Adulterant Concentration Research Articles

FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentration

Pericarpium citri reticulatae (PCR) has been used as a food and spice for many years and is known for its rich nutritional content and unique aroma. However, price increases are often accompanied by adulteration. In this study, two kinds of adulterants (Orange peel-OP and Mandarin Rind-MR) were identified by chromaticity analysis, FT-NIR and machine learning algorithm, and the doping concentration was predicted quantitatively. The results show that colorimetric analysis cannot completely differentiate between PCR and adulterants. Using spectral preprocessing combined with machine learning algorithms, PCR and two adulterants were successfully distinguished, with classification accuracy reaching 99.30 % and 98.64 % respectively. After selecting characteristic wavelengths, the R2P of the adulterated quantitative model is greater than 0.99. Generally, this study proposes to use FT-NIR to study the adulteration of PCR for the first time, which fills the technical gap in the adulteration research of PCR, and provides an important method to solve the increasingly serious adulteration problem of PCR.

Highly sensitive refractive index based biofuel adulteration sensor using multiband absorber

A multi-band electromagnetic absorber is proposed for sensing the adulterant concentration in biofuels based on refractive index variations at room temperature (300 K). The proposed absorber structure consists of a gold based metallic resonating geometry and metallic ground plane on the top and bottom layer of the cavity loaded dielectric material. The unit cell has volume of 24μm×24μm×3.57μm (0.1088λL×0.1088λL×0.01618λL where λL is wavelength at lowest operating frequency), five absorption peaks at 1.36, 3.349, 5.338, 5.887 & 9.307 THz with peak absorptivity of 96.15%, 97.55%, 95.63%, 83.60% & 96.13% and Full Width at Half Maxima (FWHM) of 0.098, 0.275, 1.01 & 0.417 THz respectively. For solid substrate, the absorption performance is polarization insensitive and with cavity inside the substrate, the absorption performance is polarization sensitive in normal incidence. The absorber’s Refractive Index (RI) and biofuel adulteration sensing performance is also analyzed by loading analyte on its top surface and inside the substrate cavity. Peak sensitivity of ∼3 THz/RIU with a maximum Figure of merit (FOM) of ∼5.69RIU−1 and maximum ∼0.16% error in ethanol based biofuel adulteration estimation are achieved. The proposed absorber has advantages of ultracompact dimensions, very high sensitivity and very less estimation error in adulteration estimation over other absorber based sensors.

Combination of gas chromatography-mass spectrometry and hyperspectral imaging for identification of adulterated Safflower seed oil

Safflower seed oil (SSO), a crucial edible oil, occupies a significant position in human diet due to its unique nutritional components. However, because of its high economic value, SSO has become a primary target for food adulteration. To rapidly and effectively detect the concentration of adulterants in SSO, a research approach using hyperspectral imaging and gas chromatography-mass spectrometry (GC-MS) was proposed as technical means, employing machine learning as the methodology. Specifically, different preprocessing methods were compared, with median filtering (MF) selected for spectral data to significantly reduce noise and improve the robustness and generalization ability of the model. Regarding the selection of feature bands, bands around 440 nm, 530 nm, and 880 nm – 950 nm were identified as more favorable for establishing a predictive model for the concentration of adulterants in SSO, while also shortening the modeling time. By constructing an ensemble learning model with ridge regression (Ridge) and partial least squares regression (PLSR) as base models and LightGBM as the meta-model, achieving high-precision prediction of the concentration of adulterants in SSO. Furthermore, by jointly modeling the linoleic acid, oleic acid and palmitic acid measured by GC-MS with hyperspectral data, the model's R2 was improved to 0.976, highlighting its outstanding performance. Therefore, this study identifies MF-Ridge-Stacking as the optimal model for predicting the concentration of adulterants in SSO. The research not only provides theoretical and technical support for adulteration identification but also presents a new method for predicting the concentration of adulterants in SSO, with potential practical application value.

Image-Based Detection of Adulterants in Milk Using Convolutional Neural Network.

