Detection Of Food Adulteration Research Articles

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.

Mid-infrared spectroscopy: Screening method for analysis of food adulterants in reconstituted skimmed milk powder

This study successfully shows that MilkoScan™ FT1 can be employed as rapid screening technology for detecting sixteen out of twenty-one food adulterants in reconstituted skimmed milk powder. The validation of untargeted fingerprinting method correctly identified 100% of unadulterated samples and 93% of samples adulterated with nitrogen-rich compounds or bulking agents at 5% economical adulteration. Furthermore, this work shows that lactose is a challenging adulterant to be detected by an untargeted screening method because it is naturally present in skimmed milk powder. However, this study showed that specific calibration can be applied to detect addition of lactose in skimmed milk powder. As a result, this method of screening displays a high potential to be employed by various food businesses (such as dairy, confectionary and infant formula, etc.) to rapid screening of raw materials and detect abnormalities, which may be linked to food adulteration or quality defects, ensuring products of better quality and safety to consumers. • Detection of food adulterants in reconstituted skim milk powder using mid-infrared. • Untargeted screening method for detection of nitrogen-rich compounds and bulking agents. • The adulterated and unadulterated samples show sensitivity of 93% and 100%, respectively.

DNA Based Molecular Detection Methods of Biological Adulterations in Commercial Teas: Successes and Obstacles

Extra to water, tea is the broadly consumable hot beverage in the biosphere. People drink more tea than coffee, cold drinks, dairy products, and alcohol combined. Availability does not effectively order the dried plant pieces, which are utilized to make tea. Numerous cases of food adulteration have been recorded in many countries, including India. This has taken to the creation of a new discipline of science, known as ‘green criminology’, to compensate violations of food law. Over the years, new techniques for identifying food adulteration have been developed Here we reviewed different standard DNA based molecular techniques to analyze their performance in identifying tea constituents. Originally, these were sensory techniques, which proved unreliable. Later, physical analysis of the product was done based on data on the label and microscopic evaluation. Later methods, based on the identification of lipids and proteins, were also not accurate due to biochemical changes during processing. These problems provoked scientists to get an interest in the potential of DNA testing. Due to the stability of DNA and the universal applicability of DNA-based methods for all cells, they are ideal for use in practice. Currently, the most reliable test for detecting food adulteration is Bar-HRM, as it is a highly sensitive and specific technique. Broad scale endorsement of the plant’s DNA barcoding may require systems that spot the journey brings about point of view of common plant species names and character for separate firmly close gatherings.

Efficient High-throughput Techniques for the Analysis of Disease- Resistant Plant Varieties and Detection of Food Adulteration.

Over the past two decades, the advances in the next generation sequencing (NGS) platforms have led to the identification of numerous genes/QTLs at high-resolution for their potential use in crop improvement. The genomic resources generated through these high-throughput sequencing techniques have been efficiently used in screening of particular gene of interest particularly for numerous types of plant stresses and quality traits. Subsequently, the identified-markers linked to particular trait have been used in Marker-Assisted Backcross Breeding (MABB) activities. Besides, these markers are also being used to catalogue the food crops for detection of adulteration to improve the quality of food. With the advancement of technologies, the genomic resources are originating with new markers; however, to use these markers efficiently in crop breeding, High-Throughput Techniques (HTT) such as multiplex PCR and Capillary Electrophoresis (CE) can be exploited. Robustness, ease of operation, good reproducibility and low cost are the main advantages of multiplex PCR and CE. The CE is capable of separating and characterizing proteins with simplicity, speed and small sample requirements. Keeping in view the availability of vast data generated through NGS techniques and development of numerous markers, there is a need to use these resources efficiently in crop improvement programmes. In summary, this review describes the use of molecular markers in the screening of resistance genes in breeding programme and detection of adulterations in food crops using high-throughput techniques.

