Detection Of Adulteration Research Articles

Detection of Vegetable Oil Adulteration in Pre-Grated Bovine Hard Cheeses via 1H NMR Spectroscopy.

Adulteration of food products is a widespread problem of great concern to society and dairy products are no exception to this. Due to new methods of adulteration being devised in order to circumvent existing detection methods, new detection methods must be developed to counter fraud. Bovine hard cheeses such as Asiago, Parmesan, and Romano are widely sold and consumed in pre-grated form for convenience. Due to being processed products, there is ample opportunity for the introduction of inexpensive adulterants and as such, there is concern regarding the authenticity of these products. An analytical method was developed using a simple organic extraction to verify the authenticity of bovine hard cheese products by examining the lipid profile of these cheeses via proton Nuclear Magnetic Resonance (NMR) spectroscopy. In this study, 52 samples of pre-grated hard cheese were analyzed as a market survey and a significant number of these samples were found to be adulterated with vegetable oils. This method is well suited to high throughput analysis of these products and relies on ratiometrics of the lipids in the samples themselves. Genuine cheeses were found to have a very consistent lipid profile from sample to sample, improving the power of this approach to detect vegetable oil adulteration. The method is purely ratiometric with no need for internal or external references, reducing sample preparation time and reducing the potential for the introduction of error.

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.

Possibilities for Detection of Adulteration of Lemon Juices and Lemon-Based Beverages

Lemon juice and other lemon-based beverages are subject to economically motivated fraud, primarily inclu­ding a reduced fruit content closely related to excessive dilution with water, undeclared addition of sugars or synthetic citric acid, and substitution of lemons with cheaper fruit. Eight tested samples of 100% juices showed good agreement between the measured concentrations and the reference values for the following markers: formol number (range from 16.9 to 26.5 mL of 0.1M NaOH 100 mL–1), ash (2.5 to 3.9 g L–1), phosphorus (84 to 145 mg L–1), potassium (931 to 1720 mg L–1), magnesium (81 to 114 mg L–1), malic acid (1.8 to 6.0 g L–1), and ᴅ‑isocitric acid (277 to 560 mg L–1). The following markers were suggested as an evidence of adulteration, processing method, and recipe alteration after the subsequent analysis of the beverage concentrates: 1) Citric acid is a substance whose content can be increased by adding synthetic citric acid. 2) Calcium content was impacted by clarification or reconstitution in some beverage concentrate samples tested. 3) Increased content of glucose and fructose confirmed exogenous sugar addition. 4) Ascorbic acid levels below 150 mg L–1 indicated severe processing or long-term storage. The flavonoid profile, characterized mainly by the ratio of eriocitrin and hesperidin (average value 0.7), serves as an indicator of the addition of another citrus species. However, the finding for the Interdonato variety suggests that this ratio is quite variable and not entirely specific because lime juice exhibits a similar value.
 
 Citronová šťáva a další nápoje na bázi citronů podléhají ekonomicky motivovaným podvodům, mezi které primárně patří snížený obsah ovoce úzce spojený s nadměrným naředěním vodou, nedeklarovaným přídavkem cukrů nebo syntetické citronové kyseliny a záměnou citronů levnějším druhem ovoce. U 8 testovaných vzorků 100% šťáv byla potvrzena dobrá shoda naměřených koncentrací s referenčními hodnotami u těchto znaků: formolové číslo (rozsah 16,9 až 26,5 ml 0,1M NaOH 100 ml–1), popel (2,5 až 3,9 g l–1), fosfor (84 až 145 mg l–1), draslík (931 až 1720 mg l–1), hořčík (81 až 114 mg l–1), jablečná kyselina (1,8 až 6,0 g l–1) a ᴅ-isocitronová kyselina (277 až 560 mg l–1). Po následném proměření nápojových koncentrátů byly jako indikátory falšování, způsobu zpracování a úpravy receptury doporučeny tyto markery: 1) obsah citronové kyseliny, jejíž nálezy mohou být zvýšeny přídavkem citronové kyseliny syntetického původu, 2) obsah vápníku, který byl u řady testovaných vzorků ovlivněn čiřením nebo rekonstitucí koncentrátu, 3) zvýšená hodnota glukosy a fruktosy potvrzovala exogenní přídavek cukrů, 4) pokles askorbové kyseliny pod 150 mg l–1 vypovídal o nešetrném zpracování nebo dlouhodobém skladování. Profil flavonoidů charakterizovaný především poměrem eriocitrinu a hesperidinu (průměrná hodnota 0,7) slouží jako indikátor přídavku jiného citrusového druhu. Je však třeba brát v potaz, že je poměrně variabilní, což ukázal nález zjištěný pro odrůdu Interdonato a ne zcela specifický, protože obdobný poměr vykazuje i šťáva z limet.

