Detection Of Food Adulteration Research Articles

Application of high-performance thin layer chromatography mass spectrometry (HPTLC MS) in foodomics authenticity

BackgroundChemometric analysis is also a powerful tool that can be used in the preliminary stages of analytical methods optimization, but is also efficient in data processing. These new approaches may be the key to the analysis of proteins, carbohydrate, sphignolipids, polyphenols in different food components. Although there are challenges in identifying and annotating those compounds due to the limited availability of analytical standards and structural diversity. Scope and approachChromatograms are a useful tool for learning more about the chemical make-up of the food being tested. This information can occasionally be implicit or presented in a subtle way. Therefore, to extract it, chemometric tools and data mining techniques are needed. A chromatogram's fingerprint allows for the possibility of performing both identity and quality testing on food. This viewpoint aims to offer a current assessment of chromatographic fingerprinting technique in the area of food authentication. Additionally, this methodology's limitations, absence from official analytical methods, and future directions are discussed. Novel techniques have been employed in the past few decades, ranging from high-pressure thin liquid chromatography (HPTLC) to mass spectrometry (MS) and other spectroscopic methods. In the last decade research developments, and the application of analytical methods in qualitative and quantitative studies of food components and their adulterants is reviewed in the present work. High-pressure Thin liquid chromatography (HPTLC) is hyphenated with high resolution mass spectrometry is particularly addressed due to its applicability in the targeted/untargeted metabolomic analysis of all food components. Key findings and conclusionsIn the current study, the HPTLC-MS technique was used to examine food components and food adulteration. The HPTLC-MS hyphenated techniques were found to be an extremely helpful method for the detection of food adulterants as well as the various food components, such as protein, carbohydrates, lipids, essential oils, and flavonoids, which were found to be effective as medicinally.

Rapid identification of adulterated camel meat by MIL-101(Cr)-based fluorescent sensing platform: Surface potential adjustment to optimize detection performance

To identify adulterated camel meat in emerging markets, there is an urgent need for sensitive, rapid, and convenient detection methods. In this study, a novel fluorescent sensing platform was developed to effectively identify pork components adulterated in camel meat by regulating the surface charge properties of MIL-101(Cr). Our data demonstrated that the modification of polyethylenimine (PEI) had minimal impact on the crystal structure of MIL-101(Cr). However, it significantly affected the adsorption behavior of MIL-101(Cr) towards 6-carboxyfluorescein (FAM) labelled DNA, thereby influencing the response of fluorescence quenching-recovery. Under optimal conditions, the proposed turn-on fluorescent biosensor, based on PEI-MIL-101(Cr) exhibited high sensitivity with a detection limit of 42 pM. It also demonstrated excellent selectivity for specific target DNA, successfully detecting a pork fraction of 0.45 % (w/w) in camel meat. This study was aimed to provide a fresh perspective on the potential application of functional materials derived from metal-organic frameworks in detecting food adulteration.

Fish DNA Sensors for Authenticity Assessment-Application to Sardine Species Identification.

Food and fish adulteration is a major public concern worldwide. Apart from economic fraud, health issues are in the forefront mainly due to severe allergies. Sardines are one of the most vulnerable-to-adulteration fish species due to their high nutritional value. Adulteration comprises the substitution of one fish species with similar species of lower nutritional value and lower cost. The detection of adulteration, especially in processed fish products, is very challenging because the morphological characteristics of the tissues change, making identification by the naked eye very difficult. Therefore, new analytical methods and (bio)sensors that provide fast analysis with high specificity, especially between closely related fish species, are in high demand. DNA-based methods are considered as important analytical tools for food adulteration detection. In this context, we report the first DNA sensors for sardine species identification. The sensing principle involves species recognition, via short hybridization of PCR-amplified sequences with specific probes, capture in the test zone of the sensor, and detection by the naked eye using gold nanoparticles as reporters; thus, avoiding the need for expensive instruments. As low as 5% adulteration of Sardina pilchardus with Sardinella aurita was detected with high reproducibility in the processed mixtures simulating canned fish products.

Food adulteration: Causes, risks, and detection techniques-review.

