Method For Rapid Identification Research Articles

Using active cooling/heating for 1C1R grey-box model parameter identification in actual environment: A proof-of-concept study

A model predictive control (MPC) has been important method for managing both comfort and energy saving in heating, ventilation, and air conditioning (HVAC) problems. However, recent data-driven models for MPC, relying on extensive training data, pose challenges for practical implementation in real-world thermal environments due to their time-consuming nature. The grey-box approach, utilizing a simple RC model as a thermal environment proxy, is well known for its data efficiency. Nevertheless, numerical instability arising from the multi-collinearity of observed data can degrade the model’s time efficiency. To deal with this issue, we propose and implement a rapid and stable parameter identification method that integrates active cooling-heating within the actual thermal environment. Our method requires only a single day of data to estimate basic RC model parameters, allowing for immediate use of the RC model in MPC as soon as an air conditioner is installed. Practical Application Using a grey-box model for MPC in HVAC systems is efficient, but the multicollinearity problem of observed data undermines its time efficiency. Our method deals with this challenge by enabling immediate model parameter identification with just 1 day’s data, incorporating active cooling-heating. We demonstrate it in an actual thermal environment. The high level of time and data efficiency afforded by our method is particularly beneficial for IoT applications like malfunction detection and adaptive room reparameterization associated with room rearrangement.

#3004 Serum miRNAs as novel biomarkers and modulators of rapidly progressing chronic kidney disease in patients with diabetes

Abstract Background and Aims Chronic kidney disease (CKD) is very common in diabetes, occurring in around 40% of patients. Without treatment, CKD can lead to end stage renal disease (ESRD), which requires costly dialysis and transplantation. Current treatments aim to prevent CKD or slow down its progression to ESRD. The speed of CKD progression is patient-specific, and at present there are no biomarkers which can predict which patient with diabetes will develop CKD; level of albuminuria is an important but variably reliable biomarker of speed of CKD progression. The current methods for rapid identification of patients prone to accelerated CKD progression are limited, as they are based on measuring estimated glomerular filtration rate (eGFR) over prolonged periods of time, as well as albuminuria. Therefore, there is a clinical need for novel biomarkers that can predict early potential rapid CKD progression in patients. The aim of this project is to determine whether serum microRNAs (miRs) can be used as biomarkers to predict the speed of CKD progression in diabetes patients, and to identify potential treatment targets associated with such dysregulated miRs. Method miRs were isolated from sera of 34 patients with type 2 diabetes classed with either slow or fast progressing CKD by their eGFR (n = 17; obtained from the Salford Kidney Study sample collection). Several miRs were found to be significantly dysregulated in fast progressing patients by using Next Generation Sequencing (NGS) of miRs in individual serum samples. An additional 49 diabetic CKD patient serum samples (slow (n = 29) or fast progressors (n = 20)) were used to validate the potential miR biomarker panel by qPCR. Results The NGS miR analysis of the initial 34 CKD patient sera produced a panel of 16 miRs that were either significantly upregulated or downregulated in the fast-progressing patients compared to slow progressors. The qPCR validation of this initial panel in 49 additional CKD patient serum samples demonstrated that 6 miRs were significantly upregulated in the fast-progressing patients. The levels of these miRs correlated with demographic and clinical parameters. ROC curve analysis revealed that the individual predictive power of each of those 6 miRs to define either a slow or fast progressing CKD patient was between 63-75%. Determination of a miR combination the strongest predictive power is ongoing. Conclusion A potential biomarker panel of 6 miRs has been identified and validated by qPCR, with each miR having a predictive power of between 63-75% for fast progression of CKD. A panel of miRs that could accurately predict progression speed of CKD patients could lead to faster and earlier diagnosis of patients to enable better treatment. Work is ongoing to explore the predictive power of this miR biomarker panel, and potential novel therapeutic targets to prevent CKD or delay its progression to ESRD.

