Development and Application of a Database of Food Ingredient Fraud and Economically Motivated Adulteration from 1980 to 2010

Myoglobin as a molecular biomarker for meat authentication and traceability

The global incidence of economically motivated meat adulteration represents a crucial issue for the food industry. Undeclared addition of cheaper or low-quality species to meat products of high commercial value has become a common practice that needs to be countered with specific measures. In this framework, myoglobin (Mb) is a sarcoplasmic haemoprotein, primarily responsible for meat colour and has been successfully used in meat fraud authentication. Mb is highly soluble in water, easily monitored at 409 nm and species-specific. Knowing that various analytical DNA-based and protein-based methods, as well as spectroscopic techniques have been developed over the years for the detection of meat fraud, the aim of the present review is to take stock of the situation regarding the possible use of Mb as a molecular biomarker for the easy and rapid detection of undeclared species in meat products, avoiding the need of sophisticated or expensive equipment and specialised operators.

Evidence of turmeric adulteration with lead chromate across South Asia

Food adulteration with toxic chemicals is a global public health threat. Lead chromate adulterated spices have been linked with lead poisoning in many countries, from Bangladesh to the United States. This study systematically assessed lead chromate adulteration in turmeric, a spice that is consumed daily across South Asia. Our study focused on four understudied countries that produce >80 % of the world's turmeric and collectively include 1.7 billion people, 22 % of the world's population. Turmeric samples were collected from wholesale and retail bazaars from 23 major cities across India, Pakistan, Sri Lanka, and Nepal between December 2020 and March 2021. Turmeric samples were analyzed for lead and chromium concentrations and maximum child blood lead levels were modeled in regions where samples had detectable lead. A total of 356 turmeric samples were collected, including 180 samples of dried turmeric roots and 176 samples of turmeric powder. In total, 14 % of the samples (n = 51) had detectable lead above 2 μg/g. Turmeric samples with lead levels greater than or equal to 18 μg/g had molar ratios of lead to chromium near 1:1, suggestive of lead chromate adulteration. Turmeric lead levels exceeded 1000 μg/g in Patna (Bihar, India) as well as Karachi and Peshawar (Pakistan), resulting in projected child blood lead levels up to 10 times higher than the CDC's threshold of concern. Given the overwhelmingly elevated lead levels in turmeric from these locations, urgent action is needed to halt the practice of lead chromate addition in the turmeric supply chain.

Research advances in detection of food adulteration and application of MALDI-TOF MS: A review

Food adulteration and illegal supplementations have always been one of the major problems in the world. The threat of food adulteration to the health of consumers cannot be ignored. Food of questionable origin causes economic losses to consumers, but the potential health risks cannot be ignored. However, the traditional detection methods are time-consuming and complex. This review mainly discusses the types of adulteration and technologies used to detect adulteration. Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is also emphasized in the detection of adulteration and authenticity of origin analysis of various types of food (milk, meat, edible oil, etc.), and the future application direction and feasibility of this technology are analyzed. On this basis, MALDI-TOF MS was compared with other detection methods, highlighting the advantages of this technology in the detection of food adulteration. The future development prospect and direction of this technology are also emphasized.

High-resolution proteomics and machine-learning identify protein classifiers of honey made by Sicilian black honeybees (Apis mellifera ssp. sicula)

Apis mellifera ssp. sicula, also known as the Sicilian black honeybee, is a Slow Food Presidium that produces honey with outstanding nutraceutical properties, including high antioxidant capacity. In this study, we used high-resolution proteomics to profile the honey produced by sicula and identify protein classifiers that distinguish it from that made by the more common Italian honeybee (Apis mellifera ssp. ligustica). We profiled the honey proteome of genetically pure sicula and ligustica honeybees bred in the same geographical area, so that chemical differences in their honey only reflected the genetic background of the two subspecies, rather than botanical environment. Differentially abundant proteins were validated in sicula and ligustica honeys of different origin, by using the so-called "rectangular strategy", a proteomic approach commonly used for biomarker discovery in clinical proteomics. Then, machine learning was employed to identify which proteins were the most effective in distinguishing sicula and ligustica honeys. This strategy enabled the identification of two proteins, laccase-5 and venome serine protease 34 isoform X2, that were fully effective in predicting whether honey was made by sicula or ligustica honeybees. In conclusion, we profiled the proteome of sicula honey, identified two protein classifiers of sicula honey in respect to ligustica, and proved that the rectangular strategy can be applied to uncover biomarkers to ascertain food authenticity.

Rapid identification method of milk powder from different animals based on Raman spectroscopy

This study developed an efficient method for identifying and quantitatively analyzing animal-origin milk powders using Raman spectroscopy combined with chemometrics. By employing the MultiClassClassifier model, the method achieved high accuracy in distinguishing various types of animal-origin milk powders, with sensitivity and specificity both exceeding 80% and an overall accuracy of 93%. Furthermore, the quantitative models based on Partial Least Squares Regression and Support Vector Machine Regression exhibited excellent linear correlations, with both Root Mean Square Error and Mean Relative Error below 0.2. These models successfully quantified adulteration in camel, mare, and donkey milk powders in comparison to goat and cow milk powders. The study's approach not only holds significant promise for detecting adulteration in specialty milk powders but also demonstrates wide applicability in analyzing other powdered adulterants.

Detection and quantification of cocoa powder adulteration using Vis-NIR spectroscopy with chemometrics approach

The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of adulteration practices aimed at achieving economic benefits. This study aimed to detect and quantify cocoa powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat. The NIRS data could not be resolved using Savitzky-Golay smoothing. Nevertheless, the application of a random forest and support vector machine successfully classified the samples with 100% accuracy. Quantification of adulteration using partial least squares (PLS), Lasso, Ridge, elastic Net, and RF regressions provided R2 higher than 0.96 and root mean square error <2.6. Coupling PLS with the Boruta algorithm produced the most reliable regression model (R2 = 1, RMSE = 0.0000). Finally, an online application was prepared to facilitate the determination of adulterants in the cocoa powder.

