Identification Of Origin Research Articles

Laparoscopic right hemicolectomy with complete mesocolon excision and cranial approach.

Complete mesocolon excision (CME) and D3-lymphadenectomy concepts have gained popularity for the surgical treatment of right colon cancer in comparison to the conventional laparoscopic right hemicolectomy (CLRH). The rationale of CME is to dissect the embryological planes between the mesenteric plane and the parietal fascia to remove the mesentery within a complete envelope of mesenteric fascia and visceral peritoneum that contains lymph nodes, the central vascular ligation, and adequate bowel length to remove involved pericolic lymph nodes in the longitudinal direction, having as the main goal to improve the oncological results. CME with D3-lymphadenectomy is challenge since involves the excision of the lymph adipose tissue covering the medial edge of the superior mesenteric vein (SMV) (trunk of Gillot, TG), and the gastrocolic trunk of Henle (GTH). We describe a LRH with CME using a cranial approach allowing an easier central vessels origin identification. Through the supramesocolic approach, gastrocolic ligament is opened and the GTH and the middle colic artery (MCA) and vein (MCV) origins are identified. Hepatic flexure is mobilized, and a gauze is placed above the mesenteric vessels. Then, the SMV is identified, dividing the ileocolic vessels origin. The plane between the Gerota and Toldt fascias is opened, identifying duodenum, pancreas, and the gauze previously placed. Following this plane and the SMV along the TG, the GTH and its branches are identified. The superior right colic vein, and the MCA and MCV right branches are divided. After that, colon is fully mobilized laterally opening the parietocolic gutter and an intracorporeal anastomosis is performed. CME could lead to an improvement of oncological results due to a wider mesocolic excision in comparison to conventional D2-lymphadenectomy. Cranial approach facilitates the vessels origin identification to perform a true central ligation.

Elemental profile of Ipê species to support timber tracing in Amazon Rainforest

Accurate identification of geographical origin and species of wood is crucial for endorsing laws to combat illegal trade. Here, the multi-element profile of Ipê species of economic interest in Brazil was investigated by neutron activation analysis (NAA). Ba, Br, Ca, Ce, Co, Eu, Fe, K, La, Na, Rb, Sc, Sm, Sr and Zn were determined in the heartwood and sapwood tissues. The Random Forest algorithm identified Sr, Ba, Sm, La, Ce and Br as the most important elements for discriminating species. The analyses revealed significant differences between species and wood tissues, indicating that multi-element profile has great potential as a forensic tool. The establishment of comprehensive reference databases that cover large geographical areas and take into account the natural variability between species is fundamental to achieving this goal.

Detection and identification of un-uniformed shape text from blurred video frames

<p><span lang="EN-GB">The identification and recognition of text from video frames have received a lot of attention recently, that makes many computer vision-based applications conceivable. In this study, we modify the picture mask and the original identification of the mask region convolution neural network and permit detection in three levels, including holistic, sequence, and at the level of pixels. To identify the texts and determine the text forms, semantics at the pixel and holistic levels can be used. With masking and detection, existences of the character and the word are separated and recognised. In addition, text detection using the results of 2-D feature space instance segmentation is done. Moreover, we explore text recognition using an attention-based optical character recognition (OCR) method with mask</span><span lang="EN-US"> r</span><span lang="EN-GB">egion convolution neural networks (R-CNN) to address and detect the problem of smaller and blurrier texts at the sequential level. Using attribute maps of the word occurrences in sequence to seq, the OCR method calculates the character sequence. At last, a fine-grained learning strategy is proposed to constructs models at word level using the annotated datasets, resulting in the training of a more precise and reliable model. The well-known benchmark datasets ICDAR 2013 and ICDAR 2015 are used to test our suggested methodology.</span></p>

Recognition of Radix Bupleuri origin using laser-induced breakdown spectroscopy (LIBS) combined with deep learning and machine learning algorithms

In this study, laser-induced breakdown spectroscopy (LIBS) was utilized to classify Radix Bupleuri from six origins to acquire good results. Several feature variable selection methods have been used to preprocess the experimental data on the LIBS. Machine learning and deep learning algorithms were used to build classification models, respectively. The accuracy of all four classification models on the prediction set is higher than 99%. Among them, the classification model constructed by the residual neural network (ResNet) algorithm demonstrated the optimal prediction accuracy both in the full waveform and feature variable selection. For the independent prediction set, the ResNet classification model still demonstrated optimal prediction with 88.33% accuracy. On the importance ranking of feature variables, the elements as Cl, Mg, Fe, Ba, Li, O, and He were considered to play an important role in the identification of the origin of Radix Bupleuri. This study demonstrated that LIBS combined with deep learning and machine learning algorithms can provide a more efficient and reliable method for the origin identification of Radix Bupleuri. In addition, it can be found that the application of deep learning algorithms can realize the prediction output more efficiently and accurately for the analysis of high-dimensional data of LIBS.

