Impact of Noodle Formulation, Boiling Methodology and Their Interactions on Stable Hydrogen and Oxygen Isotope Ratios.

Stable isotopes are commonly utilized for the geographical origin verification of foods, including wheat. However, assessing processed products poses a greater challenge due to the alterations that take place during processing and which have not been fully elucidated yet. In the current study, the effects of the formulation (the mass ratios of gluten to starch), boiling process and their interaction on the stable hydrogen (δ2H) and oxygen (δ18O) isotopic ratios of wheat noodles were evaluated. The δ2H and δ18O of noodles with different formulations (the mass ratios of gluten to starch) as raw materials, in uncooked and cooked (boiled in water) noodles, were examined. The results indicated that the δ2H of the boiled noodles ranged from -80.1‱ to -46.8‱ and were significantly lower than those of the raw materials, which ranged from -73.0‱ to -39.2‱, and the uncooked noodles, which ranged from -73.3‱ to -39.6‱. Oppositely, 18O was enriched in the boiled noodles, ranging from 27.7‱ to 31.3‱, compared with the uncooked noodles, ranging from 28.4‱ to 29.6‱. In addition, a significant interaction effect between the formulation and the boiling process was recorded for δ18O. This study shows that the hydrogen and oxygen stable isotopic compositions of noodles were significantly changed during the boiling process, and the isotopic fractionation varies with the different formulations.

An account of the occurrence of residues from veterinary drugs and contaminants in animal-derived products: a case study on Brazilian supply chains.

Brazil plays an important role in ensuring its position on the international market by assuring high food safety standards for its products, and all products should meet the requirements for residues from veterinary drugs and contaminants in animal products. Statutory monitoring provides insights into the compliance of the Brazilian industry regarding these legal requirements. The objective of this study was to provide insight into the safety of Brazilian animal products by reporting the occurrence of residues from veterinary drugs and contaminants according to an analysis of an 11-year report published by the Brazilian Ministry of Agriculture, Livestock and Food Supply (MAPA). Between 2010 and 2021, 166,647 samples from animal-derived products were analyzed in Brazil, and 624 of those samples were non-compliant (0.37%) exceeding maximum residue limits (>MRLs) or showed the presence of prohibited substances. The most common types of substances found in the non-compliant samples were heavy metals, parasiticides, and antimicrobials, accounting for 82% of all documents from the MAPA. Among Brazilian products, the challenge related to occurrence of substances varied across the food supply chain, with highest incidence rates observed in the fish chain, followed by eggs, milk, equids, sheep/goat, honey, bovine, swine, and broilers chains in decreasing order. Considering the type of substance, heavy metals were found to be more prevalent in fish products, mainly arsenic in wild fish. The prevalence of contaminants and heavy metals decreased, while that of veterinary drugs increased in Brazilian products from 2010 to 2021. From these results, it can be concluded that the number of accidental incidents including those associated with environmental contaminants decreased over the last decade, opposed to those involving human adversaries and deliberate illegal actions, such as the abuse of veterinary drugs, increased. Future monitoring plans need to take this paradigm shift into account.

Fluorescence lifetime imaging microscopy as a tool to characterize spice powder variations for quality and authenticity purposes: A ginger case study.

Spices are usually ground for applications and the resulting particle size of the powders is an important product attribute in view of the release of flavour. However, inhomogeneity of the original material may lead to variations in the physicochemical characteristics of the particles. This variation and its linkage to particle size may be examined by particular imaging techniques. This study aimed to explore the potential of Fluorescence Lifetime Imaging Microscopy (FLIM) to characterize spice powders according to particle size variations and correlation with their pigment contents to reveal the chemical information contained within the FLIM data. Ginger powder was used as a representative powder model. The FLIM profiles of the individual samples and populations revealed that FLIM coupled with the phasor approach has the capacity to characterize spice powder according to particle size. Meanwhile, Principal Component Analysis of pre-processed FLIM data revealed clustering of particle size groups. Further correlation analysis between the pigment compound contents and FLIM data of the ginger powders indicated that FLIM reflected chemical information of ginger powder and was able to visualize endogenous fluorophores. The current study revealed the potential of FLIM to characterize ginger powder particles. This approach may be extrapolated to other spice powder products. The new knowledge is a step further in paving the way for the application of innovative techniques, already prevalent in other domains, to food quality and authentication.

Establishing the relations of characteristic aroma precursors and volatile compounds for authenticating Tibetan pork.

