Food Authenticity Applications Research Articles

Comparison of data processing strategies using commercial vs. open-source software in GC-Orbitrap-HRMS untargeted metabolomics analysis for food authentication: thyme geographical differentiation and marker identification as a case study

Untargeted analysis of gas chromatography–high-resolution mass spectrometry (GC-HRMS) data is a key and time-consuming challenge for identifying metabolite markers in food authentication applications. Few studies have been performed to evaluate the capability of untargeted data processing tools for feature extraction, metabolite annotation, and marker selection from untargeted GC-HRMS data since most of them are focused on liquid chromatography (LC) analysis. In this framework, this study provides a comprehensive evaluation of data analysis tools for GC-Orbitrap-HRMS plant metabolomics data, including the open-source MS-DIAL software and commercial Compound Discoverer™ software (designed for Orbitrap data processing), applied for the geographical discrimination and search for thyme markers (Spanish vs. Polish differentiation) as the case study. Both approaches showed that the feature detection process is highly affected by unknown metabolites (Levels 4–5 of identification confidence), background signals, and duplicate features that must be carefully assessed before further multivariate data analysis for reliable putative identification of markers. As a result, Compound Discoverer™ and MS-DIAL putatively annotated 52 and 115 compounds at Level 2, respectively. Further multivariate data analysis allowed the identification of differential compounds, showing that the putative identification of markers, especially in challenging untargeted analysis, heavily depends on the data processing parameters, including available databases used during compound annotation. Overall, this method comparison pointed out both approaches as good options for untargeted analysis of GC-Orbitrap-HRMS data, and it is presented as a useful guide for users to implement these data processing approaches in food authenticity applications depending on their availability.Graphical

MALDI-ToF MS and chemometric analysis as a tool for identifying wild and farmed salmon

In this study, the difference between wild and farmed salmon production was successfully profiled and differentiated by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-ToF MS) combined with chemometric analysis. The established method based on multivariate analysis mainly involved principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) as the screening and verifying tools to provide insights into the distinctive features found in wild and farmed salmon products, respectively. The discrimination between farmed and wild salmon was accomplished with 100% classification accuracy using chemometric models, 100% identification accuracy was also achieved in distinguishing wild Salmo salar and Oncorhynchus nerka samples. The results of the present work suggest that the proposed method could serve as a reference for detecting salmon fraud relating to wild or farmed production and expand the application of MALDI-ToF technology further into food authenticity applications.

Hybrid Raman and Laser-Induced Breakdown Spectroscopy for Food Authentication Applications.

The issue of food fraud has become a significant global concern as it affects both the quality and safety of food products, ultimately resulting in the loss of customer trust and brand loyalty. To address this problem, we have developed an innovative approach that can tackle various types of food fraud, including adulteration, substitution, and dilution. Our methodology utilizes an integrated system that combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Although both techniques emerged as valuable tools for food analysis, they have until now been used separately, and their combined potential in food fraud has not been thoroughly tested. The aim of our study was to demonstrate the potential benefits of integrating Raman and LIBS modalities in a portable system for improved product classification and subsequent authentication. In pursuit of this objective, we designed and tested a compact, hybrid Raman/LIBS system, which exhibited distinct advantages over the individual modalities. Our findings illustrate that the combination of these two modalities can achieve higher accuracy in product classification, leading to more effective and reliable product authentication. Overall, our research highlights the potential of hybrid systems for practical applications in a variety of industries. The integration and design were mainly focused on the detection and characterization of both elemental and molecular elements in various food products. Two different sets of solid food samples (sixteen Alpine-style cheeses and seven brands of Arabica coffee beans) were chosen for the authentication analysis. Class detection and classification were accomplished through the use of multivariate feature selection and machine-learning procedures. The accuracy of classification was observed to improve by approximately 10% when utilizing the hybrid Raman/LIBS spectra, as opposed to the analysis of spectra from the individual methods. This clearly demonstrates that the hybrid system can significantly improve food authentication accuracy while maintaining the portability of the combined system. Thus, the successful implementation of a hybrid Raman-LIBS technique is expected to contribute to the development of novel portable devices for food authentication in food as well as other various industries.

Data fusion and multivariate analysis for food authenticity analysis

A mid-level data fusion coupled with multivariate analysis approach is applied to dual-platform mass spectrometry data sets using Rapid Evaporative Ionization Mass Spectrometry and Inductively Coupled Plasma Mass Spectrometry to determine the correct classification of salmon origin and production methods. Salmon (n = 522) from five different regions and two production methods are used in the study. The method achieves a cross-validation classification accuracy of 100% and all test samples (n = 17) have their origins correctly determined, which is not possible with single-platform methods. Eighteen robust lipid markers and nine elemental markers are found, which provide robust evidence of the provenance of the salmon. Thus, we demonstrate that our mid-level data fusion - multivariate analysis strategy greatly improves the ability to correctly identify the geographical origin and production method of salmon, and this innovative approach can be applied to many other food authenticity applications.

Design and Validation of a Portable Machine Learning-Based Electronic Nose.

Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography–mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications.

