Identification Of Adulteration Research Articles

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, the differentially expressed proteins in sheep, goat, and cow milk and their functional differences are analyzed using label-free proteomics technology to identify potential biomarkers. 770 proteins and 2914 peptide segments were identified. The statistical analysis showed significant differences in the relative abundances of the 74 proteins among the sheep, goat, and cow milk. CSN3 and LALBA can be used as potential biomarkers for goat milk, XDH can be used as potential biomarkers for cow milk, and CTSB and BPIFB1 can be used as potential biomarkers for sheep milk. The functional analysis using Gene Ontology and 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.

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.

Targeted identification of camellia oil and olive oil adulterated with sesame or rice oil based on characteristic substances by HPLC-UV

This paper aims to establish a rapid and accurate method to identify the adulteration of camellia oil and olive oil based on characteristic substances. The results showed that the sesamin and sesamolin can be selected as the characteristic substances of sesame oil, and oryzanol is identified as the characteristic substances of rice oil. A high-performance liquid chromatography (HPLC) method with UV determination was developed to detect the characteristic substances, which can identify whether sesame oil and rice oil are mixed in camellia oil or olive oil. The pretreatment and chromatographic conditions were optimized. The linear equation, LOD, LOQ, recovery and precision were studied. The LODs of sesamin, sesamolin and oryzanol were determined to be 25 mg/kg, 50 mg/kg and 50 mg/kg, respectively. Similarly, the LOQs of sesamin, sesamolin and oryzanol were 50 mg/kg, 100 mg/kg and 100 mg/kg respectively. The simulated adulterated oils with different proportion were analyzed to validate the effectiveness of method. The adulteration limit of sesame oil is 2 %, and that of rice oil is 5 %. The method has the advantages of simple operation, good specificity and high accuracy. It can provide technical support for oil quality control and adulteration identification.

Using volatile compounds for the identification of coffee adulterants: marker compounds and non-targeted analysis

Using volatile compounds for the identification of coffee adulterants: marker compounds and non-targeted analysis

Modelling and numerical methods for identifying low-level adulteration in ground beef using near-infrared hyperspectral imaging (NIR-HSI)

Owing to the inherent characteristics of ground beef, adulteration presents a substantial risk for suppliers and consumers alike. This study developed a robust and novel method for identifying replacement fraud in ground beef with beef liver, beef heart, and pork using Near Infrared-Hyperspectral Imaging (NIR-HSI) coupled with chemometric and other statistical methods. More specifically, NIR-HSI provided an efficient and accurate means of identifying each type of adulteration using the classification model Genetic Algorithm (GA) - Backpropagation Artificial Neural Network (BPANN), showing perfect sensitivity and specificity (a value of 1.00) for the calibration and the validation sets for all types of adulteration. As an alternative to chemometric analysis, Hyperspectral Imaging-Root Mean Square (HSI-RMS) value, based on the RMScut-off calculation, was determined to discriminate types of adulterations without the need of resource-intensive modelling. This HSI-RMS approach provides a simple-to-use method that avoids the complexity of HSI data processing and aims to directly understand the similarity between different spectra of one sample in the pixel level. Different types of adulteration show noticeable differences reflected in the HSI-RMS value (varying from 55 to 1439), which demonstrate the potential of HSI-RMS concept as a novel and valuable alternative for assessing the HSI data and facilitating the identification of adulterants.

Detection of lard adulteration in 3 kinds of vegetable oils by liquid chromatography-mass spectrometry with porous graphite carbon column.

Liquid chromatography‒mass spectrometry employing porous graphite carbon columns and an n-octane-isopropanol mobile phase was utilized for the separation of triacylglycerols (TAGs) in various edible oils, aiming to identify lard adulteration in soybean, corn, and sunflower seed oils. Experiments were conducted using a Hypercarb column (2.1mm × 100mm, 5µm) and an n-octane-isopropanol (70:30, V/V) mobile phase at a flow rate of 0.25mL· min-1 and a column temperature of 60°C. Detection was achieved through atmospheric pressure chemical ionization-mass spectrometry. Analysis of diverse edible oil samples revealed that oils of the same type shared similar TAG compositions, while different types exhibited distinct TAG profiles. Distinct variations in triglyceride composition were observed across different edible oils. Based on liquid chromatography‒mass spectrometry analysis, the characteristic component 1-stearic acid-2-palmitic acid-3-oleic acid glyceride (SPO), which may also include PSO, was identified in lard through principal component analysis and orthogonal partial least squares discriminant analysis. This component served as a marker for detecting as low as 0.1% lard adulteration in soybean, corn, and sunflower seed oils. The technique offers a precise and effective approach for the identification of lard adulteration in these edible oils.