Comprehensive Investigation of Salmonella spp.: Isolation, Characterization and PCR Primer Set Evaluation for Enhanced Detection and Identification

Background: Salmonellosis is a serious health concern for both animals and humans. According to a report of the Centers for Disease Control and Prevention, 40,000 human infections occur annually; however, the number of unconfirmed cases is as high as approximately 800,000-4000,000. These organisms gain entry into the gastrointestinal cells and cause serious diseases, resulting in intestinal damage and even death. Methods: In this research, 115 Salmonella isolates from different sources were tested. Using morphological, cultural, and biochemical methods, they were classified into 30 serovars. Plasmid profiling was used for quick identification, which showed similarities among the isolates.PCR primer sets targeting specific genes, such as invA, hilA, iroB, oriC, fimA, hitJ and stn, demonstrated reliability, accuracy and specificity for identifying S. enterica serovars. Result: This study recommends the use of specific primer sets in PCR-based methods for rapid and reliable identification and detection of locally isolated Salmonella serovars/strains. This research adds to the continuing efforts for improving the diagnostic techniques to tackle the challenges presented by Salmonella spp.

A rapid solid-phase extraction purification combined with UPLC[sbnd]MS/MS used for simultaneous determination of eight tetracyclines and three metabolite in chicken and pork

A method for rapid detection and identification of 8 tetracyclines (TCs) and 3 metabolite residues in chicken and pork was established and verified by combining rapid solid-phase extraction (SPE) technology with ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLCMS/MS). After a sample was extracted, it was purified with a PRiME HLB cartridge, and the cartridge did not need to be activated, balanced, or eluted. The filtrate was dried under nitrogen, redissolved, and subsequently analyzed via UPLCMS/MS. The limits of detection for chicken and pork were 0.05–0.48 μg/kg and 0.05–0.52 μg/kg, respectively. The limits of quantification were 0.16–1.61 μg/kg and 0.16–1.75 μg/kg, respectively. The recoveries were 85.15%–107.50% and 84.21%–106.37%, respectively. The established method was suitable for qualitative and quantitative analyses of TCs and their metabolite residues in chicken and pork and has been used for the detection of TC in chicken and pork samples from local markets.

Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry.

The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475-525 nm and 665-690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin.

Rapid molecular identification of Rana dybowskii by species-specific primers

Oviductus Ranae is the dried oviduct from Rana dybowskii, a forest frog species with medicinal, tonic, and cosmetic properties. Due to the high price and resource shortage, counterfeit varieties of Oviductus Ranae often appear in the market. However, traditional identification methods cannot accurately differentiate between Oviductus Ranae and its adulterants. In this study, a rapid molecular identification method has been established. The method involves extracting genomic DNA in just 30 s using filter paper purification, species-specific rapid polymerase chain reaction (PCR) amplification, and finally, fluorescence detection of the products. It can accurately identify Oviductus Ranae and its three common adulterants in about 30 min, making the process simple, fast, and highly specific.

Rapid identification of cocrystal components of explosives based on Raman spectroscopy and principal component analysis

Energetic cocrystal materials are considered to be one of the important directions for the development of energetic materials, due to their high energy density and low sensitivity. However, there is still a lack of effective methods to carry out rapid structural and purity identification. Herein, we explored a method for rapid identification and identification of unknown components extracted from CL-20/MTNP and CL-20/HMX cocrystal processes based on Raman spectroscopy combined with principal component analysis (PCA). Thirty sets of cocrystal and 30 sets of mixed explosives were randomly selected as the training set and 10 sets each as the validation set. The principal components were extracted by dimensionality reduction of the collected Raman spectra using the principal component sub-featured clustering algorithm of chemometrics. The region identification structure formed by different principal components allows intelligent output of whether the sample was cocrystal or not. The results show that the cumulative contribution rate of the three principal components in the sample set was 98.7 %. The confidence ellipses of the validation set were all well distributed within the confidence ellipses of the training set. And the structure identification results of explosive cocrystals were output quickly, accurately and intelligently. Therefore, this method shows good potential application value in the rapid structural identification of other complex mixtures such as energetic even pharmaceutical cocrystals.

Monitoring and analysis of pathogenic microorganisms and resistance genes.

The objectives are to improve the rapid identification method of microbial risk and cut off the route of transmission of resistance genes. When new pathogenic microorganisms are found, intervention can be carried out as early as possible to identify the risk of potential pathogen transmission, and timely cut off the transmission route. Hospital air samples were collected to analyse the distribution of environmental pathogenic microorganisms and the characteristics of ARGs resistance genes. The air samples were collected from 12 sampling sites in the Affiliated Hospital of Yangzhou University. In the infusion room, general ward and intensive care unit, no significant difference was found in microorganisms, and no significant difference was found in microbial resistance genes. There were some differences in resistance genes between east and west districts. Combined with the detection of pathogenic microorganisms and resistance genes in our hospital, it is necessary to improve the daily disinfection measures such as air conditioning and fresh air equipment, cut off the infection route, block the transmission of resistance genes, and monitor pathogens and resistance genes in airborne diseases.

