Diagnosis of Rotor Component Shedding in Rotating Machinery: A Data-Driven Approach.

The potential for rotor component shedding in rotating machinery poses significant risks, necessitating the development of an early and precise fault diagnosis technique to prevent catastrophic failures and reduce maintenance costs. This study introduces a data-driven approach to detect rotor component shedding at its inception, thereby enhancing operational safety and minimizing downtime. Utilizing frequency analysis, this research identifies harmonic amplitudes within rotor vibration data as key indicators of impending faults. The methodology employs principal component analysis (PCA) to orthogonalize and reduce the dimensionality of vibration data from rotor sensors, followed by k-fold cross-validation to select a subset of significant features, ensuring the detection algorithm's robustness and generalizability. These features are then integrated into a linear discriminant analysis (LDA) model, which serves as the diagnostic engine to predict the probability of rotor component shedding. The efficacy of the approach is demonstrated through its application to 16 industrial compressors and turbines, proving its value in providing timely fault warnings and enhancing operational reliability.

Factors influencing contractors' low-carbon construction behaviors in China: a LDA-DEMATEL-ISM approach.

Contractors' low-carbon construction behaviors (CLCB) are pivotal in advancing decarbonization during the construction phase. However, there exists a notable gap in the comprehensive exploration of the multifaceted factors and mechanisms influencing CLCB. Therefore, this study aims to systematically identify the factors influencing CLCB in China, examine the interrelationships among these factors, and pinpoint the key determinants. Based on topic modeling of Latent Dirichlet Allocation (LDA), influencing factors are identified firstly from the pertinent literature. Subsequently, the causality degree and centrality degree between these factors are assessed by the Decision-Making Trial and Evaluation Laboratory (DEMATEL), followed by the establishment of a hierarchical structure using the Interpretive Structural Modeling (ISM) method, culminating in the identification of pivotal factors. Findings reveal that (1) 21 influential factors influencing CLCB are identified. (2) "Incentive policies for relevant stakeholders" and "Low-carbon regulation and supervision" emerge as key influences. (3) CLCB should be guided by policy and subjective awareness, fortified by market and management support, underpinned by technology, and directly driven by economic considerations. This research furnishes valuable insights for promoting low-carbon development during the construction phase, thereby assisting the construction sector in achieving carbon peak and carbon neutrality.

Non-destructive prediction of fertility and sex in chicken eggs using the short wave near-infrared region.

The objective of this study was to evaluate the ability of a handheld near-infrared device (900-1600 nm) to predict fertility and sex (male and female) traits in-ovo. The NIR reflectance spectra of the egg samples were collected on days 0, 7, 14 and 18 of incubation and the data was analysed using principal component analysis (PCA), linear discriminant analysis (LDA) and support vector machines classification (SVM). The overall classification rates for the prediction of fertile and infertile egg samples ranged from 73 % to 84 % and between 93 % to 95 % using LDA and SVM classification, respectively. The highest classification rate was obtained on day 7 of incubation. The classification between male and female embryos achieved lower classification rates, between 62 % and 68 % using LDA and SVM classification, respectively. Although the classification rates for in-ovo sexing obtained in this study are higher than those obtained by chance (50 %), the classification results are currently not sufficient for industrial in-ovo sexing of chicken eggs. These results demonstrated that short wavelengths in the NIR range may be useful to distinguish between fertile and infertile egg samples at days 7 and 14 during incubation.

Statistical methods for discrimination of STR genotypes using high resolution melt curve data.

Despite the improvements in forensic DNA quantification methods that allow for the early detection of low template/challenged DNA samples, complicating stochastic effects are not revealed until the final stage of the DNA analysis workflow. An assay that would provide genotyping information at the earlier stage of quantification would allow examiners to make critical adjustments prior to STR amplification allowing for potentially exclusionary information to be immediately reported. Specifically, qPCR instruments often have dissociation curve and/or high-resolution melt curve (HRM) capabilities; this, coupled with statistical prediction analysis, could provide additional information regarding STR genotypes present. Thus, this study aimed to evaluate Qiagen's principal component analysis (PCA)-based ScreenClust® HRM® software and a linear discriminant analysis (LDA)-based technique for their abilities to accurately predict genotypes and similar groups of genotypes from HRM data. Melt curves from single source samples were generated from STR D5S818 and D18S51 amplicons using a Rotor-Gene® Q qPCR instrument and EvaGreen® intercalating dye. When used to predict D5S818 genotypes for unknown samples, LDA analysis outperformed the PCA-based method whether predictions were for individual genotypes (58.92% accuracy) or for geno-groups (81.00% accuracy). However, when a locus with increased heterogeneity was tested (D18S51), PCA-based prediction accuracy rates improved to rates similar to those obtained using LDA (45.10% and 63.46%, respectively). This study provides foundational data documenting the performance of prediction modeling for STR genotyping based on qPCR-HRM data. In order to expand the forensic applicability of this HRM assay, the method could be tested with a more commonly utilized qPCR platform.