Adulteration of milk poses a severe human health hazard. Existing methods for detecting adulterants such as water, urea, ammonium sulfate (AmS), oils, and surfactants in milk are selective, expensive, and often challenging to implement in rural areas. The present work shows the potential of machine learning to detect milk adulterants using patterns of evaporative milk deposits. The final deposit patterns obtained after evaporation of the adulterated milk droplets are used to create an image data set. This data set is used to develop a deep learning model that deploys a convolutional neural network (CNN/ConvNet) to classify the distinct evaporation patterns obtained for different types and concentrations of adulterants. Further, we apply implicit and explicit regularization and compare their accuracies. The models trained with different regularization optimization schemes demonstrate that a CNN can be successfully implemented to detect adulterants in milk. Additionally, we experimentally determine how the type and concentration of milk adulterants, including ammonium sulfate (AmS), urea, oil, and surfactants, affect milk evaporative deposition. Added AmS and urea in milk crystallizes during evaporation to produce recognizable patterns that can be used for their detection. The method is capable of detecting AmS added in excess of 2.4% and urea in excess of 5% in diluted milk (20 wt %) due to the crystallization of AmS and urea, respectively. In the case of milk adulterated with vegetable oil, evaporation leads to the separation and accumulation of oil at the top of the deposit, leading to the detection of oil present in excess of 2% in 20% diluted milk. Furthermore, a minimum individual amount of 5% urea, 2.4% AmS, and 2% oil concentration in diluted milk (20%) is shown to be individually detected by evaporation pattern-based technique when milk is adulterated with all the adulterants (water, urea, AmS, and oil + surfactant) together. When subjected to different regularization optimization schemes, the CNN gives varying degrees of accuracy for successful detection. The use of implicit regularization in the form of data augmentation gives the best results with a testing average accuracy of 98%, showing that a CNN can be successfully deployed to classify and detect adulterants in milk.

Gaussian process regression coupled with mRMR to predict adulterant concentration in cocaine

Street cocaine is often mixed with various substances that intensify its harmful effects. This paper proposes a framework to identify attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) intervals that best predict the concentration of adulterants in cocaine samples. Wavelengths are ranked according to their relevance through ReliefF and mRMR feature selection approaches, and an iterative process removes less relevant wavelengths based on the ranking suggested by each approach. Gaussian Process (GP) regression models are constructed after each wavelength removal and the prediction performance is evaluated using RMSE. The subset balancing a low RMSE value and a small percentage of retained wavelengths is chosen. The proposed framework was validated using a dataset consisting of 345 samples of cocaine with different amounts of levamisole, caffeine, phenacetin, and lidocaine. Averaged over the four adulterants, the GP regression coupled with the mRMR retained 1.07 % of the 662 original wavelengths, outperforming PLS and SVR regarding prediction performance.

A new spectral simulating method based on near-infrared hyperspectral imaging for evaluation of antibiotic mycelia residues in protein feeds

Antibiotic mycelia residues (AMRs) contain antibiotic residues. If AMRs are ingested in excess by livestock, it may cause health problems. To address the current problem of unknown pixel-scale adulteration concentration in NIR-HSI, this paper innovatively proposes a new spectral simulation method for the evaluation of AMRs in protein feeds. Four common protein feeds (soybean meal (SM), distillers dried grains with solubles (DDGS), cottonseed meal (CM), and nucleotide residue (NR)) and oxytetracycline residue (OR) were selected as study materials. The first step of the method is to simulate the spectra of pixels with different adulteration concentrations using a linear mixing model (LMM). Then, a pixel-scale OR quantitative model was developed based on the simulated pixel spectra combined with local PLS based on global PLS scores (LPLS-S) (which solves the problem of nonlinear distribution of the prediction results due to the 0%-100% content of the correction set). Finally, the model was used to quantitatively predict the OR content of each pixel in hyperspectral image. The average value of each pixel was calculated as the OR content of that sample. The implementation of this method can effectively overcome the inability of PLS-DA to achieve qualitative identification of OR in 2%-20% adulterated samples. In compared to the PLS model built by averaging the spectra over the region of interest, this method utilizes the precise information of each pixel, thereby enhancing the accuracy of the detection of adulterated samples. The results demonstrate that the combination of the method of simulated spectroscopy and LPLS-S provides a novel method for the detection and analysis of illegal feed additives by NIR-HSI.

