Identification Of Authenticity Research Articles

Rapid identification of cod authenticity based on hyperspectral imaging technology

The high economic value of Atlantic cod makes it prone to fraudulent activities in the market, thus achieving rapid and non-destructive identification of its authenticity has practical significance. This study investigated the hyperspectral imaging (HSI) systems with a Vis-NIR (400 – 1000 nm) and SWIR (900 – 1700 nm) spectral range, for determining the authenticity of Atlantic cod fillets in two frozen and thawed sample states. Results found that the model effect of Vis-NIR data was generally better than SWIR data. Random forest (RF) and Linear discriminant analysis (LDA) models of Vis-NIR data achieved 100 % accuracy. Variable screening algorithms of Successive projections algorithm (SPA) and Variable combination population analysis- iteratively retaining informative variables (VCPA-IRIV) maintained 100 % accuracy of the LDA model at VIS-NIR wavebands while simplifying the data operation burden. Overall, this study suggests that HSI is a promising solution for rapid and non-destructive detection of Atlantic cod authenticity.

Unveiling the mix-up: investigating species and unauthorized tissues in beef-based meat products

Customers are very concerned about high-quality products whose provenance is healthy. The identification of meat authenticity is a subject of growing concern for a variety of reasons, including religious, economic, legal, and public health. Between March and April of 2023, 150 distinct marketable beef product samples from various retailers in El-Fayoum, Egypt, were gathered. There were 30 samples of each of the following: luncheon, kofta, sausage, burger, and minced meat. Every sample underwent a histological investigation as well as subjected to a standard polymerase chain reaction (PCR) analysis to identify meat types that had not been stated by Egyptian regulations. According to the obtained data, the meat products under scrutiny contained a variety of unauthorized tissues which do not match Egyptian regulations. Furthermore, the PCR results indicated that the chicken, camels, donkeys, and pigs derivatives were detected in 60%, 30%, 16%, and 8% of examined samples, respectively. In conclusion, besides displaying a variety of illegal tissues, the majority of the meat items under examination were tainted with flesh from many species. As a result, it is crucial to regularly inspect these products before they are put on the market to ensure that they comply with the law and don’t mislead customers Furthermore, it is advisable for authorities to implement rigorous oversight of food manufacturing facilities to ensure the production of safe and wholesome meat.

Rapid in situ identification of honey authenticity based on RP-Nano-ESI-MS using online desalting

High-content sugar in honey frequently results in severe matrix effects and requires complex pretreatment prior to analysis, posing significant challenges for the rapid analysis of honey. In this study, the reversal polarity nano-electrospray ionization mass spectrometry (RP-Nano-ESI-MS) analysis was developed for the direct evaluation of honey samples. The results indicated that RP-Nano-ESI-MS significantly mitigated the matrix effects induced by high-content sugar through the implementation of online desalting. Furthermore, RP-Nano-ESI-MS has been proven capable of not only differentiating acacia honey adulterated with 10% rape honey, but also effectively distinguishing six types of honey and exhibiting remarkable proficiency in detecting honey adulteration and botanical traceability. Additionally, RP-Nano-ESI-MS exhibited strong quantitative abilities, effectively characterizing variations in amino acid composition among six types of honey with high stability and reproducibility. Our studies underscore the significant potential of RP-Nano-ESI-MS for its rapid in situ analysis of sugar-rich foods like honey, especially in their authenticity verification.

Identification of Signature Authenticity Using Binary Extraction and K-nearest Neighbor Feature Methods

This research focuses on identifying the authenticity of signatures, which is an important part of the field of biometrics. Identification of signature authenticity has wide applications, including in document security, financial transactions, and identity verification in general. The problem to be resolved is the lack of an effective and efficient method for identifying signature authenticity. The method used is the binary extraction method and the K-nearest Neighbor feature. The main contribution of this research is to propose a new approach in identifying signature authenticity by combining binary extraction methods and K-nearest Neighbor features. This approach is expected to increase the accuracy and efficiency of the signature authenticity identification process. The results of this research are the development of a new model or algorithm for identifying the authenticity of signatures. After testing and validation, the accuracy level of the results of identifying the authenticity of this signature is 75%.

Phaseolus coccineus L. Landraces in Greece: Microsatellite Genotyping and Molecular Characterization for Landrace Authenticity and Discrimination.