Food adulteration is the intentional addition of foreign or inferior substances to original food products for a variety of reasons. It takes place in a variety of forms, like mixing, substitution, hiding poor quality in packaging material, putting decomposed food for sale, misbranding or giving false labels, and adding toxicants. Several analytical methods (such as chromatography, spectroscopy, electronic sensors) are used to detect the quality of foodstuffs. This review provides concise but detailed information to understand the scope and scale of food adulteration as a way to further detect, combat, and prevent future adulterations. The objective of this review was to provide a comprehensive overview of the causes, risks, and detection techniques associated with food adulteration. It also aimed to highlight the potential health risks posed by consuming adulterated food products and the importance of detecting and preventing such practices. During the review, books, regulatory guidelines, articles, and reports on food adulteration were analyzed critically. Furthermore, the review assessed key findings to present a well-rounded analysis of the challenges and opportunities associated with combating food adulteration. This review included different causes and health impacts of food adulteration. The analytical techniques for food adulteration detection have also been documented in brief. In addition, the review emphasized the urgency of addressing food adulteration through a combination of regulatory measures, technological advancements, and consumer awareness. In conclusion, food adulteration causes many diseases such as cancer, liver disease, cardiovascular disease, kidney disease, and nervous system-related diseases. So, ensuring food safety is the backbone of health and customer satisfaction. Strengthening regulations, taking legal enforcement action, enhancing testing, and quality control can prevent and mitigate the adulteration of food products. Moreover, proper law enforcement and regular inspection of food quality can bring about drastic changes.

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.

Real-Time Detection of Dynamic Affinity between Biomolecules Using Surface Plasmon Resonance (SPR) Technology.

Surface plasmon resonance (SPR) technology is a sensitive precise method for detecting viruses, pathogenic molecular proteins, and receptors, determining blood types, and detecting food adulteration, among other biomolecular detections. This technology allows for the rapid identification of potential binding between biomolecules, facilitating fast and user-friendly, non-invasive screening of various indicators without the need for labeling. Additionally, SPR technology facilitates real-time detection for high-throughput drug screening. In this program, the application field and basic principles of SPR technology are briefly introduced. The operation process is outlined in detail, starting with instrument calibration and basic system operation, followed by ligand capture and multi-cycle analysis of the analyte. The real-time curve and experimental results of binding quercetin and calycosin to KCNJ2 protein were elaborated upon. Overall, SPR technology provides a highly specific, simple, sensitive, and rapid method for drug screening, real-time detection of related pharmacokinetics, virus detection, and environmental and food safety identification.

Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy

Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.

A Critical Overview of HPLC-MS-Based Lipidomics in Determining Triacylglycerol and Phospholipid in Foods.

With the current advancement in mass spectrometry (MS)-based lipidomics, the knowledge of lipidomes and their diverse roles has greatly increased, enabling a deeper understanding of the action of bioactive lipid molecules in plant- and animal-based foods. This review provides in-depth information on the practical use of MS techniques in lipidomics, including lipid extraction, adduct formation, MS analysis, data processing, statistical analysis, and bioinformatics. Moreover, this contribution demonstrates the effectiveness of MS-based lipidomics for identifying and quantifying diverse lipid species, especially triacylglycerols and phospholipids, in foods. Further, it summarizes the wide applications of MS-based lipidomics in food science, such as for assessing food processing methods, detecting food adulteration, and measuring lipid oxidation in foods. Thus, MS-based lipidomics may be a useful method for identifying the action of individual lipid species in foods.

Improving fraud detection in processed meats: A histology–PCR approach

Food fraud is a growing global concern that involves intentional actions aimed at deceiving consumers for financial gain by misrepresenting food products. Adulteration, substitution, mislabeling, and tampering are among the deceptive practices that can alter food products' nutritional value and composition. While various techniques, including mass spectrometry, enzyme immunoassay, histology, and DNA-based analysis, have been employed to detect food adulteration, each method has its limitations. This study presents a novel approach that combines PCR-based techniques with histology analysis to improve the detection of food adulteration in processed meats. The investigation involved the analysis of 105 randomly selected samples of sausages and beef hams in Iran using histological and PCR techniques. The results showed that 55 % of the beef ham samples and 66.15 % of the sausage samples contained unauthorized tissues. The multiplex PCR tests confirmed that 20 % of the beef ham samples and 0 % of the sausage samples had undeclared meat. Therefore, including histology as an additional method was necessary to accurately identify undeclared tissues from the same species. The findings suggest that the combined application of PCR-based and histology-based methods can effectively identify fraudulent practices within the food industry. This approach can enhance the detection of food adulteration and provide strong evidence for regulatory authorities to take necessary actions to protect consumer rights and promote food safety.

