Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Time series analysis of nucleic acid reactions via a generalized transformer model","authors":"Canfeng Liu ,&nbsp;Binhui Wang ,&nbsp;Hui Dong ,&nbsp;Yihan Pan ,&nbsp;Jiawen Lin ,&nbsp;Jintian Yang ,&nbsp;Yihui Tao ,&nbsp;Hao Sun","doi":"10.1016/j.chemolab.2025.105522","DOIUrl":"10.1016/j.chemolab.2025.105522","url":null,"abstract":"<div><div>The contemporary landscape of medical diagnostics and therapeutic interventions has witnessed a remarkable surge in the production of time series data. Artificial intelligence (AI), particularly the deep learning, has presented promising values in investigating the high-dimension and meaningful significance hidden behind these diagnostic data. In this work, we propose a novel analytics for intelligent nucleic acid amplification tests (NAAT) based on deep learning and paper microfluidics. On-chip amplification data were straightforwardly fed to a deep learning model derived from Transformer neural network. To facilitate the development and deployment of the approach, we conducted a lightweight processing of the Transformer model. Then, the capacity of the model for accurately predicting the reaction trend and end-point value was validated. We also employed ablation experiments to evaluate the effects of various parameters on prediction performance followed by optimizing the model. Then, three clinical datasets including 706 positive and 205 negative samples obtained from Fujian Provincial Hospital were used to verify the generalization of the approach. Without any modification of the model structure and hyperparameters, accuracy, sensitivity, and specificity by the presented approach were 98.28 %, 97.52 % and 99.02 %. Further comparison studies based on the nine different AI algorithms including recurrent neural network and long-short term memory were performed. The presented study holds potential to facilitating routine diagnostic tasks for preventing pandemic and propelling the development of smart portable instruments.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105522"},"PeriodicalIF":3.8,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated framework combining CenFormer and PLS regression for rapid distillate oil classification and property prediction","authors":"Yifan Wang ,&nbsp;Xisong Chen ,&nbsp;Lei Jiang ,&nbsp;Yunyun Hu","doi":"10.1016/j.chemolab.2025.105530","DOIUrl":"10.1016/j.chemolab.2025.105530","url":null,"abstract":"<div><div>Rapid and accurate classification and property prediction of distillate oil are essential for intelligent quality control and process optimization in modern refineries. Traditional methods, such as spectral analysis with chemometrics, are widely applied, but heavily depend on manual feature engineering and offer limited representation capacities. Recent advances in deep learning have shown promise for oil analysis, yet existing models often struggle to jointly capture fine-grained local patterns and long-range spectral dependencies, and rarely optimize feature space geometry. To address these challenges, an integrated framework is proposed, integrating spectral preprocessing, a dual-branch CenFormer model, a joint loss function, and dynamic property prediction. Spectral preprocessing is employed to sharpen spectral features by applying baseline correction, spectral truncation, and vector normalization. The CenFormer model leverages a CNN-Transformer dual-branch architecture, enabling the simultaneous capture of fine-grained local patterns and long-range spectral dependencies. A joint loss function, combining softmax and center loss, enforces intra-class compactness and inter-class separability, thereby improving feature discriminability. For property prediction, a similarity-based sample selection strategy is performed, followed by PLS regression, to enable adaptive modeling of physicochemical attributes. Experimental results demonstrate the effectiveness of the framework, achieving a classification accuracy of 99.51 %, low RMSEs and rRMSEs, and high <span><math><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></math></span> in property prediction, highlighting its potential for rapid and reliable spectral analysis in industrial applications.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105530"},"PeriodicalIF":3.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-attention embedded StyleGAN for virtual sample generation in sensing applications","authors":"Xue-Yu Zhang ,&nbsp;Qun-Xiong Zhu ,&nbsp;Ming-Jia Liu ,&nbsp;Feng Ma ,&nbsp;Yi Luo ,&nbsp;Wei Ke ,&nbsp;Yan-Lin He ,&nbsp;Ming-Qing Zhang ,&nbsp;Yuan Xu","doi":"10.1016/j.chemolab.2025.105519","DOIUrl":"10.1016/j.chemolab.2025.105519","url":null,"abstract":"<div><div>Given the challenges of low variability in industrial processes, which intensify data scarcity and produce anomalous distributions that compromise data-driven model accuracy. Existing sample generation methods often overlook key factors such as sparsity and correlation among data. To address these challenges, this paper proposes a StyleGAN-based virtual sample generation method with an embedded self-attention mechanism (SASG-VSG). Firstly, StyleGAN is used to map the original data space to a disentangled latent space. The output variables then act as control conditions, guiding the model to interpolate along the output dimension to ensure a more uniform distribution of generated samples. Besides, a self-attention module is incorporated into the discriminator to enhance its ability to capture the similarity between the virtual samples and the original data distribution. Finally, validation experiments on a purified terephthalic acid (PTA) solvent system and a sulfur recovery unit (SRU) confirm the capability of the proposed SASG-VSG in generating high-quality virtual samples for soft-sensing applications.