Rapid detection of pork oil adulteration in snakehead fish oil using FTIR-ATR spectroscopy and chemometrics for halal authentication

Rapid detection of pork oil adulteration in snakehead fish oil using FTIR-ATR spectroscopy and chemometrics for halal authentication

Detection of adulteration of dairy products with vegetable fats using the method of luminescent analysis

This article describes a technique for detecting pesticides in poultry meat samples using thin layer chromatography. In the course of the study, the qualitative indicators of the content of pesticides in samples of poultry carcasses treated with the insecticide esbiotrin, used to prevent the reproduction of the red chicken mite among the poultry population, were determined. The studies were carried out at various time intervals from the moment the poultry was treated with the drug; quantitative determination of the content of the residual concentration of pesticides by thin-layer chromatography will reveal the feasibility and safety of using the active substance as an insecticide in poultry farming. Chromatographic studies were carried out on the basis of the Department of Veterinary and Sanitary Expertise of the SPbGUVM.During the experiment, 3 experimental groups of laying hens and 1 control group (10 animals each) were selected. The experimental groups were treated with a 0.001% solution of esbiotrin after which the birds were slaughtered 24, 72 and 120 hours after treatment. The selected samples were subjected to chromatographic examination to detect residual concentrations of synthetic pyrethroids.

Detection of adulteration of dairy products with vegetable fats using the method of luminescent analysis

The addition of vegetable fats is one of the most common falsifications of dairy and dairy compound products. Therefore, the development of a method for detecting vegetable fats in vegetable products seems relevant. In the context of import substitution and the active development of Russian agriculture and the dairy industry, affordable, easy-to-use, accurate and reliable methods are especially relevant.In this article, we presented the results of a luminescent study of cottage cheese, sour cream, and other dairy products and compared them with milk-containing products containing milk fat using the Filin HD luminoscope device. As a result of our research, we found that the presence of vegetable fats in dairy products can be easily detected using the luminescent research method. In particular, we noted that dairy products containing vegetable fats when viewed in ultraviolet radiation with a wavelength of 365 nm. luminesces with a bright blue color, while dairy products that do not contain vegetable fat have a yellow glow.Based on our research, we concluded that luminoscopy is an affordable, accurate and fast method that meets the requirements of express analysis, giving a stable result. That allows us to recommend it as a screening tool for detecting falsifications of dairy and dairy compound products with vegetable fats.

Star enclosed circle split ring resonator-based metamaterial sensor for fuel and oil adulteration detection