Rapid detection and quantification of the adulteration of orange juice with grapefruit juice using handheld Raman spectroscopy and multivariate analysis.

Detecting food adulteration has always been an important task for food safety, especially when grapefruit is the adulterant as components in the juice have undesired interactions with many medicines. In this study we employed a handheld Raman device to detect adulteration of orange juices with grapefruit juices. Fresh fruits of orange and grapefruit were purchased from five different sources and fruit juices were made using a handheld juicer. The extracted juices were then mixed in a way that concentrations of grapefruit juices varied from 0% to 100% in 5% increments. In order to study the impact of the different sources of the fruits, three different sets of mixtures were prepared based on their spectral similarity and dissimilarity. Raman spectra were collected using a handheld instrument with an excitation laser at 785 nm and data analysed using principal component analysis (PCA), principal component-discriminant function analysis (PC-DFA) and partial least squares regression (PLS-R). PLS-R models were trained and validated on: (i) the full data set from the three different mixture sets, and (ii) each set of the three mixtures separately. The results showed that a good calibration model was obtained using full data which had a coefficient of determination (Q2) of 0.81 and a root mean square error of prediction (RMSEP) of 12.5%. Such results were improved when the PLS-R model was trained and validated on the three separate mixture combinations, where the Q2 varied from 0.85 to 0.89 and RMSEP varied from 9.9% to 11.6%. Finally, we adopted a two step approach in which a partial least squares for discriminant analysis (PLS-DA) was trained first to classify the three sample sources and then three different PLS-R models were subsequently trained on samples from the same source. This resulted in a Q2 of 0.83 and RMSEP of 12.0%. In conclusion, we have demonstrated that Raman spectroscopy can be used as a portable and rapid analytical tool for detecting adulteration of grapefruit juice added to orange juice.

DNA Accounting: Tallying Genomes to Detect Adulterated Saffron.

The EU General Food Law not only aims at ensuring food safety but also to ‘prevent fraudulent or deceptive practices; the adulteration of food; and any other practices which may mislead the consumer’. Especially the partial or complete, deliberate, and intentional substitution of valuable ingredients (e.g., Saffron) for less valuable ones is of concern. Due to the variety of products on the market an approach to detect food adulteration that works well for one species may not be easily applicable to another. Here we present a broadly applicable approach for the detection of substitution of biological materials based on digital PCR. By simultaneously measuring and forecasting the number of genome copies in a sample, fraud is detectable as a discrepancy between these two values. Apart from the choice of target gene, the procedure is identical across all species. It is scalable, rapid, and has a high dynamic range. We provide proof of concept by presenting the analysis of 141 samples of Saffron (Crocus sativus) from across the European market by DNA accounting and the verification of these results by NGS analysis.

Real-Time PCR Assay for the Detection and Quantification of Roe Deer to Detect Food Adulteration-Interlaboratory Validation Involving Laboratories in Austria, Germany, and Switzerland.

Game meat products are particularly prone to be adulterated by replacing game meat with cheaper meat species. Recently, we have presented a real-time polymerase chain reaction (PCR) assay for the identification and quantification of roe deer in food. Quantification of the roe deer content in % (w/w) was achieved relatively by subjecting the DNA isolates to a reference real-time PCR assay in addition to the real-time PCR assay for roe deer. Aiming at harmonizing analytical methods for food authentication across EU Member States, the real-time PCR assay for roe deer has been tested in an interlaboratory ring trial including 14 laboratories from Austria, Germany, and Switzerland. Participating laboratories obtained aliquots of DNA isolates from a meat mixture containing 24.8% (w/w) roe deer in pork, roe deer meat, and 12 meat samples whose roe deer content was not disclosed. Performance characteristics included amplification efficiency, level of detection (LOD95%), repeatability, reproducibility, and accuracy of quantitative results. With a relative reproducibility standard deviation ranging from 13.35 to 25.08% (after outlier removal) and recoveries ranging from 84.4 to 114.3%, the real-time PCR assay was found to be applicable for the detection and quantification of roe deer in raw meat samples to detect food adulteration.

