Detection Of Adulteration Research Articles

Honey Adulteration Detection via Ultraviolet–Visible Spectral Investigation Coupled with Chemometric Analysis

Honey Adulteration Detection via Ultraviolet–Visible Spectral Investigation Coupled with Chemometric Analysis

Smart E-Tongue Based on Polypyrrole Sensor Array as Tool for Rapid Analysis of Coffees from Different Varieties

Due to the lucrative coffee market, this product is often subject to adulteration, as inferior or non-coffee materials or varieties are mixed in, negatively affecting its quality. Traditional sensory evaluations by expert tasters and chemical analysis methods, although effective, are time-consuming, costly, and require skilled personnel. The aim of this work was to evaluate the capacity of a smart electronic tongue (e-tongue) based on a polypyrrole sensor array as a tool for the rapid analysis of coffees elaborated from beans of different varieties. The smart e-tongue device was developed with a polypyrrole-based voltammetric sensor array and portable multi-potentiostat operated via smartphone. The sensor array comprised seven electrodes, each doped with distinct counterions to enhance cross-selectivity. The smart e-tongue was tested on five Arabica coffee varieties (Typica, Bourbon, Maragogype, Tabi, and Caturra). The resulting voltammetric signals were analyzed using principal component analysis assisted by neural networks (PCNN) and cluster analysis (CA), enabling clear discrimination among the coffee samples. The results demonstrate that the polypyrrole sensors can generate distinct electrochemical patterns, serving as “fingerprints” for each coffee variety. This study highlights the potential of polypyrrole-based smart e-tongues as a rapid, cost-effective, and portable alternative for coffee quality assessment and adulteration detection, with broader applications in the food and beverage industry.

Milk Adulteration Identification using Hyperspectral Imaging and ML

Milk adulteration poses a global concern, with developing countries facing higher risks due to unsatisfactory monitoring systems and policies. Surprisingly, this common issue has often been overlooked in many countries. Contrary to popular belief, adulterants in milk can result in severe health risks, potentially leading to fatal diseases. Detecting and categorizing milk adulteration is crucial for consumer safety and the dairy industry. This research is divided into 2 breakthroughs, destructive and non-destructive methods. In the destructive method, the Lactoscan system was used for qualitative analysis: (Solid Not Fat (SNF), density, fat, lactose, conductivity, solids, protein, temperature, and pH level). The research also examines non-distractive hyperspectral imaging (HSI) through HSI Specim Fx-10 (397-1003 nm) analysis to detect various phases of milk adulteration for accurate and user-friendly imaging-based adulterants detection and categorization. Preprocessing involves radiometric correction, image resizing, region of interest (ROI) selection for feature extraction, and empirical line method (ELM) to calculate spectral reflectance signature. Machine learning techniques (Logistic Regression (LRs), Decision Tree (DTs), Support Vector Machine (SVMs), and Linear Discriminant Analysis (LDAs)), are employed, with LDA excelling in adulteration identification by learning the spectral signatures. These algorithms are trained and validated using a developed milk adulteration data set. Training, testing, and validation accuracy, precision, recall, F1-score, Kappa, and Matthew's correlation coefficient (MCC) metrics showcase the effectiveness of the proposed pipeline, outclassing numerous state-of-the-art approaches with a validation accuracy of 100%. In conclusion, this study established a multiclass model capable of detecting milk adulterant behavior, showing significant practical application for milk quality assessment.

A portable LED-induced fluorescence system for quantitative detection of different kinds of vegetable oil adulteration

This study developed a portable LED-induced fluorescence detection system for quantitative detection of various vegetable oil adulteration. Eight common vegetable oils and 14 different adulterated samples with adulteration concentrations ranging from 0 to 50% were prepared. Before quantitative analysis, different classification models were established for the determination of types of adulteration oil. The overall recognition accuracy was greater than 98%. Furthermore, with a simple calculation, the proposed normalized spectral ratio (NSR) preprocessing method was used to eliminate the light scattering effects in the raw fluorescence spectra. In addition, the competitive adaptive reweighted sampling (CARS) method was used to select characteristic wavelengths. The final oil adulteration quantitative analysis model was NSR_CARS+PLS. The range of correlation coefficient (Rp) and root-mean-square-error (RMSEP) for the prediction datasets were 0.9548-0.9974 and 1.0265%-5.0236%, respectively.