A unique shaped metamaterial sensor is proposed for detecting a wide range of pure and adulterated fuels and oils. The functionality of the designed structure has been examined both theoretically and practically ways, and the results show that it performs incredibly well. A new sample holder is built for more convenient functioning, and it is attached directly behind the constructed structure. In the field of research on liquids sensing, the purpose of this work was to present an alternative methodology. The CST microwave studio software is used to test a variety of different designs to determine which ones would result in the resonator having the best possible dimensions. The resonance frequency shifted 270, 410, and 180 MHz; between pure petrol and a mixture of 20 % kerosene and 80 % petrol, 100 % diesel and a mixture of 20 % ethanol and 80 % diesel, and clean olive oil and a mixture of 20 % corn oil and 80 % olive oil, respectively. The sensor has a quality factor of 430, high sensitivity of 1.99, and a figure of merit of 855.70, which indicates that it performs well and efficiently. In addition, the sensor that has been presented can accurately differentiate between various liquids by making use of the frequency shifting feature. The findings obtained demonstrate that this concept is readily applicable for a wide variety of electrochemical sensing applications even if the permittivity of the sample is altered; this is indicated by the findings. Because the sensor that is recommended possesses high sensitivity, a high-quality factor, and outstanding performance, it is suitable for use in the fuel industry, oil industry, and microfluidics industry.

Support vector machine-based rapid detection and quantification of butter yellow adulteration in mustard oil using NIR spectra

Butter yellow (BY; 3′-methyl-4-dimethyl-aminoazobenzene) is a carcinogenic adulterant of edible mustard oil. In this study, Fourier-transform near-infrared (FT-NIR) spectral data was combined with multivariate data analysis to both detect and quantify BY adulteration in mustard oil. Fifteen pure mustard oil and one hundred fifty mustard oil samples with BY (0.1–1% w/v) as the adulterant were evaluated. It was observed that sonication- based processing of adulterated samples provides sensitive fingerprints during FT-NIR analysis when compared to heat- based processing. Principal component analysis (PCA) of spectral regions 6000–6064 cm−1 showed well-defined discrimination of pure and adulterated mustard oil with 100% variability. For quantification of BY both NIPALS (non-linear iterative partial least squares) based partial least square (PLS) and LS-SVM (least squares support vector machine) were carried for full-spectrum as well as selected fingerprints using RCGA (real coded genetic algorithm), a variable selection method. Overall, RCGA-LS-SVM model showed best calibration and prediction model with high precision and accuracy based on low root mean square error of cross validation (RMSECV = 0.0073) and prediction (RMSEP = 0.0060). RCGA has indeed lowered the number of variables to mere 30 variables to predict BY in the range of (0.1–1% w/v) with a correlation coefficient of calibration (RC2 = 0.9997) and prediction (RP2 = 0.9996). The key fingerprints detect CH3 and CH functional groups (1st overtone) of BY. This study demonstrated that FT-NIR spectroscopy, combined with selected 30 variables using RCGA-PLS procedure could be a robust technique for the rapid quantification of butter yellow in mustard oil samples. The study would certainly help in quality control and quality assurance of mustard oil and could be emulated for other edible oils.

Classification and adulteration of mengding mountain green tea varieties based on fluorescence hyperspectral image method

For rapid nondestructive detection of classification and adulteration of Mengding Mountain green tea, an experimental method combining Fluorescence hyperspectral imaging technology and machine learning modeling method was proposed. Spectral data were preprocessed with Median Filtering (MF) and Multiplicative Scatter Correction (MSC), which can effectively reduce the effects of baseline drift and tilt. Xgboost (XGB) and Logistic Regression (LGR) were used for feature band extraction. Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and CatBoost were used to build predictive models. The results show that the MF-LGR-RF algorithm model is the best model for the classification of Mengshan green tea, and its accuracy is 100%. The MF-XGB-CatBoost algorithm model is the best model for the quantitative analysis of the doping of Laochuan tea tree Ganlu. The R2 of the test set is 0.959 and the RMSE is 0.065. In order to further improve the model performance, a multi-model stacking ensemble learning model was designed to quantitatively analyze the adulteration of Laochuan tea tree Ganlu. The R2 of the test set was increased to 0.968 and the RMSE was reduced to 0.058.

