tesis Univ. Granada

Rapid and non-destructive spatially offset Raman spectroscopic analysis of packaged margarines and fat-spread products

This work was partially supported by University of Granada (Spain) within the framemork of the funding corresponding to program 'precompetitive research projects for young researchers'. Funding for open access charge: University of Granada/CBUA. AMJC wish to acknowledge the Department of Economic Transformation, Industry, Knowledge and Universities belong to Regional Andalusia Government (Spain) for the Postdoctoral fellowship (DOC_00121). In addition, AAC wants to express their sincere gratitude to the Spanish Ministry of Universities for a pre-doctoral fellowship FPU (FPU20/04711, Formaci ' on del Profesorado Universitario).Spatially offset Raman spectroscopy (SORS) is a novel technique capable of measuring samples through the original packaging and recovering the spectra without the contribution of surface layers. Here, a portable SORS equipment was used to measure 62 samples of margarines and fat spreads through the original plastic container. Chemometric tools were used to analyse the data obtained. A total of 25 classification models were developed based on: (i) geographical origin, (ii) vegetable oils and (iii) some significant minor constituents present in the samples. Partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and soft independent modelling of class analogy (SIMCA) were used for model classification. Quantitative analysis using the partial least squares regression (PLSR) method was also performed to determine the total fat content. In parallel, a benchtop conventional Raman spectrometer was used to analyse the same samples, develop the models with the same training and validation sets in order to compare the results. The calculated classification performance metrics showed better classification models from SORS data than conventional Raman spectroscopy (CRS), highlighting the one-class SIMCA models for margarines containing phytosterols, olive oil or linseed oil. These models exhibited very high predictability (performance parameters with values equal to or higuer than 0.8, 0.9 and 1, respectively). The quantitation model developed from SORS exhibited a higher R2 than from CRS data, and prediction errors below 5% from SORS versus errors between 5 and 13% from CRS data. These results reveal the ability of SORS to avoid the influence of fluorescence, a major drawback when analysing Raman spectra, but also the potential of the technique as a fast, non-destructive and non-invasive analytical technique in the field of food analysis. In conclusion, the tandem ’SORS-chemometrics’ has been shown to be a potential tool in the food quality and food authentication fields. Thus, it is necessary to perform further investigations in this field in order to advance the knowledge of this technique and to be able to develop new methods of rapid analysis.University of Granada (Spain) University of Granada/CBUADepartment of Economic Transformation, Industry, Knowledge and Universities belong to Regional Andalusia Government (Spain) DOC_00121Spanish Government FPU20/0471