Food adulteration: Sources, health risks, and detection methods

Investigation of fraud in the production of butter: a forensic case study of criminal association

Butter is among the most popular and commercially valuable dairy products. Its high commercial value makes it a major target for adulteration, which aims to reduce production costs by using lower-quality fats and oils from other sources. The annual global market is around USD 30 billion (2023), expected to reach USD 36 billion in 2028, which also justifies the enormous interest in adulteration. In this work, a confirmed case of butter adulteration was studied by Nuclear Magnetic Resonance (NMR) and Stable Carbon Isotopic Ratio Analysis (SCIRA) techniques, employed to detect the inclusion in butter production of vegetable oils, such as soybean and palm oils. A total of 21 samples seized by the Brazilian Federal Police were analysed by NMR and SCIR, and compared to original butter obtained from commercial sources. The composition of all the seized samples was a mixture of butter (dairy fat of animal origin) with fat of vegetable origin (soybean and palm oil) and did not contain milk as a major component. While NMR was an unequivocal choice to discriminate the chemical composition of food samples, identifying the short-chain saturated fatty acids present in milk fat, including the butyryl alkyl chain, SCIRA was able to discriminate the origin of fat present in the butter samples as C3 sources, such as palm vegetable oils.

Assessment of Meat Content and Foreign Object Detection in Cattle Meatballs Using Ultrasonography, Radiography, and Electrical Impedance Tomography Imaging

Meat content and physically hazardous contaminants in the internal section of meatballs cannot be detected by the naked eye or surface detectors. This study is aimed at analyzing the meat content of cattle meatballs and detecting foreign objects using ultrasonography (USG), digital radiography (DR), and electrical impedance tomography (EIT). Meatballs were produced using four different meat formulations (0%, 25%, 50%, and 75% meat) and three treatments (no preservative (control), borax, and formalin preservatives). Cast iron and plastic beads were used as models of foreign objects embedded in the samples. The echogenicity, opacity, and resistivity values of each sample were evaluated and compared across groups. The results showed that the shelf life of the control meatballs was shorter than that of meatballs with preservatives. The echogenicity and opacity values for the different meat formulations were hypoechoic in USG and grey in DR. USG was able to distinguish between control and preservative-treated meatballs but could not differentiate meat content and detect foreign objects. Conversely, DR effectively assessed meat content and detected iron-based foreign objects, while EIT showed higher resistivity values for iron and plastic beads compared to the meatball bodies.

Analysis of the lipidomic profile of vegetable oils and animal fats and changes during aging by UPLC-Q-exactive orbitrap mass spectrometry

Vegetable oil and animal fat residues are common evidence in the cases of homicide, arson, theft, and other crimes. However, the lipid composition and content changes during aging on complex carriers remain unclear. Therefore, this study dynamically monitored the lipid composition and content changes during aging of 13 different types of vegetable oils and animal fats on five different carriers using the UPLC-Q-Exactive Orbitrap MS method. A total of 6 subclasses of 93 lipids including lysophosphatidylcholine (2 species), phosphatidylcholine (2 species), diglyceride (5 species), triglyceride (81 species), acylGlcCampesterol ester (2 species), and acylGlcSitosterol ester (1 species), were first identified in fresh vegetable oils and animal fats. By comparing the LC-MS/MS chromatograms of fresh vegetable oils and animal fats, it was found that there were significant differences between the chromatograms of vegetable oils and animal fats, but it was difficult to distinguish between the chromatograms of vegetable oils or animal fats. After aging at 60 °C for 200 days, there was a significant decrease in the content of diglyceride, triglyceride, acylGlcCampesterol ester, and acylGlcSitosterol ester, while the content of lysophosphatidylcholine and phosphatidylcholine initially increased and then decreased. Furthermore, statistical analysis of lipid differences between vegetable oils and animal fats was performed using cluster heat maps, volcanic maps, PCA, and OPLS-DA. On average, 33 significantly different lipids were screened (VIP > 1, p < 0.05), which could serve as potential biomarkers for distinguishing vegetable oils and animal fats. It was found that the potential biomarkers still existed during aging of vegetable oils and animal fats (100 and 200 days). This research provides important reference information for the identification of vegetable oil and animal fat residues in complex carriers at crime scenes.

Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry Combined with Chemometrics for Protein Profiling and Classification of Boiled and Extruded Quinoa from Conventional and Organic Crops

Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between farming and processing conditions, and the detected proteins that were critical for differentiation. They confirm the effectiveness of tracing the agricultural origins and technological treatments of quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted approach offers promising applications in food control and the food-processing industry.