Low-cost rapid workflow for honey adulteration detection by UV–Vis spectroscopy in combination with factorial design, response surface methodology and supervised machine learning classifiers

Honey is often adulterated and heat treated for higher profits. This study aims to develop a low-cost, easy to perform, and less time-consuming method for screening the honey status. Known (12: adulterated 4; unadulterated 8) and unknown (7) honey samples of different geographical and biological sources were used to simulate adulteration and heat treatment effects by factorial design. Crude methanol-chloroform extracts of simulated samples were used for generating UV–Vis spectra (200-700 nm) and the effects were assessed by response surface methodology. Four machine learning classifiers were applied to predict the sample status, and among them Neural network and Random-forest were found to be satisfactory. The selected classifiers successfully detected 2 adulterated, and 4 unadulterated honeys among 7 unknown samples. Present workflow is proved to be an effective technique in detecting honey adulteration, and can be applied in honey and other food industries for optimization and quality control of their products.

A Low-Cost Closed-Tube Method for Detection of Adulteration in Ground Meat

ABSTRACT The increased public concern about adulteration and mislabelling of meat products has fueled a need for the development of fast, reliable, and cost-effective species detection methods. Hence, we developed a high resolution melt analysis (HRMA) for the discrimination of meat species commonly used in the meat industry and their possible adulterants. A universal primer pair spanning a ⁓238 bp fragment of mitochondrial 16S rRNA gene was designed to perform HRMA. The developed workflow was validated on standard meat samples, ternary meat mixtures of eight different species, and 56 commercial meat samples. Distinct melting profiles were generated for each species, several ternary meat mixtures, and commercial food samples. The assay was sufficiently sensitive to detect 0.003 ng/µl of DNA of every targeted species. In short, the developed HRM analysis is a rapid, cost-effective, and efficient species discrimination system for confirmation of meat origin.

Detection of honey adulteration using benchtop 1H NMR spectroscopy.

High magnetic field NMR spectroscopy featuring the use of superconducting magnets is a powerful analytical technique for the detection of honey adulteration. Such high field NMR systems are, however, typically housed in specialised laboratories, require cryogenic coolants, and necessitate specialist training to operate. Benchtop NMR spectrometers featuring permanent magnets are, by comparison, significantly cheaper, more mobile and can be operated with minimal expertise. The lower magnetic fields used in such systems, however, result in limited spectral resolution, which diminishes their ability to perform quantitative composition analysis. These limitations may be overcome by implementing a recently developed field-invariant model-based fitting method which is defined by the underlying quantum mechanical properties of the nuclear spin system; this method is applied here to quantify the sugar composition of honey using benchtop 1H NMR (43 MHz) spectroscopy. The detection of adulteration of 26 honey samples with brown rice syrup is quantitatively demonstrated to a minimum adulterant concentration of 5 wt%. Honey adulteration with corn syrup, glucose syrup and wheat syrup was also quantitatively detected using this approach. Our NMR detection of adulteration was shown to be invariant with time over 60 days of storage.

Blockchain Based Solutions for Milk Procurement Management and Adulteration Detection

In our day-to-day life, milk is a fundamental component that is rudimentary for many food products. Consuming milk regularly is very important because it is a mixture of various macro and micronutrients and is a source of proteins, fats, carbohydrates, and vitamins. Particularly in our nation, women’s empowerment and milk production are closely related. Furthermore, rural Indian communities rely heavily on small holder dairy farms for their livelihoods, and women play an important role in milk production. The dairy sector is estimated to contribute around 6% of the country’s agricultural GDP, with the value of milk production in India exceeding 200 million ton by the year 2023 [1]. Milk is a highly perishable commodity, when not handled properly it is susceptible to microbial spoilage. The milk after production must be processed properly to retain its quality till consumption. Nowadays, many milk vendors are adulterating the milk for financial gain. Consumers have the rights to obtain good and safe milk, as it is largely consumed by people of all age groups. Supply chain process of a small holder dairy farming has been examined recently in Salem, Tamil Nadu, India and it has been observed that the current process in dairy involves the accumulated milk from dairy farmers are being poured into the milk cane, then being detected and sent to a larger container where it is mixed with all the other milk. In most cases, it would be impossible to separate fresh milk from un-fresh milk. Therefore, the objective of this research work aims to develop a Blockchain and IoT based solution for milk procurement and adulteration detection there by providing a complete milk traceability data to the consumers from production to consumption, and establishing a trust in providing quality milk to the society.