Tibetan pork has been favored for its unique aromas, which originate from chemical reactions between characteristic precursors in cooking. The precursors (e.g., fatty acids, free amino acids, reducing sugars, and thiamine) of Tibetan pork ((semi-) free range) from different regions in China, comprising Tibet, Sichuan, Qinghai, and Yunnan, and commercial pork (indoor reared) were compared in this study. Tibetan pork was characterized by higher ω-3 polyunsaturated fatty acids (i.e., C18:3n3), higher essential (i.e., valine, leucine, and isoleucine), aromatic (i.e., phenylalanine), and sulfur-containing (i.e., methionine and cysteine) free amino acids, higher thiamine, and lower reducing sugars. Boiled Tibetan pork exhibited higher heptanal, 4-heptenal, and 4-pentylbenzaldehyde compared with commercial pork. The results from multivariate statistical analysis revealed that precursors combined with volatiles exhibited discriminating capability for characterizing Tibetan pork. The precursors in Tibetan pork exerted a certain effect on characteristic aroma generation, probably arising from promoting chemical reactions in cooking.

A multi-analyte screening method for the rapid detection of illicit adulterants in dietary supplements using a portable SERS analyzer.

The popularity and number of dietary supplements on the health market have experienced an unprecedented boost in recent years. Simultaneously, their increased use has been accompanied by an increase in acute intoxication cases linked to the adulteration of these products with illicit and undeclared substances. In this study, a SERS-based screening methodology was developed to rapidly detect illegally added pharmaceutically active substances to dietary supplements. A portable analyzer and silver printed-SERS substrates were used to enhance the signal, requiring less than 20 min of sample preparation prior to the analysis. The method was successful in the qualitative identification of eleven out of twenty-three illicit adulterants in the dietary supplements; it could detect the target compounds at realistic adulteration levels (0.1-5.0% w/w), demonstrating the potential of SERS-based methodologies for forensic rapid screening applications. The developed method is quick, easy to use, requires no skilled technicians and little sample preparation, and allows in-situ analyses. For these reasons, it is suitable for quick screening to be performed by inspectors at customs. Moreover, the low specificity of spectroscopic methods, to which SERS belongs, would benefit the detection of newly synthesized analogues of the target adulterants, which would otherwise be more difficult using common mass spectrometry methods in absence of reference standards.

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.

Interpreting the variation in particle size of ground spice by high-resolution visual and spectral imaging: A ginger case study.

High-resolution (HR) visual imaging and spectral imaging are common computer vision-based techniques used for food quality analysis and/or authentication based on the interaction of light and material surface and/or composition. The particle size of ground spices is an important morphological feature that affects the physico-chemical properties of food products containing such particles. This study aimed to interpret the impact of particle size of ground spice on its HR visual profile and spectral imaging profile using ginger powder as a representative spice powder model. The results revealed an increase in the light reflection with the decrease of particle size of ginger powder, which was manifested by the lighter colour (higher percentage of the colour code with lighter yellow colour) of the HR visual image and stronger reflection with spectral imaging. The study also revealed that, in spectral imaging, the influence of the particle size of ginger powder increased with rising wavelengths. Finally, the results indicated a relationship between spectral wavelengths, ginger particle size, and other natural variables of the products which might be generated from cultivation to processing. Ultimately, the impact of natural variables arising during the food production process on the physico-chemical properties of the product should be fully considered or even additionally evaluated prior to the application of specific food quality and/or authentication analytical techniques.

A sense of ginger fraud: prevalence and deconstruction of the China-European union supply chain.

As an important spice, ginger has been widely distributed in the Chinese and the European Union (EU) markets, the two largest trading areas, in various forms. The ginger supply chain between China and the EU is long and complex, providing opportunities for fraudsters to deceive consumers. However, limited attention has been given to food fraud in ginger, and there is a lack of research on this topic. In this review, ginger was used as an example for interpreting the fraud issues within low-priced and high-trade volume spice products. This review aims to summarize the open access information from food and food fraud databases, literature, and stakeholders about ginger fraud, and to map, deconstruct and analyse the food fraud vulnerability in the supply chain. In addition, potential testing strategies to detect ginger fraud were also discussed. The investigation of food fraud databases, a semi-structured literature review and online interviews with stakeholders revealed that adulteration is the major fraud type in ginger products. And the most vulnerable ginger products are ground ginger and finely processed ginger. The ginger supply chain from China to the EU comprises nine stages and is medium vulnerable to food fraud, both in regard to opportunities and motivational drivers. To ensure the integrity of the ginger supply chain, there is a need to apply fraud vulnerability tools in the companies of the industry. In addition, screening and confirmatory techniques based on the characteristics of ginger should be utilised for monitoring fraud issues in the supply chain.