Food authenticity and the interactions with human health and climate change

Food authenticity and fraud, as well as the interest in food traceability have become a topic of increasing interest not only for consumers but also for the research community and the food manufacturing industry. Food authenticity and fraud are becoming prevalent in both the food supply and value chains since ancient times where different issues (e.g., food spoilage during shipment and storage, mixing decay foods with fresh products) has resulted in foods that influence consumers health. The effect of climate change on the quality of food ingredients and products could also have the potential to influence food authenticity. However, this issue has not been considered. This article focused on the interactions between consumer health and the potential effects of climate change on food authenticity and fraud. The role of technology and development of risk management tools to mitigate these issues are also discussed. Where applicable papers that underline the links between the interactions of climate change, human health and food fraud were referenced.

Simultaneous determination of subspecies and geographic origins of 110 rice cultivars by microsatellite markers

Rice varieties of different subspecies types (indica rice and japonica rice) across various geographical origins (Hunan, Jiangsu, and Northeast China) were monitored using microsatellite markers (simple sequence repeats, SSR). 110 representative rice cultivars were collected from the main crop areas. Multiple methods including clustering analysis (neighbor-joining (NJ) method, unweighted pair-group method with arithmetic mean (UPGMA) method), principal component analysis (PCA) and model-based grouping were applied. The study revealed that 25 pairs of SSR markers exhibited a broad range of polymorphism information content (PIC) values, ranging from 0.240 to 0.830. Furthermore, our study successfully achieved a higher overall mean correct rate of 99.09% in determining the geographical origin of rice. Simultaneously, it accurately classified indica rice and japonica rice. These findings are significant as they provide an SSR fingerprint of 110 high-quality rice cultivars, serving as a valuable scientific resource for the detection of rice adulteration and traceability of its origin.

Detection of honey adulteration using machine learning

Honey adulteration is a growing concern due to its health benefits and high nutritional content. Traditional methods like Melissopalynology are ineffective in detecting adulterated honey. This research presents a comparative study of machine learning algorithms for detecting adulteration in honey. The study uses hyperspectral imaging, a promising tool for food quality assurance, to classify and predict adulteration in honey. The proposed model relies on hyper-spectrum images and improves the accuracy of existing models using hyperparameter tuning. The dataset used includes segmented and pre-processed hyperspectral images of adulterated honey samples. The study found that machine learning and hyperspectral imaging can accurately identify if honey has been adulterated, with over 98% classification accuracy. The results showed that between 5% and 10% of adulterated honey samples are misclassified, with C1 Clover honey being the most frequently misclassified. This study aims to develop an efficient and accurate honey counterfeit detection technology using machine learning technologies such as Artificial Neural Networks (ANN), Support-vector machines (SVM), K Nearest Neighbors, Random Forests, and Decision trees. The proposed model relies on hyper-spectrum images and overcomes generalization to unknown honey types of problems. The dataset used includes segmented and pre-processed hyperspectral images of adulterated honey samples from seven different brands with 12 different botanical origin labels. Feature reduction techniques, such as feature ranking-based feature selection, and autoencoder techniques are employed to classify the botanical origins of honey. The model parameters are enhanced or tuned by the training process, and hyperparameters are adjusted by running the whole training data. The researchers used Python, and well-known algorithms like ANN, SVM, KNN, random forests, and decision trees. The results show that machine learning and hyperspectral imaging can accurately identify if honey has been adulterated, with over 98% classification accuracy.

Reductions in spice lead levels in the republic of Georgia: 2020-2022

Spice adulteration using yellow lead chromate-based pigments has been documented as a growing global health concern. Spices from the Republic of Georgia with extremely high levels of lead, up to an order of magnitude higher than any other spices worldwide, have been implicated as sources of child lead poisoning. The objectives of this study were to 1) evaluate lead concentrations in spices sampled across the country of Georgia between 2020 and 2022, and 2) assess factors associated with spice adulteration, specifically the role of spice quality and regulatory enforcement. Spice samples were collected from 29 cities nationwide. The most populous cities were selected in each administrative region as well as those of importance to the spice supply chain. Sampling was carried out at the largest spice bazaars in each city. The regions of Adjara and Imereti were the focus of qualitative interviews conducted in 2021 with key businesspeople selling spices with very high and low levels of lead. The same cities and bazaars were visited at each of three sampling periods between 2020 and 2022. In total, 765 spice samples were collected. Lead concentrations in spices decreased over time, with a maximum of 14,233 μg/g in 2020 down to 36 μg/g in the final sampling round of 2022. A logistic regression determined that sampling round, region and spice type were associated with elevated lead in samples. Samples from Adjara and those containing marigold had the highest lead levels. Interviews with eighteen prominent spice vendors revealed difficulties sourcing sufficient quantities of high quality, brightly colored marigold, and concerns about adulteration. Interviews with two authorities from the National Food Authority highlighted the increased attention on regulating lead in spices since 2018. Continued monitoring and periodic regulatory enforcement may adequately disincentivize further adulteration with lead chromate in the spice industry in Georgia.

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few "known" types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a 'dilute-and-shoot' approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a "dilute and shoot" method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.

An innovative molecular approach towards the cost-effective entomological authentication of honey

Honey authentication and traceability are crucial not only for economic purposes but also for ensuring safety. However, the widespread adoption of cutting-edge technologies in practical applications has been hampered by complex, time-consuming sample pre-treatment processes, the need for skilled personnel, and substantial associated expenses. This study aimed to develop a simple and cost-effective molecular technique to verify the entomological source of honey. By utilizing newly designed primers, we successfully amplified the mitochondrial 16S ribosomal RNA gene of honey bees from honey, confirming the high quality of the extracted DNA. Employing RFLP analysis with AseI endonuclease, species-specific restriction patterns were generated for honey derived from six closely related honey bees of the Apis genus. Remarkably, this method was proven equally effective in identifying heat-treated and aged honey by presenting the same RFLP profiles as raw honey. As far as we know, this is the initial research of the simultaneous differentiation of honey from closely related honey bee species using the restriction endonuclease AseI and mitochondrial 16S rRNA gene fragments. As a result, it holds tremendous potential as a standardized guideline for regulatory agencies to ascertain the insect origins of honey and achieve comprehensive traceability.