Molecular detection of beef adulteration in mutton by duplex polymerase chain reaction

A duplex polymerase chain reaction (PCR) technique was evaluated for detection of beef adulteration in mutton. A total of 22 fresh longissimus dorsi muscle samples each of mutton and beef was collected in December, 2020 from 15 retail shops located in and around Chennai. Meat sample was taken in different proportions (mutton :beef) as 100:0, 90:10, 80:20, 70:30, 60:40, 50:50 and 0:100. PCR amplified with species-specific primers for cytochrome b gene revealed species-specific bands at 331 and 274 bp for mutton and beef, respectively. The primers used in the study amplified the corresponding sequence of cytochrome b gene for the different proportions of meat and beef samples. From the study, it can be concluded that duplex PCR is a reliable technique to detect the adulteration of fresh and adulterated samples of mutton and beef.

A Comparative Analysis of Choorna and Bhavitha Choorna of Flower Buds of Japa (Hibiscus rosa-sinensis Linn.) through High-performance Thin-layer Chromatography

Introduction:High-performance thin-layer chromatography (HPTLC) is nowadays adopted by the major pharmacopoeias of the world for analysis of herbal drugs and preparations. The current use is generally limited to the visual observation of the fingerprints for identification and detection of adulterations and falsifications. It makes sense to use HPTLC to expand chromatographic fingerprints to identify the main active ingredients in medicinal plants. Compared to thin layer chromatography, the separation and resolution are significantly superior, and the outcomes are far more consistent and repeatable. Materials and Methods: Japakusuma mukula (flower buds of Japa) which is botanically identified as Hibiscus rosasinensis Linn. contains various phytoconstituents such as flavonoids, terpenoids, steroids, polysaccharides, alkaloids, amino acids, lipids, sesquiterpene, quinones, and naphthalene groups. HPTLC fingerprinting profile of choorna (powder) and bhavitha choorna (triturated powder) of flower buds of Hibiscus rosa-sinensis Linn. is demonstrated in methanolic extract in this study and bands are analyzed at 254 nm and 366 nm using CAMAG Linomat V Automatic Sample Spotter. The peaks and area are compared. Results and Conclusion: Similarity in the peak areas even in different Max Rf values, it could be inferred that the phytoconstituent obtained in both conditions will be having similarity. Since the bhavitha choorna possess, the greater number of large value peak areas in both the wavelength of visualization, it is clear that bhavitha choorna is more potent in comparison with choorna.

Toward the Non-Targeted Detection of Adulterated Virgin Olive Oil with Edible Oils via FTIR Spectroscopy & Chemometrics: Research Methodology Trends, Gaps and Future Perspectives.

Fourier-Transform mid-infrared (FTIR) spectroscopy offers a strong candidate screening tool for rapid, non-destructive and early detection of unauthorized virgin olive oil blends with other edible oils. Potential applications to the official anti-fraud control are supported by dozens of research articles with a "proof-of-concept" study approach through different chemometric workflows for comprehensive spectral analysis. It may also assist non-targeted authenticity testing, an emerging goal for modern food fraud inspection systems. Hence, FTIR-based methods need to be standardized and validated to be accepted by the olive industry and official regulators. Thus far, several literature reviews evaluated the competence of FTIR standalone or compared with other vibrational techniques only in view of the chemometric methodology, regardless of the inherent characteristics of the product spectra or the application scope. Regarding authenticity testing, every step of the methodology workflow, and not only the post-acquisition steps, need thorough validation. In this context, the present review investigates the progress in the research methodology on FTIR-based detection of virgin olive oil adulteration over a period of more than 25 years with the aim to capture the trends, identify gaps or misuses in the existing literature and highlight intriguing topics for future studies. An extensive search in Scopus, Web of Science and Google Scholar, combined with bibliometric analysis, helped to extract qualitative and quantitative information from publication sources. Our findings verified that intercomparison of literature results is often impossible; sampling design, FTIR spectral acquisition and performance evaluation are critical methodological issues that need more specific guidance and criteria for application to product authenticity testing.