Identification of lyophilized avocado powder adulteration using the cavity perturbation technique at microwave frequencies

In this paper, we propose the cavity perturbation technique using a high Q cylindrical cavity at 2.4 GHz to identify adulteration of dry-freeze avocado powder with two of the most common adulterants (chickpea powder and green color). Three different adulteration conditions are explored, namely, avocado powder mixed with color, avocado powder mixed with chickpea powder, and avocado powder mixed with 50% colour–50% powder, at various contamination ratios (100, 90, 77, 62, 50, and 0%). For all cases, the perturbation technique shows clear linear quadratic regression trends that make adulteration identification possible.

Authenticity and adulteration identification of Bubali Cornu based on DNA fingerprints

Whether adulteration exists is a difficult problem in the identification of traditional Chinese medicine(TCM). Bubali Cornu is mainly available in the medicinal material market in the form of buffalo horn silk or buffalo horn powder but lacks obvious identification characteristics, so there is a risk of adulteration. However, the method of identification of adulteration in Bubali Cornu is lacking at present. In order to ensure authenticity and identify adulteration of TCM Bubali Cornu, control the quality of TCM Bubali Cornu, and ensure the authenticity of clinical use, the DNA fingerprints of 43 batches of samples from pharmaceutical companies and medicinal material markets were identified, and the amplification primers of fluorescent DNA fingerprints of Bubali Cornu and Bovis Grunniens Cornu were screened. The DNA fingerprints of Bubali Cornu were obtained by fluorescent capillary typing. The identification effect of fluorescent capillary typing on different adulteration ratios was also tested. Two pairs of fluorescent STR typing primers, namely 16Sa and CRc, which can distinguish Bubali Cornu and Bovis Grunniens Cornu, were screened out, and a DNA fingerprint identification method was established. The 16Sa migration peaks of Bovis Grunniens Cornu and Bubali Cornu were 223.4-223.9 bp and 225.5-226.1 bp. The CRc migration peaks of Bovis Grunniens Cornu and Bubali Cornu were 518.8-524.8 bp and 535.9-542.5 bp. The peak height of the migration peak could be used for preliminary quantification of the adulterants with an adulteration ratio below 50%, and the quantitative results were similar to the adulteration ratio. In this study, a simple and quick universal DNA fingerprint method was established for the identification of Bubali Cornu and its adulterants, which could realize the identification of TCM Bubali Cornu and the semi-quantitative identification of the adulterants.

Identification and quantification of adulteration in frozen-thawed meat of different breeds by NIR spectroscopy and chemometrics

This work aims to compare the characteristic near-infrared absorption bands of different breeds of meats and to develop a qualitative and semi-quantitative method for the detection of adulteration in various types of frozen meat based on near-infrared spectroscopy and chemometrics methods. The adulterated samples were in various combinations of pork, beef and mutton adulterated with different proportions of pork, beef, lamb, duck and chicken. The results showed that the accuracy of the PLS-DA model that identified all adulteration types together was the lowest (AUCP= 0.8265), while the accuracy of the PLS-DA model of lamb-chicken and beef-chicken was the highest (AUCP= 1). AUCP of other adulteration types was higher than 0.92. And the PLSR model constructed by all the adulteration types together had the worst result (Rp2= 0.5348, RMSECV=0.1895, RPD=1.48). The results of PLSR prediction models constructed by the adulteration types separately all reached the expectation (Rp2= 0.8707–0.9703，RMSECV= 0.0637–0.1089，RPD= 2.78–5.80). The results of the PLS-DA models and PLSR models using characteristic band spectral information are basically the same or slightly improved compared with those results of the models using full-band spectral information. Therefore, NIR spectroscopy and chemometrics methods can directly qualitatively identified all adulteration types, but it is more accurate to semi-quantitatively predict the proportion of adulteration according to different adulteration types. The near-infrared absorption of moisture and fat contributed more to the detection of meat adulteration.

A multiplex DNA probe-based method for simultaneous identification of adulteration in meat samples

Meat adulteration and admixing are prevalent malpractices observed in processed and raw meat samples, where the consumption of adulterated meat has been associated with food allergies, financial losses, and consumer distrust. Meat authentication is pivotal to address these concerns. The meat authenticity can be determined through genetic, protein, and immunological markers and advanced detection methods. However, these methods often target a single species and lack the specificity to distinguish closely related species. Here, in the present study, we have developed a multiplex detection method based on the species-specific primers and probes, that can target four meat species in one reaction. The developed method amplifies the mitochondrial genomic regions of chicken, pork, sheep and goat using TaqMan multiplex probe-based RT-qPCR assay. Unique pairs of species-specific primers and probes that target specific mitochondrial DNA (mtDNA) regions of each species were designed and screened for specificity and sensitivity. The detection limit for species identification using the designed primers in real-time qPCR assays was 0.1 picogram per microliter (pg/μL) DNA detected in singleplex reaction and facilitates the simultaneous detection of closely related species, such as goat and sheep. Further, DNA-based probes were utilized in a multiplex real-time qPCR assay to identify chicken, pork, sheep and goat DNA in a single tube reaction. The multiplex assay was validated for raw and processed meat products, demonstrating its applications in ensuring the quality of meat products and safeguarding consumer interests.

