Identification Of Adulteration Research Articles

Discriminating the adulteration of varieties and misrepresentation of vintages of Pu’er tea based on Fourier transform near infrared diffuse reflectance spectroscopy

In the Pu’er tea market, the ubiquity of blending different varieties and the fraudulent representation of vintage years present a persistent challenge. Traditional sensory evaluation and experience are often inadequate for discerning the true variety and vintage of tea, highlighting the need for more sophisticated analytical methods to ensure authenticity and quality. Fourier transform near infrared diffuse reflectance spectroscopy combined with radial basis function neural network (RBFNN) was applied for determination of the varieties and vintages of Pu’er tea. For vintage identification, the accuracy, precision, recall, and F1-score of the RBFNN model for the prediction set were 99.2%, 98.2%, 98.0%, and 98.0%, respectively. For identification of varieties adulteration, the corresponding parameters were 98.9%, 97.2%, 96.7%, and 96.6%, respectively. These results illustrated the feasibility to identify the adulteration of varieties and misrepresentation of vintages of Pu’er tea with near infrared spectra and RBFNN model, proving an efficient alternative for Pu’er tea quality inspection, and offering a robust method for combating the pervasive issues within the market.

A comparative study on rapid qualitative and quantitative determination of olive oil adulteration.

Authenticity of olive oil is a significant concern for producers, consumers, and policymakers. To help address this issue, a rapid, efficient, and accurate flow injection mass spectrometric (FIMS) fingerprinting approach, combined with SVM and PLS classification and regression models, was proposed for the identification and quantitative analysis of olive oil adulteration. Based on the comprehensive comparative analysis, SVM outperformed those of PLS-DA, achieving higher values for accuracy, sensitivity, and specificity, as well as positive predictive and negative predictive values, in identifying adulterated olive oil samples. Furthermore, compared with PLSR model, the SVR model demonstrated superior performance in determining the content of adulterated olive oil, with a higher coefficient of determination and lower Root Mean Square Error. In conclusion, FIMS fingerprinting technology in combination with SVM can be effectively implemented for rapid, reliable, and accurate identification and quantification of olive oil adulteration.

A rapid identification technique for rice adulteration based on improved YOLOV8 model

Abstract Rice classification and quality testing are essential to ensure its safety and quality, and can effectively reduce food economic losses associated with rice, which is currently the subject of repeated reports of rice adulteration. For this reason, according to the experimental requirements, we selected the smallest and lightest version of YOLOv8n in the YOLOv8 series, and optimised and proposed the lightweight and efficient Faster-YOLO algorithm, which is more suitable for rice adulteration classification and recognition tasks, on the basis of the YOLOv8n algorithm. Firstly, this paper introduces the Group Convolutional Hybrid Attention Mechanism, which combines channel information and spatial information, and embeds it in the last layer of the Backbone network, which enhances the feature representation capability of the model by focusing on the key information to suppress the noise. Secondly, the C2F module in the Backbone part adopts the design of combining Faster and C2F to enhance the feature fusion capability and reduce the number of parameters and computational complexity of the model. Finally, the collected data are augmented with multiple aspects to simulate different environments and compared with multiple attention mechanisms and deep learning models. The experimental results show that the method proposed in this paper is superior in classification and recognition performance, with recognition accuracy of 93.4%, precision of 96.5%, recall of 96%, and F1 score of 96.2%. In this paper, the accuracy is improved by 1.1%, 2.4%, 6.9%, 7.5%, 13.1%, 7.9%, 7.1%, and 7.1% compared to yolov8n, yolo11, yolov5, EfficientNetV2, MobileNetV3, Restnet18, and VGG19 image classification methods, respectively. It is demonstrated that Faster-YOLO provides a strong basis for the rapid identification of rice adulteration by reducing the weight of the model while improving the detection and identification capabilities.

