Food Authenticity Research Articles

Navigating Food Fraud: A Survey of Nigerian Consumer Knowledge and Attitudes.

Food fraud presents a growing threat globally, impacting consumer health; food safety; and brand credibility. The key motivation for food fraud is usually an economic or financial advantage. Economically motivated food fraud (EMA) is the illegal deception, deliberate and intentional substitution or addition of a substance in a food product, which may pose a possible health risk to unsuspecting consumers. This study aims to assess the awareness and attitudes of Nigerian consumers towards food authenticity and the regulatory measures in place to combat food adulteration. The study comprised 2160 respondents who were asked about their knowledge of high-profile food fraud incidents, frequently adulterated products, and their opinions of the effects of food fraud and preventative measures. The findings of this study revealed that more than a third of respondents were unfamiliar with the term "food fraud". However, they were aware of local high-profile cases, such as "plastic rice" and counterfeit dairy products. Most participants relied on point-of-sale information for assessing food authenticity, with street vendors being viewed as potential sources of food adulteration. The study also found that more than half of the respondents were concerned about food fraud regardless of whether it involved local or imported products. A wide variety of food items were identified as prone to adulteration or fraudulent activity. These included alcoholic drinks, dairy products, honey, rice, and tomato paste, amongst others. These findings highlight the need for improved consumer awareness, regulatory effectiveness, and remediation efforts to combat food fraud in Nigeria.

The impact of Blockchain adoption on supply chain performance: evidence from food industry

The food supply chain (FSC) constantly needs to address persistent challenges such as information asymmetry, low transparency, food quality and authenticity, and unnecessary waste. Blockchain is perceived as a promising solution to overcome these FSC challenges. Existing literature captures a conceptual understanding of various aspects of Blockchain for FSC, such as how the technology can enhance transparency, efficiency, and food authenticity. However, a quantitative assessment of the overall impact of Blockchain adoption on the FSC operational performance is still missing. This study combines empirical and analytical approaches to investigate the evident research gap. Under the lens of systems thinking and System Dynamics (SD) modelling perspective, the study collected questionnaire and interview data to develop different FSC models for evaluating the impact of Blockchain on key operational performance metrics. The findings indicated that Blockchain positively affects inventory level and lead time in the immediate term, and cost in the long term. The results also warn that forgoing inventory buffers can come with the cost-of-service level. This study provides quantitative evidence of the positive influence of Blockchain on the FSC. This research contributes by extending the understanding of Blockchain's implications on broader supply chain performance from a systems perspective.

Rapid discrimination and authentication of Lushan Yunwu tea with different harvest periods based on Nano-ESI-MS combined with chemometric analysis

The economic value and flavor of tea with different harvest periods vary greatly. Therefore, rapid discrimination and authentication are important for quality control and commercialization of tea. Nano-electrospray ionization mass spectrometry (Nano-ESI-MS) is an emerging technique in food authenticity and traceability due to the minimal sample preparation required and short analysis time. In this study, Nano-ESI-MS method was established for rapid discrimination and authentication of Lushan Yunwu tea (LYT). The results showed that Nano-ESI-MS could not only effectively distinguishing LYT of different harvest periods but also differentiating Mingqian tea adulterated with 5% Yuhou tea and Autumn tea, respectively, with 58 and 17 compounds were considered as markers, respectively. The decision tree and linear discriminant analysis were used to identify the adulteration of tea with classification accuracy of models of training and validation sets were higher than 99.2% and 86.9%, respectively. Moreover, Nano-ESI-MS was used to track the changes of these compounds during tea processing, including amino acids, caffeine, and catechins. The Nano-ESI-MS method could be regarded as a powerful analytical tool for rapid discrimination and authentication of tea.

Ensuring Trust: The Science and Technology Behind Food Authentication and Reliability

Food authentication of halal food, which includes the manufacturing technique, technical processing, identification of undeclared components, and species replacement in halal food items, has increased rapidly as a result of growing public awareness of food quality and safety. This highlights the need for in-depth investigation into analytical techniques to produce precise and trustworthy data for observing and managing the authenticity of halal food. This study offers a concise, objective summary of current research on the analytical methods applied to the evaluation of the authenticity of halal food.