Phaseolus coccineus L. is a highly valuable crop for human consumption with a high protein content and other associated health benefits. Herein, 14 P. coccineus L. landraces were selected for genetic characterization: two Protected Geographical Indication (PGI) landraces from the Prespon area, namely "Gigantes" ("G") and "Elephantes" ("E"), and 12 additional landraces from the Greek Gene Bank collection of beans (PC1-PC12). The genetic diversity among these landraces was assessed using capillary electrophoresis utilizing fluorescence-labeled Simple Sequence Repeat (SSR) and Expressed Sequence Tag (EST); Simple Sequence Repeat (SSR) is a molecular marker technology. The "G" and "E" Prespon landraces were clearly distinguished among them, as well as from the PC1 to PC12 landraces, indicating the unique genetic identity of the Prespon beans. Overall, the genetic characterization of the abundant Greek bean germplasm using molecular markers can aid in the genetic identification of "G" and "E" Prespon beans, thus preventing any form of fraudulent practices as well as supporting traceability management strategies for the identification of authenticity, and protection of the origin of local certified products.

An experimental study on the visual identification of Fritillaria ussuriensis based on LAMP and nucleic acid colloidal gold technique

Fritillaria ussuriensis Maxim is one of the traditional Chinese valuable herbs, which is the dried bulb of Fritillaria, a plant of the lily family. The identification of authenticity about F. ussuriensis is still technically challenging. In this study, visual identification was performed by ring-mediated isothermal amplification and nucleic acid colloidal gold techniques. Firstly, multiple sequence comparative analysis was performed by DNAMAN to find the differential sites of F. ussuriensis and its mixed pseudo-products, and the specific identification primers of F. ussuriensis were designed. Genomic DNA was extracted by the modified CTAB method, and the reaction system and reaction conditions were optimized to construct LAMP for the visual detection of F. ussuriensis, meanwhile, the genuine product was cloned and the extracted plasmid was sequenced. The specificity and sensitivity were detected, and also verified by nucleic acid colloidal gold method, and 20 commercially available samples were tested. The extracted DNA met the requirements of the experiment, and the genuine F. ussuriensis PCR product titrated on a test strip showed two bands on the T and C lines, while the counterfeit and negative control showed only one band on the C line, which matched the LAMP results. The specificity was 100 %, and the sensitivity of LAMP assay was up to 0.01 ng μL−1, while that of colloidal gold assay was 0.1 ng μL−1, thus the LAMP assay had high sensitivity. 14 out of 20 commercially available samples of F. ussuriensis were qualified, and 6 were unqualified, and the results of the two methods of identification were consistent. In this study, the combined detection method of LAMP and colloidal gold for nucleic acid was established to be specific, rapid, precise and visualized, which can provide a new technical idea for the detection of F. ussuriensis.

An authentic assessment method for cordyceps sinensis

Cordyceps Sinensis, renowned for its diverse pharmacological properties and the rarity of its natural species, faces significant challenges due to rampant adulteration by counterfeit products. Consequently, there is a crucial need to reliably identify Cordyceps species to ensure their quality and efficacy. While current analytical techniques predominantly rely on LC–MS, there remains a notable deficiency and substantial demand for the development of a unified, reproducible, and fast method suitable for commercial applications. In this study, we employed a cost-effective and straightforward approach utilizing headspace GC–MS to authenticate Cordyceps sinensis. This method enables the comprehensive analysis of the chemical profile, facilitating the identification of quality and authenticity in Cordyceps samples. Through a comparative analysis of the chemical profiles of seven authentic Cordyceps samples with seven other Cordyceps samples, we propose a Quality Assessment System for Authentic Cordyceps, encompassing the following criteria: 1) the presence of 29 compounds commonly found in authentic Cordyceps within the chemical profile, and 2) the area ratio of 3-methylbutanal to 2-methylbutanal falling within the range of 2.09–3.01. This method exhibits considerable promise as a standardized, reproducible, and expeditious technique for the quality assessment and authentication of Cordyceps.

Research on the Authenticity of Mutton Based on Machine Vision Technology.