Astral-DIA proteomics: Identification of differential proteins in sheep, goat, and cow milk

Dairy products are of great benefit to human health, and the nutritional differences between different dairy products have attracted attention. In this study, DIA proteomics technique, combined with parallel reaction monitoring (PRM) as a validation method, was used for the qualitative and quantitative analysis of proteins in sheep, goat, and cow milk. In total, 4316 proteins were identified. Beta-2-glycoprotein 1 and aminopeptidase can be used as potential biomarkers for sheep milk, fibrinogen alpha chain and Alpha-1-B glycoprotein can be used as potential biomarkers for goat milk, and angiogenin-1 and Serpin family G member 1 can be used as potential biomarkers for cow milk. Functional analysis showed that these different proteins were enriched through different pathways, such as complement and coagulation cascades. These data reveal the differences in protein content and physiological functionality and provide an important basis for the study of dairy nutrition and adulteration identification.

Development of a time-resolved laser-induced fluorescence fingerprinting method for detecting low-level adulteration in extra virgin olive oil

Adulteration identification in extra virgin olive oil (EVOO) is a significant concern in the olive oil industry. This study aimed to detect low-level adulteration of EVOO with other edible oils by using a novel time-resolved laser-induced fluorescence (TRLIF) fingerprinting method. Five EVOO brands were first analyzed to assess its potential for classification based on their differing fluorophore content. The developed method effectively reduces the effects associated with the optical path length and excitation light intensity. Subsequently, three sets of binary mixtures were tested: EVOO adulterated with refined olive oil, soybean oil, and sunflower oil. Quantitative analysis was performed using parallel factor analysis, which achieved a cross-validation coefficient of determination (R2) exceeding 0.90, with a prediction error < 1.40 %. These findings demonstrate that this method has the potential to determine the purity and quality of EVOO.

Development and evaluation of a lateral flow immunochromatographic assay for the rapid detection of donkey meat in beef as a tool for meat adulteration identification

Development and evaluation of a lateral flow immunochromatographic assay for the rapid detection of donkey meat in beef as a tool for meat adulteration identification

Development of FTIR-ATR spectra and PLS regression combination model for discrimination of pure and adulterated acacia honey

A Fourier Transform Infrared Spectroscopy and attenuated total reflectance (FTIR-ATR)-based chemometric model was evaluated for the rapid identification and estimation of adulterants in honey. Two types of honey, bee honey and stingless bee honey, were adulterated with acid adulterants (acetic acid, citric acid, and tamarind extract) at different concentrations of 1%, 3%, 5%, and 7% and sugar adulterants (liquid corn syrup, cane sugar, palm sugar, and inverted sugar) at different concentrations of 1%, 2%, and 3%. The physicochemical properties (Brix, moisture content, pH, and free acidity) and sugar contents (glucose, fructose, and sucrose) of each type of honey and their adulterated samples were determined before FTIR analysis. For all the samples, FTIR spectra were acquired in the mid-infrared range (400-3600 cm−1) and the spectra obtained were subjected to partial least squares (PLS) analysis to develop the model for the determination of adulterants in honey samples. The PLS model produced high coefficient of determination (R2) for bee honey adulterated with acetic acid (0.95), citric acid (0.98), tamarind extract (0.97), and stingless bee honey adulterated with liquid corn syrup (0.99), inverted sugar (0.91), cane sugar (0.86), and palm sugar (0.77). A recognition model was developed for mixture detection and verified through the physicochemical analysis. The combination of FTIR-ATR spectra and the PLS regression model can be a fast, reliable, nondestructive method to detect honey adulteration.

Machine vision combined with deep learning-based approaches for food authentication: An integrative review and new insights.

Food fraud undermines consumer trust, creates economic risk, and jeopardizes human health. Therefore, it is essential to develop efficient technologies for rapid and reliable analysis of food quality and safety for food authentication. Machine vision-based methods have emerged as promising solutions for the rapid and nondestructive analysis of food authenticity and quality. The Industry 4.0 revolution has introduced new trends in this field, including the use of deep learning (DL), a subset of artificial intelligence, which demonstrates robust performance and generalization capabilities, effectively extracting features, and processing extensive data. This paper reviews recent advances in machine vision and various DL-based algorithms for food authentication, including DL and lightweight DL, used for food authenticity analysis such as adulteration identification, variety identification, freshness detection, and food quality identification by combining them with a machine vision system or with smartphones and portable devices. This review explores the limitations of machine vision and the challenges of DL, which include overfitting, interpretability, accessibility, data privacy, algorithmic bias, and design and deployment of lightweight DLs, and miniaturization of sensing devices. Finally, future developments and trends in this field are discussed, including the development of real-time detection systems that incorporate a combination of machine vision and DL methods and the expansion of databases. Overall, the combination of vision-based techniques and DL is expected to enable faster, more affordable, and more accurate food authentication methods.