Multidimensional fluorescence spectroscopic characterization of Spain’s top-selling gins: Category and alcohol content

Excitation-emission fluorescence spectroscopy (EEFS) is emerging as a powerful tool for food characterization and authentication. This study pioneers the use of EEFS to characterize and differentiate Spain’s top-selling gins, considering categories and alcohol content. Twenty gins, including alcoholic (ALC), light alcoholic (LIG), and non-alcoholic (NALC) types, were analyzed generating excitation-emission matrices (EEMs), which were processed with multi-way principal component analysis (MPCA) and PARAllel FACtor analysis (PARAFAC). Results revealed distinct spectral fingerprints for each gin type, with significant differences between alcoholic and non-alcoholic gins. Light gins closely resembled their full-alcohol counterparts, indicating minimal alteration in gin characteristics due to reduced alcohol content, while non-alcoholic gins showed substantial divergence. The MPCA model highlighted primary differences between ALC and NALC types, while the PARAFAC models identified specific fluorophores linked to juniper berries and other botanicals. This study confirms that EEFS, coupled with chemometrics, is effective in authenticating gin products, marking a significant advancement in spirit analysis.

Integrating different detection techniques and data analysis methods for comprehensive food authenticity verification

Traditional food testing methods, primarily confined to laboratory settings, are increasingly inadequate to detect covert food adulteration techniques. Hence, a crucial review of recent technological strides to combat food fraud is essential. This comprehensive analysis explores state-of-the-art technologies in food analysis, accentuating the pivotal role of sophisticated data processing methods and the amalgamation of diverse technologies in enhancing food authenticity testing. The paper assesses the merits and drawbacks of distinct data processing techniques and explores their potential synergies. The future of food authentication hinges on the integration of portable smart detection devices with mobile applications for real-time food analysis, including miniaturized spectrometers and portable sensors. This integration, coupled with advanced machine learning and deep learning for robust model construction, promises to achieve real-time, on-site food detection. Moreover, effective data processing, encompassing preprocessing, chemometrics, and regression analysis, remains indispensable for precise food authentication.

The Use of Ultra-Fast Gas Chromatography for Fingerprinting-Based Classification of Zweigelt and Rondo Wines with Regard to Grape Variety and Type of Malolactic Fermentation Combined with Greenness and Practicality Assessment

In food authentication, it is important to compare different analytical procedures and select the best method. The aim of this study was to determine the fingerprints of Zweigelt and Rondo wines through headspace analysis using ultra-fast gas chromatography (ultra-fast GC) and to compare the effectiveness of this approach at classifying wines based on grape variety and type of malolactic fermentation (MLF) as well as its greenness and practicality with three other chromatographic methods such as headspace solid-phase microextraction/gas chromatography-mass spectrometry with carboxen-polydimethylosiloxane fiber (SPME/GC-MS with CAR/PDMS fiber), headspace solid-phase microextraction/gas chromatography-mass spectrometry with polyacrylate fiber (SPME/GC-MS with PA fiber), and ultra performance liquid chromatography–photodiode array detector-tandem mass spectrometry (UPLC-PDA-MS/MS). Principal Component Analysis (PCA) revealed that fingerprints obtained using all four chromatographic methods were suitable for classification using machine learning (ML). Random Forest (RF) and Support Vector Machines (SVM) yielded accuracies of at least 99% in the varietal classification of Zweigelt and Rondo wines and therefore proved suitable for robust fingerprinting-based Quality Assurance/Quality Control (QA/QC) procedures. In the case of wine classification by the type of MLF, the classifiers performed slightly worse, with the poorest accuracy of 91% for SVM and SPME/GC-MS with CAR/PDMS fiber, and no less than 93% for the other methods. Ultra-fast GC is the greenest and UPLC-PDA-MS/MS is the most practical of the four chromatographic methods.

Progressive Analytical Techniques Utilized for the Detection of Contaminants Attributed to Food Safety and Security

Food analysis and authentication are very crucial to assure safety, quality, and integrity of food and food-related products. Analysis of food products not only ensures the quality of food but also gives information about composition, physical characteristics, flavor, shelf-life, safety, microstructure, and processibility. Consequently, food is now regarded as an inexpensive means of preventing disease while also serving as a source of energy. Typically, a wide range of distinct chemical components make up the chemical composition of food. Furthermore, during processing or storage, food is always susceptible to structural alterations. Therefore, to assess the nutritional value and the quality of food products, food components must be analyzed and quantified. Food analysis mainly includes the preparation, examination, and identification of major food components, such as dietary fiber, phenolic compounds, carbohydrates, proteins, amino acids, peptides, enzymes, phospholipids, pesticides, alcohols, fat-soluble and water-soluble vitamins, organic acids, organic bases, bittering agents, pigments, and aroma compounds, as well as minor components including preservatives, antioxidants, colorants. Therefore, there are several advanced analytical techniques, including spectroscopy, mass spectrometry, chromatography, NMR, HSI, MALDI-TOF, RT-PCR, biosensors, etc. have been vital in the food science domain due to their high selectivity and sensitivity. This review emphasizes implementing various analytical techniques to analyze food safety and security.