The 11 sins of seafood: Assessing a decade of food fraud reports in the global supply chain.

Due to complex, valuable, and often extremely opaque supply chains, seafood is a commodity that has experienced a high prevalence of food fraud throughout the entirety of its logistics network. Fraud detection and prevention require an in-depth understanding of food supply chains and their vulnerabilities and risks so that food business operators, regulators, and other stakeholders can implement practical countermeasures. An analysis of historical criminality within a sector, product, or country is an important component and has not yet been conducted for the seafood sector. This study examines reported seafood fraud incidents from the European Union's Rapid Alert System for Food and Feed, Decernis's Food Fraud Database, HorizonScan, and LexisNexis databases between January 01, 2010 and December 31, 2020. Illegal or unauthorized veterinary residues were found to be the most significant issue of concern, with most reports originating from farmed seafood in Vietnam, China, and India. For internationally traded goods, border inspections revealed a significant frequency of reports with fraudulent or insufficient documentation, indicating that deceptive practices are picked up at import or export but are occurring further down the supply chain. Practices such as species adulteration (excluding veterinary residues), species substitution, fishery substitution, catch method fraud, and illegal, unreported, and unregulated fishing were less prevalent in the databases than evidenced in the scientific literature. The analysis demonstrates significant differences in outcomes depending on source and underlines a requirement for a standardized and rigorous dataset through which food fraud can be scrutinized to ensure enforcement, as well as industry and research resources are directed accurately. Practical Application: Levels of historic food fraud in a product, sector, supply chain node or geographic location provide an indication of historic criminality, the methods used and the location of reported frauds. This study provides an overview of historic levels of seafood fraud that can be used to inform seafood fraud prevention and mitigation activities by the food industry, regulators and other stakeholders.

Hyperspectral Imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends.

Meat products are particularly plagued by safety problems because of their complicated structure, various production processes and complex supply chains. Rapid and non-invasive analytical methods to evaluate meat quality have become a priority for the industry over the conventional chemical methods. To achieve rapid analysis of safety and quality parameters of meat products, hyperspectral imaging (HSI) is now widely applied in research studies for detecting the various components of different meat products, but its application in meat production and supply chain integrity as a quality control (QC) solution is still ambiguous. This review presents the fresh look at the current states of HSI research as both the scope and the applicability of the HSI in the meat quality evaluation expanded. The future application scenarios of HSI in the supply chain and the future development of HSI hardware and software are also discussed, by which HSI technology has the potential to enable large scale meat product testing. With a fully adapted for factory setting HSI, the inspection coverage can reliably identify the chemical properties of meat products. With the introduction of Food Industry 4.0, HSI advances can change the meat industry to become from reactive to predictive when facing meat safety issues.

Identification of milk from different animal and plant sources by desorption electrospray ionisation high-resolution mass spectrometry (DESI-MS).

This study used desorption electrospray ionisation mass spectrometry (DESI-MS) to analyse and detect and classify biomarkers in five different animal and plant sources of milk for the first time. A range of differences in terms of features was observed in the spectra of cow milk, goat milk, camel milk, soya milk, and oat milk. Chemometric modelling was then used to classify the mass spectra data, enabling unique or significant markers for each milk source to be identified. The classification of different milk sources was achieved with a cross-validation percentage rate of 100% through linear discriminate analysis (LDA) with high sensitivity to adulteration (0.1-5% v/v). The DESI-MS results from the milk samples analysed show the methodology to have high classification accuracy, and in the absence of complex sample clean-up which is often associated with authenticity testing, to be a rapid and efficient approach for milk fraud control.

Comparison of Portable and Benchtop Near-Infrared Spectrometers for the Detection of Citric Acid-adulterated Lime Juice: A Chemometrics Approach.