Investigating the potential of Fourier transform mid-infrared spectroscopy combined with chemometrics for detecting camel’s milk adulteration

This study explores the effectiveness of mid-infrared (MIR) spectroscopy in combination with chemometrics as an alternative to sensory analysis for detecting camel’s milk adulteration with cow’s milk. A paired comparison test involving various concentrations of adulterants was initially conducted to assess consumers’ ability to detect such adulteration. The analysis successfully classified samples into adulterated and authentic camel’s milk using principal component (PC) analysis and hierarchical cluster analysis. Moreover, the application of partial least squares regression and PC regression calibration models demonstrated high-performance capabilities in revealing the level of adulteration. These findings highlight the potential of MIR spectroscopy combined with chemometrics for the authentication of camel’s milk.

Prediction of Milk Powder Raman Spectra and Ammonium Sulfate Adulteration Concentration Based on Neural Network Model

This article aims to explore the changes in Raman spectra of milk powder when different concentrations of ammonium sulfate are adulterated. By conducting neural network tests on milk powder samples with different ammonium sulfate concentrations, we studied the impact of ammonium sulfate adulteration on milk powder. The research results show that moderate concentrations of ammonium sulfate adulteration can improve the solubility and flowability of milk powder, but high concentrations of adulteration may reduce the protein content of milk powder and have negative effects on its sensory quality and safety, causing significant adverse effects on human health. Therefore, the adulteration concentration of ammonium sulfate should be strictly controlled in the production and processing of milk powder to ensure its quality and safety.

Prediction Of Raman Spectra and Adulteration Concentration of Wheat Flour Based on Neural Network Modelling

In order to quickly detect the adulteration of flour containing wheat flour quality testing, the Raman spectrum of wheat flour as the object of study, based on a number of data-based neural network to identify and determine the concentration of wheat adulteration. Firstly, the neural network system in this study borrowed the Raman wave number and Raman intensity curve from "Non-contact Detection of Benzoyl Peroxide in Flour Based on Raman Spectroscopy" as the training and testing set. Next, we created the neural network, set the training parameters, trained the network and simulated the test. Finally, the supervised learning process resulted in error analysis and comparison of results, and the Raman spectra of wheat flour and adulteration concentration were plotted visually.

Reconfigurable Ring Antenna Sensor for Detection of Adulteration in Liquids

In this research paper, a novel reconfigurable ring antenna sensor is presented to detect adulteration in liquids. The suggested antenna sensor is designed to detect and predict salt and sugar concentrations in water and adulterant concentrations in milk. The proposed antenna works in five modes and reconfigurability is achieved using pin diodes. The adulteration in liquids is determined by analyzing the connection between concentration and fluctuation in the reflection coefficient. Calibration equations and Mean relative error (MRE) of different solutions are obtained using reflection coefficients and concentrations. The investigation demonstrates that salt and sugar concentrations could be predicted using a ring sensor with mean relative errors of 2.17% and 2.67% respectively. The sensor is used to detect and predict the concentration of several adulterants in milk like water, caustic soda, sodium carbonate, ammonium sulfate, and urea. The value of MRE for adulterated milk is 2.2% for water, 2.33% for caustic soda, 2.33% for sodium carbonate, 2% for ammonium sulfate, and 3% for urea. The results show that the proposed ring antenna sensor has considerably good sensitivity and accuracy, which makes the system attractive for detecting adulteration in liquids with the lowest mean relative error.

Comparison of Multiple NIR Spectrometers for Detecting Low-Concentration Nitrogen-Based Adulteration in Protein Powders.

Protein adulteration is a common fraud in the food industry due to the high price of protein sources and their limited availability. Total nitrogen determination is the standard analytical technique for quality control, which is incapable of distinguishing between protein nitrogen and nitrogen from non-protein sources. Three benchtops and one handheld near-infrared spectrometer (NIRS) with different signal processing techniques (grating, Fourier transform, and MEM-micro-electro-mechanical system) were compared with detect adulteration in protein powders at low concentration levels. Whey, beef, and pea protein powders were mixed with a different combination and concentration of high nitrogen content compounds-namely melamine, urea, taurine, and glycine-resulting in a total of 819 samples. NIRS, combined with chemometric tools and various spectral preprocessing techniques, was used to predict adulterant concentrations, while the limit of detection (LOD) and limit of quantification (LOQ) were also assessed to further evaluate instrument performance. Out of all devices and measurement methods compared, the most accurate predictive models were built based on the dataset acquired with a grating benchtop spectrophotometer, reaching R2P values of 0.96 and proximating the 0.1% LOD for melamine and urea. Results imply the possibility of using NIRS combined with chemometrics as a generalized quality control tool for protein powders.