Using one-class autoencoder for adulteration detection of milk powder by infrared spectrum

Food adulteration detection requires quick and simple methods. Spectral detection can significantly reduce the analysis time, but it needs to construct a detection model. In this study, a one-class classification method based on an autoencoder is proposed for the detection of food adulteration by spectroscopy. In the proposed method, the autoencoder is constructed to extract low-dimensional features from high-dimensional spectral data and reconstruct the original spectrum. Then the coding error and reconstruction error are used to determine the food sample is adulterated or not. The infrared spectral data of milk powder and its adulterated forms are used to verify the performance of the proposed model. Experimental results show that the proposed method has similar effects to soft independent modeling of class analogy and one-class partial least squares, and is significantly better than support vector data description. The proposed method can be flexibly applied to the spectral detection of food adulteration.

Portable through Bottle SORS for the Authentication of Extra Virgin Olive Oil

The authenticity of olive oil has been a significant long-term challenge. Extra virgin olive oil (EVOO) is the most desirable of these products and commands a high price, thus unscrupulous individuals often alter its quality by adulteration with a lower grade oil. Most analytical methods employed for the detection of food adulteration require sample collection and transportation to a central laboratory for analysis. We explore the use of portable conventional Raman and spatially-offset Raman spectroscopy (SORS) technologies as non-destructive approaches to assess the adulteration status of EVOO quantitatively and for SORS directly through the original container, which means that after analysis the bottle is intact and the oil would still be fit for use. Three sample sets were generated, each with a different adulterant and varying levels of chemical similarity to EVOO. These included EVOO mixed with sunflower oil, pomace olive oil, or refined olive oil. Authentic EVOO samples were stretched/diluted from 0% to 100% with these adulterants and measured using two handheld Raman spectrometers (excitation at 785 or 1064 nm) and handheld SORS (830 nm). The PCA scores plots displayed clear trends which could be related to the level of adulteration for all three mixtures. Conventional Raman (at 785 or 1064 nm) and SORS (at 830 nm with a single spatial offset) conducted in sample vial mode resulted in prediction errors for the test set data ranging from 1.9–4.2% for sunflower oil, 6.5–10.7% for pomace olive oil and 8.0–12.8% for refined olive oil; with the limit of detection (LOD) typically being 3–12% of the adulterant. Container analysis using SORS produced very similar results: 1.4% for sunflower, 4.9% for pomace, and 10.1% for refined olive oil, with similar LODs ranging from 2–14%. It can be concluded that Raman spectroscopy, including through-container analysis using SORS, has significant potential as a rapid and accurate analytical method for the non-destructive detection of adulteration of extra virgin olive oil.

Novel monkey-wrench-shaped microstrip patch sensor for food evaluation and analysis.

Microwave sensor technology is considered to be a non-destructive and hygienic means for food evaluation and analysis. The rapid progress in microwave sensor technologies motivated us to present a novel monkey-wrench-shaped microstrip patch sensor for evaluating food quality. The proposed antenna is considered as a liquid sensor to detect adulteration in liquids by examining the relationship between concentration, shift in resonant frequency, and variation in reflection coefficient. The sensor has a compact size of 17 × 14 mm2 imprinted on an FR4 substrate of thickness 1.57 mm. This microwave system is proposed for monitoring overall milk quality using a microstrip sensor and has considerably good numerical sensitivity and accuracy (13.11% and 88.5% respectively), which makes the system attractive for detecting adulteration. Further, the Q-factor for the proposed sensor is 209 and has a standard deviation less than the difference between non-adulterated and adulterated values, giving resolution high enough to distinguish adulteration with an acceptable statistical accuracy. In this study, different milk categories (buffalo, goat, and cow milk) are considered as liquid samples to detect adulteration by demonstrating the variation in reflection coefficient due to change in dielectric properties for the following different cases: (i) adulteration of milk with water; (ii) adulteration of milk with synthetic milk powder; (iii) adulteration of milk with caustic soda; and (iv) adulteration of milk with vegetable oil. The referred variation in reflection coefficient and resonant frequency is due to a change in the dielectric properties of a liquid when a varying concentration of an adulterant/solute is added to the liquid sample. The simulation and measured results show good agreement that validates the proposed sensor for food adulteration detection with high sensitivity. In terms of performance, the proposed sensor shows accuracy with high spatial resolution and reduced penetration depth to detect the adulteration in various milk samples (i.e. buffalo, goat, and cow milk). © 2021 Society of Chemical Industry.