An integrated framework for flood disaster information extraction and analysis leveraging social media data: A case study of the Shouguang flood in China.

Flood disasters cause significant casualties and economic losses annually worldwide. During disasters, accurate and timely information is crucial for disaster management. However, remote sensing cannot balance temporal and spatial resolution, and the coverage of specialized equipment is limited, making continuous monitoring challenging. Real-time disaster-related information shared by social media users offers new possibilities for monitoring. We propose a framework for extracting and analyzing flood information from social media, validated through the 2018 Shouguang flood in China. This framework innovatively combines deep learning techniques and regular expression matching techniques to automatically extract key flood-related information from Weibo textual data, such as problems, floodings, needs, rescues, and measures, achieving an accuracy of 83 %, surpassing traditional models like the Biterm Topic Model (BTM). In the spatiotemporal analysis of the disaster, our research identifies critical time points during the disaster through quantitative analysis of the information and explores the spatial distribution of calls for help using Kernel Density Estimation (KDE), followed by identifying the core affected areas using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. For semantic analysis, we adopt the Latent Dirichlet Allocation (LDA) algorithm to perform topic modeling on Weibo texts from different regions, identifying the types of disasters affecting each township. Additionally, through correlation analysis, we investigate the relationship between disaster rescue requests and response measures to evaluate the adequacy of flood response measures in each township. The research results demonstrate that this analytical framework can accurately extract disaster information, precisely identify critical time points in flood disasters, locate core affected areas, uncover primary regional issues, and further validate the sufficiency of response measures, therefore enhancing the efficiency in collecting disaster information and analytical capabilities.

Nasal Drug Delivery and Nose-to-Brain Delivery Technology Development Status and Trend Analysis: Based on Questionnaire Survey and Patent Analysis.

Nasal administration is a non-invasive method of drug delivery that offers several advantages, including rapid onset of action, ease of use, no first-pass effect, and fewer side effects. On this basis, nose-to-brain delivery technology offers a new method for drug delivery to the brain and central nervous system, which has attracted widespread attention. In this paper, the development status and trends of nasal drug delivery and nose-to-brain delivery technology are deeply analyzed through multiple dimensions: literature research, questionnaire surveys, and patent analysis. First, FDA-approved nasal formulations for nose-to-brain delivery were combed. Second, we collected a large amount of relevant information about nasal drug delivery through a questionnaire survey of 165 pharmaceutical industry practitioners in 28 provinces and 161 different organizations in China. Third, and most importantly, we conducted a patent analysis of approximately 700+ patents related to nose-to-brain delivery, both domestically and internationally. This analysis was conducted in terms of patent application trends, technology life cycle, technology composition, and technology evolution. The LDA topic model was employed to identify technological topics in each time window (1990-2023), and the five key major evolution paths were extracted. The research results in this paper will provide useful references for relevant researchers and enterprises in the pharmaceutical industry, promoting the further development and application of nasal drug delivery and nose-to-brain delivery technology.

Exploring climate change discourse on social media and blogs using a topic modeling analysis.

Climate change is one of the most pressing global issues of our time, and understanding public perception and awareness of the topic is crucial for developing effective policies to mitigate its effects. While traditional survey methods have been used to gauge public opinion, advances in natural language processing (NLP) and data visualization techniques offer new opportunities to analyze user-generated content from social media and blog posts. In this study, a new dataset of climate change-related texts was collected from social media sources and various blogs. The dataset was analyzed using BERTopic and LDA to identify and visualize the most important topics related to climate change. The study also used sentence similarity to determine the similarities in the comments written and which topic categories they belonged to. The performance of different techniques for keyword extraction and text representation, including OpenAI, Maximal Marginal Relevance (MMR), and KeyBERT, was compared for topic modeling with BERTopic. It was seen that the best coherence score and topic diversity metric were obtained with OpenAI-based BERTopic. The results provide insights into the public's attitudes and perceptions towards climate change, which can inform policy development and contribute to efforts to reduce activities that cause climate change.