Background: Since the incidence of food adulteration is rising, finding a rapid, accurate, precise, low-cost, user-friendly, high-throughput, ruggedized, and ideally portable method is valuable to combat food fraud. Near-infrared spectroscopy (NIRS), in combination with a chemometrics-based approach, allows potentially rapid, frequent, and in situ measurements in supply chains. Methods: This study focused on the feasibility of a benchtop Fourier-transformation-NIRS apparatus (FT-NIRS, 1000 - 2500 nm) and a portable short wave NIRS device (SW-NIRS, 740 - 1070 nm) for the discrimination of genuine and citric acid-adulterated lime juice samples in a cost-effective manner following chemometrics study. Results: Principal component analysis (PCA) of the spectral data resulted in a noticeable distinction between genuine and adulterated samples. Wavelengths between 1100 - 1400 nm and 1550 - 1900 nm were found to be more important for the discrimination of samples for the benchtop FT-NIRS data, while variables between 950 - 1050 nm contributed significantly to the discrimination of samples based on the portable SW-NIRS data. Following partial least squares discriminant analysis (PLS-DA) as a discriminant model, standard normal variate (SNV) or multiplicative scatter correction (MSC) transformation of benchtop FT-NIRS data and SNV in combination with the second derivative transformation of portable SW-NIRS data on the training set delivered equal accuracy (94%) in the prediction of the test set. In the soft independent modeling of class analogy (SIMCA) as a class-modeling approach, the overall performances of generated models on the auto-scaled data were 98% and 94.5% for benchtop FT-NIRS and portable SW-NIRS, respectively. Conclusions: As a proof of concept, NIRS technology coupled with appropriate multivariate classification models enables fast detection of citric acid-adulterated lime juices. In addition, the promising results of portable SW-NIRS combined with SIMCA indicated its use as a screening tool for on-site analysis of lime juices at various stages of the food supply chain.

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.

Handheld Devices for Food Authentication and Their Applications: A Review.

This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.

Preliminary Study on Tracing the Origin and Exploring the Relations between Growing Conditions and Isotopic and Elemental Fingerprints of Organic and Conventional Cavendish Bananas (Musa spp.).

The stable isotopic ratios and elemental compositions of 120 banana samples, Musa spp. (AAA Group, Cavendish Subgroup) cultivar Williams, collected from six countries (Colombia, Costa Rica, Dominica Republic, Ecuador, Panama, Peru), were determined by isotope ratio mass spectrometry and inductively coupled plasma mass spectrometry. Growing conditions like altitude, temperature, rainfall and production system (organic or conventional cultivation) were obtained from the sampling farms. Principal component analysis (PCA) revealed separation of the farms based on geographical origin and production system. The results showed a significant difference in the stable isotopic ratios (δ13C, δ15N, δ18O) and elemental compositions (Al, Ba, Cu, Fe, Mn, Mo, Ni, Rb) of the pulp and peel samples. Furthermore, δ15N was found to be a good marker for organically produced bananas. A correlation analysis was conducted to show the linkage of growing conditions and compositional attributes. The δ13C of pulp and peel were mainly negatively correlated with the rainfall, while δ18O was moderately positively (R values ~0.5) correlated with altitude and temperature. A moderate correlation was also found between temperature and elements such as Ba, Fe, Mn, Ni and Sr in the pulp and peel samples. The PCA results and correlation analysis suggested that the differences of banana compositions were combined effects of geographical factors and production systems. Ultimately, the findings contribute towards understanding the compositional differences of bananas due to different growing conditions and production systems linked to a defined origin; thereby offering a tool to support the traceability of commercial fruits.

Which Company Characteristics Make a Food Business at Risk for Food Fraud?

Fraud can happen to any food business, but some sectors show more historical evidence of food fraud than others. This may be due to particular company characteristics that affect a company's level of vulnerability. In the current study, we examined the relevance of the industry segment, business size, and location of food businesses on their food fraud vulnerabilities. Over 8000 food fraud vulnerability self-assessments conducted by food businesses active in 20 industry segments located in five continents were collected and the data analyzed. Results revealed that a company's industry segment (chain and tier) affects its fraud vulnerability greatly and to a larger extent than the size of the business. The effect of industry segment on fraud vulnerability appears fairly similar across continents, whereas the effect of business size exhibits large geographical variation. The results demonstrate that those involved in animal product supply chains and end of chain nodes (catering, retail) are most vulnerable, and so are larger businesses, and businesses located in Africa and Asia. Current results imply that company characteristics are important determinants of the level of fraud vulnerability, and they may be used reversely in the future, i.e., as predictors of vulnerability.

Broadband acoustic resonance dissolution spectroscopy as a rapid tool for the compositional analysis of food powders: A case study of edible salts.

Broadband acoustic resonance dissolution spectroscopy (BARDS) is a novel method that can be used for the analysis of food-based powders, which are mainly characterized by their composition and particle morphology. This study aimed to evaluate BARDS for the compositional analysis of food powders. The changes in the BARDS spectra due to the changes in composition and particle morphology of fifteen salt mixtures (constituting of NaCl, KCl, and MgCl2) in five particle size ranges were comprehensively studied. Moreover, different regression methods were utilized to estimate each mixture component content. The results revealed that the average time-frequency spectra of each mixture in a certain particle size class were highly distinct and allowed discrimination from others. The unique spectra of each salt mixture originated from the specific dissolution rate and degassing effect of each constitutive compound. Finally, the accurate prediction of each mixture component content confirmed the consistency and efficiency of the method.