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105519"},"PeriodicalIF":3.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An expectation–maximization algorithm for spectral reconstruction under the spectral hard model","authors":"Marvin Kasterke ,&nbsp;Lea Kaufmann ,&nbsp;Maria Kateri ,&nbsp;Thorsten Brands","doi":"10.1016/j.chemolab.2025.105518","DOIUrl":"10.1016/j.chemolab.2025.105518","url":null,"abstract":"<div><div>Indirect Hard Modeling (IHM) is a physics-based evaluation method for the quantitative analysis of fluid compositions using spectroscopic techniques such as Raman spectroscopy. In this approach, mixture spectra are represented as a superposition of pure substance models, with each component described by a sum of parameterized peak functions. Nevertheless, the accuracy of the compositions prediction depends critically on user decisions regarding both the number of peak functions and the specific parameter adjustments employed. In this work, we apply an expectation–maximization (EM) based algorithm for generating spectral reconstructions of pure substance models that does not require the pre-specification of the number of peaks or any initial values. The efficient and fast performance of the used EM algorithm enables the fit of a given spectrum for an unknown number of peaks, based on a model selection criterion. In simulation studies, we demonstrate that this approach can recognize the true underlying function in settings of high noise, peak overlapping and background signals, yielding reliable results. In a validation study, the algorithm was tested using experimental data. It was integrated into an Indirect Hard Modeling framework and applied to three chemical test systems. The quality of the obtained results were in the range of other automated IHM model generating approaches while significantly reducing both time and computational effort.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105518"},"PeriodicalIF":3.8,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of artificial intelligence and multivariate analysis to analyze electrical and physicochemical properties of seawater-affected agriculture soil","authors":"Ajay L. Vishwakarma,&nbsp;Shruti O. Varma,&nbsp;M.R. Sonawane,&nbsp;Ajay Chaudhari","doi":"10.1016/j.chemolab.2025.105520","DOIUrl":"10.1016/j.chemolab.2025.105520","url":null,"abstract":"<div><div>The impact of salinity on soil has become a major environmental challenge due to global warming and urbanization. The electrical properties of soil are intricately influenced by physicochemical properties, salinity levels, moisture content, and geological features of the land. This work aimed to evaluate the electrical and chemical properties of the agricultural, riparian zone, and near-seafront salt marsh soils using a PC-based automated microwave X-band bench method at frequency 9.55 GHz with ‘infinite sample’ technique. Also, Chemical properties such as pH, sodium absorption ratio (SAR), exchangeable sodium percentage (ESP), organic carbon (OC), phosphorous (P), potassium (K), micronutrients (Fe, Mn, Cu, and Zn), and physical properties such as porosity (PO), particle and bulk density (PD and BD) of soil samples were measured using laboratory method in triplicate. Furthermore, Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) were employed to classify and differentiate samples based on their properties, providing insights into underlying patterns and groupings. To accurately estimate the dielectric constant and dielectric loss, we implemented Multiple Linear Regression (MLR) and an Artificial Neural Network (ANN) model using a feed-forward back propagation. To evaluate the performance and predictive accuracy of the developed models, statistical metrics such as Root Mean Square Error (RMSE) and the coefficient of determination (R²) were used. The R² and RMSE values of the dielectric constant obtained by the ANN model with PO, BD, PD, P, OC, K, and ESP as entered variables were 0.99 and 9.23 × 10⁻⁰⁴, and for dielectric loss, were 0.98 and 2.93 × 10⁻⁰², respectively. For MLR, the R² value of the dielectric constant and dielectric loss was 0.88 and 0.80. SHAP (SHapley Additive exPlanations) analysis, combined with an ANN model, revealed that the DC is influenced by the Exchangeable Sodium Percentage (ESP), while DL minutely affected. Thus, ANN and SHAP accurately predicted dielectric properties of soil, offering a nondestructive and efficient approach for monitoring salinity effects on soil health.