A Latent Dirichlet Allocation approach to understanding students' perceptions of Automated Writing Evaluation.

Automated writing evaluation (AWE) has shown promise in enhancing students' writing outcomes. However, further research is needed to understand how AWE is perceived by middle school students in the United States, as they have received less attention in this field. This study investigated U.S. middle school students' perceptions of the MI Write AWE system. Students reported their perceptions of MI Write's usefulness using Likert-scale items and an open-ended survey question. We used Latent Dirichlet Allocation (LDA) to identify latent topics in students' comments, followed by qualitative analysis to interpret the themes related to those topics. We then examined whether these themes differed among students who agreed or disagreed that MI Write was a useful learning tool. The LDA analysis revealed four latent topics: (1) students desire more in-depth feedback, (2) students desire an enhanced user experience, (3) students value MI Write as a learning tool but desire greater personalization, and (4) students desire increased fairness in automated scoring. The distribution of these topics varied based on students' ratings of MI Write's usefulness, with Topic 1 more prevalent among students who generally did not find MI Write useful and Topic 3 more prominent among those who found MI Write useful. Our findings contribute to the enhancement and implementation of AWE systems, guide future AWE technology development, and highlight the efficacy of LDA in uncovering latent topics and patterns within textual data to explore students' perspectives of AWE.

Cross-cultural study of hotel practices as perceived by Chinese and North American travellers: A data mining analysis.

This paper aims to unearth the different perception styles of Chinese and North American travellers from analytic versus holistic thinking perspectives. Python was utilized to gather online textual data from Chinese and North American travellers, while word frequency analysis, latent Dirichlet allocation (LDA) topic modelling analysis and content analysis were employed to elucidate the perception styles in a cross-cultural context. In general, North American travellers mainly leaned towards analytic thinking, whereas Chinese travellers showcased a blend of holistic and analytic thought processes. The topic of travel, leisure and accommodation showed both holistic and analytic thinking styles. The topics of nature and environment, front desk service, and travel routes and scenic spot areas mainly represented a holistic thinking style. The topics of convenience and facilities, breakfast, transportation, hotel theme and features, and decoration and amenities mainly suggested an analytic thinking style. Hotels should consider the different perception styles of Chinese and North American travellers to facilitate strategies accordingly and to maximize the experience of travellers from different cultural backgrounds.

Hyperspectal imaging technology for phenotyping iron and boron deficiency in Brassica napus under greenhouse conditions.

Introduction: The micronutrient deficiency of iron and boron is a common issue affecting the growth of rapeseed (Brassica napus). In this study, a non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established. Methods: The recognition accuracy was compared using the Fisher Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) recognition models. Recognition results showed that Multiple Scattering Correction (MSC) could be applied for the full band hyperspectral data processing, while the LDA models presented better performance on establishing the leaf iron and boron deficiency symptom recognition than the SVM models. Results: The recognition accuracy of the training set reached 96.67%, and the recognition rate of the prediction set could be 91.67%. To improve the model accuracy, the Competitive Adaptive Reweighted Sampling algorithm (CARS) was added to construct the MSC-CARS-LDA model. 33 featured wavelengths were selected via CARS. The recognition accuracy of the MSC-CARS-LDA training set was 100%, while the recognition accuracy of the MSC-CARS-LDA prediction set was 95.00%. Discussion: This study indicates that, it is capable to identify the iron and boron deficiency in rapeseed using hyperspectral imaging technology.

Analyzing political party positions through multi-language twitter text embeddings.

Traditional monolingual word embedding models transform words into high-dimensional vectors which represent semantics relations between words as relationships between vectors in the high-dimensional space. They serve as productive tools to interpret multifarious aspects of the social world in social science research. Building on the previous research which interprets multifaceted meanings of words by projecting them onto word-level dimensions defined by differences between antonyms, we extend the architecture of establishing word-level cultural dimensions to the sentence level and adopt a Language-agnostic BERT model (LaBSE) to detect position similarities in a multi-language environment. We assess the efficacy of our sentence-level methodology using Twitter data from US politicians, comparing it to the traditional word-level embedding model. We also adopt Latent Dirichlet Allocation (LDA) to investigate detailed topics in these tweets and interpret politicians' positions from different angles. In addition, we adopt Twitter data from Spanish politicians and visualize their positions in a multi-language space to analyze position similarities across countries. The results show that our sentence-level methodology outperform traditional word-level model. We also demonstrate that our methodology is effective dealing with fine-sorted themes from the result that political positions towards different topics vary even within the same politicians. Through verification using American and Spanish political datasets, we find that the positioning of American and Spanish politicians on our defined liberal-conservative axis aligns with social common sense, political news, and previous research. Our architecture improves the standard word-level methodology and can be considered as a useful architecture for sentence-level applications in the future.