Development and application of a TaqMan-probe-based multiplex real-time PCR assay for simultaneous detection of porcine circovirus 2, 3, and 4 in Guangdong province of China.

Porcine circoviruses disease (PCVD), caused by porcine circovirus (PCVs), is an important swine disease characterized by porcine dermatitis, nephrotic syndrome and reproductive disorders in sows. However, diseases caused by PCV2, PCV3, or PCV4 are difficult to distinguish, so a simple, rapid, accurate and high-throughput diagnostic and identification method is urgently needed to differentiate these three types. In this study, specific primers and probes were designed based on the conserved region sequences of the Rep gene of PCV2, and the Cap gene of PCV3 and PCV4. A multiplex qPCR assay was developed and optimized that the limit of detection concentration could reach as low as 3.8 copies/μL, with all correlation coefficients (R2) exceeding 0.999. Furthermore, the method showed no cross-reaction with other crucial porcine viral pathogens, and both intra-repeatability and inter-reproducibility coefficients of variation were below 2%. The assay was applied to the detection of 738 pig samples collected from 2020 to 2021 in Guangdong Province, China. This revealed positive infection rates of 65.18% for PCV2, 29.27% for PCV3, and 0% for PCV4, with a PCV2/PCV3 co-infection rate of 23.17%. Subsequently, complete genome sequences of 17 PCV2 and 4 PCV3 strains were obtained from the above positive samples and pre-preserved positive circovirus samples. Nucleotide sequence analysis revealed that the 17 PCV2 strains shared 96.7-100% complete nucleotide identity, with 6 strains being PCV2b and 11 strains being PCV2d; the 4 PCV3 strains shared 98.9-99.4% complete nucleotide identity, with 2 strains being PCV3a-1 and 2 strains being PCV3b. This research provides a reliable tool for rapid PCVs identification and detection. Molecular epidemiological investigation of PCVs in pigs in Guangdong Province will help us to understand PCV2 and PCV3 epidemiological characteristics and evolutionary trends.

Application of the MALTI-TOF MS Method for Identification of Vibrio spp. in Aquaculture

Aquaculture is developing rapidly and plays an important role in providing animal protein to the world’s growing population. However, increasing mortality from bacterial disease outbreaks in important species poses a challenge to production progress in this sector. In order to reduce the impact of these diseases, rapid and accurate pathogen identification is essential for disease management, early detection and the continued health of aquaculture. The aim of this review is to summarise studies on the identification and diagnosis of Vibrio pathogens in aquatic organisms by MALTI-TOF MS (Matrix-Assisted Laser Desorption Ionisation Time-of-Flight Mass Spectrometry), a rapid identification method based on protein profiling of bacteria. The profiles of bacterial protein obtained are compared with a global microbial protein library for identification. This study demonstrates the potential of using MALDI-TOF MS for the detection of Vibrio pathogens in aquaculture in studies published between 2015 and 2024. While purchasing a time-of-flight mass spectrometer is expensive when compared to conventional and molecular identification methods. It also appears to be much more efficient in terms of time spent on identification. MALDI-TOF MS has been shown to be simple to use in fish identification laboratories.

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

After a disaster, ascertaining the operational state of extensive infrastructures and building clusters on a regional scale is critical for rapid decision-making and initial response. In this context, the use of remote sensing imagery has been acknowledged as a valuable adjunct to simulation model-based prediction methods. However, a key question arises: how to link these images to dependable assessment results, given their inherent limitations in incompleteness, suboptimal quality, and low resolution? This article comprehensively reviews the methods for post-disaster building damage recognition through remote sensing, with particular emphasis on a thorough discussion of the challenges encountered in building damage detection and the various approaches attempted based on the resultant findings. We delineate the process of the literature review, the research workflow, and the critical areas in the present study. The analysis result highlights the merits of image-based recognition methods, such as low cost, high efficiency, and extensive coverage. As a result, the evolution of building damage recognition methods using post-disaster remote sensing images is categorized into three critical stages: the visual inspection stage, the pure algorithm stage, and the data-driven algorithm stage. Crucial advances in algorithms pertinent to the present research topic are comprehensively reviewed, with details on their motivation, key innovation, and quantified effectiveness as assessed through test data. Finally, a case study is performed, involving seven state-of-the-art AI models, which are applied to sample sets of remote sensing images obtained from the 2024 Noto Peninsula earthquake in Japan and the 2023 Turkey earthquake. To facilitate a cohesive and thorough grasp of these algorithms in their implementation and practical application, we have deliberated on the analytical outcomes and accentuated the characteristics of each method through the practitioner’s lens. Additionally, we propose recommendations for improvements to be considered in the advancement of advanced algorithms.

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.

Rapid identification,fluconazole and micafungin susceptibility testing of Candida species from blood culture by a short incubation method

This study aimed to develop and validate a rapid method for identification by MALDI-TOF system and determination of the susceptibility to Fluconazole and Micafungin by broth microdilution among Candidaspecies causing bloodstream infections. Subcultures from blood culture bottles were incubated for 5 hours (+/- 1h) and used to perform the tests, so that the turnaround time of rapid identification and susceptibility profile was about 5 and 24 hours, respectively. The rapid identification showed agreement of 92.05 %. Regarding the rapid broth microdilution for Fluconazole and Micafungin, the agreement was 97.06 % (p<0.001) and 100 % (p<0.001), and the Kappa coefficient was 0.91 (p<0.001) and 1.0 (p<0.001), respectively. To conclude, both rapid methods showed to be reproducible, inexpensive, easy to perform and time-saving. Thus, these methodologies could be useful to guide and adjust empirical antifungal therapy.

Detection and Identification of Nitrile Compounds via Recognition-Enabled Chromatographic 19F NMR.

The surge in applications of nitrile compounds across diverse fields, such as pharmaceuticals, agrochemicals, dyes, and functional materials, necessitates the development of rapid and efficient detection and identification methods. In this study, we introduce a chemosensing strategy employing a novel 19F-labeled probe, facilitating swift and accurate analysis of a broad spectrum of nitrile-containing analytes. This approach leverages the reversible interaction between the 19F-labeled probe and the analytes to produce chromatogram-like outputs, ensuring the precise identification of various pharmaceuticals and pesticides within complex matrices. Additionally, this dynamic system offers a versatile platform to investigate through-space 19F-19F interactions, showcasing its potential for future applications in mechanistic studies.

RAIN: a Machine Learning-based identification for HIV-1 bNAbs.

Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infection. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoire is still lacking. Here, we developed a straightforward computational method for Rapid Automatic Identification of bNAbs (RAIN) based on Machine Learning methods. In contrast to other approaches using one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of novel HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires.

Progress in Identification of UDP-Glycosyltransferases for Ginsenoside Biosynthesis.

Ginsenosides, the primary pharmacologically active constituents of the Panax genus, have demonstrated a variety of medicinal properties, including anticardiovascular disease, cytotoxic, antiaging, and antidiabetes effects. However, the low concentration of ginsenosides in plants and the challenges associated with their extraction impede the advancement and application of ginsenosides. Heterologous biosynthesis represents a promising strategy for the targeted production of these natural active compounds. As representative triterpenoids, the biosynthetic pathway of the aglycone skeletons of ginsenosides has been successfully decoded. While the sugar moiety is vital for the structural diversity and pharmacological activity of ginsenosides, the mining of uridine diphosphate-dependent glycosyltransferases (UGTs) involved in ginsenoside biosynthesis has attracted a lot of attention and made great progress in recent years. In this paper, we summarize the identification and functional study of UGTs responsible for ginsenoside synthesis in both plants, such as Panax ginseng and Gynostemma pentaphyllum, and microorganisms including Bacillus subtilis and Saccharomyces cerevisiae. The UGT-related microbial cell factories for large-scale ginsenoside production are also mentioned. Additionally, we delve into strategies for UGT mining, particularly potential rapid screening or identification methods, providing insights and prospects. This review provides insights into the study of other unknown glycosyltransferases as candidate genetic elements for the heterologous biosynthesis of rare ginsenosides.

