Visual Identification Method Research Articles

An easier life to come for mosquito researchers: field-testing across Italy supports VECTRACK system for automatic counting, identification and absolute density estimation of Aedes albopictus and Culex pipiens adults

BackgroundDisease-vector mosquito monitoring is an essential prerequisite to optimize control interventions and evidence-based risk predictions. However, conventional entomological monitoring methods are labor- and time-consuming and do not allow high temporal/spatial resolution. In 2022, a novel system coupling an optical sensor with machine learning technologies (VECTRACK) proved effective in counting and identifying Aedes albopictus and Culex pipiens adult females and males. Here, we carried out the first extensive field evaluation of the VECTRACK system to assess: (i) whether the catching capacity of a commercial BG-Mosquitaire trap (BGM) for adult mosquito equipped with VECTRACK (BGM + VECT) was affected by the sensor; (ii) the accuracy of the VECTRACK algorithm in correctly classifying the target mosquito species genus and sex; (iii) Ae. albopictus capture rate of BGM with or without VECTRACK.MethodsThe same experimental design was implemented in four areas in northern (Bergamo and Padua districts), central (Rome) and southern (Procida Island, Naples) Italy. In each area, three types of traps—one BGM, one BGM + VECT and the combination of four sticky traps (STs)—were rotated each 48 h in three different sites. Each sampling scheme was replicated three times/area. Collected mosquitoes were counted and identified by both the VECTRACK algorithm and operator-mediated morphological examination. The performance of the VECTRACK system was assessed by generalized linear mixed and linear regression models. Aedes albopictus capture rates of BGMs were calculated based on the known capture rate of ST.ResultsA total of 3829 mosquitoes (90.2% Ae. albopictus) were captured in 18 collection-days/trap/site. BGM and BGM + VECT showed a similar performance in collecting target mosquitoes. Results show high correlation between visual and automatic identification methods (Spearman Ae. albopictus: females = 0.97; males = 0.89; P < 0.0001) and low count errors. Moreover, the results allowed quantifying the heterogeneous effectiveness associated with different trap types in collecting Ae. albopictus and predicting estimates of its absolute density.ConclusionsObtained results strongly support the VECTRACK system as a powerful tool for mosquito monitoring and research, and its applicability over a range of ecological conditions, accounting for its high potential for continuous monitoring with minimal human effort.Graphical

Development of methodology for the visual identification of Tributyltin (TBT) in antifouling paint matrices.

Organotin compounds (OTC), mainly tributyltin (TBT), have been used since the 1970s as biocides in the composition of antifouling paints. Due to its physical-chemical characteristics, TBT has high toxicity to the marine environment affecting non-target organisms. The present study aims to develop a method of direct visual identification of TBT in antifouling paints using the cyclopalladate complex, 4- (2-thiazolylazo) resorcinol (TAR-Pd), synthesized in our laboratory. Tests were performed in blank and in the paint matrix with the following OTC: TBT-O; TBT-Cl; TPT-Cl; DBT-Cl (tributyltin oxide, tributyltin chloride, triphenyltin chloride, dibutyltin chloride), in addition to the SnCl4 and SnCl2 compounds (tin IV chloride and tin II chloride), all at a concentration of approximately 20g/ kg of dry paint). The test was performed by applying paint samples to test bodies and scraping a few tens of milligrams of the dry paint film. The scraped paint samples were submitted to the test, showing a different staining reaction for the TBT-Cl and SnCl4 samples concerning blank and other samples (TBT-O, TPT, DBT-Cl, and SnCl2). Solution tests were performed to characterize reaction products by spectroscopy in the visible band. The method developed has potential for application in real samples, being selective for TBT-Cl and SnCl4 in an acid medium, obtaining a limit of detection, in the range of 1-10mg/kg dry paint.

Distribution, flux, and risk assessment of microplastics at the Anzali Wetland, Iran, and its tributaries.