Personal Strategies to Reduce the Cardiovascular Impacts of Environmental Exposures

Ubiquitous environmental exposures increase cardiovascular disease risk via diverse mechanisms. This review examines personal strategies to minimize this risk. With regard to fine particulate air pollution exposure, evidence exists to recommend the use of portable air cleaners and avoidance of outdoor activity during periods of poor air quality. Other evidence may support physical activity, dietary modification, omega-3 fatty acid supplementation, and indoor and in-vehicle air conditioning as viable strategies to minimize adverse health effects. There is currently insufficient data to recommend specific personal approaches to reduce the adverse cardiovascular effects of noise pollution. Public health advisories for periods of extreme heat or cold should be observed, with limited evidence supporting a warm ambient home temperature and physical activity as strategies to limit the cardiovascular harms of temperature extremes. Perfluoroalkyl and polyfluoroalkyl substance exposure can be reduced by avoiding contact with perfluoroalkyl and polyfluoroalkyl substance-containing materials; blood or plasma donation and cholestyramine may reduce total body stores of perfluoroalkyl and polyfluoroalkyl substances. However, the cardiovascular impact of these interventions has not been examined. Limited utilization of pesticides and safe handling during use should be encouraged. Finally, vasculotoxic metal exposure can be decreased by using portable air cleaners, home water filtration, and awareness of potential contaminants in ground spices. Chelation therapy reduces physiological stores of vasculotoxic metals and may be effective for the secondary prevention of cardiovascular disease.

Database of Food Fraud Records: Summary of Data from 1980 to 2022

Food fraud prevention and detection remains a challenging problem, despite recent developments in regulatory and auditing requirements. In 2012, the United States Pharmacopeial Convention created a database of food ingredient fraud. The objective of this research was to report on updates made to the database structure and to provide an updated analysis of food fraud records. The restructured database was relational and included four tables: ingredients, adulterants, adulteration records, and references. Four adulteration record types were created to capture the variety of information that can be found in public food fraud reports. Information was searched and extracted from the peer-reviewed scientific literature, media publications, regulatory reports, judicial records, trade association reports, and other public sources covering 1980-present. Over an almost seven-year data entry period, a total of 15,575 records were entered, sourced primarily from the peer-reviewed literature and media reports. The percentage of records that included at least one potentially hazardous adulterant ranged from 34% to 60%, depending on the record type. The ingredients with the highest number of incident and inference records included fluid cow's milk, extra virgin olive oil, honey, beef, and chili powder. The ingredient groups with the highest number of incident and inference records included Dairy Ingredients, Seafood Products, Meat and Poultry Products, Herbs, Spices, and Seasonings, Milk and Cream, and Alcoholic Beverages. This database was created to serve as a standardized source of information about publicly documented occurrences of food fraud and other information relevant to fraud risk to support food fraud vulnerability assessments, mitigation plans, and food safety plans. These data support the contention that food fraud presents a public health risk that should continue to be addressed by food safety systems worldwide.

An analysis of emerging food safety and fraud risks of novel insect proteins within complex supply chains

Food consumption play a crucial role in human life, yet conventional food production and consumption patterns can be detrimental to the environment. Thus, research and development has been directed towards alternative proteins, with edible insects being promising sources. Edible insects have been recognised for their sustainable benefits providing protein, with less emission of greenhouse gas, land and water usage compared to sources, such as beef, chicken, and dairy products. Among the over 2000 known edible insect species, only four, namely yellow mealworm (Tenebrio molitor), migratory locust/grasshopper (Locusta migratoria), grain mould beetle, also known as lesser mealworm which is a larval form of Alphitobius diaperinus (from the family of Tenebrionidae of darkling beetles) and house cricket (Acheta domesticus), are currently authorised in specific products through specific producers in the EU. The expansion of such foods into Western diets face challenges such as consumer barriers, gaps in microbiological and chemical safety hazard data during production and processing, and the potential for fraudulent supply chain activity. The main aim of this study was to map the supply chain, through interviews with personnel along the supply chain, coupled with searches for relevant publications and governmental documents. Thus, the main potential points of food safety and fraud along the edible insect supply chain were identified. Feed substrate was identified as the main area of concern regarding microbiological and chemical food safety and novel processing techniques were forecast to be of most concern for future fraudulent activity. Despite the on-going authorisation of insect species in many countries there are substantial food safety and authenticity information gaps in this industry that need to be addressed before edible insects can be viewed as a safe and sustainable protein sources by Western consumers.

Authentication of pure and adulterated edible oils using non-destructive ultrasound

At present, the quality of edible oil is evaluated using traditional analysis techniques that are generally destructive. Therefore, efforts are being made to find alternative methods with non-destructive techniques such as Ultrasound. This work aims to confirm the feasibility of non-destructive ultrasonic inspection to characterise and detect fraudulent practices in olive oil due to adulteration with two other edible vegetable oils (sunflower and corn). For this purpose, pulsed ultrasonic signals with a frequency of 2.25 MHz have been used. The samples of pure olive oil were adulterated with the other two in variable percentages between 20% and 80%. Moreover, the viscosity and density values were measured. Both these physicochemical and acoustic parameters were obtained at 24 °C and 30 °C and linearly correlated with each other. The results indicate the sensitivity of the method at all levels of adulteration studied. The responses obtained through the parameters related to the components of velocity, attenuation, and frequency of the ultrasonic waves are complementary to each other. This allows concluding that the classification of pure and adulterated oil samples is possible through non-destructive ultrasonic inspection.