Wafer-scale fibrous SERS substrates allow label-free, portable detection of food adulteration and diagnosis of pesticide poisoning

Herein, a method for constructing three-dimensional (3D) fibrous surface-enhanced Raman scattering (SERS) substrates was developed. This strategy enables the wafer-scale, direct formation of monolayer gold nanoparticles (Au NPs) on filter paper. The process begins with coating of the fiber surface with perfluorooctyltrichlorosilane (FOS) to manipulate the surface energy. During subsequent sputter deposition, the large difference in surface energy between the Au atoms and the FOS-modified fibers leads to the thermodynamically driven formation of discrete Au NPs, thereby generating an abundance of hotspots. Moreover, the Au NP–decorated paper maintained its great hydrophobicity, condensing the analyte molecules nearby hot spots after solvent evaporation. In addition, the 3D fibrous structure provided a large detection space to efficiently utilize the large depth of field of a portable Raman spectrometer. These features resulted in the as-designed SERS paper having high sensitivity, with the enhancement factor reaching 1.63 × 108 and the detection limit reaching the nanomolar level when using a portable 785-nm Raman spectrometer. We used this approach for the trace-level, label-free detection of a diverse range of substances as well as their practical on-site detection in multiple-component systems, including illegal rhodamine B in commercial beverages and lethal paraquat in poisoning patients.

Application of Electronic Nose and Eye Systems for Detection of Adulteration in Olive Oil based on Chemometrics and Optimization Approaches

In this study, a combined system of electronic nose (e-nose) and computer vision was developed for the detection of adulteration in extra virgin olive oil (EVOO). The canola oil was blended with the pure EVOO to provide adulterations at four levels of 5, 10, 15, and 20%. Data collection was carried out using an e-nose system containing 13 metal oxide gas sensors, and a computer vision system. Applying principal component analysis (PCA) on the e-nose-extracted features showed that 93% and 92% of total data variance was covered by the three first PCs generated from Maximum Sensor Response (MSR), Area Under Curve (AUC) features, respectively. Cluster analysis verified that the pure and impure EVOO samples can be categorized by e-nose properties. PCA-Quadratic Discriminant Analysis (PCA-QDA) classified the EVOOs with an accuracy of 100%. Multiple Linear Regression (MLR) was able to estimate the adulteration percentage with the R2 of 0.8565 and RMSE of 2.7125 on the validation dataset. Moreover, factor analysis using Partial Least Square (PLS) introduced the MQ3 and TGS2620 sensors as the most important e-nose sensors for EVOO adulteration monitoring. Application of Response Surface Methodology (RSM) on RGB, HSV, L*,a*, and b* as color parameters of the EVOO images revealed that the color parameters are at their optimal state in the case up to 0.1% of canola impurity, where the obtained desirability index was 97%. Results of this study demonstrated the high capability of e-nose and computer vision systems for accurate, fast and non-destructive detection of adulteration in EVOO and detection of food adulteration may be more reliable using these artificial senses. 

Improving the performance of the model developed from the classification of adulterated honey with different botanical origins based on near-Infrared hyperspectral imaging system and supervised classification algorithms

Honey is a high-value product that proscribes adulteration. Authentication of pure honey is an imperative act that can disregard the adverse impacts brought by fraudsters on honey producers and consumers. A reliable method that can detect food adulteration that is nondestructive, rapid, and efficient can help maintain product authenticity. This study investigated the potential of a near-infrared hyperspectral imaging (NIR-HSI) system combined with supervised classification techniques for the classification of honey samples from different botanical origins that were adulterated with sugar cane syrup at 20 % and 40 % w/w. Reflectance and transflectance modes of measurement were used to obtain honey spectral images from 864.53 nm to 1700 nm. Supervised classification algorithms, namely, stepwise discriminant analysis (SDA), support vector machine (SVM), and k-nearest neighbors (kNN) were used for the classification based on whole spectral data and feature selected spectral data. The best classification model from reflectance mode was observed from SDA based on a leave-one-out cross-validation (LOOCV) method with a test accuracy of 95 %. For transflectance mode, the classification model developed from SDA based on a LOOCV method provided better test set accuracy of 100 %. The classification performance levels of the models developed from SVM and kNN were lower with the use of the whole wavelength region. Moreover, the classification performance levels of SVM and kNN using the selected features by the SDA greatly increased, however, they were not as good as from using SDA alone. The classifier maps created using SDA and predicting the averaged spectra of all pixels in the image with 10-discriminant functions for each mode of measurement displayed a two-dimensional image that had a distinct colour pertaining to a particular class that could easily discriminate one class from another.

Packaged butter adulteration evaluation based on spatially offset Raman spectroscopy coupled with FastICA

Optical detection technology has been widely used in unpackaged food adulteration detection. However, due to the interference of packaging materials on the internal food optical signal, including signal occlusion, mixing and overlap precluded the accurate detection of internal food quality. In this study, a method of packaged butter adulteration evaluation based on spatially offset Raman spectroscopy (SORS) combined with fast independent component analysis (FastICA) was proposed. The adulterated butter from 0% to 100% w/w margarine at 10% intervals was covered with packaging sheets as test samples. A line-scan Raman hyperspectral imaging system was used to obtain a scattering spectral image of the packaged butter samples. The region of interest of the scattering image is extracted as the input of FastICA model to separate the internal butter signals. The extracted butter Raman features were input into four quantitative analysis models to assess the content of butter adulteration. The results showed that the ensemble model Extra-tree has the best performance with RMSEp, Rp2, and RPD values of 0.6, 0.93, and 4.73, respectively. Additionally, the applicability of the method was validated with four types of packaging materials. This rapid non-destructive testing method is beneficial to the effective testing method of packaged butter and other products industry.

IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart Approach

Food adulteration refers to the practice of deliberately adding substances to food to increase its volume, weight, or to improve its appearance, texture, or flavor; it is a significant issue that affects the health and safety of consumers. With the increasing demand for food, the risk of contamination and the intentional addition of harmful substances has increased. There are several existing methods for detecting food adulteration, including chemical analysis, microscopy, sensory analysis, etc. While these methods are helpful, they can be time-consuming, labor-intensive, and may not provide Real-time results. Using the Internet of Things (IoT), Machine Learning (ML) can significantly enhance the ability to identify food adulteration.Within this Framework, we are propose a solution to detect food adulteration using IoT and machine learning. The system comprises IoT sensors and devices to gather data on various parameters such as color, pH, gas content, etc. The collected data is fed into machine learning algorithms for preprocessing, analysis, and testing. Any anomalies or deviations from the standard patterns are flagged for further investigation. ML algorithms can continuously learn from the collected data, enabling them to enhance their accuracy and effectiveness over time. By implementing this system, we aim to create a Real-time, data- driven approach to detecting food adulteration, ensuring food safety and quality for consumers by creating a warning system.

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.

Identification of s9ap used as an endogenous reference gene in qualitative and real-time quantitative PCR detection of Pleurotus eryngii.

Pleurotus eryngii is a kind of edible fungi with good quality, and it is popular among consumers. At present, some adulterated edible fungi are available in the market. The rights and interests of consumers can be ensured by establishing a practical edible fungi detection system. Among the existing methods for detecting food adulteration, endogenous reference gene amplification is convenient and reliable. However, no ideal endogenous reference gene is available for P. eryngii. In this study, s9ap was screened as an endogenous reference gene through sequence alignment. Qualitative and quantitative PCR analysis of this gene was carried out in one P. eryngii variety and 18 other species. The detection limit of quantitative PCR was 400 pg, and no s9ap amplification products were detected in the 18 other species. This study confirmed that s9ap was an ideal endogenous reference gene for the detection of P. eryngii. This method was also suitable for processed food products.

Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose.

Food adulteration is the most serious problem found in the food industry as it harms people’s healths and undermines their beliefs. The present study is focused on designing and developing a smart electronic nose (SE-Nose) for the qualitative and quantitative fast-track detection of food adulteration. The SE-Nose methodology is comprised of a dataset, sample slicing window protocol, normalization, pattern recognition, and output blocks. The dataset pork adulteration in beef is used to validate the SE-Nose methodology. The sample slicing window protocol extracts the early part of the signal. The sample slicing window protocol and pattern recognition models (classification and regression models) together achieved the high-performance and fast-track detection of pork adulteration in beef. With classification models, the qualitative analysis of adulteration is measured, and with regression models, the quantitative analysis of adulteration is measured. An accuracy of 99.996% and an RMSE of 0.02864 were achieved with the SVM classification and regression model. The recognition time in detecting pork adulteration in beef with SVM models is 40 s. With the proposed SE-Nose methodology, the recognition time is reduced by one-third. To validate the classification and regression models, a 10-fold cross-validation method was used.

Application of Quantum Cascade Laser to Rapid Detection of Food Adulteration

Application of Quantum Cascade Laser to Rapid Detection of Food Adulteration

A Molecularly Imprinted Polymer-Based Thermal Sensor for the Selective Detection of Melamine in Milk Samples.

In recent years, melamine-sensing technologies have increasingly gained attention, mainly due to the misuse of the molecule as an adulterant in milk and other foods. Molecularly imprinted polymers (MIPs) are ideal candidates for the recognition of melamine in real-life samples. The prepared MIP particles were incorporated into a thermally conductive layer via micro-contact deposition and its response towards melamine was analyzed using the heat-transfer method (HTM). The sensor displayed an excellent selectivity when analyzing the thermal response to other chemicals commonly found in foods, and its applicability in food safety was demonstrated after evaluation in untreated milk samples, demonstrating a limit of detection of 6.02 μM. As the EU/US melamine legal limit in milk of 2.5 mg/kg falls within the linear range of the sensor, it can offer an innovative solution for routine screening of milk samples in order to detect adulteration with melamine. The results shown in this work thus demonstrate the great potential of a low-cost thermal platform for the detection of food adulteration in complex matrices.

Detection of Adulteration in Food Using Recurrent Neural Network with Internet of Things

Food is an essential need for human survival. Throughout history, food has been recognised as a crucial need for people in order to maintain good health as well as to treat illness. As with all living things, it is one of the most basic necessities that man has as well as those of all other living creatures. In a recent publication, it was said that an extremely affordable, robust, and biocompatible impedance sensor that serves as a fractional-order element has been created and may be used to distinguish milk and tainted milk. A complete study on milk adulteration includes more than 160 academic articles on the topic. A comprehensive study on milk adulteration is available online. Specifically, the goal of this research is to discover various types of milk adulterants, different approaches for detecting each kind of adulterant, as well as the health hazards associated with milk product adulteration. In the proposed project, the fractional-order element would be investigated for its potential use in the detection of milk adulteration. With this fractional-order element-based impedance sensor, you can distinguish between different types of contaminated milk and different types of faking it, which is quite useful in the detection and differentiation of fake and real milk. According to the researchers, they have created a low-cost, user-friendly instrumentation system for detecting milk adulteration. They hope to commercialise it soon. An automated sensing system for the detection of synthetic milk, based on a microcontroller, has been created in order to reduce the reliance on specialised labour and to improve efficiency. In order to model the sensor, the dipole layer capacitance at the interface of the impedance sensor immersed in milk and the contaminated milk must be taken into account throughout the modelling process. In this study, an electrical equivalent circuit is built, and the correctness of the circuit is shown by both theoretical and experimental investigation. The detection of milk adulteration is classified with the use of Recurrent Neural Networks, and the status is updated in the cloud server with the help of the Internet of Things and Recurrent Neural Networks. It is estimated that the proposed work will have an accuracy rate of 92.31 percent, a sensitivity rate of 75.23 percent, and a specificity rate of 90.12 percent, all of which are higher than the present rate.