Microplastic pollution has raised significant concerns among scientific communities and society in recent years due to its increase and lesser-known effects on the environment. To improve the knowledge of microplastic pollution in freshwater, we investigated microplastics in Anzali Wetland, a Ramsar site in northern Iran, as well as its nine main entering rivers. The extracted microplastics were characterized via visual identification, SEM-EDX, and μ-Raman methods. Microplastics (size range: 50-5000 μm) were found in all water and sediment samples with concentration of fibrous particles as well as polypropylene and polyethylene polymers. The mean concentration of microplastics in bottom sediment and surface water samples of the wetland was 301 ± 222 particles∙kg-1 d.w. and 235 ± 115 particles∙m-3 (0.23 particles∙L-1), respectively. The microplastic concentration in the central and eastern parts of the wetland was higher than in other areas; however, the mean concentrations revealed homogeneity across the wetland area. Water properties (dissolved oxygen, pH, temperature, electrical conductivity, and salinity in water) did not affect the concentration of microplastic particles, though correlational analysis revealed a strong positive association between microplastic quantity and turbidity. There was a significant positive relationship between microplastic concentration and the percentage of clay in sediment samples. The quantity of microplastics in river water was higher than in wetland water, but the difference between the results was not significant. However, the quantity of microplastics in the river's littoral sediment was higher than in the bottom sediment of the wetland where the difference between the results was significant. Microplastic ecological risk assessment showed high potential ecological risk. The findings underscore the importance of effective management strategies and the implementation of policies to mitigate the negative impact of MP pollution on ecosystems and human health.

Diet components associated with specific bacterial taxa shape overall gut community compositions in omnivorous African viverrids.

Gut bacterial communities provide flexibility to hosts during dietary changes. Despite the increasing number of studies exploring the associations between broader dietary guilds of mammalian hosts and their gut bacteria, it is generally unclear how diversity and variability in consumed diets link to gut bacterial taxa in wild non-primate mammals, particularly in omnivores. Here, we contribute to filling this gap by exploring consumed diets and gut bacterial community compositions with metabarcoding of faecal samples for two African mammals, Civettictis civetta and Genetta spp., from the family Viverridae. For each individual sample, we characterised bacterial communities and identified dietary taxa by sequencing vertebrate, invertebrate and plant markers. This led us to establish diet compositions that diverged from what has previously been found from visual identification methods. Specifically, while the two genera have been categorised into the same dietary guild, we detected more animal dietary items than plant items in C. civetta, while in Genetta spp., we observed the opposite. We further found that individuals with similar diets have similar gut bacterial communities within both genera. This association tended to be driven by specific links between dietary items and gut bacterial genera, rather than communities as a whole, implying diet-driven selection for specific gut microbes in individual wild hosts. Our findings underline the importance of molecular tools for improving characterisations of omnivorous mammalian diets and highlight the opportunities for simultaneously disentangling links between diets and gut symbionts. Such insights can inform robustness and flexibility in host-microbe symbioses to dietary change associated with seasonal and habitat changes.

A visual identification method of the growth posture of young peach fruits in orchards

A network model was proposed for identifying the growth posture of young peach fruits in orchards (YOLON) with the YOLOv5 network model in view of identifying the growth posture of young peach fruits in the natural growth state of orchards. First of all, Coordinate Attention (CA) was added based on the original backbone network due to the influence of peach young fruits on the near-color background to enhance the feature extraction ability of the image. Then, a small-target detection layer was added to the backbone network to optimize the detection effect of smaller young fruits. Finally, the rotating frame was selected to replace the original horizontal frame to detect the young fruits with directional attributes. Experiments showed that proposed YOLON in the visual recognition of the growth posture of multi-directional peach young fruits had the average accuracy rate of the recognition of peach young fruit and the estimation error of its growth angle of 93.04% and ±3.94°, respectively. It has a good recognition effect, which provides a reference for the visual recognition of the growth posture of other young fruits and vegetables.

Recognition of Yuan blue and white porcelain produced in Jingdezhen based on graph anomaly detection combining portable X-ray fluorescence spectrometry

The blue and white porcelain produced in Jingdezhen during China’s Yuan Dynasty is an outstanding cultural heritage of ceramic art that has attracted wide attention for its identification. However, the traditional visual identification method is susceptible to misjudgment, thermoluminescence dating damages the samples, and the methods based on chemical analysis are limited by the accuracy and specificity of the elemental features. In this paper, we address the identification challenge by using machine learning techniques combined with portable X-ray Fluorescence Spectrometer (pXRF) analysis. We collect a large dataset of chemical compositions of Yuan blue and white porcelain from Jingdezhen using pXRF, and propose a graph anomaly detection method based on gradient attention map (GRAM) to identify the porcelain from different dynasties. We treat the porcelain produced in the Yuan dynasty as normal samples and those from other dynasties as abnormal samples. For GRAM, we merely train the variational graph autoencoder (VGAE) model with normal graphs and then use its encoder to extract graph features and compute the anomaly scores by utilizing the GRAM of the graph representations with respect to the node feature embeddings. Finally, we compare GRAM with state-of-the-art graph anomaly detection techniques and show that it achieves superior performance.