Essential spectral pixels-based improvement of UMAP classifying hyperspectral imaging data to identify minor compounds in food matrix

Classifying big data in hyperspectral imaging (HSI) can be challenging when minor (low-concentrated) compounds are present in actual samples, as for chemical additives and adulterants in food matrix. Herein, we propose a new strategy to classify HSI data for the identification of adulterants in food material for the first time. This strategy is based on the selection of essential spectral pixels of full HSI data followed by the feature space construction using uniform manifold approximation and projection as well as the data clustering utilizing hierarchical clustering analysis on the reduced data (named ESPs-UMAP-HCA). We apply our approach to analyze two real NIR datasets and four new Raman datasets. Compared with non-ESPs UMAP-HCA and t-distributed stochastic neighbor embedding combined with ESPs and HCA (ESPs-t-SNE-HCA), the developed strategy provides well-separated clusters for major and minor compounds in food matrix. Finally, the adulterants as minor compounds are accurately identified, which is confirmed by the fact that the extracted spectra of them perfectly match with their pure spectra. In addition, their locations are found in the contribution map even though they are present in a few pixels. What's more, the proposed strategy does not need any a priori knowledge of the data structure and the class memberships and therefore reduced the studied difficulty and confirmation bias in the analysis of big HSI datasets. Overall, the proposed ESPs-UMAP-HCA method could be a potential approach for food adulteration detection.

Comparison of competitive and sandwich immunochromatographic analysis in the authentication of chicken in meat products

Cheap chicken meat is often used as an undeclared substitute in meat products. In this study, two formats of the immunochromatographic assay (ICA) of immunoglobulins of class Y (IgY) as a biomarker for chicken authentication were developed. In both competitive ICA (cICA) and sandwich ICA (sICA), gold nanoparticles (GNP) were conjugated with anti-species antibodies. A simple procedure of sample preparation, which took only 30 min, was proposed. Test systems demonstrated high sensitivity and rapidity: visual limits of detection of IgY and assay durations were 12/14 ng/mL and 10/15 min for cICA and sICA, respectively. The absence of cross-reactivity with the mammalian species confirmed the high specificity of the test systems. Good applicability of the assays was confirmed for the detection of chicken in raw meat mixtures: as low as 3% and 0.2% (w/w) of chicken could be revealed in beef and pork by cICA and sICA, respectively. The influence of heat processing of meat-based products on immune recognition and, consequently, the analytical performance of the test systems was revealed. It was shown that sICA is preferable for the detection of IgY even in thermally processed meat. The proposed ICAs can be recommended for rapid on-site control of meat products' composition.

Double lateral flow immunosensing of undeclared pork and chicken components of meat products

Adulteration of meat products is a serious problem in the modern society. Consumption of falsified meat products can be hazardous to health and/or lead to violating religious dietary principles. To identify such products, rapid and simple test systems for point-of-need detection are in demand along with complex laboratory methods. This study presents the first double lateral flow (immunochromatographic) test system, which allows simultaneous revealing two prevalent types of falsifications-undeclared addition of pork and chicken components to meat products. In the proposed test system, porcine myoglobin (MG) and chicken immunoglobulin Y (IgY) were used as specific biomarkers recognizable by antibodies. Within the optimization of the analysis, the concentrations of the immune reagents and regimes of their application on the working membrane were selected, which provided minimal limits of detection (LODs) for both analytes. The developed test system enables the detection of MG and IgY with the LODs of 10 and 12 ng/mL, respectively, which accords to addition of 0.1% of the undeclared meat compounds. The applicability of the test system to control the composition of raw meat mixtures and cooked food products was confirmed. The developed approach can be considered as a promising tool for monitoring composition of meat products.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-024-05944-y.

Microfluidic paper analytic device (μPAD) technology for food safety applications

Foodborne pathogens, food adulterants, allergens, and toxic chemicals in food can cause major health hazards to humans and animals. Stringent quality control measures at all stages of food processing are required to ensure food safety. There is, therefore, a global need for affordable, reliable, and rapid tests that can be conducted at different process steps and processing sites, spanning the range from the sourcing of food to the end-product acquired by the consumer. Current laboratory-based food quality control tests are well established, but many are not suitable for rapid on-site investigations and are costly. Microfluidic paper analytical devices (μPADs) are a fast-growing field in medical diagnostics that can fill these gaps. In this review, we describe the latest developments in the applications of microfluidic paper analytic device (μPAD) technology in the food safety sector. State-of-the-art μPAD designs and fabrication methods, microfluidic assay principles, and various types of μPAD devices with food-specific applications are discussed. We have identified the prominent research and development trends and future directions for maximizing the value of microfluidic technology in the food sector and have highlighted key areas for improvement. We conclude that the μPAD technology is promising in food safety applications by using novel materials and improved methods to enhance the sensitivity and specificity of the assays, with low cost.