A comparative study regarding the adulteration detection of honey: Physicochemical parameters vs. impedimetric data

Honey adulteration is a major issue for European Union and its members because of an unfair practice of different producers and beekeepers, many adulterations involve the addition of sweet, concentrated syrups which may appear like honey. In our study we analysed the influence of adulteration of tilia honey with different syrups (such as corn, rice, inverted sugar, agave, maple syrups) in different percentages (5%, 10%, and 20% respectively) on physicochemical parameters (moisture content, L*, hab,cab, pH, free acidity, electrical conductivity (EC), 5-hydroxymetilfurfural (HMF), fructose, glucose, sucrose, turanose, trehalose, melesitose and raffinose) and impedimetric properties using electrochemical impedance spectroscopy. The impedimetric sensing was made using an electrochemical cell composed of two gold electrodes, and the frequency ranged between 0.1 kHz and 100 kHz. The impedimetric parameters (Z', Z″ and phase) and Randal circuit parameters can distinguish the authentic honeys from the adulterated ones (based on the adulteration agent and adulteration percentage, respectively). The partial least squares - discriminant analysis (PLS-DA) and support vector machines (SVM) were used in a binary mode to separate the authentic honeys from the adulterated ones, and the SVM proved to separate much better than PLS-DA.

Application of Microsatellites to Trace the Dairy Products Back to the Farm of Origin

The increasing number of food frauds, mainly targeting high quality products, is a rising concern among producers and authorities appointed to food controls. Therefore, the development or implementation of methods to reveal frauds is desired. The genetic traceability of traditional or high-quality dairy products (i.e., products of protected designation of origin, PDO) represents a challenging issue due to the technical problems that arise. The aim of the study was to set up a genetic tool for the origin traceability of dairy products. We investigated the use of Short Tandem Repeats (STRs) to assign milk and cheese to the corresponding producer. Two farms were included in the study, and the blood of the cows, bulk milk, and derived cheese were sampled monthly for one year. Twenty STRs were selected and Polymerase Chain Reactions for each locus were carried out. The results showed that bulk milk and derived cheese express an STR profile composed of a subset of STRs of the lactating animals. A bioinformatics tool was used for the exclusion analysis. The study allowed the identification of a panel of 20 markers useful for the traceability of milk and cheeses, and its effectiveness in the traceability of dairy products obtained from small producers was demonstrated.

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Comparing the potential of IR-spectroscopic techniques to gas chromatography coupled to ion mobility spectrometry for classifying virgin olive oil categories

Virgin olive oil (OO) can be classified into three different categories: extra virgin, virgin and lampante. The official method for this classification, based on physicochemical analysis and sensory tasting, is considered useful and effective, although it is a costly and time-consuming process. The aim of this study was to assess the potential of some analytical techniques for classifying and predicting different OO categories to support official methods and to provide olive oil companies with a rapid tool to assess product quality. Thus, mid and near infrared spectroscopies (MIR and NIR) have been compared by using different instruments and with head-space gas chromatography coupled to an ion mobility spectrometer (HS-GC-IMS). High classification success rates in validation models were obtained using IR spectrometers (>70% and > 80% in average for ternary and binary classifications, respectively), although HS-GC-IMS showed greater classification potential (>85% and > 90%).

Incidents and Potential Adverse Health Effects of Serious Food Fraud Cases Originated in Asia

Food fraud has long been regarded as a major issue within the food industry and is associated with serious economic and public health concerns. Economically motivated adulteration, the most common form of food fraud, has consequences for human health, ranging from mild to life-threatening conditions. Despite the potential harm and public health threats posed by food fraud, limited information on incidents causing illness has been reported. Enhancing the food control system on the Asian continent has become crucial for global health and trade considerations. Food fraud databases serve as valuable tools, assisting both the food industry and regulatory bodies in mitigating the vulnerabilities associated with fraudulent practices. However, the availability of accessible food fraud databases for Asian countries has been restricted. This review highlights detrimental food fraud cases originating in Asian countries, including sibutramine in dietary supplements, plasticizer contamination, gutter oil, and the adulteration of milk. This comprehensive analysis encompasses various facets, such as incident occurrences, adverse health effects, regulatory frameworks, and mitigation strategies.

Laser Sensing and Chemometric Analysis for Rapid Detection of Oregano Fraud

World health is increasingly threatened by the growing number of spice-related food hazards. Further development of reliable methods for rapid, non-targeted identification of counterfeit ingredients within the supply chain is needed. ENEA has developed a portable, user-friendly photoacoustic laser system for food fraud detection, based on a quantum cascade laser and multivariate calibration. Following a study on the authenticity of saffron, the instrument was challenged with a more elusive adulterant, olive leaves in oregano. The results show that the reported method of laser sensing and chemometric analysis was able to detect adulterants at mass ratios of at least 20% in less than five minutes.

Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil

Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25-100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography-mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82-91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines (SVMs) were also used for statistical modeling. The sensitivity and specificity obtained for SVM were 0.987 and 0.977, respectively, while these values for the ANN method were 0.949 and 0.953, respectively. E-nose-based technology is a quick and effective method for the detection of sesame oil adulteration due to its simplicity (ease of application), rapid analysis, and accuracy. GC-MS data provided corroborative chemical evidence to show differences in volatile emissions from virgin and adulterated sesame oil samples and the precise VOCs explaining differences in e-nose signature patterns derived from each sample type.

Food safety policy enforcement and associated actions reduce turmeric lead chromate adulteration across Bangladesh

Turmeric adulterated with lead chromate pigment has been previously identified as a primary source of lead exposure in Bangladesh. This study assesses the impact of a multi-faceted intervention between 2017 and 2021 to reduce lead-tainted turmeric in Bangladesh. The intervention involved: i) disseminating findings from scientific studies via news media that identified turmeric as a source of lead poisoning, ii) educating consumers and businesspeople about the risks of lead chromate in turmeric via public notices and face-to-face meetings, and iii) collaborating with the Bangladesh Food Safety Authority to utilize a rapid lead detection technology to enforce policy disallowing turmeric adulteration. Before and after the intervention, evidence of lead chromate turmeric adulteration was assessed at the nation's largest turmeric wholesale market and at turmeric polishing mills across the country. Blood lead levels of workers at two mills were also assessed. Forty-seven interviews were conducted with consumers, businesspeople, and government officials to assess changes in supply, demand, and regulatory capacity. The proportion of market turmeric samples containing detectable lead decreased from 47% pre-intervention in 2019 to 0% in 2021 (n = 631, p < 0.0001). The proportion of mills with direct evidence of lead chromate adulteration (pigment on-site) decreased from 30% pre-intervention in 2017 to 0% in 2021 (n = 33, p < 0.0001). Blood lead levels dropped a median of 30% (IQR: 21-43%), while the 90th percentile dropped 49% from 18.2 μg/dL to 9.2 μg/dL 16 months after the intervention (n = 15, p = 0.033). Media attention, credible information, rapid lead detection tools and swift government action to enforce penalties all contributed to the intervention's success. Subsequent efforts should evaluate if this is an example of an effective intervention that can be replicated to reduce lead chromate adulteration of spices globally.