Detection of adulteration of goat milk sold in the Turkish market by real-time Polymerase Chain reaction

Detection of milk adulteration is important to maintaining the quality of milk and milk products regarding pricing and consumer protection. Thus, in the current study, we investigated the adulteration of goat milk sold in the market and labeled it as “100% Goat Milk” with cows and sheep milk by real-Time PCR reaction (RT-PCR). This study is the first investigation on goat milk in Turkey and has particular importance as it was carried out using TaqMan probe RT-PCR. In total, 60 milk samples sold in the market and labeled as “100% Goat Milk” were collected from 12 different provinces of Turkey. The RT-PCR TaqMan probe detected the adulteration of goat milk with cow and sheep milk. In the study, 18 (30%) samples were found to be compatible with the label. It was determined that 42 (70%) samples did not comply with the “100% Goat Milk” statement on the label. It was determined that 6 (10%) of the samples contained only sheep milk, 18 (30%) contained only cow milk, another 6 (10%) contained goat and cow milk, 12 (20%) contained sheep and cow milk, and 36 (60%) did not contain any goat milk. The results of this study revealed high levels of adulteration in goat milk products. Therefore, careful continuous monitoring of these products’ production and sales is necessary regarding deception of consumers and public health.

Infrared Spectroscopy-Based Chemometric Analysis for Lard Differentiation in Meat Samples

One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness. The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary, for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis. Fourier Transform Infrared Spectroscopy (FTIR) is used in this work to identify lard adulteration in cow, lamb, and chicken samples. A simplified extraction method was implied to obtain the lipids from pure and adulterated meat. Adulterated samples were obtained by mixing lard with chicken, lamb, and beef with different concentrations (10%–50% v/v). Principal component analysis (PCA) and partial least square (PLS) were used to develop a calibration model at 800–3500 cm⁻¹. Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken, lamb, and beef samples. The corresponding FTIR peaks for the lard have been observed at 1159.6, 1743.4, 2853.1, and 2922.5 cm⁻¹, which differentiate chicken, lamb, and beef samples. The wavenumbers offer the highest determination coefficient R² value of 0.846 and lowest root mean square error of calibration (RMSEC) and root mean square error prediction (RMSEP) with an accuracy of 84.6%. Even the tiniest fat adulteration up to 10% can be reliably discovered using this methodology.

Detection of Adulteration in Spices

Detection of Adulteration in Spices

Development of a Smart Sensor Array for Adulteration Detection in Black Pepper Seeds using Machine Learning

Black pepper is an expensive commodity with a high risk of adulteration. Ground papaya seed is the main adulterant in pepper because it cannot be discriminated visually. There are few destructive methods. Since pepper is costlier, non-destructive method of adulteration is must but it is challenging one. The existing non-destructive method uses costlier equipment, bulky, involve laboratory-based testing, time consuming in the process. To overcome the above issues, this article presents the development of Non-destructive E- nose gas sensor for pepper adulteration detection. This system determines the VOC in a controlled environment. The proposed system utilizes MQ2 and MQ3 gas sensor arrays to identify Volatile Organic Compounds present in pepper seeds to discriminate adulterant and non-adulterant sample. The sensor data are utilized to perform the qualitative analysis to determine the adulteration using a support vector machine learning algorithm. The proposed sensor system with Support Vector Machine learning algorithm outperforms in comparison with existing methods with 100% classification accuracy. Conclusion: The developed gas sensor system is connected to the internet via the IoT application model to show results on the web pages and enables access by the authenticated user from anywhere. Client server model with MQTT protocol is used for developing IoT application.