Graphene based T-shaped monopole antenna sensor for food quality evaluation

BACKGROUND

Food adulteration has long been considered a major problem. It compromises the quality, safety, and nutritional value of food, posing significant risks to public health. Novel techniques are required to control it.

RESULTS

A graphene-based T-shaped monopole antenna sensor was tested for its ability to detect adulteration in liquid foods. Mustard oil was the pure reference sample used for product quality analysis. Olive oil and rice bran oil were adulterants added to the pure sample. It was found that the sensor could be immersed easily in the liquid sample and provided precise results.

CONCLUSION

The graphene-based T-shaped monopole antenna sensor can be used for the quality assessment of liquid food products and is suitable for real-time monitoring. © 2024 Society of Chemical Industry.

Evaluation of different regression models for detection of adulteration of mustard and canola oil with argemone oil using fluorescence spectroscopy coupled with chemometrics

Mustard and canola oils are commonly used cooking oils in Asian countries such as India, Nepal, and Bangladesh, making them prone to adulteration. Argemone is a well-known adulterant of mustard oil, and its alkaloid sanguinarine has been linked with health conditions such as glaucoma and dropsy. Utilising a non-destructive spectroscopic method coupled with a chemometric approach can serve better for the detection of adulterants. This work aimed to evaluate the performance of various regression algorithms for the detection of argemone in mustard and canola oils. The spectral dataset was acquired from fluorescence spectrometer analysis of pure as well as adulterated mustard and canola oils with some local and commercial samples also. The prediction performance of the eight regression algorithms for the detection of adulterants was evaluated. Extreme gradient boosting regressor (XGBR), Category gradient boosting regressor (CBR), and Random Forest (RF) demonstrate potential for predicting adulteration levels in both oils with high R2 values.

Designing a novel method based on multiplex PCR for detecting various meat of birds in processed ground meat products

Falsified food directly influences wildlife, fair trade, religion, and the health of society. Here, we report a multiplex polymerase chain reaction to evaluate the accurate determination of seven species of bird meat in meals on a single assay platform. To amplify segments of DNA from Columba livia, Corvus moneduloides, Gallus gallus, Coturnix japonica, Phasianus colchicus, Struthio camelus, and Meleagris gallopavo meats, respectively, a total of seven sets of species-specific primers targeting the mitochondrial and cytochrome b genes were developed. Gel photographs and electrochromatography from an Experion Bioanalyzer were used to identify all PCR products. Species specificity checks discovered no cross-species amplification. The applicability of its screening to find target species in processed food was shown in commercial and model meatballs. A validation study revealed that the test is reliable, quick, affordable, repeatable, specific, and accurate down to 50,000 mitochondrial copies. It might be used for raw meats and products involving processed and severely deteriorated food samples. The customers, the food business, and law enforcement would all benefit immensely from this suggested approach.

Selected food items adulteration, their impacts on public health, and detection methods: A review

Every living thing requires food to survive. Clean, fresh, and healthy foods are important to human health. Today, food is affected by various counterfeits. Adulteration of food is the intentional deterioration of the quality of food offered for sale by either the addition or substitution of an inferior substance or by the omission of a valuable ingredient. Economically motivated adulteration is the intentional adulteration of food for financial gain, and has enormous public health implications, making it an important issue in food science. Almost every food, including milk and dairy products, fats and oils, fruits and vegetables, grain foods, coffee, tea, honey, etc., is susceptible to adulteration. It is difficult to find food that is free from adulteration. Consumption of adulterated food contributes to numerous diseases in society, ranging from mild to life threatening. Therefore, detection of adulteration in food is essential to ensure the safety of the food we consume. To provide consumers with food that is free of adulterants, various detection methods such as physical, chemical, biochemical, and molecular techniques are used to identify adulterants in food. This review aims to provide up-to-date information on food adulteration, its impact on health, and the analytical techniques used to detect adulteration in food.

Highly Sensitive Immunochromatographic Detection of Porcine Myoglobin as Biomarker for Meat Authentication Using Prussian Blue Nanozyme

This study was aimed at the sensitive immunodetection of porcine myoglobin (MG) as a species-specific biomarker in meat products. The enhanced lateral flow immunoassay (LFIA) was created in the sandwich format using monoclonal antibodies (Mab) with specificity to porcine MG and labeled by Prussian blue nanoparticles (PBNPs) as peroxidase-mimicking nanozymes. Signal amplification was provided by the colored product of oxidation catalyzed by the PBNPs. Several Mab-PBNP conjugates with different antibody loads were synthesized; the one that provided the best analytical characteristics of the LFIA was selected. Advanced optimization of the test system was carried out. As a result, the visual limit of detection (LOD) of MG was 1.5 ng/mL. Involvement of the catalytic nanozyme properties allowed the LOD to be decreased by ~9 times in comparison to the LFIA based on gold nanomarkers, and by ~27 times compared to the LFIA based on PBNP coloration. The assay time was 30 min, including catalytic enhancement. A simple technique of meat sample pre-treatment aimed at effective MG extraction and matrix disposal was proposed. The specificity of the LFIA towards the pork meat was demonstrated. The applicability of the created test system was shown by testing extracts obtained from finished meat products.