Visual investigation of static contact conditions at bolted joints

Accurate numerical models of physical systems are vital in engineering applications, and those for single components are generally accurate. However, there are significant errors in modelling the dynamics of bolted joints, which is a key source of friction in real-life systems, because existing experimental techniques are still far from offering sufficient understanding of the contact conditions of bolted joints. In the current study, static contact conditions of bolted joints are investigated with at least one component in the assembly being transparent. A novel visual identification method is proposed to (i) measure the clearance between plates in the vicinity of bolted joints and (ii) identify the contact conditions in receding contacts by applying a paste (known as Engineer’s blue) to the contact surface. The paste is thinner than most media applied in previous studies. Clearance results obtained by visual inspection are checked against micrometer profile tracing, and the contact regions predicted by the method are used to validate simulation results obtained from two contact mechanics algorithms. Difference between simulation and experiment is as low as 9% in terms of contact region diameters. Last but not least, the reproducibility of the method has been demonstrated, especially if the clearance is in the range of 2 to 5 μm.

Visual identification of silver ornament by the structural color based on Mie scattering of ZnO spheres

Structural colors originated from Mie scattering of dielectric spheres can be regulated by the coupling effect between them and substrates. Here a rapid visual identification method of silver ornaments was proposed by the coupling effect of ZnO spheres with them. Both simulation and experimental results proved that, by coupling with different metal substrates, the Mie resonance scattering peaks of ZnO spheres with dimeter of 700 nm showed different degrees of redshift, which lead to different structural color appeared when ZnO spheres deposited on different metal surfaces with a similar appearance. A red structural color was displayed on the surface of the real silver ornament and a yellow-green structural color was shown on the surface of the cupronickel ornament. This method is quite simple and low-cost because it only needs to spray the dispersion of ZnO spheres on the ornament surface. Due to the mild chemical properties of the ZnO, covering and erasing ZnO spheres on the surface of silver would not corrode the silver ornament. Finally, an atomizer method was used for portable and daily testing. This work opens new perspectives on the visual identification of silver.

Development and application of a visualization method for identification of Panax species with LAMP and a DNAzyme

Panax ginseng and Panax quinquefolium are two valuable Chinese herbal medicines that should not be mixed because they differ in drug properties and efficacy. The traditional identification method is easily affected by subjective factors and cannot effectively distinguish between ginseng products. This study aimed to develop a new chemical analysis method to visually identify P. ginseng and P. quinquefolium. In this method, a large number of sequences containing G-quadruplex were generated by loop-mediated isothermal amplification, and the combination of G-quadruplex and hemin was used to form deoxyribozyme, which catalyzed the color change of H2O2. Artificial simulation of adulteration experiments revealed that this method could detect more than 20% adulterated P. quinquefolium. Compared with the traditional identification methods, this technology was simpler and more efficient, providing a reference for developing rapid visual identification methods and reagents for P. ginseng and P. quinquefolium.

An advanced approach for rapid visual identification of Liposcelis bostrychophila (Psocoptera: Liposcelididae) based on CRISPR/Cas12a combined with RPA.

Liposcelis bostrychophila Badonnel (Psocoptera: Liposcelididae) is a booklouse pest that is a threat to commodity storage security worldwide. Accurate and sensitive methods of L. bostrychophila on-site identification are essential prerequisites for its effective management. Evidence suggests that L. bostrychophila contains 3 intraspecific biotypes that are morphologically indistinguishable but can be discriminated at the level of mitochondrial genome organization and sequences. The traditional molecular identification methods, such as DNA barcoding and PCR-RFLP, are instrumentally demanding and time-consuming, limiting the application of the identification in the field. Therefore, this study developed a new CRISPR/Cas12a-based visual nucleic acid system based on the mitochondrial gene coding for NADH dehydrogenase subunit 2 (nad2), combined with recombinase polymerase amplification (RPA) to accurately identify L. bostrychophila from 4 other common stored-product booklice, and also differentiate 3 biotypes of this species at the same time. The entire identification process could be completed at 37 °C within 20 min with high sensitivity. The system could stably detect at least 1 ng/μl of DNA template. The green fluorescence signal produced by the trans-cleaving of the single-stranded DNA reporter could be observed by the naked eye under blue light. Additionally, the suggested system combined with the crude DNA extraction method to extract DNA rapidly, enabled identification of all developmental stages of L. bostrychophila. With crude DNA, this novel diagnostic system successfully identified an unknown booklouse by holding the reaction tubes in the hand, thus can be considered as an accurate, rapid, highly sensitive, and instrument-flexible method for on-site visual identification of L. bostrychophila.