Diagnosis progress of carcinoma of unknown primary

Carcinoma of unknown primary (CUP) is a common and complex type of tumor in clinical practice, where the primary site cannot be determined through conventional diagnostic methods, posing significant challenges for clinical diagnosis and treatment. In recent years, advancements in gene expression profiling and genetic testing technologies have provided new perspectives for CUP research, driving progress in this field. By analyzing gene expression profiles, researchers can more effectively identify the tissue origin of tumors, thereby improving diagnostic accuracy. At the same time, the potential application of genetic testing is continuously being explored, offering new possibilities for personalized treatment. This article aims to discuss the latest advancements in the diagnosis of CUP, analyze the importance of gene expression profiling and genetic testing in tumor origin identification and their clinical applications, and summarize current research progress and future research directions, with the goal of providing a theoretical basis for the early diagnosis and treatment of CUP.

Limitation of Unipolar Electrograms in Guiding Successful RFCA of Idiopathic Ventricular Arrhythmias Associated With Discrete Pre-Potential.

Activation mapping for idiopathic ventricular arrhythmias (IVAs) typically relies on identifying the earliest bipolar electrograms and unipolar electrograms characterized by an initial QS morphology preceding the intrinsic deflection. However, the utility of unipolar electrogram morphology, particularly when associated with discrete pre-potentials, in guiding IVA mapping is not well understood. We retrospectively analyzed 537 patients who underwent successful radiofrequency catheter ablation (RFCA) for IVAs between March 2016 and August 2023. Among them, 23 patients (4.3%) exhibited discrete bipolar pre-potentials with an isoelectric line between the end of the discrete pre-potential and the onset of the QRS complex during IVAs at the successful RFCA site. These cases were included in this study. The time interval from the onset of the discrete pre-potential to the onset of the QRS complex was 66.4 ± 18.3 ms. The duration of the isoelectric line between the end of the discrete pre-potential and the onset of the QRS complex was 36.9 ± 13.6 ms. Unipolar electrograms associated with discrete pre-potentials exhibited an initial negative morphology in only 13 out of 23 patients (56.5%). In contrast, initial positive and isoelectric configurations in unipolar electrograms were observed in two patients (8.7%) and eight patients (34.8%), respectively. Perfect pace mapping was obtained in 21 patients (91.3%). RFCA was successful at the earliest site of the pre-potential within 3.5 ± 1.5 s in all 23 patients. During the 2.4 ± 1.2 years follow-up period, only one patient (4.3%) experienced a recurrence of clinical IVAs, with no complications reported during RFCA or follow-up. Unipolar electrograms show limited reliability in guiding the identification of IVAs origins compared to bipolar electrograms associated with discrete pre-potentials.

Rapid Identification of Geographical Origin of Sliced Polygonati Rhizoma by Auto-focus Laser-induced Breakdown Spectroscopy Combined with Interpretable Machine Learning

Rapid Identification of Geographical Origin of Sliced Polygonati Rhizoma by Auto-focus Laser-induced Breakdown Spectroscopy Combined with Interpretable Machine Learning

Aroma Identification and Traceability of the Core Sub-Producing Area in the Helan Mountain Eastern Foothills Using Two-Dimensional Gas Chromatography and Time-of-Flight Mass Spectrometry and Chemometrics.