Discrimination of bovine milk from non-dairy milk by lipids fingerprinting using routine matrix-assisted laser desorption ionization mass spectrometry

An important sustainable development goal for any country is to ensure food security by producing a sufficient and safe food supply. This is the case for bovine milk where addition of non-dairy milks such as vegetables (e.g., soya or coconut) has become a common source of adulteration and fraud. Conventionally, gas chromatography techniques are used to detect key lipids (e.g., triacylglycerols) has an effective read-out of assessing milks origins and to detect foreign milks in bovine milks. However, such approach requires several sample preparation steps and a dedicated laboratory environment, precluding a high throughput process. To cope with this need, here, we aimed to develop a novel and simple method without organic solvent extractions for the detection of bovine and non-dairy milks based on lipids fingerprint by routine MALDI-TOF mass spectrometry (MS). The optimized method relies on the simple dilution of milks in water followed by MALDI-TOF MS analyses in the positive linear ion mode and using a matrix consisting of a 9:1 mixture of 2,5-dihydroxybenzoic acid and 2-hydroxy-5-methoxybenzoic acid (super-DHB) solubilized at 10 mg/mL in 70% ethanol. This sensitive, inexpensive, and rapid method has potential for use in food authenticity applications.

Mass Spectrometry: Status Quo in Food Allergen and Food Authenticity Applications

Mass Spectrometry: Status Quo in Food Allergen and Food Authenticity Applications

Species detection using HyBeacon® probe technology: Working towards rapid onsite testing in non-human forensic and food authentication applications

Identifying individual species or determining species’ composition in an unknown sample is important for a variety of forensic applications. Food authentication, monitoring illegal trade in endangered species, forensic entomology, sexual assault case work and counter terrorism are just some of the fields that can require the detection of the biological species present. Traditional laboratory based approaches employ a wide variety of tools and technologies and exploit a number of different species specific traits including morphology, molecular differences and immuno-chemical analyses. A large number of these approaches require laboratory based apparatus and results can take a number of days to be returned to investigating authorities. Having a presumptive test for rapid identification could lead to savings in terms of cost and time and allow sample prioritisation if confirmatory testing in a laboratory is required later. This model study describes the development of an assay using a single HyBeacon® probe and melt curve analyses allowing rapid screening and authentication of food products labelled as Atlantic cod (Gadus morhua). Exploiting melt curve detection of species specific SNP sites on the COI gene the test allows detection of a target species (Atlantic cod) and closely related species which may be used as substitutes. The assay has been designed for use with the Field Portable ParaDNA system, a molecular detection platform for non-expert users. The entire process from sampling to result takes approximately 75min. Validation studies were performed on both single source genomic DNA, mixed genomic DNA and commercial samples. Data suggests the assay has a lower limit of detection of 31 pg DNA. The specificity of the assay to Atlantic cod was measured by testing highly processed food samples including frozen, defrosted and cooked fish fillets as well as fish fingers, battered fish fillet and fish pie. Ninety-six (92.7%) of all Atlantic cod food products, tested, provided a correct single species result with the remaining samples erroneously identified as containing non-target species. The data shows that the assay was quick to design and characterise and is also capable of yielding results that would be beneficial in a variety of fields, not least the authentication of food

Review on metabolomics for food authentication

European and global food policies require that food put on the market is authentic, which means that the label declaration matches the composition and provenance of the food item. During the last decades conventional approaches such as the determination of certain major and minor components by wet chemistry of chromatographic methods have been used for food authentication. Although many of the traditional methods are still used because they are part of product standards, new approaches that could complement existing methodologies, such as metabolomics are emerging in food authentication applications. Metabolomics aims to study multiple metabolites in a cell, a tissue or an organism. The main advantage of metabolomic applications in food authentication makes use of its untargeted nature, which can enable the detection of emerging frauds. The purpose of this review is to summarise and describe the recent metabolomic applications in the area of food authentication and to discuss its current limitations and future potential.

Laser-based measurements of 18O/16O stable isotope ratios (δ18O) in wine samples

Wine counterfeiting is an international, multi-billion dollar issue, with some estimates suggesting that up to 5% of wines sold at auctions or secondary markets are fraudulent. Isotope ratio mass spectrometer (IRMS) measurements of the 18O/16O stable isotope ratio (δ18O) of water-in-wine have been used for wine authentication; however, these analyses are time-consuming and costly. In this preliminary study, off-axis integrated cavity output spectroscopy (OA-ICOS) is used to quantify δ18O in wines. This laser-based method has been extensively used to study water isotopes for hydrological and medical applications. Recently, the development of a spectral contaminant identifier (SCI) has extended the application of these OA-ICOS analyzers to contaminated water samples (eg, plant, soil, and leaf waters). Here, we utilize OA-ICOS with the SCI to characterize wine samples (9%–15% ethanol), and show that the laser-based instrument provides a δ18O measurement precision of ±0.07‰ (1σ) and agrees with IRMS to within ±0.63‰ (1σ). Moreover, by training the SCI on isotopically-characterized wines, the agreement with IRMS improves to within ±0.30‰ (1σ). The utility of the instrument is demonstrated by measuring watered and mixed wines. The method presented here can be readily extended to address other food authentication applications.

Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications

Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins.