Method For Identification Of Species Research Articles

A Multi-Scale Convolutional Neural Network Combined with a Portable Near-Infrared Spectrometer for the Rapid, Non-Destructive Identification of Wood Species

The swift and non-destructive classification of wood species holds crucial significance for the utilization and trade of wood resources. Portable near-infrared (NIR) spectrometers have the potential for rapid and non-destructive wood species identification, and while several studies have explored related methodologies, further research on their practical application is needed. To address this research gap, this study proposes a multi-scale convolutional neural network (CNN) combined with a portable NIR spectrometer (wavelengths range: 908 to 1676 nm) for wood species identification. To enhance the capability of directly extracting robust features from NIR spectral data collected by a portable spectrometer, the Gramian angular field (GAF) method is introduced to transform 1-dimensional (1D) NIR spectral data into 2-dimensional (2D) data matrices. Furthermore, a multi-scale CNN model is utilized for direct feature extraction. The representation by 2D matrices, instead of 1D NIR spectral data, aligns with 2D convolutional operations and enables a more robust extraction of discriminative features. In the experimental phase, eight wood species were identified using the proposed method, alongside commonly used multivariate data analysis and machine learning (ML) methods. The StratifiedGroupKFold dataset partitioning approach and five-fold cross-validation were used. Additionally, nine spectral preprocessing methods were compared, and principal component analysis (PCA) was used for feature extraction in the ML method. Evaluation metrics, such as accuracy, precision, and recall, were adopted to assess the performance of the methods. The proposed multi-scale CNN model, in combination with 2D GAF matrices of the 1D spectral data, yielded the most accurate results with a mean accuracy of 97.34% in the five-fold validation. These findings present a new approach for the construction of a rapid, non-destructive, and automatic wood species identification method using a portable NIR spectrometer.

Species Identification of Inuit Skin and Fur Clothing: Analyses of DNA, Hair Microscopy, and Macroscopical Identification

From approximately 1830 to 1940, through various expeditions to Siberia, Arctic North America, and Greenland, and through donations, the National Museum of Denmark acquired its collection of historical Inuit skin clothing. Unfortunately, original provenance information has been lost for 14% of the garments. In order to document the extensive collection, this study investigates three methods for species identification of animal skin: microscopy of hair, macroscopic identification, and DNA validation. Thus, the present study has two aims: first, to optimise and test hair microscopy for species identification by validating identifications by DNA analyses, and second, to use species identification to estimate the geographic and cultural provenance of Inuit skin clothing. Based on a dataset of well-documented clothing (for positive controls), this study describes an optimised species identification protocol via hair microscopy using transmitted light microscopy (TLM). We demonstrate that the TLM hair protocol is a reliable and inexpensive alternative to molecular approaches when macroscopic identification is doubtful and DNA validation or protein analyses are impossible. In this study we used photomicrographs to document the identifications of caribou (Rangifer tarandus), musk ox (Ovibos moschatus), species of the true seal family (Phocidae), domestic dog (Canis lupus familiaris), wolf (Canis lupus), Arctic fox (Vulpes lagopus), polar bear (Ursus maritimus), wolverine (Gulo gulo), ground squirrel (Urocitellus parryii richardsonii), ermine (Mustela erminea), and cattle (Bos taurus); these identifications can be used for future reference. We conclude that species identification analyses secure the documentation of garments and allow most objects to be contextualized by culture and geography. The studied garments are accessible at the museum website.

Bioinformatic Analysis Strategy in Restriction Enzyme Selection for Indonesian Panulirus homarus Identification by PCR-RFLP

The spiny lobster (Panulirus homarus) is a valuable fishery commodity in Indonesia, particularly in its juvenile life form. However, identifying the early life forms of the spiny lobster can be challenging, as it exhibits similar morphological features compared to the juveniles of other Panilurid lobsters. Molecular-based identification, specifically DNA sequencing, is the best method for species identification, but it requires advanced instruments and is costly. An alternative method is proposed here, using the PCR-RFLP technique, which is low-cost, rapid, and has standard instrumentation requirements. The challenge with this method is selecting the appropriate restriction enzyme to determine the targeted species’ identity. This study proposes using the REfind (https://github.com/indriatmoko07/REfind), R package to select the best restriction enzyme for identifying P. homarus, applicable to other species. The bioinformatics workflow used in this study successfully identified BseSI or BmgI as the most suitable restriction enzymes among 739 restriction enzymes to differentiate P. homarus from other Panilurid species. This result was validated by employing a wet lab test using the BseSI enzyme and successfully validated the bioinformatics result. These findings may be useful for biologists in conducting various studies that require rapid, low-cost, and identification of specific species in the future.

DNA BARCODING OF Caranx senegallus FROM THREE WATER BODIES ADJOINING THE GULF OF GUINEA, NIGERIA

Fish species show astonishing phenotypic and genetic diversity in populations. The delimitation and recognition of fish species are not only of interest to taxonomists but also a requirement in studies of ecology. In this study, the genetic structure of Caranx senegallus collected from three water bodies adjoining the Gulf of Guinea, Nigeria was characterized using the cytochrome c oxidase I (COI) barcode gene region. Fish samples collected from Qua Iboe estuary, Escravos estuary, and Lagos lagoon were identified morphologically, and muscle tissues were used for DNA barcoding using Fish1 and Fish2 primers. Evolutionary analysis showed the formation of diverging clades in the neighbor-joining tree and indicated the presence of multiple species. The result also revealed that morphological identification was not entirely successful, as the Basic Local Alignment Search Tool (BLAST) prediction confirmed the presence of three specimens initially identified as C. senegallus but genetically matching C. fischeri and Trachinotus goreensis. This observation further emphasized the need for an integrative method for species identification.

Nontuberculous mycobacteria testing and culture positivity in the United States

BackgroundNontuberculous mycobacteria (NTM) are environmental bacteria which may cause chronic lung disease. The prevalence of NTM pulmonary infection and disease has been increasing in the United States and globally. The predominant clinically relevant species of NTM in the United States are Mycobacterium avium complex (MAC) species and Mycobacterium abscessus. With the development of rapid species identification methods for NTM (e.g. PCR probes), more testing for NTM is being conducted through commercial labs, such as Laboratory Corporation of America (Labcorp), which provides deidentified real-time testing data to the Centers for Disease Control (CDC) pursuant to a data sharing agreement. Because NTM lung infections are not reportable in most states, other data sources are key to understanding NTM testing patterns, positivity rates, and species distributions to track infection trends and identify clinical care needs.MethodsWe obtained national Labcorp data for the period January 2019 through mid-April 2022. We subset the data to only respiratory samples sent for Acid Fast Bacilli (AFB) cultures. NTM positive results were defined as those which identified an NTM species and are not Mycobacterium tuberculosis, Mycobacterium bovis, or Mycobacterium gordonae.ResultsOverall, 112,528 respiratory samples were sent for AFB testing during the study period; 26.3% were from the Southeast U.S., identified as HSS Region IV in the Labcorp dataset, and 23.0% were from the Pacific and South Pacific region (Region IX). The culture positive prevalence ranged from 20.2% in the Southeast to 9.2% in the East North Central region (Region V). In the Southeast US, M. abscessus prevalence was 4.0%. For MAC, the highest prevalence was observed in the Mountain region (Region VII) (13.5%) and the lowest proportion was in the East South Central region (7.3%, Region III). Among positive tests, the proportion which was MAC varied from 61.8% to 88.9% and was highest in the Northeast U.S. The proportion of positive samples which were M. abscessus ranged from 3.8% to 19.7% and was highest in the Southeast.ConclusionsThe Southeastern region of the U.S. has the highest rate of culture positivity in Labcorp tests for total NTM and, of all positive tests, the highest proportion of M.abscessus. These estimates may underrepresent the true number of M.abscessus infections because M. absesscus-specific probes are not commercially available and not all NTM testing in the United States is done by Labcorp. Analysis of real-time testing data from commercial laboratories may provide insights into risk factors for NTM culture positivity in ‘hotspot’ areas.