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.

A rapid identification method for soft tissue markers of dentofacial deformities based on heatmap regression

ObjectiveThe purpose of this study was to construct a facial deformity dataset and a network model based on heatmap regression for the recognition of facial soft tissue landmarks to provide a basis for clinicians to perform cephalometric analysis of soft tissue.Materials and methodsA 34-point face marker detection model, the Back High-Resolution Network (BHR-Net), was constructed based on the heatmap regression algorithm, and a custom dataset of 1780 facial detection images for orthognathic surgery was collected. The mean normalized error (MNE) and 10% failure rate (FR10%) were used to evaluate the performance of BHR-Net, and a test set of 50 patients was used to verify the accuracy of the landmarks and their measurement indicators. The test results were subsequently validated in 30 patients.ResultsBoth the MNE and FR10% of BHR-Net were optimal compared with other models. In the test set (50 patients), the accuracy of the markers excluding the nose root was 86%, and the accuracy of the remaining markers reached 94%. In the model validation (30 patients), using the markers detected by BHR-Net, the diagnostic accuracy of doctors was 100% for Class II and III deformities, 100% for the oral angle plane, and 70% for maxillofacial asymmetric deformities.ConclusionsBHR-Net, a network model based on heatmap regression, can be used to effectively identify landmarks in maxillofacial multipose images, providing a reliable way for clinicians to perform cephalometric measurements of soft tissue objectively and quickly.

A Non-Global Traversal Method for Dynamic Load Rapid Localization and Identification

Dynamic load localization and identification technology is very important in the structural design and optimization of aircraft. This paper proposes a non-global traversal method (NTM) for the fast positioning and recognition of dynamic loads on continuous beams. This method separates the load’s position and amplitude information in the modal space. Then, it constructs an interpolation function about position information, and converts load positioning to solving the zero point of the interpolation function. After determining the position of the dynamic load, the amplitude of the dynamic load is recognized. This method does not need to traverse all the position points globally, thereby greatly improving the efficiency of load positioning. Numerical simulations and experiments show that compared with the original variable separation fast positioning method (VSRPM), this method improves the calculation efficiency by more than 80% while maintaining the same recognition accuracy. NTM is a new method of great application value.

Strategy on rapid discrimination of different varieties based on the combination of HS-GC-IMS and DNA Mini-barcode, spore powder of Ganoderma as a case study

The demand for the consumption of Ganoderma (LingZhi) is increasing significantly nowadays. While the wild resources of Ganoderma lucidum and Ganoderma sinense have been reduced severely, and artificially cultivated products are gradually occupying the market. However, it is usually difficult to distinguish the different species of Ganoderma spore powder (GSP). In this research, a rapid and accurate identification method which combined headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) with the DNA mini-barcode was successfully developed to discriminate different GSP. The feasibility of the method was evaluated by both GSP and its Chinese patent medicines. HS-GC-IMS was used to characterize GSP volatile organic compounds (VOCs) and a total of seventy-one VOCs were identified. Principal component analysis could be utilized to show the significantly difference between Korean LingZhi (KL) and American LingZhi (AL) or Taishan LingZhi (TL). Then, we clarified the species information of all samples based on the internal transcribed spacer (ITS) regions. The identification results showed that TL and KL were Ganoderma lucidum, and the AL was Ganoderma resinaceum. Furthermore, according to the mutation sites of the above ITS sequences, the specialized DNA mini-barcode was successfully developed and applied. The artificial mixtures of the three types of GSP and Chinese patent medicines containing GSP were used to authenticate the barcode capability. The mixing experiment found that there was a linear relationship between the sequence numbers and the mixed biomass, indicating that the qualitative and quantitative ability of DNA mini-barcode performed well. The investigation of the ingredients of commercially available GSP products found that the commercial products maybe contain Ganoderma resinaceum, Ganoderma applanatum and Daedaleopsis confragosa, except for Ganoderma lucidum. Therefore, the results suggested that the available GSP products might be adulterated according to Chinese Pharmacopoeia. In this paper, we established a novel approach to distinguish the different GSP samples based on HS-GC-IMS and DNA mini-barcode, and the results indicated that our strategy could not only discriminate different GSP samples, but also identify the origins in their related products.