Detection and estimation of adulterated gasoline fuel in India using FTIR-ATR spectroscopy with chemometric methods

In this work, Fourier Transform Infrared Attenuated Total Reflectance (FTIR-ATR) spectroscopy was combined with multivariate techniques, such as Principal Component Analysis (PCA), Soft Independent Modelling of Class Analogy (SIMCA), and Partial Least Squares Discriminant Analysis (PLS-DA), to classify and quantify the gasoline adulteration with kerosene. The Calibration models of Kerosene blended on gasoline fuel of 12 standard samples (2–24% v/v) were prepared. The chemometric tools were used to compare 142 samples from Mizoram and 12 samples from neighbouring states. The PCA and SIMCA approaches did not detect adulteration. The PLS-DA classification model found 7 samples were adulterated, with excellent specificity and sensitivity. The PLS regression model had the ability to predict adulterant concentrations with prediction errors less than 2% for all adulterants. The predicted concentration range of the PLS-R model is 0.3–13 % v/v, with high significant values of R-Square (0.996) and RMSE (0.405).

Aflatoxin M1 Concentrations, Adulterants, Microbial Loads, and Physicochemical Properties of Raw Milk Collected From Nekemte City, Ethiopia.

Milk is an essential part of the human diet and is a nutrient-rich food that improves nutrition and food security. The aim of this study was to determine the presence and concentration of aflatoxin M1 (AFM1), adulterants, microbial loads, and physicochemical properties of raw cow's milk (CM) in Nekemte City, Ethiopia. A total of 12 samples of fresh CM were purposefully collected from four kebeles in the city (Bake Jama, Burka Jato, Cheleleki, and Bakanisa Kese) based on the potential of each milk production and distributor site. The AFM1 concentration was determined by high-performance liquid chromatography (HPLC) with a Sigma-Aldrich standard (St. Louis, MO, USA). The concentrations of AFM1 in Bake Jama, Burka Jato, Cheleleki, and Bakanisa Kese were found to be 0.01-0.03 g/L, 0.31-0.35 g/L, 0.19-0.21 g/L, and 0.04-0.07 g/L, respectively. The concentrations of AFM1 in the present study varied significantly (p < 0.05) and ranged from 0.01 g/L to 0.35 g/L. These results show that of the 12 samples tested, all were positive for AFM1 and contaminated to varying degrees. The results of this study also revealed that the concentration of AFM1 in 7 (58%) of the 12 milk samples was above the European Union's (EU) maximum tolerance limit (0.05 g/L). The present study also revealed that of the investigated adulterants, only the addition of water had positive effects on three milk samples, while the remaining adulterants were not detected in any of the milk samples. The total bacterial count (TBC) and total coliform count (TCC) were significantly (p < 0.05) different and ranged from 5.53 to 6.82 log10cfumL-1 and from 4.21 to 4.74 log10cfumL-1, respectively. The physicochemical properties of the milk samples in the present study were significantly (p < 0.05) different and ranged from 2.8% to 5.75% fat, 7.03% to 9.75% solid-not-fat (SNF), 2.35% to 3.61% protein, 3.33% to 5.15% lactose, 11.54% to 13.69% total solid, 0.16% to 0.18% titratable acid, 26.7 to 32.1°C, 6.35 to 6.55 pH, and 1.027 to 1.030 specific gravity. The physicochemical parameters of the raw milk in the study area met the required quality standards. Hence, further studies are required to determine the extent of the problem and the factors associated with high levels of AFM1 in raw milk in the study areas, including the detection of aflatoxin B1 (AFB1) in animal feed.