Visual Metaphor in Early Second Language Education

The paper adapts the theory of knowledge enablement in knowledge-sharing companies to the needs of the second-language classroom to explicate the association between pedagogical interaction and the effectiveness of the teaching process. The goal of the inquiry is to alert language teachers to the hidden opportunities visualization and layout suggest in designing a stress-free educational environment conducive to amplifying language knowledge and skills in a context stimulating knowledge enablement. Visualization is explored at two levels: 1) the level of text layout and illustrations; and 2) the level of visual images and visual metaphors. The method of structural and functional modelling is used to present the layout of exercises and language content and the visual metaphor identification method is employed in the analysis of visual images. A conclusion is asserted of the instrumental role of visuals in English coursebooks for young learners as a medium of visual literacy and as a factor in streamlining the development of productive second language skills. This claim is substantiated by a case study that 1) demonstrates the contextualizing function of images and visual metaphors in English language coursebooks for young learners; and 2) explores the means, instruments and ways of visualizing the instructional content of two TEYL coursebooks published in 2014 and 2015.

A stress defect state measurement method based on low-frequency ACMFL excitation and Hall sensor array collection

The safety testing of ferromagnetic materials, which are the main materials for various machines and equipment, is particularly important. Stress concentration zones (stress defects) cause stress corrosion of ferromagnetic materials, and also have the potential to cause direct damage to ferromagnetic materials. Estimation of stress sources state using electromagnetic nondestructive measurement methods is a critical and difficult problem. In this paper, a visual and intelligent identification method of stress defects in ferromagnetic materials by low frequency AC magnetic flux leakage (ACMFL) technique is proposed. A new three-point compression experiment was designed in this paper. Time-difference vision is established to analyze the ACMFL signal caused by stress defects. A visual transformed convolutional neural network deep learning algorithm has been proposed to identify grayscale patterns pre-processed by the time-difference vision. The results show that the method proposed in this paper elucidates the relationship between the time-difference vision of a stress defect and the stress source state of the mechanical stress. Our proposed method allows to analyze the pressure indenter size of the pressure source of stress defects.

Multimodal imaging-based diagnostic approach for MRI-negative posterior cortex epilepsy.

Posterior cortex epilepsy (PCE) primarily comprises seizures originating from the occipital, parietal, and/or posterior edge of the temporal lobe. Electroclinical dissociation and subtle imaging representation render the diagnosis of PCE challenging. Improved methods for accurately identifying patients with PCE are necessary. To develop a novel voxel-based image postprocessing method for better visual identification of the neuroimaging abnormalities associated with PCE. Multicenter, retrospective study. Clinical and imaging features of 165 patients with PCE were retrospectively reviewed and collected from five epilepsy centers. A total of 37 patients (32.4% female, 20.2 ± 8.9 years old) with magnetic resonance imaging (MRI)-negative PCE were finally included for analysis. Image postprocessing features were calculated over a neighborhood for each voxel in the multimodality data. The postprocessed maps comprised structural deformation, hyperintense signal, and hypometabolism. Five raters from three different centers were blinded to the clinical diagnosis and determined the neuroimaging abnormalities in the postprocessed maps. The average accuracy of correct identification was 55.7% (range from 43.2 to 62.2%) and correct lateralization was 74.1% (range from 64.9 to 81.1%). The Cronbach's alpha was 0.766 for the correct identification and 0.683 for the correct lateralization with similar results of the interclass correlation coefficient, thus indicating reliable agreement between the raters. The image postprocessing method developed in this study can potentially improve the visual detection of MRI-negative PCE. The technique could lead to an increase in the number of patients with PCE who could benefit from the surgery.