Pattern recognition of multiple metal ions using fluorescent Perylenyl nanoaggregates and application in food traceability

Metal ions (MIs) identification is essential for the safety assessment, traceability and authentication of food. Most current approaches for detecting MIs are difficult to reconcile the simplicity, sensitivity and stability simultaneously. In this work, we proposed a novel strategy for discriminating MIs based on fluorescent supramolecular nanoaggregates (SNAs). In the presence of MI, perylene diimide derivatives (PDI)-based SNAs could be formed through the multiple non-covalent interactions between them including electrostatic, coordination and π-π interactions. With the assistance of discriminant analysis (LDA), different MIs (Hg2+, Ag+, Cd2+, Fe3+, Cr3+, Fe2+, Zn2+, Cu2+ and Pb2+) were successfully identified at three different concentration levels. It featured good quantitative sensing abilities in buffer solutions and practical samples. Furthermore, a water-quality evaluation model was successfully constructed for the distinction of different sources of drinking water, and the fluorescence array sensor technology was applied for the first time to the geographical traceability of apples.

Food authenticity, culture, and tourism: analysing the dimensions of local gastronomic image among foreign tourists in Fiji

Food authenticity, culture, and tourism: analysing the dimensions of local gastronomic image among foreign tourists in Fiji

Rapid headspace analysis of commercial spearmint and peppermint teas using volatile 'fingerprints' and an electronic nose.

Spearmint and peppermint teas are widely consumed around the world for their flavor and therapeutic properties. Dynamic headspace sampling (HS) coupled to gas chromatography/mass spectrometry (GC-MS) with principal component analysis (PCA) of 'fingerprint' volatile profiles were used to investigate 27 spearmint and peppermint teas. Additionally, comparisons between mint teas were undertaken with an electronic nose (enose). Twenty compounds, all previously known in the literature, were identified using HS-GC-MS. PCA found distinct differences between the fingerprint volatile profiles of spearmint, peppermint and spearmint/peppermint combination teas. HS-GC-MS analysis performed with an achiral column allowed faster processing time and yielded tighter clustering of PCA tea groups than the analysis which used a chiral column. Two spearmint outliers were detected. One showed a high degree of variation in volatile composition and a second wholly overlapped with the peppermint PCA grouping. Enose analysis separated all treatments with no overlaps. Characterizing the volatile fingerprints of mint teas is critical to quality control for this valuable agricultural product. The results of this study show that fingerprint volatile profiles and enose analysis of mint teas are distinctive and could be used to rapidly identify unknown samples. With specific volatile profiles identified for each tea, samples could be tested in the laboratory, or potentially on a farm or along the supply chain, to confirm the provenance and authenticity of mint food or beverage commodities. © 2024 Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Exploring the critical attributes of robot chef restaurants and their impact on customer perceived value: a mixed-methods study

Purpose This study aims to explore the key attributes of robot chef restaurants and their influencing factors from the perspective of customers and analyzes how these key attributes affect customer perceived value. Design/methodology/approach A mixed-methods research design was used in this study. Using 473 online reviews and ratings (Study 1), the research summarized customers’ evaluations on three types of attributes (environment, service and food) and identified the key attributes along with their influencing factors. Subsequently, through field questionnaires (Study 2) involving 269 actual customers, structural equation modeling was used to analyze how the identified key attributes and their influencing factors impact customer perceived value. Findings This study reveals that customers in robot chef restaurants prioritize food attributes, particularly valuing food authenticity alongside food quality. In contrast to traditional restaurants, customers’ evaluations of food attributes in robot chef restaurants are significantly influenced by the competence of robot chefs. Notably, customers’ negative attitudes toward robots diminish the positive effects on both food quality and food authenticity. Practical implications To enhance customer perceived value, robot chef restaurants should concentrate on food attributes. They can achieve this by fostering a high-quality, authentic food experience through the elevation of robot chefs’ competence and by providing customer education. Originality/value This study expands research on the customer experience in robotic restaurants by proposing an integrated model determining factors that affect the perceived customer value.