Detection of adulteration of Alpaca (Vicugna pacos) meat using a portable NIR spectrometer and NIR-hyperspectral imaging

Alpaca meat has high protein content; good tenderness and low intramuscular fat content, being more expensive than meat from other animals (e.g., beef). Hence, alpaca meat may face adulteration, which demands chemical analytical methods to be identified. In this study, chemical-free methods, e.g., portable NIR spectrometer and NIR-HSI were employed to detect adulteration of alpaca meat with pork, chicken, and beef (0 – 50% w/w). Spectral analysis revealed significant differences in the spectra of pure alpaca meat samples using NIR-HSI. Principal Component Analysis (PCA) grouped samples into pure and non-pure alpaca meat using both devices as sources of spectra. Next, we developed and validated one-class Data Driven Soft Independent Class Analogy (DD-SIMCA) models to authenticate pure alpaca meat. DD-SIMCA models using spectra acquired by both devices achieved 100% sensitivity and 100% specificity for external set of samples. Besides, Partial Least Squares Regression (PLSR) based on NIR-HSI outperformed the portable NIR spectrometer to predict the concentration of adulterant in alpaca meat. In conclusion, both devices supported by chemometric approaches can be implemented as screening methods to detect adulteration in alpaca meat.

Rapid and non-destructive detection of ponceau 4R red colored pork

The characteristic colour of pork desired by consumers is a widespread phenomenon on the Ghanaian market that has led to some suspected adulteration practices. Currently available methods for monitoring pork quality are time consuming but above all, destructive (destroys the integrity of meat). This study aimed to develop rapid models that can be used to detect, classify and predict the presence of ponceau 4R in fresh pork in the Kumasi metropolis of Ghana using near-infrared spectroscopy together with chemometrics. Fresh pork samples, 120 obtained from the markets and 120 adulterated artificially in the laboratory, were subjected to near-infrared measurements. The spectra obtained were evaluated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Partial Least Square Regression (PLSR). PCA and LDA showed that scanning the skin of the pork and pretreating the spectra with Savitzky-Golay smoothing sufficed for further chemometric analysis. The classification models built using LDA showed similarities between samples obtained from the markets and the artificially adulterated samples, indicating the presence of colour adulterant. The models also revealed the importance of processing time in making the adulterated meat more appealing to consumers. PLSR, however, yielded poor results for predicting colour and adulterant concentration. In effect, PCA and LDA methods proved to be better alternatives for the detection of colored pork adulteration and can be adopted for quality control applications together with near infrared spectroscopy.

Oregano herb adulteration detection through rapid spectroscopic approaches: Fourier transform-near infrared and laser photoacoustic spectroscopy facilities

Oregano is a culinary herb known and used worldwide, whose demand and cost encourage illegal activities in terms of fraud, especially adulteration. The scope of this study is to provide a rapid, cost-effective and easy-to-use approach to detect the adulteration of oregano with a common matrix, the olive leaves. Two analytical solutions were considered, Fourier transform near infrared (FT-NIR), using two different instruments, and laser photoacoustic spectroscopy (LPAS). Different raw data preprocessing procedures were applied, Principal Component Analysis (PCA) was used for preliminary data visualisation, while Partial Least Squares Regression (PLS-R) and Discriminant Analysis (PLS-DA) allowed the authors to classify samples with different adulteration percentages. FT-NIR performed better than LPAS, perhaps also because the photoacoustic system is still an experimental prototype. For example, the coefficient of determination R2 between actual and predicted adulterant concentrations was in the range 0.93–1.00 for FT-NIR and in the range 0.86–0.96 for LPAS. Nevertheless, this allowed the use of a low level data fusion approach to increase the consistency of the statistical model. The fusion between raw data from the LPAS prototype and one of the FT-NIR instruments gave optimal results, confirming the potential of these analytical solutions for the identification of oregano adulterants. The approaches used in the present work could be applied to the identification of other adulterants in herbs and spices by food companies for quality control measurements, possibly for on line and in field analysis.