Development of rapid and sensitive loop-mediated isothermal method for on-site visual identification of tissue origin of pig by using mitochondrial COI gene sequences

Context The loop-mediated isothermal amplification (LAMP) methods have great potential to identify the species origin of the tissue in meat and meat products at isothermal temperature and are also suitable for field conditions. Aim The present study aimed to develop a rapid, specific, and sensitive assay based on the LAMP technique for identification of tissue of pig origin. Methods The pig-specific primers were designed by targeting the mitochondrial COI gene. The amplification temperature and time for the LAMP reaction were optimised as 64°C and 45 min. The analysis of the amplified product was performed on the basis of the development of colour after the addition of intercalating SYBR Green I dye, and also by the ladder-like pattern on agarose-gel electrophoresis. Key results The assay was found to be highly specific for DNA templates of pig origin and showed no cross-reactivity with other food animals, viz. cattle, buffalo, sheep, and goats. The analytical sensitivities of the LAMP and PCR assays were recorded as up to 0.00001 ng and 0.1 ng respectively, of the absolute DNA content. The laboratory validation of the developed method was performed on blind samples and an admixture of meat from different food animals, viz. cattle, buffalo, sheep, goat and pig. The analysis could be performed in an hour by using supernatant from Phire Animal Tissue Direct PCR kit-treated tissue, excluding the complex process of nucleic acid extraction. Conclusion The LAMP assay was found to be cost-effective, easy to perform, and highly species-specific for pig tissue in meat and meat admixture. The result of the assay can be analysed with the naked eye without the need for sophisticated equipment. Compared with pre-standardised PCR assay, the developed LAMP method was quite sensitive and could be performed within 1 h, from sampling to analysis results. Implications The developed LAMP assay is low resource-based single-tube approach that could be exploited significantly in the fields of diagnostics, agriculture, and aquaculture.

Visual and Intelligent Identification Methods for Defects in Underwater Structure Using Alternating Current Field Measurement Technique

As the ocean engineering structure serves in the critical underwater environment, a variety of defects, such as cracks and corrosions, always cause damage to the structure. It is still a key challenge to identify and evaluate these defects accurately in the underwater environment. In this article, the visual and intelligent identification methods are presented for the inspection of defects in underwater structures using the alternating current field measurement (ACFM) technique. The current perturbation theory is developed to analyze the disturbed current field and the distorted magnetic field caused by defects. The gradient imaging algorithm is presented as an image preprocessing method to highlight the visual morphology of defects. The underwater intelligent ACFM system is set up. The experiments are carried out to verify the gradient imaging algorithm. The convolutional neural network (CNN) deep learning algorithm is presented to identify the grey-scale map samples preprocessed by the gradient imaging algorithm. The results show that the current perturbation theory clarifies the relationship between the characteristic signal and the morphology of various defects. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bz</i> image reflects the surface morphology of defects. The gradient imaging algorithm can achieve visual detection of defects. The single crack, the irregular crack, and the corrosion can be identified intelligently by the CNN deep learning algorithm. These defects can be evaluated accurately after classification.

A visual identification method for the apple growth forms in the orchard

The work aimed at the visual identification of the growth forms of fruits to facilitate the subsequent use of different harvesting mechanisms for different growth forms of fruits by robots. The improved YOLOv5 deep learning algorithm was used to propose a visual identification method for the growth forms of apples in the orchard. Specifically, the feature extraction module of the YOLOv5 algorithm imitated the BiFPN model to propose the BiFPN-S structure. The spread of features and feature reuse were enhanced to better fit features. The improved algorithm was called YOLOv5-B. The network SiLU activation function was replaced with the ACON-C activation function to improve its network performance. The COCO data set was used to pre-train the network, and then the data set of the work was trained by the transfer learning method. After the training, the generated optimal model was applied for the visual-identification test of the growth of apple fruits. The results showed that the improved algorithm model considered high accuracy and real-time performance, with the map reaching 98.4% and the F1 value of 0.928. The average accuracy of identifying the growth forms of apples for the test set was 98.45%, and the processing speed was 71 FPS.