Analysis of Whole-Genome as a Novel Strategy for Animal Species Identification.

Survival crises stalk many animals, especially endangered and rare animals. Accurate species identification plays a pivotal role in animal resource conservation. In this study, we developed an animal species identification method called Analysis of whole-GEnome (AGE), which identifies species by finding species-specific sequences through bioinformatics analysis of the whole genome and subsequently recognizing these sequences using experimental technologies. To clearly demonstrate the AGE method, Cervus nippon, a well-known endangered species, and a closely related species, Cervus elaphus, were set as model species, without and with published genomes, respectively. By analyzing the whole genomes of C. nippon and C. elaphus, which were obtained through next-generation sequencing and online databases, we built specific sequence databases containing 7,670,140 and 570,981 sequences, respectively. Then, the species specificities of the sequences were confirmed experimentally using Sanger sequencing and the CRISPR-Cas12a system. Moreover, for 11 fresh animal samples and 35 commercially available products, our results were in complete agreement with those of other authoritative identification methods, demonstrating AGE's precision and potential application. Notably, AGE found a mixture in the 35 commercially available products and successfully identified it. This study broadens the horizons of species identification using the whole genome and sheds light on the potential of AGE for conserving animal resources.

Using capillary electrophoresis to identify Anopheline species in routine sampling sites.

In the Anopheles genus, various mosquito species are able to transmit the Plasmodium parasites responsible for malaria, while others are non-vectors. In an effort to better understand the biology of Anopheles species and to quantify transmission risk in an area, the identification of mosquito species collected in the field is an essential but problematic task. Morphological identification requires expertise and cannot be checked after processing samples in a destructive treatment, while sequencing of numerous samples is costly. Here, we introduce a method of Species identification via Simple Observation Coupled with Capillary Electrophoresis Technology (SOCCET). This molecular technique of species identification is based on precise determination of ITS2 length combined with a simple visual observation, the colour of mosquito hindleg tip. DNA extracted from field-collected Anopheles mosquitoes was amplified with universal Anopheles ITS2 primers and analysed with a capillary electrophoresis device, which precisely determines the size of the fragments. We defined windows of amplicon sizes combined with fifth hind tarsus colour, which allows discrimination of the major Anopheles species found in our collections. We validated our parameters via Sanger sequencing of ITS2 amplicons. Using the SOCCET method, we characterised the composition of Anopheles populations in five locations of French Guiana, where we detected a total of nine species. Anopheles braziliensis and Anopheles darlingi were detected in four locations each and represented 13 and 67% of our samples, respectively. The SOCCET method can be particularly useful when working with routine sampling sites with a moderate species diversity, that is, when the number of local species is too high to define species-specific primers but low enough to avoid individual ITS2 sequencing. This tool will be of interest to evaluate local malaria transmission risk and this approach may be further implemented for other mosquito genera.

A Metric-Based Few-Shot Learning Method for Fish Species Identification with Limited Samples.

Fish species identification plays a vital role in marine fisheries resource exploration, yet datasets related to marine fish resources are scarce. In open-water environments, various fish species often exhibit similar appearances and sizes. To solve these issues, we propose a few-shot learning approach to identifying fish species. Our approach involves two key components. Firstly, the embedding module was designed to address the challenges posed by a large number of fish species with similar phenotypes by utilizing the distribution relationships of species in the embedding space. Secondly, a metric function was introduced, effectively enhancing the performance of fish species classification and successfully addressing the issue of limited sample quantity. The proposed model is trained end to end on fish species public datasets including the Croatian fish dataset, Fish4Knowledge and WildFish. Compared with the prototypical networks, our method performs more effectively and improves accuracy by 2% to 10%; it is able to identify fish effectively in small samples sizes and complex scene scenarios. This method provides a valuable technological tool for the development of fisheries resources and the preservation of fish biodiversity.

Geometric morphometric methods for identification of oyster species based on morphology.

Both genetic and environmental factors affect the morphology of oysters. Molecular identification is currently the primary means of species identification, but it is inconvenient and costly. In this research, we evaluated the effectiveness of geometric morphometric (GM) techniques in distinguishing between two oyster species, Crassostreagigas and C.ariakensis. We used traditional morphometric and GM methods, including principal component analysis (PCA), thin-plate spline analysis (TPS) and canonical variable analysis (CVA), to identify specific features that distinguish the two species. We found that differences in shape can be visualised using GM methods. The Procrustes analysis revealed significant differences in shell morphology between C.gigas and C.ariakensis. The shells of C.ariakensis are more prominent at the widest point and are more scattered and have a greater variety of shapes. The shells of C.gigas are more oval in shape. PCA results indicated that PC1 explained 45.22%, PC2 explained 22.09% and PC3 explained 10.98% of the variation between the two species, which suggests that the main morphological differences are concentrated in these three principal components. Combining the TPS analysis function plots showed that the shell shape of C.ariakensis is mainly elongated and spindle-shaped, whereas the shell shape of C.gigas is more oval. The CVA results showed that the classification rate for the two species reached 100% which means that C.ariakensis and C.gigas have distinct differences in shell morphology and can be completely separated, based on morphological characteristics. Through these methods, a more comprehensive understanding of the morphological characteristics of different oyster populations can be obtained, providing a reference for oyster classification and identification.

Geometry of posterior larval spiracles to identify medically and forensically important calliphorids in Thailand

Fly identification is the primary step of analysis in forensic entomology. Although morphology and molecular techniques are considered satisfactory methods, some constraints may arise from a financial or even human point of view. Over the past decade, the geometric morphometric approach has been increasingly advocated for the classification and identification of arthropods. This study explored the method for species identification of 800 third-instar larvae of eight blow fly species of medical and forensic importance: Chrysomya chani Kurahashi, Chrysomya megacephala (Fabricius), Chrysomya (Ceylonomyia) nigripes Aubertin, Chrysomya pinguis (Walker), Chrysomya (Achoetandrus) rufifacies (Macquart), Hemipyrellia ligurriens (Wiedemann), Lucilia cuprina (Wiedemann), and Lucilia porphyrina (Walker). Based on the posterior spiracles geometry, the cross-validation revealed a relatively high percentage of correct classification in most species, ranking from 86% to 100%. The results of this study confirmed that the geometric morphometric (GM) analysis of posterior spiracles might be utilized as a larva identification tool. Therefore, this GM method represents one way of overcoming difficulties with the identification of blow fly larvae and can support further studies of these flies.

Artificial neuronal networks are revolutionizing entomological research

AbstractThe application of artificial intelligence (AI) in entomological research has gained significant attention in recent years. This review summarizes the current state of research on the potential of AI methods in various subfields of entomology, such as behavioural biology, biodiversity research, climate change research, pest management, and disease vector control. In some cases, AI‐based species identification methods based on deep learning neuronal network models have been shown to outperform traditional morphological identification methods in terms of accuracy and speed. Behavioural biology research has been enhanced through the use of AI‐based tracking systems that can classify insect behaviour and movement patterns. Habitat modelling has also been improved with the use of AI, allowing for the creation of more accurate models that can predict insect distribution and abundance. Climate change and biodiversity research have benefited from AI‐driven tools that can analyse large datasets and predict the impact of environmental changes on insect populations. In agriculture, pest management has been revolutionized by AI methods, with the development of smart traps and monitoring systems that can detect and identify pest species in real‐time, enabling targeted control measures. Disease vector control has also been improved through the use of AI‐based predictive models, which can identify areas at risk of disease transmission and aid in the development of effective control strategies. In conclusion, AI methods have the potential to revolutionize many aspects of entomological research. Future research should focus on developing more sophisticated AI tools and integrating them into entomological research to address even more complex ecological questions.

Molecular based identification and phylogenetic relationship by using cytochrome b gene of Pangasius pangasius.

Molecular appraoch for identification of unknown species by using Cytochrome b gene is an effective and reliable as compared with morphological based identification. For DNA barcoding universal molecular genes were used to identify the species. Cytochrome b is a specific gene used for identification purpose. DNA barcoding is a reliable and effective method compared to the different traditional morphological methods of specie identification. So,in the present study which was conducted to identify the species, a total of 50 fish samples were collected from five different sites. DNA was extracted by using the Phenol Chloroform method from muscle tissue. Five sequences were sequenced (one from each site), analyzed, and identified specific species as Pangasius pangasius. Identified sequences were variable in length from 369 bp (Site 1), 364 bp (Site 2), 364 bp (Site 3), 352 bp (Site 4), and 334 bp (Site 5). Identity matches on the NCBI database confirmed the specific specie as P. pangasius. A distancing tree was drawn to show maximum likelihood among the same and different species. Yet, in many cases fishes on diverse development stages are difficult to identify by morphological characters. DNA-based identification methods offer an analytically powerful addition or even an alternative tool for species identification and phylogenetic study. This work intends to provide an updated and extensive overview on the DNA based methods for fish species identification by using Cytochrome b gene as targeted markers for identification purpose.

Challenges and knowledge gaps in the management of non-tuberculous mycobacterial pulmonary disease in sub-Saharan African countries with a high tuberculosis burden: a scoping review

IntroductionIn sub-Saharan African (SSA) countries endemic for tuberculosis (TB), previous TB is a significant risk factor for non-tuberculous mycobacterial pulmonary disease (NTM-PD). The deployment of GeneXpert MTB/RIF in pulmonary TB...

Evaluation of the wing cell contour to distinguish between Stomoxys bengalensis and Stomoxys sitiens (Diptera: Muscidae) using outline-based morphometrics

The blood-sucking flies of the genus Stomoxys Geoffroy, 1762 (Diptera: Muscidae) are significant ectoparasites that can cause irritation and transmit pathogens to both animals and humans. Within the genus Stomoxys, two species, Stomoxys bengalensis and Stomoxys sitiens, have similar morphology and coexist in the same habitat. Accurate species identification of these flies is crucial for understanding disease vectors and implementing effective control measures. In this study, we assessed the effectiveness of outline-based geometric morphometrics (GM) by analyzing the wing cell contour of the first posterior cell (R5) to distinguish between species and sexes of S. bengalensis and S. sitiens. Our results demonstrate that the outline-based GM method is highly effective in distinguishing between species and sexes of these flies based on contour shape, with accuracy scores ranging from 90.0% to 97.5%. Therefore, outline-based GM emerges as a promising alternative to landmark-based GM or as a supplementary tool in conjunction with traditional morphology-based methods for species identification.

Comparison of the VITEK® 2 System with Conventional Methods for Species Identification and Antimicrobial Susceptibility Pattern of Staphylococcal Carrier Isolates

Staphylococcus aureus, an opportunistic pathogen that inhabits 20% to 70% of human anterior nares and can cause infections, ranging from mild, superficial infections of the skin and soft tissues to severe conditions like sepsis, necrotizing pneumonia and toxic shock syndrome. Healthcare workers are potential asymptomatic nasal carriers, contribute to major infection transmission. The emergence of multidrug-resistant strains necessitates advanced treatment approaches. The study aimed to identify staphylococcal nasal carriers among dental students, and to compare the results obtained from automated identification system, VITEK® 2 with conventional methods for identifying Staphylococcus species and determining antibiotic susceptibility patterns. Anterior nasal swabs were collected from 42 study participants were processed as per standard microbiological culture techniques. All the conventionally identified S. aureus isolates were subjected to a further analysis using an automated VITEK® 2 SYSTEM. Surprisingly, the automated identification system classified all conventionally proven S. aureus isolates as other Staphylococci species (S. epidermidis (60%), S. warneri, and S. lentus), raising concerns about potential misdiagnosis. Overall, there was a 97.5% categorical agreement between VITEK® 2 system and the reference method, with minimal errors (0.8% very major, 0.6% major, and 1.17% minor). These discrepancies underscore the importance of accurate species identification and highlight the necessity for advanced techniques in infection control strategies, emphasizing the potential impact on decolonization decisions.

A snail species identification method based on deep learning in food safety.

In daily life, snail classification is an important mean to ensure food safety and prevent the occurrence of situations that toxic snails are mistakenly consumed. However, the current methods for snail classification are mostly based on manual labor, which is inefficient. Therefore, a snail detection and classification method based on improved YOLOv7 was proposed in this paper. First, in order to reduce the FLOPs of the model, the backbone of the original model was improved. Specifically, the original 3×3 regular convolution was replaced with 3×3 partial convolution, and the Conv2D_BN_SiLU module in the partial convolution was replaced with the Conv2D_BN_FReLU module. FReLU could enhance the model's representational capacity without increasing the number of parameters. Then, based on the specific features of snail images, in order to solve the problems of small and dense targets of diverse shapes, a receptive field enhancement module was added to the head to learn the different receptive fields of the feature maps and enhance the feature pyramid representation. In addition, the CIoU was replaced with the WIoU to make the model pay more attention to targets at the edge or difficult-to-regress accurate bounding boxes. Finally, the images of nine common types of snails were collected, including the Pomacea canaliculata, the Viviparidae, the Nassariidae, and so on. These images were then labeled using LabelImg software to create a snail image dataset. Experiments were conducted based on the dataset, and the results showed that the proposed method demonstrated the best performance compared to other state-of-the-art methods.

Electrophoretic Methods for Identifying the Species of Seafood and Its Derivatives

The identification of the species of seafood and their products, whether they are fresh or cooked, is one of the key concerns of food regulations in many countries that have a significant intake of seafood. In point of fact, a commercial fraud happens when a species that is less value is intentionally substituted for a species that is more valuable, and a sanitary fraud takes place when a product that is potentially harmful is introduced into the market. A primary responsibility of veterinary inspection of seafood products is the detection of harmful species with the aim of removing them from the retail trade (Council Directive 91/493/ECC). Two efficient methods for seafood species identification are molecular biology methods and protein electrophoresis. Classic electrophoretic methods have been used for a long time to authenticate seafood; they are simple, accurate, and inexpensive compared to molecular biological methods, which are the wave of the future in food safety labs. The purpose of this article is to offer an overview of the electrophoretic methods commonly used to identify different species of seafood, including sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE), native or urea-isoelectric focusing electrophoresis (IEF), two-dimensional electrophoresis (2-DE), and capillary electrophoresis (CE).

Optimization of Molecular Genetic Method for Identification of Dairy Raw Materials