A highly sensitive SERS substrate of porous membrane-Ag NPs for kidney cancer detection.

BACKGROUND: The substrate employed in surface-enhanced Raman spectroscopy (SERS) constitutes an essential element in the cancer detection methodology. In this research, we introduce a three-dimensional (3D) structured SERS substrate that integrates a porous membrane with silver nanoparticles to enhance SERS spectral signals through the utilization of the aggregation effect of silver nanoparticles. This enhancement is crucial because accurate detection results strongly depend on the intensity of specific peaks in Raman spectroscopy. A highly sensitive SERS substrate can significantly improve the accuracy of detection results. RESULTS: We collected 66 plasma samples from individuals with kidney cancer and control individuals, including both bladder cancer patients and healthy individuals. Then, we utilized substrates with and without porous membranes to acquire the SERS spectra of the samples, enabling us to evaluate the enhancement effect of our SERS substrate. The spectral analysis demonstrated enhanced peak intensities in the experimental group (with porous substrate) compared to the control group (without porous substrate). The uniformity and reproducibility of the SERS substrate are also significantly enhanced, which is very helpful for improving the accuracy of detection results. Additionally, the Principal Component Analysis-Linear Discriminant Analysis algorithm (PCA-LDA) was employed to classify the SERS spectra of both groups. In the experimental group, the classification accuracy was 98.5 % for kidney cancer, and 83.3 % for kidney and bladder cancer. Compared to the control group, it improved by 3 % and 12.6 % respectively. SIGNIFICANT: This indicates that our 3D structured SERS substrate combined with multivariate statistical algorithms PCA-LDA can not only improve the accuracy of SERS detection technology in single cancer detection, but also has great potential in multiple cancer detection. This 3D structured SERS substrate is expected to become a new auxiliary means for cancer detection.

Computational evaluation of novel XCuH3 (X = Li, Na and K) perovskite-type hydrides for hydrogen storage applications using LDA and GGA approach.

Hydrogen energy has attracted a lot of interest from researchers as a sustainable and renewable energy source, but there are some technical challenges related to its storage. Hydride materials demonstrate the ability to store hydrogen adequately and safely. In the current study, we have investigated the structural and optoelectronic properties of the XCuH3 (where X = Li, Na and K) perovskite-type hydride using LDA and GGA formalisms for hydrogen storage application. Electronic properties such as band structure, density of states reveal the metallic character of the studied XCuH3 hydrides. Various optical parameters such as the complex dielectric function, refractive index, extinction coefficient, absorption coefficient, reflectivity, optical conductivity, energy loss function, and joint density of states have been computed and compared. The gravimetric hydrogen storage capacity for LiCuH3, NaCuH3 and KCuH3 are found to be 4.11, 3.37 and 2.86 wt%, respectively. The computed values of the gravimetric ratio manifest that XCuH3 hydrides are potential candidates for hydrogen storage applications. These calculations are made for the first time for XCuH3 hydrides and will be inspirational in the future for comparison and for hydrogen storage purposes.

Exploring hot topics and evolutionary paths in the Diagnosis-Related Groups (DRGs) field: a comparative study using LDA modeling.

BACKGROUND: This study reviews the research status of Diagnosis-related groups (DRGs) payment system in China and globally by analyzing topical issues in this field and exploring the evolutionary trends of DRGs in different developmental stages. METHODS: Abstracts of relevant literature in the field of DRGs were extracted from the China National Knowledge Infrastructure (CNKI) database and the Web of Science (WoS) core database and used as text data. A probabilistic distribution-based Latent Dirichlet Allocation (LDA) topic model was applied to mine the text topics. Topical issues were determined by topic intensity, and the cosine similarity of the topics in adjacent stages was calculated to analyze the topic evolution trend. RESULTS: A total of 6,758 English articles and 3,321 Chinese articles were included. Foreign research on DRGs focuses on grouping optimization, implementation effects, and influencing factors, whereas research topics in China focus on grouping and payment mechanism establishment, medical cost change evaluation, medical quality control, and performance management reform exploration. CONCLUSIONS: Currently, the field of DRGs in China is developing rapidly and attracting deepening research. However, the implementation depth of research in China remains insufficient compared with the in-depth research conducted abroad.