To realize the real-time automatic identification of adulterated minced mutton, a convolutional neural network (CNN) image recognition model of adulterated minced mutton was constructed. Images of mutton, duck, pork and chicken meat pieces, as well as prepared mutton adulterated with different proportions of duck, pork and chicken meat samples, were acquired by the laboratory's self-built image acquisition system. Among all images were 960 images of different animal species and 1200 images of minced mutton adulterated with duck, pork and chicken. Additionally, 300 images of pure mutton and mutton adulterated with duck, pork and chicken were reacquired again for external validation. This study compared and analyzed the modeling effectiveness of six CNN models, AlexNet, GoogLeNet, ResNet-18, DarkNet-19, SqueezeNet and VGG-16, for different livestock and poultry meat pieces and adulterated mutton shape feature recognition. The results show that ResNet-18, GoogLeNet and DarkNet-19 models have the best learning effect and can identify different livestock and poultry meat pieces and adulterated minced mutton images more accurately, and the training accuracy of all three models reached more than 94%, among which the external validation accuracy of the optimal three models for adulterated minced mutton images reached more than 70%. Image learning based on a deep convolutional neural network (DCNN) model can identify different livestock meat pieces and adulterated mutton, providing technical support for the rapid and nondestructive identification of mutton authenticity.

A review: role of mass spectrometry in the quality and authenticity identification of olive oil

SummaryQuality and authenticity identification of olive oil is a vital issue for the olive oil industry on account of its great economic value, unique sensory characteristics, and rich nutritional value. Therefore, it is of great significance to effectively detect olive oil's quality and authenticity. Mass spectrometry, based on the advantages of specificity, sensitivity, and fast analysis speed, has played an increasingly important role in the identification of olive oil quality and authenticity in recent years. Chromatography tandem–mass spectrometry (LC–MS, GC–MS) and direct mass spectrometry (DIMS, DART, MALDI, and PTR‐MS) techniques provide rich detection information in the identification of main compounds (fatty acids, triacylglycerols, etc.) and minor compounds (phenols, sterols, tocopherols, diacylglycerols, etc.) in olive oil. And these techniques are widely applied in whole oils analysis and characteristic compounds identification in olive oil. The literature results show that the combination of mass spectrometry data and chemometrics has made a significant contribution to determining the geographical traceability, adulteration identification, and grade classification of olive oil. This review aims to provide a certain reference for mass spectrometry techniques in the identification of olive oil quality and authenticity, and forecasts the development prospects.

Detection and characterization of meat adulteration in various types of meat products by using a high-efficiency multiplex polymerase chain reaction technique.

Identification of meat authenticity is a matter of increasing concerns due to religious, economical, legal, and public health reasons. However, little is known about the inspection of eight meat species in one tube reaction due to technological challenge of multiplex polymerase chain reaction (PCR) techniques. Here, a developed multiplex PCR method can simultaneously authenticate eight meat species including ostrich (753 bp), cat (564 bp), goose (391 bp), duck (347 bp), chicken (268 bp), horse (227 bp), dog (190 bp), and sheep (131 bp). The detectable deoxyribonucleic acid (DNA) contents for each target species was as low as 0.01 ng in both raw and heat-treated meat or target meat down to 0.1% (w/w) of total meat weight reflecting high stability of the assay in heat processing condition, indicating that this method is adequate for tracing meat origin in real-world meat products, which has been further validated by authenticity assays of commercial meat products. Overall, this method is a powerful tool for accurate evaluation of meat origin with a good application foreground.

The development of a joint neural network to the Raman spectroscopy identification of stamp‐pad ink brands and aging

AbstractThe identification of seal authenticity is an important part of document inspection. Confocal laser Raman spectroscopy combined with convolutional neural (CNN) and recurrent neural (RNN) networks was used to distinguish red stamp‐pad ink of different brands and aging. A total of 536 spectral samples from 16 brands were collected in this study and 53,600 amplification data samples were obtained by adding noise. The joint neural network has significant classification performance compared to partial least squares (PLS) discriminant and common CNN. In the three kinds of stamp‐pad inks, photosensitive, atomic, and common, the recognition rate for different brands and aging both reached 100%.

Application of 3D Printing Reconstruction Algorithm in Ancient Ceramic Restoration

The study of the ancient ceramic is of great significance to the identification of authenticity, value recognition, cultural types, and dissemination channels of the ceramic. In this study, a series of technical problems such as ceramic contour extraction, image distortion correction, and nonlinear contour modeling for 3D printing of rotating body under complex background were solved and a restoration algorithm for the shape of ancient ceramics was proposed. Then, an accurate contour model is established by using the two-dimensional images of the rotating ancient ceramics to reconstruct the three-dimensional model of the ceramic shape. The experimental results show that the modeling algorithm for three-dimensional printed ceramics can accurately obtain the three-dimensional model of rotating ancient ceramics, which is of certain significance to explore a new direction of the research and development of three-dimensional printed ancient ceramics.