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.

Identification of fenfluramine adulteration in slimming foods using a highly sensitive immunoassay

Fenfluramine, an appetite suppressant used for weight loss, can cause significant harm if overdosed. The unauthorized addition of fenfluramine to slimming foods has raised concerns. Currently, no rapid screening method is available for the quick detection of fenfluramine in the field. This study proposes six new haptens with varying spacer arm lengths and hydrophobicity, promising to elicit antibodies capable of being highly specific and strongly binding to fenfluramine. The study found that highly hydrophobic haptens with long spacer arms favored the generation of highly sensitive antibodies. Key interaction forces for antibody recognition of fenfluramine were revealed by intrinsic molecular mechanisms. Based on the above results, monoclonal antibody for fenfluramine was prepared and an ultrasensitive icELISA method with heterologous coating strategies was developed in slimming foods. The IC50 of the method was 6.25 ng/mL, the linear detection range was 0.47–83.51 ng/mL and the detection limit was 0.10 ng/mL. Recovery rates in tea bags, tablets, capsules, coffee, and beverages ranged from 96.56 % to 108.90 %. The method was successfully applied to blind samples, showing strong correlation with HPLC-MS/MS results. Thus, the developed method is suitable for identifying fenfluramine adulteration in slimming foods.

Identification and Quantification of Common Adulterants in Extra Virgin Olive Oil Using Microwave Dielectric Spectroscopy Aided by Feedforward Neural Networks

Identification and Quantification of Common Adulterants in Extra Virgin Olive Oil Using Microwave Dielectric Spectroscopy Aided by Feedforward Neural Networks

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.

Identification of white wine adulteration with apple juice and apple cider using cyclic voltammetry on a screen-printed electrode aided by chemometric analysis

An electroanalytical approach has been developed for the identification and quantification of adulteration of white wine with apple juice and apple cider. After addition of LiClO4 as electrolyte and deoxygenation, the sample was analysed via cyclic voltammetry conducted on a screen-printed carbon electrode modified with gold nanoparticles. Cyclic voltammograms (CVs) of white wine samples displayed consistency regardless of their grape variety, mono-, bi- or multi-varietal status as well as geographical origin. In contrast, CVs of apple juice and apple cider exhibited similarities but were distinct from those of white wine. They were particularly characterized by the presence of a cathodic peak at about −0.50 V, attributed to sugars and organic acids, predominantly malic acid. Subsequently, cyclic voltammograms were exported into data points and they were subjected to chemometric analysis. Principal Component Analysis effectively grouped samples into two clusters: white wine and apple juice/ apple cider. Class-modelling demonstrated the ability to detect adulteration in white wine samples, with a detection threshold of 5 % v/v or lower, contingent upon the adulterant type (apple juice or apple cider). Partial Least Squares regression facilitated quantification of the adulteration level with acceptable accuracy. This approach is both cost-effective and straightforward, necessitating minimal sample preparation.

Paper strips-based narrowing colorimetry: An all-in-one strategy for rapid visual detection and adulteration identification of food dyes

Multi-task instrument-free technique for fast detection of harmful elements is of great significance for food safety, but it is still scarce. Herein, an all-in-one colorimetric strategy was initiated for rapid visual detection and adulteration identification of food dyes using narrow-band colorimetric paper strips as sorbents. Colorimetry of food dyes with improved sensitivity was achieved by compressing coloration region of full-length paper strips into a narrow band, while differential dyeing behaviors of natural/synthetic dyes toward paper strips enabled adulteration identification of food dyes in a single paper strip. As concept-to-proof assays, in-situ rapid detection of dyes in real foods and dye effluents were realized with LODs as low as 10−7 g/mL level, and the identifications of natural dyes adulterated with same-colored synthesized dyes were also achieved using fruit/vegetable juices as real samples. Finally, fine consistence between colorimetric results and the outcomes of smartphone-based digital image analysis along with instrumental techniques like spectrometry and HPLC consolidated the feasibility and reliability of this strategy.