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105520"},"PeriodicalIF":3.8,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beer's linguistics and chemistry: an investigation opening new research perspectives","authors":"Nicola Cavallini ,&nbsp;Francesco Savorani ,&nbsp;Rasmus Bro ,&nbsp;Marina Cocchi","doi":"10.1016/j.chemolab.2025.105521","DOIUrl":"10.1016/j.chemolab.2025.105521","url":null,"abstract":"<div><div>In the last two decades, interest in food production and consumption has progressively grown, alongside the booming popularity of craft beer, fueled by micro-breweries and home brewing. Beer is a complex mixture of compounds — from carbohydrates to proteins and ethanol — shaped by the recipe, ingredients, and production process. Less obvious is that the human tongue, in synergy with the oral cavity and nose, acts as a powerful sensor array. Tasting experiences can be viewed as “analytical sessions”, where sensory signals processed by the brain determine not only if the beer is appreciated but also which tastes and flavours are perceived.</div><div>In our study, we investigated the connection between the “objective” chemical profile of beer and the “subjective” sensory descriptions from user reviews. We analysed 88 beers using near-infrared (NIR), visible, and nuclear magnetic resonance (NMR) spectroscopy, pairing them with text reviews processed through natural language processing (NLP) tools and converted into numerical data via a bag-of-words approach. Principal Component Analysis-Generalized Canonical Analysis (PCA-GCA) revealed correlations between chemical signals and topics like “hops,” “brown colour,” and “booze”. NMR data showed the strongest correlations, especially for hops-related terms, while visible spectra linked to colour descriptors. Automated topic extraction often performed comparably to manual term selection, suggesting potential for scalable studies. Despite limitations like dataset size and beer variety, this approach shows promise for aligning chemical composition with sensory perception, with applications for product development and broader food analysis.</div><div>A novel approach integrates text corpora with analytical data through chemometrics, linking language complexity to instrumental responses. Results showed strong correlations, like NMR signals with hops-related terms and visible spectra with beer colour. This previously unexplored connection opens the door to designing food products tailored to consumer preferences. The approach is broadly applicable, from food science to medical diagnosis or aligning expert opinions with factual data.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105521"},"PeriodicalIF":3.8,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sharpness-aware minimization with physics-informed regularizations for predicting semiconductor material properties in molecular dynamics","authors":"Dong-Hee Shin,&nbsp;Young-Han Son,&nbsp;Tae-Eui Kam","doi":"10.1016/j.chemolab.2025.105511","DOIUrl":"10.1016/j.chemolab.2025.105511","url":null,"abstract":"<div><div>In recent years, the growing adoption of artificial intelligence across diverse scientific fields has significantly increased demand for advanced semiconductor chips, necessitating innovations in semiconductor material design. Accurate prediction of semiconductor material properties is essential for improving chip performance, as these properties directly affect electrical, thermal, and mechanical characteristics. Traditionally, density functional theory has been the gold standard for atomic-scale simulations in material property prediction; however, its high computational cost limits scalability. Molecular dynamics simulations provide a scalable alternative by leveraging the power of machine learning force fields (MLFFs); however, semiconductor systems present unique challenges due to non-equilibrium dynamics, surface defects, and impurities. These factors often result in out-of-distribution (OOD) atomic configurations, which can significantly degrade model performance. To address this challenge, we propose Physics-Informed Sharpness-Aware Minimization (PI-SAM), a novel framework designed to enhance the prediction of semiconductor material properties across diverse datasets and challenging OOD scenarios. Specifically, PI-SAM leverages sharpness-aware minimization to achieve flatter loss minima, improving the model’s generalization. Additionally, it incorporates physics-informed regularizations to enforce energy-force consistency and account for potential energy surface curvature, ensuring alignment with the underlying physical principles governing semiconductor behavior. Experimental results demonstrate that our PI-SAM outperforms competing methods, especially on OOD datasets, underscoring its effectiveness in improving generalization.