Detection Of Adulteration Research Articles

Design & development of adulteration detection system by fumigation method & machine learning techniques

A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil. Each and every edible oil has a certain value of refractive index. When such oils are contemned in a change adding adulterants, the value of its refractive indices also changes. This value of refractive index serves as a feature for testing the oil and helps us in detecting the adulteration. If Oil is adulterated with vegetable oils, the refractive index will be lower and with animal fats, the refractive index will be higher than that of pure Oil. While in Fumigation Method a hardware module is develop in which adulterated & pure oil samples are heated at 40–50 °C for 4.66 min and the volatiles that are generated by varying gas concentrations are forcefully passed through to the MEMS Gas Sensor-MISC-2714 and Multichannel Gas sensor. The conductance of the sensors changes according to the gases sensed by the sensors contributes to features extraction. The conductance value serves as a feature for the classifier to determine whether the sample is highly, moderately, or lowly contaminated. Thus, in proposed methods we use different algorithms based on machine learning like KNN, Random Forest, CATBOOST and XGBOOST to accurately reveal the adulteration. Amongst all the applied algorithm Random Forest (RF) Classifier & XGBOOST algorithm outperform well and gives 100% accuracy. The proposed work is used for identifying food adulteration in edible food products which helps us to feed Society with high-quality food.

Exploring AI-enhanced NMR dereplication analysis for complex mixtures and its potential use in adulterant detection

Exploring AI-enhanced NMR dereplication analysis for complex mixtures and its potential use in adulterant detection

Independently Operational Dual-Band Metamaterial-Based Smart EM Sensing System for Detection and Quantification of Adulteration in Cooking Oils

Independently Operational Dual-Band Metamaterial-Based Smart EM Sensing System for Detection and Quantification of Adulteration in Cooking Oils

Application of Multivariate Data Analysis Methods for Rapid Detection and Quantification of Adulterants in Lavender Essential Oil Using Infrared Spectroscopy

Application of Multivariate Data Analysis Methods for Rapid Detection and Quantification of Adulterants in Lavender Essential Oil Using Infrared Spectroscopy

Analysis of cow and goat milk by spr: adulteration detection and differences in protein adsorption

The article discusses using Surface Plasmon Resonance (SPR) to detect adulteration of goat milk with cow milk. The SPR technique is based on changes in the refractive index and can be used to monitor the adsorption of proteins on the sensor surface. The SPR technique can differentiate between pure cow milk and pure goat milk by measuring the initial refractive index, which is higher for cow milk. The article then describes the changes in the refractive index that occur when goat milk is adulterated with cow milk and how adding a bovine casein film on the sensor surface can make it more selective for cow milk. The differences in the chemical compositions and molecular structures of cow and goat milk and how these differences affect the adsorption of proteins and fatty acids on the sensor surface. Finally, other proteins, such as α-lactalbumin and β-lactoglobulin, are used as indicators of adulteration.

Strategies for identifying adulterated Acanthopanax senticosus using portable mass spectrometry and chemometrics

Acanthopanax senticosus is widely cultivated in China, Japan, South Korea, and Russia, and is extensively researched worldwide. Illegal activities, particularly adulteration have been fostered due to its large market demand and high price. This study successfully identifies authentic and adulterated Acanthopanax senticosus samples using a portable mass spectrometer combined with chemometrics. Different data processing and feature selection methods were evaluated for authenticity and adulteration models, with SNV-VIP being selected as the optimal method for both models. Excellent classification performance was achieved using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), K-nearest Neighbors (KNN), Random Forest (RF), and Convolutional Neural Networks-Long Short-Term Memory-Attention (CNN-LSTM-Attention) models, with prediction accuracies for the authenticity model validation set being 1.00, 1.00, 1.00, and 1.00, respectively. The prediction accuracies for the adulteration model validation set were 0.86, 0.73, 0.89, and 0.92, respectively. Additionally, the models identified the most important ions based on variable importance. The combination of portable mass spectra (PMS) and chemometrics provides a simple, rapid, and reliable method for identifying adulteration in Acanthopanax senticosus samples. This study provides a new basis into the application of PMS for adulteration detection in the food field.

Study on detection of adulteration in milk and some milk products.

Study on detection of adulteration in milk and some milk products.

Experimental investigation of a mixed metal oxide (MMO)-based sensor for the detection of adulterants (urea and melamine) in milk at room temperature

Experimental investigation of a mixed metal oxide (MMO)-based sensor for the detection of adulterants (urea and melamine) in milk at room temperature

Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study

Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study

Raman spectroscopy and multivariate analysis for the waste and edible vegetable oil classification

Twelve samples of waste cooking oil (WCO) were prepared by four different deep-frying procedures. The edible and the waste oil samples were characterised by Raman spectroscopy, revealing few and almost negligible differences between them. Therefore, the possibility of classifying the different groups of samples by extracting valuable data from the Raman spectra through statistical multivariate analysis was explored. Even if the number of samples was not enough to draw definitive conclusions, unsupervised principal component analysis (PCA) and supervised partial least square discriminant analysis (PLS-DA) conducted on the raw Raman signals, allowed to distinguish within edible and waste vegetable oil, and to select the most relevant combination of variables associated with each family. Using sparse partial least square discriminant analysis (S-PLS-DA), we determined a chemical fingerprint characteristic of each sample by creating a Variable In Projection (VIP) plot. The methodology herein presented could find relevant application in the detection of waste adulteration in vegetable oils sold for industrial purposes other than food.