Introduction: This article addresses the pressing issue of ensuring the quality and safety of dairy products by combating adulteration, a challenge that continues to plague the dairy industry. Adulteration, often driven by cost-cutting motives, involves altering the composition, quality, or origin of dairy products, even in the face of tightened control measures and improved monitoring systems. Specifically, this study hones in on the problem of species-specific adulteration, particularly the substitution of goat milk with cow milk due to the significant cost differential. Detecting and identifying such adulteration is crucial, and while traditional methods like electrophoresis and chromatography have been used, they are often expensive and labor-intensive. The article explores the use of molecular genetic methods, particularly polymerase chain reaction (PCR), as a more efficient and accurate means of identifying species-specific milk adulteration. Methods like PCR offer high specificity, sensitivity, speed, and the ability to perform quantitative and multiplex analyses.Purpose: The objective of this study is to optimise a method for species identification of dairy products by PCR-based screening using DNA isolated from cow’s and goat’s milk comatic cells.Materials and Methods: This research conducted at the Central Laboratory of Microbiology, All-Russian Research Institute of Dairy Industry, focuses on milk species identification of cattle and small ruminants using a specific set of primers targeting the polymorphic amylogenin gene. The study involves PCR screening with DNA extracted from cow’s and goat’s raw milk utilizing a MiniAmp instrument for the analysis. Qualitative and quantitative assessments of DNA preparations were performed, measuring DNA concentration with a Qubit 4 fluorimeter and Qubit dsDNA BR Assay Kit. PCR analysis was carried out in 25 μl reactions, including key components such as 5xScreen Mix, specific primers, DNA samples, and mQ H2O. The amplification program comprised denaturation, annealing, and elongation steps over a series of cycles.Results: The experiment was aimed at assessing the suitability of primers SE47 and SE48 for amplification of nuclear DNA of milk somatic cells of cattle (Bos taurus) and goats (Capra hircus). Initially, a calculated annealing temperature of 56 °C was used in the PCR reaction, yielding in numerous nonspecific fragments appeared on the electropherogram. To solve this problem, we gradually increased the annealing temperature, which resulted in a significant decrease in the nonspecific fragments number and their complete absence at annealing temperature of 70°C.Conclusion: We succeeded in optimizing a PCR-based detection system for the milk species identification of cattle and small ruminants. The results obtained confirm the possibility of using genomic DNA of milk somatic cells for the successful amplification of species-specific nuclear markers, but there remains a need for further research to determine the sensitivity of the PCR system and the possibility of its use in the analysis of milk processing products.

Development of specific barcodes for identification of Aloe species based on chloroplast DNA barcoding

DNA barcoding is a widely used taxonomic tool for correctly identifying plant species. The chloroplast matK and rbcL are some of the most extensively studied variable coding genes of angiosperms and have been recommended as a universal barcode for terrestrial plants. Genus Aloe contains around 500 species, and most Aloe species have extensive traditional medicinal usage worldwide, viz., A. arborescens, A. ferox, A. maculate, and A. vera. This study evaluated the two chloroplast DNA barcodes (matK and rbcL) to develop a theoretical base for species identification and germplasm conservation of Aloe species. The Maximum Parsimony analysis was conducted to study the evolutionary relatedness of Aloe sequences using matK and rbcL sequences obtained from the NCBI nucleotide database. The results revealed that 49 Aloe accessions were segregated into four major clades using matK phylogenetic analysis. Likewise, the 29 Aloe accessions were allocated into two clades based on the rbcL phylogeny. Additionally, specific barcodes have been developed for the following species: A. vera, A. boylei, A. ciliaris, A. karasbergensis, and A. sinkatana based on SNP (Single Nucleotide Polymorphism) site analysis of barcode sequences and the corresponding DNA QR (quick response) code of each sequence was generated. This study provides a unique method for species identification of the genus Aloe and indicates the possibility of correctly identifying, discriminating, and documenting the Aloe species.

Qualitative and quantitative studies of multicomponent gas by CNN-KPCA-RF model

To improve the accuracy of multi-component gas analysis in infrared spectroscopy and simplify the workflow, an infrared spectroscopy gas detection method based on an improved convolutional neural network is proposed. This method can not only identify a variety of gas categories but also finely identify the concentration of gas. To verify the model identification effect proposed in this paper, eight kinds of gases such as CH4 and C2H6 were used as the sample gases for gas identification and concentration classification, and the corresponding hardware was used to complete the development of the system. The experimental results show that the accuracy of the model method for gas species identification can reach 90%, and the accuracy for concentration identification is the same. In addition, compared with the traditional CNN method, the recognition effect is significantly improved. With the improvement of the data set, the number of gas categories detected by this method and the measurement accuracy will be improved.