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.

Development and application of lipidomics for food research

Lipidomics is an emerging and promising omics derived from metabolomics to comprehensively analyze all of lipid molecules in biological matrices. The purpose of this chapter is to introduce the development and application of lipidomics for food research. First, three aspects of sample preparation are introduced: food sampling, lipid extraction, and transportation and storage. Second, five types of instruments for data acquisition are summarized: direct infusion-mass spectrometry (MS), chromatographic separation-MS, ion mobility-MS, MS imaging, and nuclear magnetic resonance spectroscopy. Third, data acquisition and analysis software are described for the lipidomics software development. Fourth, the application of lipidomics for food research is discussed such as food origin and adulteration analysis, food processing research, food preservation research, and food nutrition and health research. All the contents suggest that lipidomics is a powerful tool for food research based on its ability of lipid component profile analysis.

Surface Environment and Energy Density Effects on the Detection and Disinfection of Microorganisms Using a Portable Instrument

Real-time detection and disinfection of foodborne pathogens are important for preventing foodborne outbreaks and for maintaining a safe environment for consumers. There are numerous methods for the disinfection of hazardous organisms, including heat treatment, chemical reaction, filtration, and irradiation. This report evaluated a portable instrument to validate its simultaneous detection and disinfection capability in typical laboratory situations. In this challenging study, three gram-negative and two gram-positive microorganisms were used. For the detection of contamination, inoculations of various concentrations were dispensed on three different surface types to estimate the performance for minimum-detectable cell concentration. Inoculations higher than 10³~10⁴ CFU/mm² and 0.15 mm of detectable contaminant size were estimated to generate a sufficient level of fluorescence signal. The evaluation of disinfection efficacy was conducted on three distinct types of surfaces, with the energy density of UVC light (275-nm) ranging from 4.5 to 22.5 mJ/cm² and the exposure time varying from 1 to 5 s. The study determined the optimal energy dose for each of the microorganisms species. In addition, surface characteristics may also be an important factor that results in different inactivation efficacy. These results demonstrate that the proposed portable device could serve as an in-field detection and disinfection unit in various environments, and provide a more efficient and user-friendly way of performing disinfection on large surface areas.

Exploring customers' perceptions of food adulteration at bazaars and supermarkets in Dhaka, Bangladesh; a qualitative exploration

BACKGROUND

Food adulteration is an increasingly recognized global public health problem. In low- and middle-income countries like Bangladesh, adulteration is difficult to detect and respond to. We explored customers' perceptions on food adulteration, perception of risk and connections between information, participant characteristics and patterns of adulterated food concerns that impact risk perception in urban Bangladesh.

METHODS

A formative study was conducted in Dhaka, between June and August 2015 at a supermarket and a wet market. We explored community awareness and response to chemical contaminants (adulterants) among participants from a range of socio-economic backgrounds. The team conducted 38 in-depth interviews with 12 customers and 4 staff from a supermarket, and 12 customers and 10 vendors from a wet market. Participants were selected purposively. Audio recorded data were coded based on thematic content and analyzed manually.

RESULTS

We asked participants how common foods were likely adulterated, and most gave figures of 70% or more. They reported that foods were adulterated with chemicals or artificial colors, especially fish, milk, and vegetables. The supermarket more commonly sold packaged foods with nutritional and expiry information on the label; and offered convenience in terms of building size, layout, and cleanliness. All customers from the wet market thought that foods were cheaper and fresher than from supermarkets. Supermarket customers expressed greater concern about adulterated foods than wet market customers. Most participants from both markets reported that food adulteration is invisible, adulterated foods cannot be avoided, and have long-term negative health impacts including cancer, diabetes, paralysis, heart attack, and others. Nearly half of customers from both markets were concerned about the poor nutritional value of adulterated food. Participants from both settings expressed the need for access to credible information about adulteration to help choose safe foods. The majority expressed the need for government action against those who are responsible for adulteration.

CONCLUSIONS

Food adulteration was considered a major health threat. The government could act on food adulteration prevention if provided credible population-based data on disease burden, a model food sampling and testing protocol, a model for inspections, organizational strengthening and training, example social and behavioral change communications with estimated costs.