Identification of liquor adulteration based on machine learning and electrochemical sensor

Identification of liquor adulteration based on machine learning and electrochemical sensor

Extending the Quality Control Landscape of Royal Jelly Products by Simultaneous MRJP Proteotype Quantification and Adulterant Identification

Extending the Quality Control Landscape of Royal Jelly Products by Simultaneous MRJP Proteotype Quantification and Adulterant Identification

Identification of liquor adulteration by Raman spectroscopy method based on ICNAFS

The health of consumers can be impacted by the additives placed into the liquor. To address the issues of poor accuracy, low reliability, and complex operational procedures in identifying adulteration in existing liquor, an improved convex non-negative matrix factorization (ICNAFS) with an adaptive graph constraint for unsupervised feature extraction is proposed in this paper, with the goal of achieving rapid identification of adulteration in liquor by Raman spectroscopy through dimensionality reduction. For the sake to streamline the calculation process for effective feature extraction and increase the accuracy of the analyzed model, the proposed ICNAFS method incorporates two fundamental models, such as ridge regression and convex non-negative matrix factorization (NMF). In particular, dimensionality reduction of the original spectrum is initially conducted using Principal Component Analysis (PCA), Sequential Projection Algorithm (SPA), Convex Non-Negative Matrix Factorization with an Adaptive Graph Constraint (CNAFS), and ICNAFS respectively. k-means is subsequently employed to merge the four models for clustering analysis. The results suggest that the accuracy of the presented ICNAFS-assisted k-means model is higher than the other techniques, with a clustering accuracy of 98.67%, exhibiting a 4% improvement over the existing CNAFS, through examination of 150 sets of tainted liquor data from five categories of samples. This demonstrates the potency of the proposed ICNAFS-assisted k-means clustering model in conjunction with Raman spectroscopy as a method for detecting tainted liquor.

Adulteration identification of Hainan camellia (Camellia oleifera Abel.) oil based on comprehensive two‐dimensional gas chromatography and quadrupole time‐of‐flight mass coupled with chemometrics spectrometry

SummaryThe nutritional value of Hainan camellia oil is incomparable to other places, and Hainan camellia oil is expensive, so unscrupulous businessmen often mix other edible oils into them for illegal profit. This study detected the changes of volatile components in Hainan camellia oil adulterated with five edible oils (peanut, soybean, corn, rapeseed, and sunflower oils) using comprehensive two‐dimensional gas chromatography and quadrupole time‐of‐flight mass spectrometry (HS‐SPME‐GC × GC‐Q‐TOFMS). The principal component analysis (PCA) score plot showed that pure Hainan camellia oil (PHCO) and Hainan camellia oil samples were adulterated with other edible oils clearly to identify the adulteration separated from each other. The adulteration of PHCO could be accurately predicted using the developed PCA and OPLS‐DA models for concentrations as low as 5%. More importantly, several potential markers were found to identify the impersonate PHCO. Therefore, the volatile compound determination based on HS‐SPME‐GC × GC‐Q‐TOFMS proved to be an effective method for detecting adulteration of other edible oils in PHCO.

Rapid Identification and Quantification of Adulteration in Methyl Eugenol using Raman Spectroscopy Coupled with Multivariate Data Analysis.

Identification of adulterants in commercial samples of methyl eugenol is necessary because it is a botanical insecticide, a tephritid male attractant lure that is used to attract and kill invasive pests such as oriental fruit flies and melon flies on crops. In this study, Raman spectroscopy was used to qualitatively and quantitatively assess commercial methyl eugenol along with adulterants. For this purpose, commercial methyl eugenol was adulterated with different concentrations of xylene. The Raman spectral features of methyl eugenol and xylene in liquid formulations were examined, and Raman peaks were identified as associated with the methyl eugenol and adulterant. Principal component analysis (PCA) and partial least-squares regression analysis (PLSR) have been used to qualitatively and quantitatively analyze the Raman spectral features. PCA was applied to differentiate Raman spectral data for various concentrations of methyl eugenol and xylene. Additionally, PLSR has been used to develop a predictive model to observe a quantitative relationship between various concentrations of adulterated methyl eugenol and their Raman spectral data sets. The root-mean-square errors of calibration and prediction were calculated using this model, and the results were found to be 1.90 and 3.86, respectively. The goodness of fit of the PLSR model is found to be 0.99. The proposed approach showed excellent potential for the rapid, quantitative detection of adulterants in methyl eugenol, and it may be applied to the analysis of a range of pesticide products.