Feasibility study of detecting some milk adulterations using a LED-based Vis-SWNIR photoacoustic spectroscopy system

The aim of this study is to evaluate a previousely developed photoacoustic spectroscopy system with light sources of visible to short-wave near infrared (Vis-SWNIR, 395-940 nm) for detection of adulterations in cow's milk including formalin, urea, hydrogen peroxide, starch, sodium hypochlorite, and detergent powder. The results of principal component analysis (PCA) showed a very good visual differentiation of different adulterations. The artificial neural networks (ANN) showed the highest classification accuracy (97.6 %) in detection of adulteration type and adulteration level (nearly 100 %). It can be generally concluded that the Vis-SWNIR photoacoustic spectroscopy system is a reliable and potent instrument for detecting various types of milk adulterations. Further studies are suggested with including cow's milk of different sources with probable variations in composition to generalize the findings of the present study. With the extension of the light sources to the range of long-wave NIR, the system can be applied as a diagnostic tool for quality evaluation of other liquid foods.

On the Traceability of Honey by Means of Lanthanide Distribution

Honey is a natural food appreciated all over the world since antiquity due to its well-recognised beneficial properties. However, it is also considered among the most counterfeited foods. Therefore, analytical methods are currently being developed to allow the verifying of its geographic provenance and its botanical origin. Trace- and ultra-trace elements are usually exploited as chemical descriptors in authentication studies, as they allow the properties declared in the label to be verified. A different matter is to trace a food by means of traceability, that is, to find the link between a food and the soil in which this food originates. For traceability, it has been demonstrated in several studies that the lanthanides are particularly useful to find this link. In the present study, the traceability of the honey chain has been studied by means of ICP-MS and ICP-OES analysis, by comparing the lanthanide distributions of 17 different monofloral honey chains, each one composed of honey, flowers and soil in which such flowers grew. The results show that, while the fingerprint of soil, described by the lanthanide distribution, is transmitted unaltered from soil to flowers, a slight fractionation on the heavier lanthanides (from Dy to Lu) occurs in the passage from flowers to honey.

Establishment and evaluation of multiple adulteration detection of camellia oil by mixture design

Multiple adulteration is a common trick to mask adulteration detection methods. In this study, the representative multiple adulterated camellia oils were prepared according to the mixture design. Then, these representative oils were employed to build two-class classification models and validate one-class classification model combined with fatty acid profiles. The cross-validation results indicated that the recursive SVM model possessed higher classification accuracy (97.9%) than PLS-DA. In OCPLS model, the optimal percentage of RO, SO, CO and SUO was 2.8%, 0%, 7.2%, 0% respectively in adulterated camellia oil, which is the most similar to the authentic camellia oils. Further validation showed that five adulterated oils with the optimal percentage could be correctly identified, indicating that the OCPLS model could identify multiple adulterated oils with these four cheaper oils. Moreover, this study serves as a reference for one class classification model evaluation and a solution for multiple adulteration detection of other foods.

Review on Sensor Array-Based Analytical Technologies for Quality Control of Food and Beverages

Food quality control is an important area to address, as it directly impacts the health of the whole population. To evaluate the food authenticity and quality, the organoleptic feature of the food aroma is very important, such that the composition of volatile organic compounds (VOC) is unique in each aroma, providing a basis to predict the food quality. Different types of analytical approaches have been used to assess the VOC biomarkers and other parameters in the food. The conventional approaches are based on targeted analyses using chromatography and spectroscopies coupled with chemometrics, which are highly sensitive, selective, and accurate to predict food authenticity, ageing, and geographical origin. However, these methods require passive sampling, are expensive, time-consuming, and lack real-time measurements. Alternately, gas sensor-based devices, such as the electronic nose (e-nose), bring a potential solution for the existing limitations of conventional methods, offering a real-time and cheaper point-of-care analysis of food quality assessment. Currently, research advancement in this field involves mainly metal oxide semiconductor-based chemiresistive gas sensors, which are highly sensitive, partially selective, have a short response time, and utilize diverse pattern recognition methods for the classification and identification of biomarkers. Further research interests are emerging in the use of organic nanomaterials in e-noses, which are cheaper and operable at room temperature.

Meat food fraud risk in Chinese markets 2012-2021

Food fraud is a major concern worldwide, and the majority of cases include meat adulteration or fraud. Many incidences of food fraud have been identified for meat products both in China and abroad over the last decade. We created a meat food fraud risk database compiled from 1987 pieces of information recorded by official circular information and media reports in China from 2012 to 2021. The data covered livestock, poultry, by-products, and various processed meat products. We conducted a summary analysis of meat food fraud incidents by researching fraud types, regional distribution, adulterants and categories involved, categories and sub-categories of foods, risk links and locations, etc. The findings can be used not only to analyze meat food safety situations and study the burden of food fraud but also help to promote the efficiency of detection and rapid screening, along with improving prevention and regulation of adulteration in the meat supply chain markets.