Pesticide Residues and Unauthorized Dyes as Adulteration Markers in Chilli Pepper and Tomato

To assess the contamination of processed chilli pepper and tomatoes, a report over the past four decades since the establishment of the Rapid Alert System for Food and Feed (RASFF) was retrieved and analysed. Out of the 887 notification reports assessed for eligibility, 446 were found regarding chilli pepper and tomato contamination. This study identified India as the country of origin with the highest number of reported cases relating to chilli pepper contamination. Italy and Türkiye were the countries with the highest number of reported cases regarding the exportation of adulterated tomatoes to other countries according to the RASFF report. Unauthorized dyes such as Sudan I, III, IV, orange II, rhodamine B, and para red were reported to have been detected in either chilli pepper or tomato in the supply chain. Almost all unauthorized dyes in this study were found to be more than the range (0.5 to 1 mg/kg) of the detection limit of Sudan dye and other related dyes using analytical methods set by the European Union. Unapproved pesticides by the European Union (EU) found in this study were acetamiprid, chlorothalonil, chlorpyrifos, dimethoate, methomyl, monocrotophos, omethoate, oxamyl, and thiophanate methyl. The present study indicates the persistence of chilli pepper and tomato contamination with harmful dyes and pesticide residues despite the ban on the use of certain chemicals in the food chain.

Rapid detection of duck ingredient in adulterated foods by isothermal recombinase polymerase amplification assays

Duck is often used in meat fraud as a substitute for more expensive meats. Rapid detection of duck ingredient in meat products is of great significance for combating meat fraud and safeguarding the interests of consumers. Therefore, we aim to develop duck-specific recombinase polymerase amplification (RPA)-based assays for the rapid detection of duck ingredient in animal-derived foods. Using Cytb gene as target, the real-time RPA and RPA combined with lateral flow strips (LFS RPA) were developed successfully for the rapid detection of ducks in 20 min at 39 °C and 40 °C, respectively. The assays did not show cross-reactions with 6 other livestock and poultry. The developed RPA assays could detect 10 pg duck genomic DNA per reaction and 0.1 % (w/w) duck ingredient in duck and mutton mixed powder within 30 min, including a rapid nucleic acid extraction. Furthermore, duck ingredient could be detected in 30 different actual foods including heat-processed meats and blood products. Therefore, duck-specific real-time RPA and LFS RPA assays were successfully developed with good specificity and sensitivity, which could enable rapid detection of duck ingredient in the field and provide technical support for combating the meat fraud.

High-Throughput Real-Time qPCR and High-Resolution Melting (HRM) Assay for Fungal Detection in Plant Matrices

Crop producers are under great pressure to produce more and better food items. Effective control of crop pathogens is fundamental to guaranteeing food safety and reducing economic losses. Therefore, their identification throughout the production chain is of utmost interest. To achieve this goal, genomic analysis tools are currently being developed allowing to control crop production more effectively.Genomic analysis in some samples is difficult, mostly due to the sample's intrinsic characteristics, i.e., high levels of phenols, fatty acids (e.g., oleaginous fruits, such as olives), and carbon hydrates (e.g., honey), among others. Additionally, some samples yield very low DNA recovery with high content of contaminants, imposing protocol improvements to overcome these difficulties.Here we present protocols focused on qPCR and HRM to detect the presence of fungal pathogens collected from plant-derived samples.

A Comprehensive Review on Nutraceuticals: Therapy Support and Formulation Challenges

Nutraceuticals are the nourishing components (hybrid of nutrition and pharmaceuticals) that are biologically active and possess capability for maintaining optimal health and benefits. These products play a significant role in human health care and its endurance, most importantly for the future therapeutic development. Nutraceuticals have received recognition due to their nutritional benefits along with therapeutic effects and safety profile. Nutraceuticals are globally growing in the field of services such as health care promotion, disease reduction, etc. Various drug nutraceutical interactions have also been elaborated with various examples in this review. Several patents on nutraceuticals in agricultural applications and in various diseases have been stated in the last section of review, which confirms the exponential growth of nutraceuticals' market value. Nutraceuticals have been used not only for nutrition but also as a support therapy for the prevention and treatment of various diseases, such as to reduce side effects of cancer chemotherapy and radiotherapy. Diverse novel nanoformulation approaches tend to overcome challenges involved in formulation development of nutraceuticals. Prior information on various interactions with drugs may help in preventing any deleterious effects of nutraceuticals products. Nanotechnology also leads to the generation of micronized dietary products and other nutraceutical supplements with improved health benefits. In this review article, the latest key findings (clinical studies) on nutraceuticals that show the therapeutic action of nutraceutical's bioactive molecules on various diseases have also been discussed.