The combination of volatile compounds endows wines with unique aromatic characteristics and is closely related to their geographical origins. In the pursuit of origin identification and the subdivision of homogeneous production areas, clarifying the characteristics of production areas is of great significance for improving wine quality and commercial value. In this study, GC×GC-TOFMS technology was used to analyze the aroma characteristics of "Cabernet Sauvignon" wines from 26 wineries in the Helan (HL), Yinchuan (YC), Yongning (YN), Qingtongxia (QTX), and Hongsibu (HSP) sub-producing areas in the eastern foothills of Helan Mountain in Ningxia, China. The results indicate a gradual increase in relative humidity from the southern part of Ningxia, with the YN sub-region showing optimal fruit development and the QTX region having the highest maturity. A total of 184 volatile compounds were identified, with 36 compounds with an OAV > 1, crucial for the aroma profiles of primarily fermentation-derived alcohols and esters. An aromatic vector analysis revealed that "floral" and "fruity" notes are the primary characteristics of Cabernet Sauvignon wines from the Helan Mountain East region, with lower maturity aiding in the retention of these aromas. By constructing a reliable OPLS-DA model, it was determined that 15 substances (VIP > 1) played a crucial role in identifying production areas, among which phenylethyl alcohol and isoamyl alcohol were the main contributors. In addition, a Pearson correlation analysis showed a negative correlation between sunlight duration during the growing season and benzyl alcohol accumulation, while a significant positive correlation was observed during the ripening period. Due to the critical role of phenyl ethanol in identifying producing areas, this further demonstrates that sunshine conditions may be a key factor contributing to the differences in wine flavor across regions. This study offers a theoretical foundation for understanding the relationship between climatic factors and flavor characteristics, addressing the issue of wine homogenization in small production areas, clarifying typical style characteristics, and establishing a traceability technology system based on characteristic aroma.

Classifiability Analysis of Spectroscopic Profiling Datasets in Food Safety-related Discriminative Tasks

Discriminative tasks, i.e., the identification of different food materials, brands, and origins, have become an essential part of food safety control. In recent years, spectroscopic profiling combined with machine learning is becoming popular for food-related discriminative tasks, but finding an appropriate classification model can be challenging. Compared to the current “trial-and-error” practice, this paper proposes a dedicated two-step classifiability analysis framework to address this issue. The first step collects more than 90 diversified metrics to measure the dataset separability from different perspectives. The second step synthesizes these metrics into a quantitative score using meta-learner and decomposition-based strategies. Finally, two Raman spectroscopic profiling case studies were conducted to validate the method, demonstrating higher scores for the easily separable liquor dataset (around 1.0) compared to the more challenging table salt dataset (<0.5). This score can guide researchers to determine the required model complexity and assess the adequacy of the current physio-chemical profiling instrument. We expected the classifiability analysis framework proposed in this research to be generalized to a wide range of machine learning applications within the realm of food, where data-driven classification or discriminative tasks are involved.

Abstract A053: Liquid biopsy-based detection of triple negative breast cancer using DNA methylation biomarkers

Abstract Triple-negative breast cancer (TNBC) presents significant clinical challenges due to its aggressive phenotype, rapid progression, and limited targeted treatment options. TNBC often mimics benign lesions on ultrasound and metastatic features can be missed in routine mammography screening leading to misdiagnosis and delayed treatment. These limitations led us to investigate whether a blood-based liquid biopsy could provide a non-invasive, sensitive method for early TNBC detection, potentially overcoming the constraints of conventional imaging techniques. To discover biomarkers for TNBC, we performed targeted bisulfite sequencing on a cohort of 46 breast cancer solid tissue biopsy samples, comprising 19 TNBC and 27 non-TNBC cases. We identified 150 differentially methylated regions (mean length 703bp) separating TNBC and non-TNBC samples. Upon linking each region to its nearest gene, we observed several genes previously associated with prognosis and survival (HOXB3, PAX9, SOX9) substantiating the clinical relevance of these biomarkers. Using public TCGA data (369 breast cancer samples), we integrated expression and methylation data and observed directionally consistent changes. For example, HOXB3 showed increased methylation at its associated differential regions and decreased expression in TNBC cases. To ascertain whether these biomarkers could be useful in a liquid biopsy approach, we performed targeted bisulfite- sequencing on cell-free DNA samples derived from prospectively collected patients with breast cancer. All samples had independent tissue-based assessment of ER, PR and HER2 using immunohistochemistry or in situ hybridization. We then built a feed-forward neural network classifier with classes TNBC and non-TNBC that used transfer-learning and the identified biomarkers to learn methylation signatures in the biopsy samples and map them to cell-free DNA. To emulate clinical workflow, where subtyping follows cancer diagnosis and tissue of origin identification, we evaluated cfDNA samples correctly identified as breast cancer using two independent machine learning models trained using 2,393 prospectively collected multi- indication cancer and non-cancer samples (NCT05435066). Subsequent TNBC subtyping of breast cancer-positive cases (N=56) accurately identified 77% (10/13) of TNBC samples and 91% (39/43) of non-TNBC cases, with overall accuracy of 84%. The estimated tumor content range for correctly predicted TNBC samples was 0.5-33% with the three mis-classified samples all showing very low tumor content (0.22%, 0.28%, 0.59%). Non-TNBC cases showed a tumor content range of 0.07-36% for correct and 2.3-14% for misclassified samples. In conclusion, we identified novel methylation biomarkers that distinguish TNBC in the cell-free DNA of patients without needing invasive tissue biopsies. The high sensitivity of our assay at low tumor fractions has the potential to improve outcomes through earlier intervention and may enable earlier screening in high-risk groups, monitoring of minimal residual disease and longitudinal monitoring during treatment. Citation Format: Jocelyn Charlton, Shiva Farashahi, Kade Pettie, Elie Massaad, Josh Hubbell, Feras Hantash, Kieran I Chacko. Liquid biopsy-based detection of triple negative breast cancer using DNA methylation biomarkers [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A053.