Authentication of edible herbal materials and food products using mass spectrometry based metabolites and inorganic constituents

Medicinal food homologous (MFH) substances not only provide nutrition but also serve as a traditional means to overcome many health issues. Authentication of these products verifies their efficacity and assures consumers of a genuine product. In this review paper, we focus the determination of MFH authenticity including geographical identification and adulteration detection using mass spectrometry (liquid and gas chromatography) based metabolites and inorganic constituents (muti-elements and stable isotopes). The application of these techniques to determine product identification characteristics combined with chemometrics are discussed, along with the limitations of these techniques. Multi-elements, stable isotopes, and metabolite analysis are shown to provide an effective combination of techniques to resolve the origin of various MFH products. Most organic compounds from MFH products are identified using chromatographic separation techniques (HPLC, GC) combined with different detection methods. Chemometric analysis of organic and inorganic fingerprints offers a robust method to detect and classify mislabeled and suspected fraudulent samples of different MFH products.

Qualitative and quantitative detection of camellia oil adulteration using electronic nose based on wavelet decomposition humidity correction

This study addresses the challenge of environmental humidity impacting the accuracy of metal oxide semiconductor (MOS)-based electronic nose systems in detecting adulterated camellia oil. A self-developed electronic nose platform with eight MOS sensors was used to detect adulteration in camellia oil mixed with varying ratios of rapeseed, soybean, and corn oils at different humidity levels (20%, 40%, and 60%). Wavelet decomposition using fourth-order Symlet wavelet was applied to correct response signals, mitigating humidity's effect on detection accuracy. Linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) were employed to build qualitative identification models using pre- and post-correction datasets, while partial least squares regression (PLSR) and backpropagation neural network (BPNN) were used for quantitative prediction. Results showed that both qualitative and quantitative models significantly improved performance after correcting signal drift with wavelet decomposition. The qualitative model's 10-fold cross-validation prediction accuracy increased to 98.67% from 88.67%, while the quantitative model's average R2 reached 0.99 with RMSE reduced to 3.64 mL/100 mL, compared to pre-correction R2 of 0.86 and RMSE of 9.63 mL/100 mL. Notably, the RF and BPNN models outperformed others in qualitative and quantitative detection, respectively. These findings highlight the potential of electronic nose technology with humidity correction for authenticating camellia oil.

Enhanced Fuel Adulteration Detection Using Surface Plasmon Resonance Biosensor with Machine Learning Optimization in the terahertz regime

Enhanced Fuel Adulteration Detection Using Surface Plasmon Resonance Biosensor with Machine Learning Optimization in the terahertz regime

The use of PCR method for adulteration detection of goat dairy products manufactured by smallholders

The analysis of food adulteration is an important element of improving health safety. Both accidental and intentional adulteration of food, can cause serious health consequences for humans and can be considered as health hazard. In recent years, molecular biology techniques has been increasingly used in food analysis due to their high precision and the ability to obtain results in a short time, as well as the identification and quantitative determination of food adulterants. The samples tested in the study were goat's milk, yogurts, fresh and ripening cheeses produced by smallholders all over Poland. The analysis included DNA isolation, which was used as template for duplex PCR and detection of goat’s and cow’s mitochondrial DNA fragments. The obtained results indicate that the applied technique enabled the rapid identification of adulteration in analyzed goat’s milk products, among which in only one product out of thirty five the presence of undeclared cow’s milk was detected. This indicates good food safety standards applied on small-scale farms.

Identification and quantification of adulteration in collagen powder by terahertz spectroscopy − the effect of spectral characteristics on performance is considered

Terahertz spectroscopy is an emerging rapid detection method that can be used to detect and analyze food quality issues. However, models developed based on various spectral characteristics of terahertz have shown different performances in food identification. Therefore, we preliminarily analyzed the effect of terahertz spectral characteristics on the identification and quantification of collagen powder adulterated with food powders (plant protein powder, corn starch, wheat flour) with the use of random forest (RF), linear discriminant analysis (LDA), and partial least squares regression (PLSR), and determined the spectral characteristics suitable for identification and quantitative analysis. Then, the selected spectral characteristics data were preprocessed using baseline correction (BC), gaussian filter (GF), moving average (MA), and savitzky-golay (SG). Feature variables were extracted from preprocessed spectral characteristics data using genetic algorithm (GA), random forest (RF), and least angle regression (LAR). The study indicated that the BC-GA-LDA classification model based on the absorption coefficient spectra achieved an accuracy of 96.96% in identifying adulterated collagen powder. Additionally, the GA-PLSR model developed based on the power spectra demonstrated excellent performance in predicting adulteration levels, with the coefficient of determination (Rp2) values ranging from 0.93 to 0.99. The results showed that the rational selection of terahertz spectral characteristics is highly feasible for the accurate detection of collagen powder adulteration.

Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods

Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of traditional chemometrics and deep learning methods in identifying and quantifying adulterated CAO were compared from a statistical perspective, and no significant difference were founded in the identification of CAO at different levels of adulteration. The recognition rate of pure and adulterated CAO was 100 %, but there were misclassifications among different adulterated CAOs. The deep learning models outperformed chemometrics methods in quantitative prediction of adulteration level, with RP2, RMSEP, and RPD of the optimal ConvLSTM model achieved 0.999, 0.9 % and 31.5, respectively. The classifiers and models developed in this study based on deep learning have wide applicability and reliability, and provide a fast and accurate method for adulteration detection in CAO.

Urea Concentration Detection in Milk Using Microscopic Image Processing Algorithm under Ultraviolet Light Approach

The adulteration of milk is a pressing concern for the citizens of India and people all around the globe. Due to a lack of regulation compliance and insufficient surveillance infrastructure, it is noticeably worse in emerging and slow-growing nations. One of the most common and dangerous adulterants in milk is urea. If the permissible quantity of urea in milk is surpassed, it could have a major negative impact on people's health. All existing methods of urea detection require time, expertise, costly chemicals, and enzymes, along with exorbitant instruments and instrument-specific expertise. The key to overcoming this challenge is having the infrastructure to detect adulterated milk. This study aims to identify a cost- effective and largely implementable system for quantitative detection of urea content to identify adulterated milk primarily for milk distribution centers in India. The proposed milk adulteration detection system, dubbed the MADS, entails a cost-effective, rapid, accurate, precise, and completely novel method for the quantitative computation of urea levels in adulterated milk. It is a device that detects the concentration of particles of urea in milk using a microscopic image processing algorithm under ultraviolet light. Using ultraviolet light and a proprietary program in Python, the isolation of the urea particle from the rest of the milk solids is done and the area concentration, as an average of the value calculated in each of the frames of the video captured through the microscopic camera, is computed. This gives the final urea concentration in milk, which can be used to check whether the concentration follows the government guidelines and exceeds the legal limit.

Classification of wheat flour levels in powdered spices using visual imaging

The present research aims to investigate the ability of image processing in the detection of wheat flour adulterant in different spices. Different samples of black pepper, red pepper, and cinnamon were prepared with different levels of wheat flour adulteration. The images of the samples were captured using a visible camera. An image processing algorithm was designed and codded in MATLAB software to process the acquired images. Different color and textural features were extracted. The efficient features were selected and classified using artificial neural network and support vector machine. In the present research, the classification accuracies of artificial neural network analysis for black pepper, red pepper, and cinnamon products were 97.8, 100.00, and 100.00 %, respectively. During the experiment, these results were better than the performance of the support vector machine, (93.33, 100.00, and 98.88 %, respectively). Visible image processing can be used accurately to detect different adulterant levels that increase product quality while reducing operating costs.

Analytical Method Development and Validation Based on Simple Color Reactions and Microwave Plasma–Atomic Emission Spectrometry (MP-AES) for the Detection of Adulteration in Teff Injera (Ethiopian Flatbread)

Analytical Method Development and Validation Based on Simple Color Reactions and Microwave Plasma–Atomic Emission Spectrometry (MP-AES) for the Detection of Adulteration in Teff Injera (Ethiopian Flatbread)

Design of a Portable Electronic Nose for Identification of Minced Chicken Meat Adulterated With Soybean Protein Isolate

ABSTRACTThe study aimed to develop a portable electronic nose system for detecting adulteration with soybean protein isolate (SPI) in chicken meat. The system mainly consisted of three parts: the gas sensor array, the DSP28335 control board, and the upper computer. The DSP28335 control board, developed using C language, included analog to digital converter (ADC) module, digital output (DO) module, pulse width modulation (PWM) module, controller area network (CAN) module, power module, drive circuit, and so forth. The upper computer, developed using LabVIEW, facilitated user interaction with the user by primarily handling CAN configuration and monitoring, displaying and storing sensor data, temperature and flow data, and sending and monitoring electronic nose commands. The feasibility of the proposed electronic nose for characterizing adulterated chicken meat was tested on six classes of chicken meat that had been adulterated with varied quantities of SPI. The mass fractions of SPI were 0%, 5%, 10%, 15%, 20%, and 25%, respectively. On the basis of odor data from the electronic nose, K‐nearest neighbor (KNN), linear discriminant analysis (LDA), and support vector machine (SVM) were applied to qualitatively distinguish minced chicken meat with different adulteration ratios. The results showed that the SVM model had the best recognition effect. When the best parameters (c, g) were c = 16 and g = 1, the accuracy of SVM model was 97.22% and 93.75% in the training and testing sets, respectively. These results demonstrated that the portable electronic nose designed in this paper effectively identifies minced chicken meat under various adulteration conditions, enabling rapid and nondestructive detection of chicken meat adulteration.