Quantification of the adulteration concentration of palm kernel oil in virgin coconut oil using near-infrared hyperspectral imaging

The adulteration concentration of palm kernel oil (PKO) in virgin coconut oil (VCO) was quantified using near-infrared (NIR) hyperspectral imaging. Nowadays, some VCO is adulterated with lower-priced PKO to reduce production costs, which diminishes the quality of the VCO. This study used NIR hyperspectral imaging in the wavelength region 900–1,650 nm to create a quantitative model for the detection of PKO contaminants (0–100%) in VCO and to develop predictive mapping. The prediction equation for the adulteration of VCO with PKO was constructed using the partial least squares regression method. The best predictive model was pre-processed using the standard normal variate method, and the coefficient of determination of prediction was 0.991, the root mean square error of prediction was 2.93%, and the residual prediction deviation was 10.37. The results showed that this model could be applied for quantifying the adulteration concentration of PKO in VCO. The prediction adulteration concentration mapping of VCO with PKO was created from a calibration model that showed the color level according to the adulteration concentration in the range of 0–100%. NIR hyperspectral imaging could be clearly used to quantify the adulteration of VCO with a color level map that provides a quick, accurate, and non-destructive detection method.

Chemistry and Bonding between Air Quality and Climate Change

Abstract: When air quality isn't bad Only a small amount of solid flyspeck and some chemical adulterants are present in the otherwise clean air. Poor air quality, which has many high concentrations of adulterants, is hazardous to human health and the environment. The Air Quality Index (AQI), which is based on the position of attention of adulterants present in the air at each point, is used to describe the quality of the air. Environmental concerns for the present and the foreseeable future include air pollution and climate change. According to a survey, if no action is made, air pollution would rank as the leading global cause of unexpected death by 2055. formerly, air pollution situations in Asia are above respectable situations for mortal beings’ health, and indeed in other part of world, the most maturity of the civic population was exposed to air pollution attention exceeding the diurnal limit values, and especially the stricter WHO air quality guidelines in the once decade

Rapid quantification of the adulteration of pomegranate juices by Raman spectroscopy and chemometrics

The juice drink industry has repeatedly been exposed to adulteration. Unscrupulous producers, for example, use cheap juice for substitution in the pursuit of more significant economic benefits, which presents a tremendous challenge for the control of the quality of drinks. The objective of this study was to apply Raman spectroscopy combined with chemometrics to rapidly quantify the adulteration concentration of apple juice or grape juice in pomegranate juice. Two supervised learning algorithms: partial least squares regression (PLSR) and support vector machine regression (SVR) were used to analyze the Raman spectra of 114 samples. The coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) of the prediction set when using PLSR and SVR to predict the adulterated concentration of apple juice in pomegranate juice were 0.9357 and 0.9465, 6.446% and 5.974%, 3.945 and 4.322, respectively. The R2, RMSE, and RPD of the prediction set when using PLSR and SVR to predict the adulteration concentration of grape juice in pomegranate juice were 0.9501 and 0.9502, 6.334% and 5.571%, and 4.475 and 4.481, respectively. It was concluded that Raman spectroscopy combined with chemometrics has excellent potential for application as a rapid quantitative method to detect adulterated concentrations of pomegranate juice.

Detection of dextran, maltodextrin and soluble starch in the adulterated Lycium barbarum polysaccharides (LBPs) using Fourier-transform infrared spectroscopy (FTIR) and machine learning models

Due to the similar chemical structures and physicochemical properties, it is challenging to distinguish dextran, maltodextrin, and soluble starch from the polysaccharide products of plant origin, such as Lycium barbarum polysaccharides (LBPs). Using the first-order derivatives of Fourier-transformed infrared spectroscopy (FTIR, wave range 1800–400 cm−1), this study proposed a two-step pipeline to identify dextran, maltodextrin, and soluble starch from adulterated LBPs samples qualitatively and quantitatively. We applied principal component analysis (PCA) to reduce the dimensionality of FTIR features. For the qualitative step, a set of machine learning models, including logistic regression, support vector machine (SVM), Naïve Bayes, and partial least squares (PLS), were used to classify the adulterants. For the quantitative step, linear regression, LASSO, random forest, and PLS were used to predict the concentration of LBPs adulterants. The results showed that logistic regression and SVM are suitable for classifying adulterants, and random forests is superior for predicting adulterant concentrations. This would be the first attempt to discriminate the adulterants from the polysaccharide's product of plant origin. The proposed two-step methods can be easily extended to other applications for the quantitative and qualitative detection of samples from adulterants with similar chemical structures.