Identifying potential introduced and natural sources of pollution in Delaware watersheds.

Managing water quality with microbial impairment caused by Enterococcus poses unique challenges regarding the determination of fecal host origin. Most water monitoring is performed based on Enterococcus counts that neither detect the location of the introduction of pollution nor identify the type of contaminating Enterococcus. The use of sequenced-based microbial source tracking could allow for identification of fecal origin and potential remediation of pollution. The state of Delaware has numerous waterways with high microbial impairment from unknown sources, so we used sequence-based microbial source tracking to investigate potential microbial pollution in three watersheds with significant variation in land use and population density. In this study, we use a 16S rRNA sequence reference library of microbial communities from relevant fecal sources (wild animal, domestic animal, sediment, and septic/wastewater) to determine the most likely sources of microbial impairment in three Delaware watersheds. This study assigned sources of microbial contamination to mostly human-related sources (septic and wastewater) or unknown sources indicating that waste infrastructure may have a larger influence on microbial community structure in Delaware watersheds than previously considered. Our results suggest that long-term source tracking is valuable for ruling out native or domesticated animals as contributors to water pollution.IMPORTANCETraditional microbial pollution monitoring utilizes specific fecal indicator bacteria that need to grow in the laboratory for detection. Here, we show the use of sequence information from whole microbial communities and an expanded reference library in microbial source tracking. Expanding the host detection range by including the whole microbial community may allow for a wider range of potential fecal origin identification even when specific fecal indicators are absent or in low concentration. We show that many Delaware waterways bear the signature of human influence compared to natural sources. In addition, the robust reference library built in this study can be used to conduct source tracking studies in the mid-Atlantic USA.

A Data-Driven Model for Classification of Traditional Chinese Medicine Materials

This paper proposes a data-driven classification model for traditional Chinese medicinal herbs based on mid-infrared spectral data. Addressing the limitations of traditional identification methods when the herbs’ appearance is damaged or incomplete, this study employs machine learning techniques to achieve accurate classification of medicinal herb types through spectral data preprocessing and feature extraction. Firstly, the Savitzky-Golay convolution smoothing method and Standard Normal Variate (SNV) transformation were used for denoising the spectral data. Then, Principal Component Analysis (PCA) was employed to reduce the dimensionality of the high-dimensional spectral data and extract the key features. Finally, the Gaussian Mixture Model (GMM) was applied to cluster the reduced data, categorizing the medicinal herbs into six classes. The results show that this method produces the accurate and stable classification. The constructed model is not only applicable to the classification and origin identification of medicinal herbs but also provides an important reference value for the classification and origin identification of other plant species.

Genetic structure of Saccharina japonica in Japan and evaluation of a potential mitochondrial region for identification of geographic origin

Genetic structure of Saccharina japonica in Japan and evaluation of a potential mitochondrial region for identification of geographic origin

Who says ejque?