A deep neural network and random forests driven computer vision framework for identification and prediction of metanil yellow adulteration in turmeric powder

SummaryTurmeric (Curcuma longa) is a popular food ingredient which is widely used in powdered form. Despite different food and medicinal advantages it is often adulterated. Metanil yellow (MET) is one such synthetic chemical which can be easily mixed with turmeric powder and such mixing is difficult to detect. This paper presents a computer vision framework using the potential of deep neural network towards detection of MET adulteration in turmeric powder and random forests regressor to predict the possible amount of adulterant. An in‐house database consisting of features from turmeric images of five variants of pure and adulterated turmeric powder has been used for experimentations. A new frequency domain annular‐mean filter‐based feature extraction has been used. The results show the potential of the presented method that can perform with more than 98% accuracy in both identification and prediction tasks. The reported technique can be considered as a motivating step towards development of a non‐invasive and low‐cost mobile device towards food adulteration detection in future.

A Review of Recent Studies Employing Hyperspectral Imaging for the Determination of Food Adulteration

Applications of hyperspectral imaging (HSI) methods in food adulteration detection have been surveyed in this study. Subsequent to the research on existing literature, studies were evaluated based on different food categories. Tea, coffee, and cocoa; nuts and seeds; herbs and spices; honey and oil; milk and milk products; meat and meat products; cereal and cereal products; and fish and fishery products are the eight different categories investigated within the context of the present study. A summary of studies on these topics was made, and articles reported in 2019 and 2020 were explained in detail. Research objectives, data acquisition systems, and algorithms for data analysis have been introduced briefly with a particular focus on feature wavelength selection methods. In light of the information extracted from the related literature, methods and alternative approaches to increasing the success of HSI based methods are presented. Furthermore, challenges and future perspectives are discussed.

Undeclared (Poly)phosphates Detection in Food of Animal Origin as a Potential Tool toward Fraud Prevention

(Poly)phosphates are approved as water-preserving and emulsifying agents that improve the appearance and consistency of many food products. The labelling of added (poly)phosphates is essential for protecting vulnerable population groups and to prevent unfair trade practices resulting in economic fraud. The problems with (poly)phosphates’ utilisation concerns both analytical and legislative issues, such as: (1) their straightforward detection; (2) excessive addition altering freshness perception and misleading consumers; (3) uncontrolled usage increasing foodstuff weight; (4) application in products where they are not permitted; and (5) no indication on the label. Bearing all these issues in mind, the main purpose of this study was the quantification and screening of the (poly)phosphates profile in meat, marine and dairy products (160 samples), of which 43 were without declared (poly)phosphate treatment. Analysis was completed by high-performance ion-exchange chromatography either with conductometric detection or coupled to Q-Exactive Orbitrap high-resolution mass spectrometry. Although the (poly)phosphates profiles varied greatly according to species and processing type, the following criteria for detection of illicit treatment were established: high orthophosphate level, quantified short-chain (poly)phosphate anions and the presence of long-chain forms. In conclusion, the instrumental platforms used in this study can be recommended to inspection bodies as reliable methods for the detection of food adulteration with (poly)phosphates.