Dual-Functional Solar-to-Steam Generation and SERS Detection Substrate Based on Plasmonic Nanostructure

Solar-to-steam (STS) generation based on plasmonic materials has attracted significant attention as a green method for producing fresh water. Herein, a simple in situ method is introduced to fabricate Au nanoparticles (AuNPs) on cellulose filter papers as dual-functional substrates for STS generation and surface-enhanced Raman spectroscopy (SERS) sensing. The substrates exhibit 90% of broadband solar absorption between 350 and 1800 nm and achieve an evaporation rate of 0.96 kg·m^-2·h^-1 under 1-sun illumination, room temperature of 20 °C, and relative humidity of 40%. The STS generation of the substrate is stable during 30 h continuous operation. Enriched SERS hotspots between AuNPs endow the substrates with the ability to detect chemical contamination in water with ppb limits of detection for rhodamine 6G dye and melamine. To demonstrate dual-functional properties, the contaminated water was analyzed with SERS and purified by STS. The purified water was then analyzed with SERS to confirm its purity. The developed substrate can be an improved and suitable candidate for fresh water production and qualification.

Prediction of Mineral Composition in Wheat Flours Fortified with Lentil Flour Using NIR Technology

Lentil flour is an important source of minerals, including iron, so its use in food fortification programs is becoming increasingly important. In this study, the potential of near infrared technology to discriminate the presence of lentil flour in fortified wheat flours and the quantification of their mineral composition is evaluated. Three varieties of lentils (Castellana, Pardina and Guareña) were used to produce flours, and a total of 153 samples of wheat flours fortified with them have been analyzed. The results show that it is possible to discriminate fortified flours with 100% efficiency according to their lentil flour content and to discriminate them according to the variety of lentil flour used. Regarding their mineral composition, the models developed have shown that it is possible to predict the Ca, Mg, Fe, K and P content in fortified flours using near infrared spectroscopy. Moreover, these models can be applied to unknown samples with results comparable to ICP-MS determination of these minerals.

Hyperspectral Imaging and Chemometrics for Authentication of Extra Virgin Olive Oil: A Comparative Approach with FTIR, UV-VIS, Raman, and GC-MS

Limited information on monitoring adulteration in extra virgin olive oil (EVOO) by hyperspectral imaging (HSI) exists. This work presents a comparative study of chemometrics for the authentication and quantification of adulteration in EVOO with cheaper edible oils using GC-MS, HSI, FTIR, Raman and UV-Vis spectroscopies. The adulteration mixtures were prepared by separately blending safflower oil, corn oil, soybean oil, canola oil, sunflower oil, and sesame oil with authentic EVOO in different concentrations (0-20%, m/m). Partial least squares-discriminant analysis (PLS-DA) and PLS regression models were then built for the classification and quantification of adulteration in olive oil, respectively. HSI, FTIR, UV-Vis, Raman, and GC-MS combined with PLS-DA achieved correct classification accuracies of 100%, 99.8%, 99.6%, 96.6%, and 93.7%, respectively, in the discrimination of authentic and adulterated olive oil. The overall PLS regression model using HSI data was the best in predicting the concentration of adulterants in olive oil with a low root mean square error of prediction (RMSEP) of 1.1%, high R²_pred (0.97), and high residual predictive deviation (RPD) of 6.0. The findings suggest the potential of HSI technology as a fast and non-destructive technique to control fraud in the olive oil industry.

The Analytical Possibilities of FT-IR Spectroscopy Powered by Vibrating Molecules

This paper discusses the state of advancement in the development of spectroscopic methods based on the use of mid (proper) infrared radiation in the context of applications in various fields of science and technology. The authors drew attention to the most important solutions specific to both spectroscopy itself (ATR technique) and chemometric data processing tools (PCA and PLS models). The objective of the current paper is to collect and consistently present information on various aspects of FT-IR spectroscopy, which is not only a well-known and well-established method but is also continuously developing. The innovative aspect of the current review is to show FT-IR's great versatility that allows its applications to solve and explain issues from both the scientific domain (e.g., hydrogen bonds) and practical ones (e.g., technological processes, medicine, environmental protection, and food analysis). Particular attention was paid to the issue of hydrogen bonds as key non-covalent interactions, conditioning the existence of living matter and determining the number of physicochemical properties of various materials. Since the role of FT-IR spectroscopy in the field of hydrogen bond research has great significance, a historical outline of the most important qualitative and quantitative hydrogen bond theories is provided. In addition, research on selected unconventional spectral effects resulting from the substitution of protons with deuterons in hydrogen bridges is presented. The state-of-the-art and originality of the current review are that it presents a combination of uses of FT-IR spectroscopy to explain the way molecules vibrate and the effects of those vibrations on macroscopic properties, hence practical applications of given substances.

Fourier transform infrared spectroscopy and chemometrics for the rapid screening of economically motivated adulteration of honey spiked with corn or rice syrup

Due to its high price, increased consumption, and limited production, honey has been a main target for economically motivated adulteration (EMA). An approach combining Fourier-Transform infrared spectroscopy (FTIR) and chemometrics was evaluated to develop a rapid screening tool to detect potential EMA of honey with either rice or corn syrup. A single-class soft independent modeling of class analogy (SIMCA) model was developed using a diverse set of commercial honey products and an authentic set of honey samples collected at four different U.S. Department of Agriculture (USDA) honey sample collection locations. The SIMCA model was externally validated with a set of calibration-independent authentic honey, typical commercial honey control samples, and those spiked with rice and corn syrups in the 1-16% concentration range. The authentic honey and typical commercial honey test samples were correctly predicted with an 88.3% classification rate. High accuracy was found in predicting the rice and corn syrup spiked samples above the 7% concentration range, yielding 97.6% and 94.8% correct classification rates, respectively. This study demonstrated the potential for a rapid and accurate infrared and chemometrics method that can be used to rapidly screen for either rice or corn adulterants in honey in less than 5 min.

How Chemometrics Can Fight Milk Adulteration

Adulteration and fraud are amongst the wrong practices followed nowadays due to the attitude of some people to gain more money or their tendency to mislead consumers. Obviously, the industry follows stringent controls and methodologies in order to protect consumers as well as the origin of the food products, and investment in these technologies is highly critical. In this context, chemometric techniques proved to be very efficient in detecting and even quantifying the number of substances used as adulterants. The extraction of relevant information from different kinds of data is a crucial feature to achieve this aim. However, these techniques are not always used properly. In fact, training is important along with investment in these technologies in order to cope effectively and not only reduce fraud but also advertise the geographical origin of the various food and drink products. The aim of this paper is to present an overview of the different chemometric techniques (from clustering to classification and regression applied to several analytical data) along with spectroscopy, chromatography, electrochemical sensors, and other on-site detection devices in the battle against milk adulteration. Moreover, the steps which should be followed to develop a chemometric model to face adulteration issues are carefully presented with the required critical discussion.