Abstract This paper examines language attitudes of 386 participants from all regions of Spain toward a phonetic variant used in the Madrid area, velarized coda /s/. Results show that velarized /s/ aids in the identification of a Madrid origin and, moreover, that velarized /s/ speakers are considered lower status, less pleasant, less confident, more of a tough-guy, less desirable as friends, less compatible with the listener and worse storytellers. Nearly all of these traits interact with listener gender in which women show the strongest variable effect. In addition, ethnocentrism is found to play a role in listener judgments, irrespective of the coda /s/ variant used.

Molecular compositional variation of organic matter deposited on the East Tasman Plateau during the Paleocene–Eocene Thermal Maximum

Molecular compositions in sediments collected by Ocean Drilling Program Leg 189 at Site 1172 in the Tasmanian Gateway, Australia have been characterised by gas chromatography–mass spectrometry to assess the impact of the Paleocene–Eocene Thermal Maximum (PETM) on organic matter origins and evolution. Total organic carbon (TOC) and sulfur contents reach the lowest values immediately before the initiation of the negative carbon isotope excursion (CIE) in the shallow marine environment on the East Tasman Plateau. The TOC content increases slightly while the sulfur content increases more substantially during deposition of the CIE, suggesting the occurrence of low-oxygen environments during the PETM event. The studied section is thermally immature, as shown by the dominance of biological configuration biomarkers such as 17β(H),21β(H)-hopanes (ββ) and hopenes. Changes in relative abundance of compound classes and isomer ratios are largely caused by the variation of source inputs and depositional conditions. Very low abundance of marine steroids during the CIE, but very enriched hopanoids, indicate low marine productivity, in accord with substantial remineralisation of organic matter in the bottom waters. The ββ-hopanes are the most abundant hopanoid series, followed by the neohop-13(18)-ene and hop-17(21)-ene series, further verifying the immaturity of the organic constituents in the samples. Hopane ratios (ββ/(βα + αβ) and βα/(βα + αβ) with different carbon numbers are systematically higher in sediments deposited during the CIE than those formed pre- and post-CIE, suggest increased bacterial inputs. Higher C30/C29 and C30/C31 hopanoid ratios in the CIE sediments are consistent with a greater contribution from in situ bacterial organic matter, rather than continental soil erosion by flooding. This is supported by the higher C27–32 hopanoids/(C27 + C29 + C31n-alkanes) ratio and sulfur content during the CIE. A relatively lower TOC content in the sediments formed during the PETM is inferred to have been caused by dilution by both enhanced detrital input and intensified bacterial consumption. This study demonstrates that the biomarker approach can play an important role in the identification of organic matter origin and characterisation of depositional environment for PETM sequences.

Feature extraction of fluorescence excitation-emission matrices using PCA fused with Wilks Λ-statistic and FDA for origin identification and active components content prediction of sweet basil

Feature extraction of fluorescence excitation-emission matrices using PCA fused with Wilks Λ-statistic and FDA for origin identification and active components content prediction of sweet basil

Ferroptosis and Intrinsic Drug-induced Liver Injury by Acetaminophen and Other Drugs: A Critical Evaluation and Historical Perspective.

Drug-induced hepatotoxicity is a significant clinical issue worldwide. Given the limited treatment options for these liver injuries, understanding the mechanisms and modes of cell death is crucial for identifying novel therapeutic targets. For the past 60 years, reactive oxygen species and iron-dependent lipid peroxidation (LPO) have been hypothesized to be involved in many models of acute drug-induced liver injury. However, this mechanism of toxicity was largely abandoned when apoptosis became the primary focus of cell death research. More recently, ferroptosis-a novel, non-apoptotic form of cell death-was identified in NRAS-mutant HT-1080 fibrosarcoma cells exposed to erastin and other NRLs. Ferroptosis is characterized by glutathione depletion and the impairment of glutathione peroxidase 4 activity, which hinders the detoxification of lipid hydroperoxides. These hydroperoxides then serve as substrates for iron-dependent LPO propagation. This cell death mechanism is now receiving widespread attention, extending well beyond its original identification in cancer research, including in the field of drug-induced liver injury. However, concerns arise when such mechanisms are applied across different cell types and disease states without sufficient validation. This review critically evaluated the historical evidence for iron-dependent LPO as a mechanism of drug-induced hepatotoxicity and explored how these earlier findings have led to the current concept of ferroptosis. Overall, the published data support the idea that multi-layered endogenous antioxidant defense mechanisms in the liver limit the occurrence of pathophysiologically relevant LPO under normal conditions. Only when these defense mechanisms are severely compromised does ferroptosis become a significant mode of drug-induced cell death.