Comparative Efficacy of Advanced Therapies in the Treatment of Radiographic Axial Spondyloarthritis or Ankylosing Spondylitis as Evaluated by the ASDAS Low Disease Activity Criteria.

INTRODUCTION: With an increasing number of biologic/targeted synthetic disease-modifying antirheumatic drug options available for the treatment of active ankylosing spondylitis (AS), also known as radiographic axial spondyloarthritis, it is of clinical interest to determine the comparative efficacy of these advanced therapies among populations with differing prior advanced therapy exposure. This study aimed to assess the comparative efficacy of approved advanced therapies for AS in tumor necrosis factor inhibitor (TNFi)-naïve and, separately, in TNFi inadequate responder/intolerant (-IR) populations. METHODS: A systematic literature review was conducted to identify randomized clinical trials for TNFis, interleukin-17A inhibitors, and Janus kinase inhibitors used as advanced therapies for active AS. Clinical efficacy was considered by the Ankylosing Spondylitis Disease Activity Score low disease activity (ASDAS LDA) criteria, defined as ASDAS score less than 2.1, among approved therapies. Comparative efficacy in the TNFi-naïve population was assessed utilizing network meta-analysis, while comparative efficacy in the TNFi-IR population was assessed utilizing matching-adjusted indirect comparison. Odds ratios were calculated, from which absolute rates and numbers needed to treat were calculated. Safety in the form of trial-reported and placebo-adjusted rates of discontinuation due to adverse events (AEs) was reviewed. RESULTS: Among the TNFi-naïve population, the estimated ASDAS LDA rate between week 12 and 16 was highest for patients treated with upadacitinib (52.8%) and lowest for patients treated with placebo (11.6%). Among the TNFi-IR population, the estimated ASDAS LDA rate was 41.3% for patients treated with upadacitinib and 17.5% for patients treated with ixekizumab. The trial-reported and placebo-adjusted rates of discontinuation due to AEs were generally low across included advanced therapies. CONCLUSIONS: Relative to other assessed therapies, upadacitinib demonstrated greater clinical efficacy per ASDAS LDA in the treatment of active AS in both TNFi-naïve and TNFi-IR populations. Head-to-head and real-world data comparisons are warranted to both validate these findings and aid medical decision makers.

Identifying the origin of Yemeni green coffee beans using near infrared spectroscopy: a promising tool for traceability and sustainability.

Yemeni smallholder coffee farmers face several challenges, including the ongoing civil conflict, limited rainfall levels for irrigation, and a lack of post-harvest processing infrastructure. Decades of political instability have affected the quality, accessibility, and reputation of Yemeni coffee beans. Despite these challenges, Yemeni coffee is highly valued for its unique flavor profile and is considered one of the most valuable coffees in the world. Due to its exclusive nature and perceived value, it is also a prime target for food fraud and adulteration. This is the first study to identify the potential of Near Infrared Spectroscopy and chemometrics-more specifically, the discriminant analysis (PCA-LDA)-as a promising, fast, and cost-effective tool for the traceability of Yemeni coffee and sustainability of the Yemeni coffee sector. The NIR spectral signatures of whole green coffee beans from Yemeni regions (n = 124; Al Mahwit, Dhamar, Ibb, Sa'dah, and Sana'a) and other origins (n = 97) were discriminated with accuracy, sensitivity, and specificity ≥ 98% using PCA-LDA models. These results show that the chemical composition of green coffee and other factors captured on the spectral signatures can influence the discrimination of the geographical origin, a crucial component of coffee valuation in the international markets.

Computational vs. qualitative: analyzing different approaches in identifying networked frames during the Covid-19 crisis.

Despite the increasing adaption of automated text analysis in communication studies, its strengths and weaknesses in framing analysis are so far unknown. Fewer efforts have been made to automatic detection of networked frames. Drawing on the recent developments in this field, we harness a comparative exploration, using Latent Dirichlet Allocation (LDA) and a human-driven qualitative coding process on three different samples. Samples were comprised of a dataset of 4,165,177 million tweets collected from Iranian Twittersphere during the Coronavirus crisis, from 21 January, 2020 to 29 April, 2020. Findings showed that while LDA is reliable in identifying the most prominent networked frames, it misses to detects less dominant frames. Our investigation also confirmed that LDA works better on larger datasets and lexical semantics. Finally, we argued that LDA could give us some primary intuitions, but qualitative interpretations are indispensable for understanding the deeper layers of meaning.