A Novel FTIR-Based Chemometric Solution for the Assessment of Saffron Adulteration with Non-Fresh Stigmas

The development of fast, non-destructive, and green methods with adequate sensitivity for saffron authentication has important implications in the quality control of the entire production chain of this precious spice. In this context, the highly suitable sensitivity of a spectroscopic method coupled with chemometrics was verified. A total number of 334 samples were analyzed using attenuated-total-reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy; the collected spectra were processed by partial-least-squares discriminant analysis (PLS-DA) to evaluate the feasibility of this study for the discrimination between compliant saffron (fresh samples produced in 2020) and saffron samples adulterated with non-fresh stigmas produced in 2018 and 2016. PLS-DA was able to classify the saffron samples in accordance with the aging time and to discriminate fresh samples from the samples adulterated with non-fresh (legally expired) stigmas, achieving 100% of both sensitivity and specificity in external prediction. Moreover, PLS regression was able to predict the adulteration level with sufficient accuracy (the root-mean-square error of prediction was approximately 3-5%). In summary, ATR-FTIR and chemometrics can be employed to highlight the illegal blending of fresh saffron with unsold stocks of expired saffron, which may be a common fraudulent practice not yet considered in the scientific literature.

Non-targeted detection of food adulteration using an ensemble machine-learning model

Recurrent incidents of economically motivated adulteration have long-lasting and devastating effects on public health, economy, and society. With the current food authentication methods being target-oriented, the lack of an effective methodology to detect unencountered adulterants can lead to the next melamine-like outbreak. In this study, an ensemble machine-learning model that can help detect unprecedented adulteration without looking for specific substances, that is, in a non-targeted approach, is proposed. Using raw milk as an example, the proposed model achieved an accuracy and F1 score of 0.9924 and 0. 0.9913, respectively, when the same type of adulterants was presented in the training data. Cross-validation with spiked contaminants not routinely tested in the food industry and blinded from the training data provided an F1 score of 0.8657. This is the first study that demonstrates the feasibility of non-targeted detection with no a priori knowledge of the presence of certain adulterants using data from standard industrial testing as input. By uncovering discriminative profiling patterns, the ensemble machine-learning model can monitor and flag suspicious samples; this technique can potentially be extended to other food commodities and thus become an important contributor to public food safety.

Nitrogen factors for rainbow trout (Oncorhynchus mykiss) and brook trout (Salvelinus fontinalis) fillets

Measures for consumer protection against food adulteration and misleading labelling are integrated into EU legislation, including methods for detecting misleading practices. Verification of the meat content is available for marine products, but not for salmonid fish due to the lack of standard nitrogen factors. This study aimed to establish nitrogen factors for rainbow trout (Oncorhynchus mykiss) and brook trout (Salvelinus fontinalis). The study analysed 340 fish from Czech fisheries obtained in the summer of 2018-2020. According to the established ISO methods, fillet samples with and without skin were analysed for their nitrogen content (protein), dry matter, ash, and fat. The recommended nitrogen factor for rainbow trout fillets with and without the skin is 3.07 ± 0.12 and 3.06 ± 0.14, respectively, and the nitrogen factor for fat-free rainbow trout fillets with and without the skin is 3.33 ± 0.15 and 3.29 ± 0.15, respectively. The recommended nitrogen factor for brook trout fillets with and without the skin is 3.16 ± 0.10 and 3.12 ± 0.09, respectively, and the nitrogen factor for fat-free brook trout fillets with and without the skin is 3.42 ± 0.13 and 3.36 ± 0.12, respectively. The established nitrogen factors will enable the analysis of the meat content to ensure that consumers purchase correctly described and labelled fish products.

Research on the Authenticity of Mutton Based on Machine Vision Technology

To realize the real-time automatic identification of adulterated minced mutton, a convolutional neural network (CNN) image recognition model of adulterated minced mutton was constructed. Images of mutton, duck, pork and chicken meat pieces, as well as prepared mutton adulterated with different proportions of duck, pork and chicken meat samples, were acquired by the laboratory's self-built image acquisition system. Among all images were 960 images of different animal species and 1200 images of minced mutton adulterated with duck, pork and chicken. Additionally, 300 images of pure mutton and mutton adulterated with duck, pork and chicken were reacquired again for external validation. This study compared and analyzed the modeling effectiveness of six CNN models, AlexNet, GoogLeNet, ResNet-18, DarkNet-19, SqueezeNet and VGG-16, for different livestock and poultry meat pieces and adulterated mutton shape feature recognition. The results show that ResNet-18, GoogLeNet and DarkNet-19 models have the best learning effect and can identify different livestock and poultry meat pieces and adulterated minced mutton images more accurately, and the training accuracy of all three models reached more than 94%, among which the external validation accuracy of the optimal three models for adulterated minced mutton images reached more than 70%. Image learning based on a deep convolutional neural network (DCNN) model can identify different livestock meat pieces and adulterated mutton, providing technical support for the rapid and nondestructive identification of mutton authenticity.

Differentiation of Monofloral Honey Using Volatile Organic Compounds by HS-GCxIMS

Honey is a natural product and can be described by its botanical origin, determined by the plants from which the bees collect nectar. It significantly influences the taste of honey and is often used as a quality criterion. Unfortunately, this opens up the possibility of food fraud. Currently, various methods are used to check the authenticity of monofloral honey. The laborious, manual melissopalynology is considered an essential tool in the verification process. In this work, the volatile organic compounds obtained from the headspace of honey are used to prove their authenticity. The headspace of 58 honey samples was analyzed using a commercial easy-to-use gas chromatography-coupled ion mobility spectrometer with a headspace sampler (HS-GCxIMS). The honey samples were successfully differentiated by their six different botanical origins using specific markers with principal component analysis in combination with linear discriminant analysis. In addition, 15 honey-typical compounds were identified using measurements of reference compounds. Taking a previously published strategy, retention times of marker compounds were correlated with GC-coupled mass spectrometry (GC-MS) measurements to assist in the identification process.