Identification of the electrical connection in the catalyst layer of the polymer electrolyte membrane water electrolyzer

Mechanical deterioration of the catalyst layer (CL) in contact with the porous transport layer (PTL) can disconnect it from the electrical network and deactivate it during proton exchange membrane water electrolysis (PEMWE). This study presents a method for visual identification of the electrically connected region in the CL using in situ electrochemical reduction of iridium oxide to iridium, coupled with energy dispersive X-ray spectroscopy (EDS) mapping and environmental scanning electron microscopy (E-SEM) analyses. Because the iridium oxides in the catalyst layer can be electrochemically reduced to iridium only when they are in both ionic and electronic connection, the reduced portion corresponds to electrically connected region. Our results revealed that the region under the rib not in contact with porous transport layer (PTL) (non-PTL-contact region) is the most prone to electric disconnection because of the cracks formed by PTL compression. Finer CL fragmentation at lower catalyst loadings aggravates such disconnection. Cell polarization of a low catalyst-loaded CL is greatly reduced when the electrical connection at the non-PTL-contact region under the rib is improved with the addition of carbon nano-fibers to the CL. These results emphasize the importance of the electrical connection in the non-PTL-contact region for achieving high-performance low-catalyst-loaded PEMWE.

Characteristics of burned phytolith from representative plants in Northeast China and implications for paleo-fire reconstruction

Studies of the burned phytolith identification have provided a theoretical basis for paleo-fire reconstruction. However, as the most widely used method, the applicability of the visual identification method is still controversial due to its subjectivity. To gain a better identification of burned phytolith, this study used CIELAB color model to quantitatively analyze and distinguish the color characteristics of phytoliths from burned and unburned plant samples (n = 86) in northeast China. The results demonstrate that more than 60% of phytolith in burned plant samples appeared color changes. The burned phytolith percentages (BP/P) of different morphotypes were also with obvious differences, the phytoliths derived from hair cells and stomata have the highest BP/P values while those values of phytoliths from tracheary elements were relatively low, which may be due to the different anatomical origin of phytoliths, and the original cells closer to the outer epidermis were more prone to coloration. After measuring and comparing the chromatism values of phytoliths, it is concluded that the chromatism values of those phytoliths greater than 30 can be regarded as being exposed to fire. Moreover, the various hues from different species may be related to the flammability and combustion adequacy of plants. Finally, we proposed the phytolith morphotypes suitable for paleo-fire reconstruction in the study area according to the above results. Overall, our study provides a reliable basis for the identification of burned phytolith and a reference for reconstructing paleo-fires.

Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks.

In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to determine the most significant IR regions that distinguished benign from malignant samples. The result of the NN-based sensitivity analysis was compared to the obtained results from FTIR visual peak identification. In training each NN models, a 10-fold cross validation was performed and the performance metrics–area under the curve (AUC), accuracy, positive predictive value (PPV), specificity rate (SR), negative predictive value (NPV), and recall rate (RR)–were averaged for comparison. The NN models were compared to six (6) machine learning models–logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA)–for benchmarking. The NN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics; while only surpassing the SVM in accuracy, NPV and SR. The best performance metric among the NN models was 90.48% ± 10.30% for AUC, 96.06% ± 7.07% for ACC, 92.18 ± 11.88% for PPV, 94.19 ± 10.57% for NPV, 89.04% ± 16.75% for SR, and 94.34% ± 10.54% for RR. Results from the proposed sensitivity analysis were consistent with the visual peak identification. However, unlike the FTIR visual peak identification method, the NN-based method identified the IR region associated with C–OH C–OH group carbohydrates as significant. IR regions associated with amino acids and amide proteins were also determined as possible sources of variability. In conclusion, results show that ATR-FTIR via NN is a potential diagnostic tool. This study also suggests a possible more specific method in determining relevant regions within a sample’s spectrum using NN.

Paris polyphylla var. yunnanensis genotype classification and fluorescence visual identification

AbstractParis polyphylla Smith. var. yunnanensis (Franch.) Hand.‐Mazz (PPY) is the legal species of Rhizoma Paridis recorded in the Chinese pharmacopeia. Therefore, accurate and convenient identification of PPY and its adulterants is key to the quality control of Rhizoma Paridis. In this research, according to single nucleotide polymorphism of internal transcribed spacer, two genotypes (YN‐I and YN‐II) of PPY are in existence. To avoid false‐negative results from the omission of the genotype during authenticity identification of PPY, two pairs of specific primers for YN‐I and YN‐II were designed, respectively. Furthermore, with the fluorescent dye SYBR Green I introduced into the polymerase chain reaction (PCR) system, the identification results were directly observed in virtue of the green fluorescent color of the PCR products. After methodological validation based on 25 batches of commercial medicinal material, a rapid and specific visual fluorescent identification method for two genotypes of PPY from its adulterants was established. This research also provided a model for accurate and intuitive identification of traditional Chinese medicine from multiple species of the same genus.