Socioeconomic Status and Local Biodiversity as Predictors of Attitudes towards Nature among High School Students in a Latin American Metropolis.

Urbanization and biodiversity loss disconnect humans from nature and may limit pro-environmental actions. Although, a growing body of knowledge exists on people's attitudes towards nature, the majority focuses on the influence of socio-environmental factors. Significant variations in socioeconomic and educational levels in urban areas affect access to biodiversity. In this study, we aim to identify the socio-environmental variables that are associated with people's attitudes towards nature (student's knowledge about urban birds and their participation in conservation actions). We collected data on local biodiversity, socioeconomical status, and high school students' attitudes towards nature (15 schools, n = 175 students) in a large Latin American capital city. Structural equation modeling (SEM) was utilized to identify relationships among all variables. Socioeconomic characterization was assessed using levels of socio-materiality and school performance scores. Definitions of ecological complexity were based on bird species richness and vegetation density. Knowledge of urban birds was evaluated using two indicators: urban bird identification (recognition of names) and identification of the biogeographic origin of urban birds (native or exotic). Participation in conservation actions by students was assessed based on implementation scale and implementation frequency. According to our results, respondents' knowledge of birds was poor: 42% of students identified only two species (the domestic pigeon and the monk parakeet, both exotic). Only 26% of total students participated in conservation actions. SEM analysis revealed that socioeconomic status was positively associated with the ecological complexity of surrounding areas. In turn, ecological complexity showed a positive relationship with students' knowledge on urban birds. Finally, students' knowledge of urban birds showed a positive influence on conservation actions. Our findings suggest that, the improvement of students' knowledge about local biodiversity and ensuring equitable access to biodiversity could be an effective way to foster students' involvement in conservation actions, especially in neighborhoods of lower socioeconomic levels.

Elemental fingerprinting based on ICP-MS/OES and machine learning: Sample digestion-free and standard-free strategy for geographical origin identification of Chinese liquor

The significant price disparity of Chinese baijiu originating from various regions has unfortunately partially given rise to the proliferation of counterfeit and substandard products, and this may cause disturbance in the market economy. Ensuring the traceability and differentiation of baijiu by its origin is therefore of paramount importance. In this study, the Baijiu samples were straightforward dilution without sample digestion, followed by the detection of elemental signal intensities using ICP-MS and ICP-OES without accurate quantification, to rapidly construct their multi-elemental fingerprints. Heatmap and HCA (hierarchical cluster analysis) were used for data visualization and tentative classification, respectively. PCA (principal component analysis) was implemented to corroborate the classification outcomes. Subsequently, the supervised OPLS-DA (orthogonal partial least squares discrimination analysis) was deployed to enhance the identification and predictive capabilities for the Baijiu samples. The unequivocal discrimination of the three distinct Baijiu origins with 100% classification accuracy. The validation values R2 and Q2 approached 1.0, indicating a great fit and prediction capability of the OPLS-DA model. The VIP plot evaluated the importance of elemental variables, revealing that the key contributions were primarily from elements K, Ca, Na, Mg, and Al. Collectively, these findings demonstrate the efficacy of chemometric techniques, when integrated with multi-elemental fingerprints based on their signal intensities only, in identifying the geographical origins of the Baijiu. This approach holds promise for applications in quality control and anti-counterfeiting measures in the baijiu industry.

Geographical origin identification of dendrobium officinale based on NNRW-stacking ensembles

Dendrobium officinale is a well-recognized functional food material. Considering its therapeutic effect and price vary among different geographical origins, this paper proposed an origin identification method based on Raman spectroscopy and NNRW (neural network with random weights)-stacking ensemble model. In a case study of dendrobium officinale samples from three different geographical origins, we compare both single estimators, i.e., KNN (k-nearest neighbors), MLP (multi-layer perceptron), DTC (decision tree classifier), and NNRW, and their stacking ensemble counterparts. The results showed that the NNRW-stacking ensemble has the best test accuracy (96.3%) and an impressive fitting speed (the fastest among all ensembles). In conclusion, the NNRW-stacking ensemble model combined with Raman spectroscopy can be a promising method for herb geographical original identification. The proposed model has demonstrated the speed advantage of NNRW (no need for gradient-based iterations) and the generalization power of stacking ensembles (reduce single-estimator bias).