Isolation and Barcoding of Trace Pollen-free DNA for Authentication of Honey

Adulteration and mislabeling of honey to mask its true origin have become a global concern. Pollen microscopy, the current gold standard for identifying honey's geographical and plant origins, is laborious, requires extensive training, and fails to identify filtered honey and honey spiked with pollen from a more favorable plant to disguise its origins. We successfully isolated pollen-free DNA from filtered honey using three types of adsorbents: (i) anti-dsDNA antibodies coupled to magnetic microspheres; (ii) anion-exchange adsorbent; and (iii) ceramic hydroxyapatite. The internal transcribed spacer 2 region of the captured pollen-free DNA was polymerase chain reaction-amplified and subjected to next-generation sequencing. Using an in-house bioinformatics pipeline, initial experiments showed that anion exchange had the greatest capacity to capture trace pollen-free DNA, and it was successfully applied to isolate DNA from five honey samples. Enrichment of trace pollen-free DNA from filtered honey samples opens a new approach for identifying the true origins of honey.

Authenticity and Typicity of Traditional Cheeses: A Review on Geographical Origin Authentication Methods

Food fraud, corresponding to any intentional action to deceive purchasers and gain an undue economical advantage, is estimated to result in a 10 to 65 billion US dollars/year economical cost worldwide. Dairy products, such as cheese, in particular cheeses with protected land- and tradition-related labels, have been listed as among the most impacted as consumers are ready to pay a premium price for traditional and typical products. In this context, efficient food authentication methods are needed to counteract current and emerging frauds. This review reports the available authentication methods, either chemical, physical, or DNA-based methods, currently used for origin authentication, highlighting their principle, reported application to cheese geographical origin authentication, performance, and respective advantages and limits. Isotope and elemental fingerprinting showed consistent accuracy in origin authentication. Other chemical and physical methods, such as near-infrared spectroscopy and nuclear magnetic resonance, require more studies and larger sampling to assess their discriminative power. Emerging DNA-based methods, such as metabarcoding, showed good potential for origin authentication. However, metagenomics, providing a more in-depth view of the cheese microbiota (up to the strain level), but also the combination of methods relying on different targets, can be of interest for this field.

LC-HRMS-Based Non-Targeted Metabolomics for the Assessment of Honey Adulteration with Sugar Syrups: A Preliminary Study

Honey is a natural product that is in great demand and has a relatively high price, thus making it one of the most common targets of economically motivated adulteration. Its adulteration can be obtained by adding cheaper honey or sugar syrups or by overfeeding honeybees with sugar syrups. Adulteration techniques are constantly evolving and advanced techniques and instruments are required for its detection. We used non-targeted metabolomics to underscore potential markers of honey adulteration with sugar syrups. The metabolomic profiles of unadulterated honeys and sugar beet, corn and wheat syrups were obtained using hydrophilic interaction liquid chromatography high-resolution mass spectrometry (LC-HRMS). The potential markers have been selected after data processing. Fortified honey (5%, 10% and 20%), honey obtained from overfeeding, and 58 commercial honeys were analyzed. One potential marker appeared with a specific signal for syrups and not for honey. This targeted analysis showed a linear trend in fortified honeys with a calculated limit of quantification around 5% of fortification.

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.

Amino Acid Fingerprinting of Authentic Nonfat Dry Milk and Skim Milk Powder and Effects of Spiking with Selected Potential Adulterants

A collaborative study was undertaken in which five international laboratories participated to determine amino acid fingerprints in 39 authentic nonfat dry milk (NFDM)/skim milk powder (SMP) samples. A rapid method of amino acid analysis involving microwave-assisted hydrolysis followed by ultra-high performance liquid chromatography-ultraviolet detection (UHPLC-UV) was used for quantitation of amino acids and to calculate their distribution. The performance of this rapid method of analysis was evaluated and was used to determine the amino acid fingerprint of authentic milk powders. The distribution of different amino acids and their predictable upper and lower tolerance limits in authentic NFDM/SMP samples were established as a reference. Amino acid fingerprints of NFDM/SMP were compared with selected proteins and nitrogen rich compounds (proteins from pea, soy, rice, wheat, whey, and fish gelatin) which can be potential economically motivated adulterants (EMA). The amino acid fingerprints of NFDM/SMP were found to be affected by spiking with pea, soy, rice, whey, fish gelatin and arginine among the investigated adulterants but not by wheat protein and melamine. The study results establish an amino acid fingerprint of authentic NFDM/SMP and demonstrate the utility of this method as a tool in verifying the authenticity of milk powders and detecting their adulteration.

Exploring smart phone based colorimetric technology for on-site quantitative determination of adulterant (neutralizer) in milk

There is a great concern regarding the safety of milk not only for human health but also for its economic consequences. The portable sensing devices, which can collect and analyze data within food supply chains at critical control points are lacking. Smart phones have now emerged as an integral part of each home, lab, farm and factory. It is having a provision of digital camera and computation; with widespread applicability including food analysis. The use of soda as a milk neutralizer is a usual practice but has a detrimental human health impact. This investigation explored an easy, economic, fast, repeatable, and field applicable Smartphone-based sensing technology, which was standardized and in-house validated for the quantitative determination of neutralizer in milk samples. The method had simple steps of spot-test response and digital image evaluation with the Red Green Blue process. The linearity of the method was shown by analytical curves ranging from 0.125% (1250 ppm) to 1% (10,000 ppm) that were characterized by R2 > 0.99. The limit of detection of 0.11% demonstrated the sensitivity of the method which was found better than the existing wet chemical spot test. Comparison with the existing spectroscopic method revealed no statistically significant difference between the observations using paired t-test at a confidence level of 95%.

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