Identification of adulterants in açaí pulp samples using laser-induced fluorescence spectroscopy combined with linear discriminant analysis (LDA) method: A demonstration of the potential applicability of the technique

Identification of adulterants in açaí pulp samples using laser-induced fluorescence spectroscopy combined with linear discriminant analysis (LDA) method: A demonstration of the potential applicability of the technique

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.

Identification of meat adulteration in minced meat samples labeled as beef and mutton in Tehran stores using duplex PCR.

Food fraud and profiteering are becoming increasingly common in the meat industry. Therefore, it is essential to identify such practices to prevent consumer deception and maintain food safety. This study aimed to determine the contents of minced meat samples labeled as beef and mutton in retail stores across Tehran province, Iran, to identify instances of meat adulteration. To this end, this study randomly collected 300 minced meat samples labeled as beef and mutton from Tehran stores over 4 years (2018-2022) and analyzed them using duplex polymerase chain reaction (PCR). The results revealed that more than 95% of the samples only contained beef, while only 5% of the samples matched the label and contained a mixture of beef and mutton. This discrepancy between the label and actual contents could be attributed to the price difference between beef and mutton, providing a financial incentive for producers to maximize profits. Given the potential for meat adulteration, increased monitoring of meat products is necessary, including detailed tests such as PCR, which is a fast, easy, sensitive, specific, and highly effective method for detecting meat adulteration. The findings of this study can assist in developing effective strategies to prevent meat adulteration and maintain consumer confidence in the meat industry.

Label-free-based proteomics analysis reveals differential proteins of sheep, goat, and cow milk

Regarding the limited information on species protein differences between sheep, goat, and cow milk, we analyzed the differentially expressed proteins in sheep, goat, and cow milk and their functional differences using label-free proteomics technology to identify potential biomarkers. In all, 770 proteins and 2,914 peptide segments were identified. Statistical analysis showed significant differences in the relative abundances of the 74 proteins among sheep, goat, and cow milk. CSN3 and LALBA can be used as potential biomarkers for goat milk, XDH can be used as a potential biomarker for cow milk, and CTSB and BPIFB1 can be used as potential biomarkers for sheep milk. Functional analysis using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes showed that these significantly different proteins were enriched by different pathways, including thyroid hormone synthesis and glycerol phospholipid metabolism. The data revealed differences in the amounts and physiological functions of the milk proteins of different species, which may provide an important basis for research on the nutritional composition of dairy products and adulteration identification technology.

A novel rapid detection method for chicken adulteration based on recombinant polymerase amplification and multicomponent nuclease (MNAzyme)

The identification of meat adulteration is of great significance to food safety. This work reports a novel rapid detection platform for chicken adulteration based on recombinase polymerase amplification (RPA) and multicomponent nuclease (MNAzyme). The conserved sequence target of the Cytb gene was used to design RPA primers and MNAzyme. First, the RPA amplification products were introduced into the detection system, which included MNAzyme and DNA-RNA composite probes. When the chicken-derived RPA amplification product was present in the sample, it generated an “input” signal that triggered MNAzyme's catalytic core to cleave the DNA-RNA complex probe, leading to a fluorescence signal. The entire procedure, from nucleic acid extraction to detection, took just 60 min, and the results could be easily interpreted using a portable fluorescent thermostatic amplifier. The RPA-MNAzyme assay detected 8 copies/µL of chicken DNA in 60 min, which is consistent with the results of the RPA assay, demonstrating that the method is highly accurate and specific (does not cross-react with nucleic acids from other animal sources). And it could detect as low as 0.5% chicken in beef and lamb. By harnessing MNAzyme to specifically identify target genes in RPA amplification products, the platform eliminates the need for RPA primer screening and RPA probe design, reducing experimental costs.