Slice spectra approach to synchronous Two-dimensional correlation spectroscopy analysis for milk adulteration discriminate

The discrimination approach of adulterated milk was proposed combined synchronous two-trace two-dimensional (2T2D) correlation slice spectra at the characteristic wavebands of adulterant in milk with multivariate method. Two common adulterants, melamine and urea, were analyzed to demonstrate useful by the method. 2T2D (near infrared) NIR slice spectra at characteristic wavebands of adulterant were extracted from the synchronous 2T2D correlation spectra, and were input to construct the N-way partial least squares discriminant analysis (NPLS-DA) models. One-dimensional (1D) spectroscopy featuring all the present components in the samples combined with partial least squares discriminant analysis (PLS-DA) was also evaluated for comparison. The results indicated that for one kind of adulterant in model, prediction accuracies of slice spectral models were both 100% for melamine-adulterated and urea-adulterated samples discrimination. Moreover, for two kinds of adulterants in model, prediction accuracies of slice spectral models were 90.57% and 100% for melamine-adulterated and urea-adulterated discrimination, respectively, which was better than those of 1D whole models based on PLS-DA (only 81.13% and 98.15%, respectively). The comparison informs that the 2T2D slice spectra extracted at the characteristic wavebands of adulterant highlighted the adulterant spectral features and was obviously advantage to improve the discrimination accuracy. Meanwhile, the complexity of slice spectra is significantly reduced compared with the whole matrix of synchronous 2T2D correlation spectra.

Review of Recent Modern Analytical Technology Combined with Chemometrics Approach Researches on Mushroom Discrimination and Evaluation

Mushroom is a macrofungus with precious fruiting body, as a food, a tonic, and a medicine, human have discovered and used mushrooms for thousands of years. Nowadays, mushroom is also a "super food" recommended by the World Health Organization (WHO) and Food and Agriculture Organization (FAO), and favored by consumers. Discrimination of mushroom including species, geographic origin, storage time, etc., is an important prerequisite to ensure their edible safety and commodity quality. Moreover, the effective evaluation of its chemical composition can help us better understand the nutritional properties of mushrooms. Modern analytical technologies such as chromatography, spectroscopy and mass spectrometry, etc., are widely used in the discrimination and evaluation researches of mushrooms, and chemometrics is an effective means of scientifically processing the multidimensional information hidden in these analytical technologies. This review will outline the latest applications of modern analytical technology combined with chemometrics in qualitative and quantitative analysis and quality control of mushrooms in recent years. Briefly describe the basic principles of these technologies, and the analytical processes of common chemometrics in mushroom researches will be summarized. Finally, the limitations and application prospects of chromatography, spectroscopy and mass spectrometry technology are discussed in mushroom quality control and evaluation.

A new honey adulteration detection approach using hyperspectral imaging and machine learning

This paper develops a new approach to fraud detection in honey. Specifically, we examine adulterating honey with sugar and use hyperspectral imaging and machine learning techniques to detect adulteration. The main contributions of this paper are introducing a new feature smoothing technique to conform to the classification model used to detect the adulterated samples and the perpetration of an adulterated honey data set using hyperspectral imaging, which has been made available online for the first time. Above $$95\%$$ 95 % accuracy was achieved for binary adulteration detection and multi-class classification between different adulterant concentrations. The system developed in this paper can be used to prevent honey fraud as a reliable, low cost, data-driven solution.

Accurate species identification of food-contaminating beetles with quality-improved elytral images and deep learning

Food samples are routinely screened for food-contaminating beetles (i.e., pantry beetles) due to their adverse impact on the economy, environment, public health and safety. If found, their remains are subsequently analyzed to identify the species responsible for the contamination; each species poses different levels of risk, requiring different regulatory and management steps. At present, this identification is done through manual microscopic examination since each species of beetle has a unique pattern on its elytra (hardened forewing). Our study sought to automate the pattern recognition process through machine learning. Such automation will enable more efficient identification of pantry beetle species and could potentially be scaled up and implemented across various analysis centers in a consistent manner. In our earlier studies, we demonstrated that automated species identification of pantry beetles is feasible through elytral pattern recognition. Due to poor image quality, however, we failed to achieve prediction accuracies of more than 80%. Subsequently, we modified the traditional imaging technique, allowing us to acquire high-quality elytral images. In this study, we explored whether high-quality elytral images can truly achieve near-perfect prediction accuracies for 27 different species of pantry beetles. To test this hypothesis, we developed a convolutional neural network (CNN) model and compared performance between two different image sets for various pantry beetles. Our study indicates improved image quality indeed leads to better prediction accuracy; however, it was not the only requirement for achieving good accuracy. Also required are many high-quality images, especially for species with a high number of variations in their elytral patterns. The current study provided a direction toward achieving our ultimate goal of automated species identification through elytral pattern recognition.