Exploring different classification-dependent QSAR modelling strategies for HDAC3 inhibitors in search of meaningful structural contributors.

Histone deacetylase 3 (HDAC3), a Zn2+-dependent class I HDACs, contributes to numerous disorders such as neurodegenerative disorders, diabetes, cardiovascular disease, kidney disease and several types of cancers. Therefore, the development of novel and selective HDAC3 inhibitors might be promising to combat such diseases. Here, different classification-based molecular modelling studies such as Bayesian classification, recursive partitioning (RP), SARpy and linear discriminant analysis (LDA) were conducted on a set of HDAC3 inhibitors to pinpoint essential structural requirements contributing to HDAC3 inhibition followed by molecular docking study and molecular dynamics (MD) simulation analyses. The current study revealed the importance of hydroxamate function for Zn2+ chelation as well as hydrogen bonding interaction with Tyr298 residue. The importance of hydroxamate function for higher HDAC3 inhibition was noticed in the case of Bayesian classification, recursive partitioning and SARpy models. Also, the importance of substituted thiazole ring was revealed, whereas the presence of linear alkyl groups with carboxylic acid function, any type of ester function, benzodiazepine moiety and methoxy group in the molecular structure can be detrimental to HDAC3 inhibition. Therefore, this study can aid in the design and discovery of effective novel HDAC3 inhibitors in the future.

Sentiment analysis of online reviews of energy-saving products based on transfer learning and LBBA model.

With the exacerbation of global climate change and the growing environmental awareness among the general public, the concept of green consumption has gained significant attention across various sectors of society. As a representative example of green consumer products, energy-saving products play a crucial role in the timely realization of dual carbon goals. However, an analysis of online comments regarding energy-saving products reveals that the majority of these products still exhibit shortcomings in terms of efficacy, noise level, cost-effectiveness, and particularly, energy-saving appliances. This study focuses on the user-generated online comments data from the Taobao e-commerce platform for Grade 1 energy-saving refrigerators. By employing text mining techniques, the study aims to extract the essential information and sentiments expressed in the comments, in order to explore the consumption characteristics of Grade 1 energy-saving refrigerators. Moreover, the LBBA (LDA-Bert-BiLSTM-Attention) model is utilized to investigate the consumer topics of interest and emotional features. Initially, the LDA model is adopted to identify the attributes and weights of consumer concerns. Subsequently, the Bert model is pre-trained with the online comment data, and combined with the BiLSTM algorithm and Attention mechanism to predict sentiment categories. Finally, a transfer learning approach is utilized to determine the sentiment inclination of user-generated online comments and to identify the primary driving factors behind each sentiment category. This research employs sentiment analysis on online comments data regarding energy-saving products to uncover consumer sentiment attributes and emotional characteristics. It provides decision-makers with a comprehensive and systematic understanding of public consumption intentions, offering decision support for the efficient operation and management of the energy-saving product market.

Comprehensive Two-Dimensional Gas Chromatography with a TOF MS Detector-An Effective Tool to Trace the Signature of Grape Varieties.

Varietal volatile compounds are characteristic of each variety of grapes and come from the skins of the grapes. This work focuses on the development of a methodology for the analysis of free compounds in grapes from Trincadeira, Cabernet Sauvignon, Syrah, Castelão and Tinta Barroca from the 2021 and 2022 harvests, using HS-SPME-GC × GC-TOFMS. To achieve this purpose, a previous optimization step of sample preparation was implemented, with the optimized conditions being 4 g of grapes, 2 g of NaCl, and 2 mL of H2O. The extraction conditions were also optimized, and it was observed that performing the extraction for 40 min at 60 °C was the best for identifying more varietal compounds. The fiber used was a triple fiber of carboxen/divinylbenzene/polydimethylsiloxane (CAR/DVB/PDMS). In addition to the sample preparation, the analytical conditions were also optimized, enabling the adequate separation of analytes. Using the optimized methodology, it was possible to identify fifty-two free volatile compounds, including seventeen monoterpenes, twenty-eight sesquiterpenes, and seven C13-norisoprenoids. It was observed that in 2021, more free varietal volatile compounds were identifiable compared to 2022. According to the results obtained through a linear discriminant analysis (LDA), the differences in volatile varietal signature are observed both among different grape varieties and across different years.