Photoacoustic identification of blood authenticity based on quantum-behaved particle swarm optimized wavelet neural network.

To accurately identify the blood authenticity, a set of photoacoustic detection system was established. In experiments, five kinds of blood in total of 125 groups were used, the time-resolved photoacoustic signals and peak-to-peak spectra were obtained in 700 to 1064 nm. Experimental results showed the accurate identification of blood authenticity was limited due to overlap of signals and spectra. To solve the problem, wavelet neural network (WNN) was employed to supervised train peak-to-peak spectra of 100 samples. The correct rate was 72% for 25 test samples. To improve correct rate, the parameters of WNN were optimized by quantum-behaved particle swarm optimization (QPSO) algorithm. Meanwhile, the effects of neurons number, learning rate factors, iteration times and training times on correct rate were studied and compared with WNN and WNN-PSO algorithms. Results showed the correct rate of WNN-QPSO was increased to 96%. Then, three kinds of dynamic contraction-expansion coefficients were used. Under the optimal dynamic coefficient, the correct rate reached 100%. Moreover, the truncated mean stabilization strategy (TMSS) was coupled to improve the convergent speed. Finally, 10 algorithms were compared. Results demonstrated that photoacoustic spectroscopy combined with WNN-QPSO coupled with TMSS and dynamic contraction-expansion coefficient had an excellent performance in the identification of blood authenticity.

Identification of oil authenticity and adulteration using deep long short-term memory-based neural network with seagull optimization algorithm

Identification of oil authenticity and adulteration using deep long short-term memory-based neural network with seagull optimization algorithm

Differentiation and classification of Chinese Luzhou-flavor liquors with different geographical origins based on fingerprint and chemometric analysis.

In order to differentiate and characterize Chinese Luzhou-flavor liquor according to geographical origins, the volatile flavor compounds were analyzed for forty commercial Luzhou-flavor liquor samples from Sichuan, Jiangsu, and Hubei provinces. A total of 113 volatile flavor compounds were quantified; among them, 29 flavor compounds were quantified according to the internal standard method. The differences in flavor composition among different brands of Luzhou-flavor liquor were compared. A data matrix of 64 (flavor components) × 40 (samples) was studied and interpreted using chemometric analysis. The research object could be naturally clustered according to geographical origin (brand) based on the hierarchical cluster analysis (HCA), principal component analysis (PCA) and multivariate analysis of variance (MANOVA) methods. A 100% of predication ability was obtained by the application of K-nearest neighbor model (KNN) for study sample classification. The results demonstrate that the abundance of volatile flavor components in liquors combined with appropriate multivariate statistical methods could be used for the division and traceability of liquors from different geographic origins. PRACTICAL APPLICATION: This study can provide the basis for the identification of liquor authenticity and the traceability of liquor.

Potassium di-hydrogen phosphate identification based on wide energy X-ray absorption spectrum and an artificial neural network

In order to accurately distinguish potassium di-hydrogen phosphate (KH2PO4) from common counterfeit products (NH4H2PO4, KCl, Na2SO4, MgSO4 and white sugar), a wide X-ray absorption spectrum detection system based on photon count detector (PCD) was used to scan six samples leading to wide X-ray absorption spectrum (XAS). Principal component analysis (PCA) was used to extract the main features of XAS (normalized) and an artificial neural network (ANN) was used for automatic identification. The experimental results showed that the absorption of X-rays in the energy range of 12 keV to 40 keV was different among samples. The overall accuracy was 86.67%. The precision, recall and F1-score of potassium di-hydrogen phosphate all reached 100%. Therefore, it is feasible to use the difference of XAS between potassium di-hydrogen phosphate and other counterfeit products to realize product identification, and PCA-ANN algorithm can achieve an ideal automatic recognition result. Wide XAS combined with PCA-ANN automatic classification algorithm provides a new idea for the identification of the potassium di-hydrogen phosphate authenticity.

Performance of zinc oxide quantum dots coated paper and application of fluorescent anti-counterfeiting.