Barcoding High Resolution Melting (Bar-HRM) enables the discrimination between toxic plants and edible vegetables prior to consumption and after digestion

The consumption of poisonous plants can lead to serious health problems or even casualties due to various factors, including easy access to poisonous plants due to their common distribution, co-occurrence and resemblance with edible plants, and the lack of regulation in the food product supply chain. Clinical diagnosis of intoxications usually relies on the availability of the plant consumed by the patient and on the morphology of the plant parts found in the patient's stomach. Therefore, given the fragmented nature of ingested plant material, species identification may face serious difficulties, can be inaccurate, and time-consuming. This highlights the need for rapid and reliable tools to identify toxic species. In the present study, we developed an ITS2-high-resolution melting (HRM) assay for: (1) the discrimination of common toxic plants and their edible lookalikes, and (2) the detection of toxic plants in digested samples. More specifically, we designed species-specific ITS2 primers for the authentication of poisonous species in simulated mixtures and verified them with Bar-HRM. Moreover, the developed HRM-based molecular tool was capable of quantifying the toxic species Datura stramonium in simulated mixtures with the edible Amaranthus retroflexus down to at least 0.5% v/v. This study shows that species-specific ITS2 primers can amplify the DNA from fragmented and/or artificially digested samples and that Bar-HRM is capable of detecting poisonous plant species in digested samples even after 4 h. The developed Bar-HRM protocol has important implications for application in medicine, forensics, and the agricultural industry, either to accurately detect the cause of plant intoxications or as a tool for quality control in the supply chain. PRACTICAL APPLICATION: In this work, we established a high-resolution melting DNA-based protocol capable of discriminating between phenotypically similar common toxic and edible plant species in mixtures, even at very low quantities. This technology also proved efficient in detecting the toxic species in mixtures digested in artificial gastric acid, as it would be the case after accidental ingestion. This work is expected to have important implications for application in medicine, forensics, and the agricultural industry, either for identifying the cause of plant intoxications or as a tool for quality control in different steps of the supply chain.

CRISPR-Cas-based detection for food safety problems: Current status, challenges, and opportunities

Food safety is one of the biggest public issues occurring around the world. Microbiological, chemical, and physical hazards can lead to food safety issues, which may occur at all stages of the supply chain. In order to tackle food safety issues and safeguard consumer health, rapid, accurate, specific, and field-deployable detection methods meeting diverse requirements are one of the imperative measures for food safety assurance. CRISPR-Cas system, a newly emerging technology, has been successfully repurposed in biosensing and has demonstrated huge potential to establish conceptually novel detection methods with high sensitivity and specificity. This review focuses on CRISPR-Cas-based detection and its current status and huge potential specifically for food safety inspection. We firstly illustrate the pending problems in food safety and summarize the popular detection methods. We then describe the potential applications of CRISPR-Cas-based detection in food safety inspection. Finally, the challenges and futuristic opportunities are proposed and discussed. Generally speaking, the current food safety detection methods are still unsatisfactory in some ways such as being time-consuming, displaying unmet sensitivity and specificity standards, and there is a comparative paucity of multiplexed testing and POCT. Recent studies have shown that CRISPR-Cas-based biosensing is an innovative and fast-expanding technology, which could make up for the shortcomings of the existing methods or even replace them. To sum up, the implementation of CRISPR-Cas and the integration of CRISPR-Cas with other techniques is promising and desirable, which is expected to provide "customized" and "smart" detection methods for food safety inspection in the coming future.

An Integrative Glycomic Approach for Quantitative Meat Species Profiling

It is estimated that food fraud, where meat from different species is deceitfully labelled or contaminated, has cost the global food industry around USD 6.2 to USD 40 billion annually. To overcome this problem, novel and robust quantitative methods are needed to accurately characterise and profile meat samples. In this study, we use a glycomic approach for the profiling of meat from different species. This involves an O-glycan analysis using LC-MS qTOF, and an N-glycan analysis using a high-resolution non-targeted ultra-performance liquid chromatography-fluorescence-mass spectrometry (UPLC-FLR-MS) on chicken, pork, and beef meat samples. Our integrated glycomic approach reveals the distinct glycan profile of chicken, pork, and beef samples; glycosylation attributes such as fucosylation, sialylation, galactosylation, high mannose, α-galactose, Neu5Gc, and Neu5Ac are significantly different between meat from different species. The multi-attribute data consisting of the abundance of each O-glycan and N-glycan structure allows a clear separation between meat from different species through principal component analysis. Altogether, we have successfully demonstrated the use of a glycomics-based workflow to extract multi-attribute data from O-glycan and N-glycan analysis for meat profiling. This established glycoanalytical methodology could be extended to other high-value biotechnology industries for product authentication.