Non-Targeted Nuclear Magnetic Resonance Analysis for Food Authenticity: A Comparative Study on Tomato Samples.

Non-targeted NMR is widely accepted as a powerful and robust analytical tool for food control. Nevertheless, standardized procedures based on validated methods are still needed when a non-targeted approach is adopted. Interlaboratory comparisons carried out in recent years have demonstrated the statistical equivalence of spectra generated by different instruments when the sample was prepared by the same operator. The present study focused on assessing the reproducibility of NMR spectra of the same matrix when different operators performed individually both the sample preparation and the measurements using their spectrometer. For this purpose, two independent laboratories prepared 63 tomato samples according to a previously optimized procedure and recorded the corresponding 1D 1H NMR spectra. A classification model was built using the spectroscopic fingerprint data delivered by the two laboratories to assess the geographical origin of the tomato samples. The performance of the optimized statistical model was satisfactory, with a 97.62% correct sample classification rate. The results of this work support the suitability of NMR techniques in food control routines even when samples are prepared by different operators by using their equipment in independent laboratories.

DNA Metabarcoding Approach as a Potential Tool for Supporting Official Food Control Programs: A Case Study.

Food authentication significantly impacts consumer health and the credibility of Food Business Operators (FBOs). As European regulations mandate the verification of food authenticity and supply chain integrity, competent authorities require access to innovative analytical methods to identify and prevent food fraud. This study utilizes the DNA metabarcoding approach on meat preparations, sampled during an official control activity. It assesses animal and plant composition by amplifying DNA fragments of the 12S rRNA and trnL (UAA) genes, respectively. The results not only confirmed the declared species but also revealed undeclared and unexpected taxa in products labelled as containing a single animal species and various unspecified plant species. Notable findings such as the presence of Murinae, Sus scrofa, Ovis aries, and Pisum sativum could raise public health concerns, compromise consumer choices made for ethical or religious reasons, and reflect the hygienic conditions of the processing plant. This study demonstrates that the DNA metabarcoding approach looks to be a promising support tool for official control authorities to ensure food authenticity and safety, and to develop risk profiles along the supply chain.

Classification of kimchi using laser-induced breakdown spectroscopy and k-nearest neighbors modeling

This study introduces a novel application of Laser-Induced Breakdown Spectroscopy (LIBS) combined with k-nearest neighbors (KNN) modeling to classify the origin of kimchi. Using the spectral intensities of Mg II at 279 nm and K I at 766 nm, we achieved a classification accuracy of 92.8 %. This method effectively leverages regional differences in salt supply chains impacting kimchi's elemental composition. The innovation lies in applying the interclass distance method for variable selection in LIBS analysis, enhancing the interpretability and accuracy of food classification. Compared to traditional elemental analysis techniques, LIBS offers a practical, cost-effective solution for rapid field analysis with minimal sample preparation. This study not only demonstrates the potential of LIBS for food authenticity but also provides insights for developing accurate methods for detecting Mg and K in various food products, contributing to advancements in food quality control.

Advancements in Analytical Strategies for Authentication and Quality Control of Grappa and Wine Brandy with Geographical Indications

In recent years, food authentication has acquired significant importance due to the increase in the incidence of fraud and counterfeiting. Alcoholic beverages are among the food products most susceptible to these kinds of illicit practices due to their high commercial value. In the EU alone, there are 47 categories of spirit drinks and approximately 250 geographical indications (GIs). The production and labeling of GIs are strictly regulated, and developing analytical procedures that can ensure compliance with the legislation is essential to guarantee the typicality of these products. The aim of this review is to summarize the most relevant analytical techniques used for the authentication and quality control of two well-renowned GIs: “Grappa” and wine brandy. It considers the last decade of advancements for both conventional targeted chromatographic techniques and less common methods mainly based on spectrometry coupled with chemometrics for quick and non-destructive discrimination of samples. Novel approaches and future perspectives are also highlighted.

Decentralized food safety and authentication on cellulose paper-based analytical platform: A review.