AfroPALM - Afrocentric Palm oil Adulteration Learning Models: an End-to-end Deep Learning Approach for Detection of Palm Oil Adulteration

In Africa, rapid population growth and a focus on increasing food production have often overlooked the crucial aspect of food safety, leading to the highest per capita incidences of foodborne illness globally. This underscores the imperative to address the food safety challenges on the continent. A critical concern is the widespread adulteration of red palm oil across West Africa, often involving harmful red azo dyes, posing significant health risks. Current detection methods, reliant on laboratory procedures, are not only time-consuming and expensive but also impractical for broad implementation in local markets. To address these limitations, we propose an end-to-end deep learning approach that bypasses the need for manual feature extraction and laboratory-based analyses. Utilizing high- resolution imaging technology, our approach can objectively and reliably detect palm oil adulteration directly from raw image data. In our study, we developed a deep convolutional neural network dubbed AfroPALM-Custom, specifically designed for detecting palm oil adulteration in African markets. AfroPALM-Custom was pivotal in refining our deep learning approach, achieving a test accuracy of 90.63% and an F1 score of 90.98%. We later adapted mobile-efficient pretrained models, namely SqueezeNet1.1 and GhostNetV1—mobile—fine-tuned and as well dubbed AfroPALM-GhostNet and AfroPALM-SqueezeNet. In performance, AfroPALM-GhostNet and AfroPALM-SqueezeNet achieved test accuracies of 96.29% and 91.16%, respectively, and F1-scores of 96.57% and 91.95%. This proficiency in identifying adulterated palm oil demonstrates a significant advancement in food safety solutions for African markets, offering a practical and scalable approach for implementation within local marketplaces where resources are limited and laboratory methods are impractical.© 2001 Elsevier Science. All rights reserved

Rapid qualitative and quantitative detection for adulteration of Atractylodis Rhizoma using hyperspectral imaging combined with chemometric methods

In the field of traditional Chinese medicine, Atractylodis Rhizoma (AR) is commonly used for various diseases due to its excellent ability to dry dampness and strengthen the spleen, especially popular in East Asia. The aim of this study is to proposed Hyperspectral Imaging (HSI) in combination with chemometric methods for the rapid qualitative and quantitative detection of AR adulteration with other types of powder. Partial Least Squares Discriminant Analysis (PLS-DA) was used to construct the classification models the best, with the First-order Derivative (F-D) preprocessing method. The accuracy values of the test sets for classification models were above 99%. Furthermore, Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and BP Neural Network (BPNN) were used to quantitatively analyze the adulteration level. On the whole, the BPNN model has a relatively stable effect. The R-square (R2) values of different models were all greater than 0.97, the Root Mean Square Error (RMSE) values were all less than 0.0300, and the Relative Percentage Difference (RPD) values were over 6.00. After applying three characteristic wavelength selection algorithms, namely Iterative Retained Information Variable (IRIV), Successive Projections Algorithm (SPA), and Variable Iterative Space Shrinkage Approach (VISSA) algorithms, the classification accuracy values remained over 99.00% while the quantification models’ RPD values were over 4.00. These results demonstrate the reliability of using hyperspectral imaging combined with chemometrics methods for the adulteration problems in AR.

Camellia oil grading adulteration detection using characteristic volatile components GC-MS fingerprints combined with chemometrics

Camellia oil grading adulteration detection using characteristic volatile components GC-MS fingerprints combined with chemometrics

Optical Characteristics of the Black Seed Oil: Fluorescence and Adulteration Detection.

Understanding the optical characteristics, especially the fluorescence properties of vegetable oils, particularly black seed oil (BSO), is an essential prerequisite for the development of the future applications in both medicinal and nutritional fields. In this way, it is essential to identify the roles played by the components such as unsaturated fatty acids, carotenoids, flavonoids, vitamin E, and chlorophylls in the BSO fluorescence spectra. In the current landscape, challenges arise from the adulteration of BSO with impurities such as sunflower oil (SO), complicating efforts to obtain pure BSO. Here, dependence of the BSO fluorescence on excitation wavelength has been examined using UV- visible diode lasers (λ = 355, 405, 440, 532 and 660nm) as excitation sources. Though conjugated unsaturated fatty acids, flavonoids and chlorophylls are mainly contributed to the fluorescence due to UV excitation, wavelengths in the visible range specifically excite carotenoids, vitamin E, and chlorophylls. By utilizing the laser-induced fluorescence (LIF) technique, we explored the effects of inner filters and setup geometry to gain deeper insights into the BSO fluorescence dynamics. Differential spectral analysis (DSA) revealed that adulteration of BSO with SO alters its fluorescence features. As a result, a novel approach is proposed for adulteration detection, based on the simultaneous excitation of BSO and SO by a 405nm laser, benefit to indirect excitation of the carotenoids of BSO by fluorescence emission of SO within the spectral range of 400-500nm, which results in the enhancement of BSO fluorescence in the region of 500-600nm.

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.