Novel Application of Near-infrared Spectroscopy and Chemometrics Approach for Detection of Lime Juice Adulteration.

The aim of this study is to investigate the novel application of a handheld near infra-red spectrophotometer coupled with classification methodologies as a screening approach in detection of adulterated lime juices. For this purpose, a miniaturized near infra-red spectrophotometer (Tellspec®) in the spectral range of 900-1700 nm was used. Three diffuse reflectance spectra of 31 pure lime juices were collected from Jahrom, Iran and 25 adulterated juices were acquired. Principal component analysis was almost able to generate two clusters. Partial least square discriminant analysis and k-nearest neighbors algorithms with different spectral preprocessing techniques were applied as predictive models. In the partial least squares discriminant analysis, the most accurate prediction was obtained with SNV transforming. The generated model was able to classify juices with an accuracy of 88% and the Matthew's correlation coefficient value of 0.75 in the external validation set. In the k-NN model, the highest accuracy and Matthew's correlation coefficient in the test set (88% and 0.76, respectively) was obtained with multiplicative signal correction followed by 2nd-order derivative and 5th nearest neighbor. The results of this preliminary study provided promising evidence of the potential of the handheld near infra-red spectrometer and machine learning methods for rapid detection of lime juice adulteration. Since a limited number of the samples were used in the current study, more lime juice samples from a wider range of variability need to be analyzed in order to increase the robustness of the generated models and to confirm the promising results achieved in this study.

Study on the Relations between Hyperspectral Images of Bananas (Musa spp.) from Different Countries, Their Compositional Traits and Growing Conditions.

Bananas are some of the most popular fruits around the world. However, there is limited research that explores hyperspectral imaging of bananas and its relationship with the chemical composition and growing conditions. In the study, the relations that exist between the visible near-infrared hyperspectral reflectance imaging data in the 400-1000 nm range of the bananas collected from different countries, the compositional traits and local growing conditions (altitude, temperature and rainfall) and production management (organic/conventional) were explored. The main compositional traits included moisture, starch, dietary fibre, protein, carotene content and the CIE L*a*b* colour values were also determined. The principal component analysis showed the preliminary separation of bananas from different geographical origins and production systems. The compositional and spectral data revealed positively and negatively moderate correlations (r around ±0.50, p < 0.05) between the carotene, starch content, and colour values (a*, b*) on the one hand and the wavelength ranges 405-525 nm, 615-645 nm, 885-985 nm on the other hand. Since the variation in composition and colour values were related to rainfall and temperature, the spectral information is likely also influenced by the growing conditions. The results could be useful to the industry for the improvement of banana quality and traceability.

Opportunities for fraudsters: When would profitable milk adulterations go unnoticed by common, standardized FTIR measurements?

Milk is regarded as one of the top food products susceptible to adulteration where its valuable components are specifically identified as high-risk indicators for milk fraud. The current study explores the impact of common milk adulterants on the apparent compositional parameters of milk from the Dutch market as measured by standardized Fourier transform infrared (FTIR) spectroscopy. More precisely, it examines the detectability of these adulterants at various concentration levels using the compositional parameters individually, in a univariate manner, and together in a multivariate approach. In this study we used measured boundaries but also more practical variance-adjusted boundaries to set thresholds for detection of adulteration. The potential economic impact of these adulterations under a milk payment scheme is also evaluated. Twenty-four substances were used to produce various categories of milk adulterations, each at four concentration levels. These substances comprised five protein-rich adulterants, five nitrogen-based adulterants, seven carbohydrate-based adulterants, six preservatives and water, resulting in a set of 360 samples to be analysed. The results showed that the addition of protein-rich adulterants, as well as dicyandiamide and melamine, increased the apparent protein content, while the addition of carbohydrate-based adulterants, whey protein isolate, and skimmed milk powder, increased the apparent lactose content. When considering the compositional parameters univariately, especially protein- and nitrogen-based adulterants did not raise a flag of unusual apparent concentrations at lower concentration levels. Addition of preservatives also went unnoticed. The multivariate approach did not improve the level of detection. Regarding the potential profit of milk adulteration, whey protein and corn starch seem particularly interesting. Combining the artificial inflation of valuable components, the resulting potential profit, and the gaps in detection, it appears that the whey protein isolates deserve particular attention when thinking like a criminal.