Food safety and authenticity analysis play a pivotal role in guaranteeing food quality, safeguarding public health, and upholding consumer trust. In recent years, significant social progress has presented fresh challenges in the realm of food analysis, underscoring the imperative requirement to devise innovative and expedient approaches for conducting on-site assessments. Consequently, cellulose paper-based devices (PADs) have come into the spotlight due to their characteristics of microchannels and inherent capillary action. This review summarizes the recent advances in cellulose PADs in various food products, comprising various fabrication strategies, detection methods such as mass spectrometry and multi-mode detection, sampling and processing considerations, as well as applications in screening food safety factors and assessing food authenticity developed in the past 3 years. According to the above studies, cellulose PADs face challenges such as limited sample processing, inadequate multiplexing capabilities, and the requirement for workflow integration, while emerging innovations, comprising the use of simplified sample pretreatment techniques, the integration of advanced nanomaterials, and advanced instruments such as portable mass spectrometer and the innovation of multimodal detection methods, offer potential solutions and are highlighted as promising directions. This review underscores the significant potential of cellulose PADs in facilitating decentralized, cost-effective, and simplified testing methodologies to maintain food safety standards. With the progression of interdisciplinary research, cellulose PADs are expected to become essential platforms for on-site food safety and authentication analysis, thereby significantly enhancing global food safety for consumers.

Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome

Complex microbiomes are part of the food we eat and influence our own microbiome, but their diversity remains largely unexplored. Here, we generated the open access curatedFoodMetagenomicData (cFMD) resource by integrating 1,950 newly sequenced and 583 public food metagenomes. We produced 10,899 metagenome-assembled genomes spanning 1,036 prokaryotic and 108 eukaryotic species-level genome bins (SGBs), including 320 previously undescribed taxa. Food SGBs displayed significant microbial diversity within and between food categories. Extension to >20,000 human metagenomes revealed that food SGBs accounted on average for 3% of the adult gut microbiome. Strain-level analysis highlighted potential instances of food-to-gut transmission and intestinal colonization (e.g., Lacticaseibacillus paracasei) as well as SGBs with divergent genomic structures in food and humans (e.g., Streptococcus gallolyticus and Limosilactobabillus mucosae). The cFMD expands our knowledge on food microbiomes, their role in shaping the human microbiome, and supports future uses of metagenomics for food quality, safety, and authentication.

Carbon-13-isotopomics and metabolomics of fatty acids from triacylglycerols: overcoming the limitations of GC-C-IRMS for short- and medium-acyl chains.

Carbon-13 isotopomics of triacylglycerol (TAG) fatty acids or free fatty acids in biological matrices holds considerable potential in food authentication, forensic investigations, metabolic studies, and medical research. However, challenges arise in the isotopic analysis of short- and medium-chain (C4 to C10) fatty acid methyl esters (SMCFAMEs) through gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS). The high volatility of these esters results in losses during their preparation, leading to isotopic fractionation. Moreover, the methoxy group added to acyl chains requires the correction of δ13C values, thereby increasing the uncertainty of the final results. Analyzing free fatty acids (FFAs) addresses both issues encountered with SMCFAMEs. To achieve this objective, we have developed a new protocol enabling the isotopomics of individual fatty acids (FAs) by GC-C-IRMS. The same experiment also provides the FA profile, i.e., the relative percentage of each FA in the TAG hydrolysate or its concentration in the studied matrix. The method exhibited high precision, as evidenced by the repeatability and within-lab reproducibility of results when tested on TAGs from both animal and vegetal origins. Compared to the analysis of FAMEs by GC-C-IRMS, the current procedure also brings several improvements in alignment with the principles of green analytical chemistry and green sample preparation. Thus, we present a two-in-one method for 13C-isotopomic and metabolomic biomarker quantitation within quasi-universal TAG compounds, encompassing the short- and medium-acyl chains.

Smartphone video imaging: A versatile, low-cost technology for food authentication

This study presents a low-cost smartphone-based imaging technique called smartphone video imaging (SVI) to capture short videos of samples that are illuminated by a colour-changing screen. Assisted by artificial intelligence, the study develops new capabilities to make SVI a versatile imaging technique such as the hyperspectral imaging (HSI). SVI enables classification of samples with heterogeneous contents, spatial representation of analyte contents and reconstruction of hyperspectral images from videos. When integrated with a residual neural network, SVI outperforms traditional computer vision methods for ginseng classification. Moreover, the technique effectively maps the spatial distribution of saffron purity in powder mixtures with predictive performance that is comparable to that of HSI. In addition, SVI combined with the U-Net deep learning module can produce high-quality images that closely resemble the target images acquired by HSI. These results suggest that SVI can serve as a consumer-oriented solution for food authentication.