Recent Advances and Potential Applications of Atmospheric Pressure Cold Plasma Technology for Sustainable Food Processing

In a circular economy, products, waste, and resources are kept in the system as long as possible. This review aims to highlight the importance of cold plasma technology as an alternative solution to some challenges in the food chain, such as the extensive energy demand and the hazardous chemicals used. Atmospheric cold plasma can provide a rich source of reactive gas species such as radicals, excited neutrals, ions, free electrons, and UV light that can be efficiently used for sterilization and decontamination, degrading toxins, and pesticides. Atmospheric cold plasma can also improve the utilization of materials in agriculture and food processing, as well as convert waste into resources. The use of atmospheric cold plasma technology is not without challenges. The wide range of reactive gas species leads to many questions about their safety, active life, and environmental impact. Additionally, the associated regulatory approval process requires significant data demonstrating its efficacy. Cold plasma generation requires a specific reliable system, process control monitoring, scalability, and worker safety protections.

Rapid Identification of Fupenzi (Rubus chingii Hu) and Its Adulteration by AuNP Visualization

Fupenzi (Rubus chingii Hu) is a dried and immature fruit in East China, which has effects of nourishing kidneys, solidifying essence, and otherwise. Because Fupenzi was often adulterated and replaced with inferior things, this paper had researched Fupenzi and its adulterant raspberry. A new type of visible sensor was constructed by using Au nanoparticles (AuNPs), which was modified by the surface-active agent and combined with the ultraviolet-visible (UV-vis) spectrum technology. It was found that the change in particle size after the interaction of AuNPs and adulterants will lead to color change. In this paper, the RGB (red, green, and blue) values of the solution were extracted to correlate the color with the concentration of adulterants, and the relationship between the absorption peak intensity and the concentration of adulterants was established. The results showed that the intensity of an absorption peak is related to adulteration concentration, and the color of the solution changed from red to gray as the particle size changed. The visual sensor constructed based on the above principle is a fast and precise method to detect adulteration with different concentrations, which has a potential application value in real-time and rapid detection of Fupenzi’s quality.

Paper-based microfluidic devices for food adulterants: Cost-effective technological monitoring systems

Analytical sciences have witnessed emergent techniques for efficient clinical and industrial food adulterants detection. In this review, the contributions made by the paper-based devices are highlighted for efficient and rapid detection of food adulterants and additives, which is the need of the hour and how different categories of techniques have been developed in the past decade for upgrading the performance for point-of-care testing. A simple strategy with an arrangement for detecting specific adulterants followed by the addition of samples to obtain well-defined qualitative or quantitative signals for confirming the presence of target species. The paper-based microfluidics-based technology advances and prospects for food adulterant detection are discussed given the high-demand from the food sectors and serve as a valued technology for food researchers working in interdisciplinary technological frontiers.

Baobab pulp authenticity and quality control by multi-imaging high-performance thin-layer chromatography

Globalization of trade and increasing demand for baobab fruit pulp powder (Adansonia digitata) has led to more adulteration incidence with physically similar products, e.g. sifted cereal flours. In this study, 135 baobab samples drawn from trees in Kitui and Kilifi (Kenya) and North and South Kordofan (Sudan) were used as the reference and compared with adulterated (with 10-30% sifted rice, maize and wheat flours) baobab samples using multi-imaging by high-performance thin-layer chromatography. The ethanol - water extracts were separated on a normal phase. Any differences were detected via multi-imaging (UV/Vis/FLD) including diphenylamine alanine o-phosphoric acid, p-anisaldehyde sulfuric acid and p-amino benzoic acid reagents. Raffinose was identified as a marker compound for cereal-based adulteration. The method accuracy (recovery of 95%) and detection from 10-30% flour addition onwards are sufficient to curb economically motivated adulteration, to control product quality and to ensure consumer protection for local and international trade.

Consumers’ Knowledge of Food Adulteration and Commonly Used Methods of Detection

Food adulteration has become a threat to many countries as most individuals have consumed food items without knowing that it has been adulterated, leaving the consumer with various ailments. This study identifies the degree of adulteration in some commonly used food items bought by consumers and the means of detection. The study comprised 384 women who patronized various food items for food preparation. They were asked if they have come into contact with adulterated food products before and to illustrate how they detect if a food item is adulterated. From the findings of this study, the respondents indicated that they will not consume a food item if they get to know that it has been adulterated, and 50.5% indicated that they have come into contact with adulterated food during preparing food. Various reasons were given by the respondents why they will not consume adulterated food, and the reasons included the following: the food may be dangerous to consume and not healthy for consumption and can cause stomach disorders. Few numbers (11 out of 384) of the respondents also indicated that they will still go ahead and consume adulterated food items since not all adulterants are toxic. Food items that are prone to adulteration as mentioned by the respondent included groundnut paste, chilli pepper, tomato powder, and honey with their adulterants ranging from flour, colour, Sudan IV dye, chalk powder, foam, cola nut powder, avocado pear seed powder, and many more. Means of detecting the presence of adulterants as indicated by the respondent were sensory and textural characteristics due to the cost involved in the use of other advanced techniques.