DNA barcoding of Notopterygii Rhizoma et Radix (Qiang-huo) and identification of adulteration in its medicinal services

Safety concerns, stemming from the presence of complex and unpredictable adulterants, permeate the entire industrial chain of traditional Chinese medicines (TCMs). The Notopterygii Rhizoma et Radix (NReR) from the Apiaceae family, commonly known as “Qiang-huo”, is a widely used herbal medicine. The recent surge in its demand has given rise to a proliferation of counterfeit and substituted products in the market. Traditional identification presents inherent limitations, while DNA mini-barcoding, reliant on sequencing a short-standardized region, has received considerable attention as a new potential means to identify processed medicinal materials. In this study, we constructed a comprehensive Internal Transcribed Spacer 2 (ITS2) matrix encompassing genuine NReR and their commonly found adulterants for the first time. Leveraging this matrix, we conducted a thorough assessment of the genetic profiles and sources of NReR available in the Chinese herbal medicine market. Following established DNA barcoding protocols, the intra-specific genetic divergences within NReR species were found to be lower than the inter-specific genetic divergences from other species. Among the 120 samples that were successfully amplified, ITS2 exhibits an outstanding species-level identification efficiency of 100% when evaluated using both the BLASTN and neighbor-joining (NJ) tree methods. We concluded that ITS2 is a mini-barcode that has shown its potential and may become a universal mini-barcode for the quality control of “Qiang-huo”, thereby ensuring the safety of clinical medication.

A Review on High Performance Thin Layer Chromatography

A common excellent replacement for GC and HPLC is High-Performance Thin Layer Chromatography (HPTLC), an improved and automated technique of thin-layer chromatography (TLC) that provides better separation performance and detection limits. Applications of HPTLC include the study of biological materials and phytochemicals, the measurement of herbal medications and active components, formulation fingerprinting, and the identification of adulterants in formulations. Using HPTLC, chemicals of forensic importance can be located. It is more sensitive and feasible to run many samples in a little period of time by using a small volume of solvent. It is one of the more intricate instrumental procedures, utilizing every feature available in thin-layer chromatography.

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Driven by economic interests, adding adulterations in chili powder is a problem which threatens people’s health. Thanks to its nondestructive, rapid, and portable advantages, electronic nose has more potential to be used for adulteration detection than the traditional methods. An approach for identifying the adulterants in chili powder was proposed in this paper. Firstly, an electronic nose system with 10 gas sensors was designed, and then the response images were drawn based on the response signals of the electronic nose. Afterwards, gas features were extracted from those response images by using a histogram of oriented gradients (HOG) algorithm. Finally, an SVM-based identification model was constructed to achieve the identification of plant adulterants in chili powder. The experimental results showed that the identification accuracy of the adulterant categories (almond shell, red beetroot, and tomato peel) based on the HOG features could reach up to 98.3%, and the identification results for adulterant content were 94.2%, 93.3%, and 95%, respectively. Furthermore, in order to compare the efficiency of the proposed identification approach, the widely used model AlexNet was also investigated and discussed.

The identification of aged-rice adulteration by support vector machine classification combined with characteristic wavelength variables

The aging of rice during long-term storage leads to a decline in quality and a decrease in consumption value. Therefore, the efficient and rapid detection of aged-rice adulteration is crucial for rice quality control. This paper aims to utilize near-infrared spectroscopy (NIRS) in conjunction with chemometrics to identify two types of rice adulteration: Late japonica rice and Southern japonica rice. To achieve this, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were utilized to select characteristic wavelengths from the spectral data, both with and without pretreatment methods. In order to establish a support vector machine classification (SVC) model with exceptional predictive capability, cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO) were adopted to find the best combination of parameters (C, g). Notably, the prediction accuracy achieved by MMN-CARS-CV-SVC, SNV-CARS-CV-SVC, SNV-CARS-GA-SVC, and SNV-CARS-PSO-SVC models was 98% for Southern japonica rice adulteration, with varying proportions of aging rice. Conversely, the prediction accuracy of the MSC-CARS-CV-SVC model reached 96% for Late japonica rice adulteration, with varying proportions of aged rice. In conclusion, the combination of NIRS and SVC enhances the discriminative analysis of aged-rice adulteration, and the outstanding generalization ability of the SVC model offers new possibilities for the analysis of adulteration in other cereal grains.