Fluorescent anti-counterfeiting is one of the most widely used anti-counterfeiting technologies at present. The demand to develop new anti-counterfeiting materials and technology is more and more urgent. Zinc oxide quantum dots (ZnO QDs) have superior fluorescent properties under ultraviolet light, making them a suitable replacement for traditional phosphors for anti-counterfeiting printing, which is environmentally friendly and meets the needs of sustainable development. In this paper, water-soluble ZnO QDs with an average particle size of 5.64 nm were prepared. Paper coated by ZnO QDs was obtained after ultrasonic treatment, which could emit bright yellow fluorescence when excited by ultraviolet light. As the concentration of ultrasonic solution is increased, the loading amount of ZnO QDs on the coated paper increased gradually, reaching the maximum when the concentration is increased to 1molL-1, which then does not change with an increase in concentration. The fluorescent intensity of the coated paper was consistent with the changing trend of the loading amount. The coated paper has excellent optical stability, is easy to recycle, and provides simple identification of authenticity by ultraviolet light and anti-copy functionality. Their application in packaging and printing is of great significance to the development of complex, concealed and non-repeatable anti-counterfeiting technology.

DNA authentication technologies for product quality monitoring in the wine industry

Identification of wine product authenticity is a topical question in theRussian Federation. A solution to this problem can be DNA authentication of wines, which is a technological process of product authenticity control using genetic identification of the main plant ingredient — wine grape varieties. This type of wine verification is carried out by analyzing residual amounts of Vitis vinifera L. nucleic acids extracted from cell debris of final products by molecular genetic methods. The aim of this work is the analysis of the existing methods for extraction of nucleic acids from grapes, wine raw materials and commercial wines, as well as description of the molecular genetic approaches to technical genetic identification of grape varieties and authentication of wines made from them. The obtained data suggest suitability of DNA authentication of wine products as a supplement to earlier approved analytical methods (documentary, visual, sensory, physico-chemical).

Spatial distribution and content determination of Ganoderic acid F in tablets using confocal Raman microspectroscopy

This study determines the spatial distribution and content of Ganoderic acid F in tablets without destroying the tablet. Confocal Raman microspectroscopy was conducted on the raw materials for Ganoderic acid F, starch, hydrated magnesium silicate, and magnesium stearate using live video imaging. Five different concentrations of Ganoderic acid F tablets (18.0–54.0%) were scanned at a spectral resolution of 1000 × 1000 µm. After surface scanning imaging of the tablets at five different concentrations, the characteristic Raman spectra were obtained, which were used for the rapid and accurate identification of the tablets. The spatial distribution of the drug in the tablet was determined, and the uniformity of drug mixture was confirmed. The content of each component in the tablet was successfully calculated according to the single-peak spectra of the raw and auxiliary materials. In conclusion, the characteristic Raman scanning spectra of the tablets were obtained, and the authenticity of the tablets was determined rapidly. In the tablets of different concentrations, Ganoderic acid F was evenly distributed in the low-concentration tablets. Without destroying the drug, the confocal microscopic Raman spectrometer and function calculation can be used to understand the spatial distribution and content of each component in the drug, which can be used for the validation of drug preparation process, the detection of drug content, and the identification of drug authenticity.

A rapid identification of authenticity and specifications of Chinese medicine Fritillariae Cirrhosae Bulbus based on E-eye technology

The quality of traditional Chinese medicine tablets is correlated with clinical efficacy and drug safety, and plays a great role in promoting the development of traditional Chinese medicine. However, the existing traditional artificial identification and modern instrument detection in terms of accuracy and timeliness have both advantages and disadvantages. Therefore, how to quickly and accurately identify the quality of traditional Chinese medicine tablets has become a high-profile issue. The purpose of this paper is to explore the feasibility of the application of electronic eye technology in the study of rapid identification of traditional Chinese medicine quality. A total of 80 batches of samples were collected and tested by Fritillariae Cirrhosae Bulbus for traditional empirical identification(M_1) and modern pharmacopeia(M_2). The optical data was collected from electronic eyes, and the chemical metrology was used to establish suitable discrimination models(M_3). Four authenticity and commodity specification models, namely identification analysis(DA), minimum bidirectional support vector machine(LS-SVM), partial minimum two-multiplier analysis(PLS-DA), main component analysis identification analysis(PCA-DA), were established, respectively. The accuracies of the authenticity identification models were 82.5%, 90.0%, 96.2% and 93.8%, while the accuracies of the commodity specification identification models were 89.3%, 96.0%, 90.7% and 97.3%, respectively. The models were well judged, the authenticity identification was based on the final identification model of PLS-DA, and the commodity specification was based on the final identification model of PCA-DA. There was no significant difference between its accuracy and M_1, and the time of determination was much shorter than M_2(P<0.01). Therefore, electronic-eye technology could be used for the rapid identification of the quality of Fritillariae Cirrhosae Bulbus.