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105511"},"PeriodicalIF":3.8,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Not from scratch: Explainable deep transfer learning fine-tunning with domain adaptation enables trustworthy COVID-19 prediction","authors":"Bingqiang Zhao ,&nbsp;Honglin Zhai ,&nbsp;Tianhua Wang ,&nbsp;Haiping Shao ,&nbsp;Ling Zhu","doi":"10.1016/j.chemolab.2025.105517","DOIUrl":"10.1016/j.chemolab.2025.105517","url":null,"abstract":"<div><div>Medical image analysis can help diagnose Coronavirus Disease 2019 (COVID-19) early and save patient lives before the disease worsens. However, there are various limitations to manual inspection of these medical images, such as dependence on physician experience and subjectivity of assessment. To enable fast and precise disease diagnosis, we propose XDTLMI-Net, a framework using four CNNs (GoogLeNet, ResNet18, ResNet50, ResNet101) skilled in image data processing. This framework uses existing medical domain knowledge to guide transfer learning for COVID-19 Computed tomography (CT) scan images and Chest X-rays (CXR) images. XDTLMI-Net performed three tasks of medical image classification of COVID-19 on three public datasets: COVID-19 CT, SARS-COV-2 CT and COVID-19 CXR. It achieved an average classification accuracy of 0.9897, 0.9752 and 0.9397, and an average classification F1-score of 0.9 guide transfer learning with 898, 0.9741 and 0.9394, respectively. Moreover, we employed the Shaply Additive exPlanations and Gradient-weighted Class Activation Mapping to interpret the COVID-19 predictions and help understand the predictive models’ decision-making process. Generally, a general end-to-end framework called XDTLMI-Net based on CNN and transfer learning was developed, which works on small datasets of medical images, and does not require any segmentation or image preprocessing procedures. Moreover, XDTLMI-Net outperformed on three datasets in fine-tuning course and gave reasonable importance to each input COVID-19 image, showing its potential for application in different clinical scenarios.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"266 ","pages":"Article 105517"},"PeriodicalIF":3.8,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FT-NIR combined with multiple intelligent algorithms for rapid identification and quantitative analysis of Iron Mineral Decoction Pieces","authors":"Yangqian Wu ,&nbsp;Yi Wan ,&nbsp;Jin Li ,&nbsp;Xiangyi Wen ,&nbsp;Xiaolan Zhang ,&nbsp;Can Zhang ,&nbsp;Xiaoli Zhao","doi":"10.1016/j.chemolab.2025.105512","DOIUrl":"10.1016/j.chemolab.2025.105512","url":null,"abstract":"<div><div>Calcined and Vinegar-quenched Magnetite (CVQM), Calcined and Vinegar-quenched Hematite (CVQH), Calcined and Vinegar-quenched Pyrite (CVQP), Calcined and Vinegar-quenched Limonite (CVQL) are all iron-containing mineral decoction pieces, which are easily be confused because of their similar primary compositions and appearances. However, their medicinal values differ significantly, misuse in clinical settings could pose substantial safety risks to patients. In this study, E-eye and Fourier transform near infrared (FT-NIR) combined with multivariate algorithms were employed for the qualitative identification and quantitative prediction of iron content in these four kinds of mineral decoction pieces. The results indicated that the PCA model alongside machine learning classification models with E-eye was ineffective for distinguishing among the four types of decoction pieces, achieving an accuracy rate below 80 %. Furthermore, by utilizing FT-NIR technology with SNV + ICO optimization on raw spectra, we achieved machine-learning classification model accuracies around 90 %, which were improved by 28 %–36 % compared to analyses based solely on raw spectra. Additionally, the quantitative prediction regression (PLSR) model for predicting iron content demonstrated R²_C = 0.9627 and R²_P = 0.9451, indicating strong linearity and predictive accuracy of the model. Overall, this study demonstrated that FT-NIR combined with multivariate algorithms provided an effective approach for identifying and evaluating the quality of mineral medicines with similar appearances and compositions.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"266 ","pages":"Article 105512"},"PeriodicalIF":3.8,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-destructive aging evaluation of transformer insulation oil via Raman spectroscopy and ensemble learning with KPCA feature extraction","authors":"Feng Hu ,&nbsp;Ziyue Pu ,&nbsp;Rongying Dai ,&nbsp;Wendou Gan ,&nbsp;Junchao Liang ,&nbsp;Yulong Zhang ,&nbsp;Mengxiao Ni ,&nbsp;Yan Ge ,&nbsp;Hang Wu ,&nbsp;Penghui Chen","doi":"10.1016/j.chemolab.2025.105514","DOIUrl":"10.1016/j.chemolab.2025.105514","url":null,"abstract":"<div><div>Transformer insulating oil aging critically impacts power system reliability. This study develops a non-destructive aging evaluation method using Raman spectroscopy with kernel principal component analysis (KPCA) and ensemble learning. Raman spectral data were obtained through accelerated thermal aging experiments and a spectral detection platform; subsequently, the data were preprocessed using Moving Average Sliding, Savitzky-Golay, and Gaussian filtering. Then, Raman features were extracted using KPCA with four kernel functions (Linear, Polynomial, Gaussian and Sigmoid), and evaluation performance was compared using a decision tree; eventually, four weak classifiers (DT, LDA, SVM, and BPNN) were integrated to construct the final ensemble learning evaluation model. Results showed Gaussian filtering achieved the highest signal-to-noise ratio (35.23 dB); Gaussian kernel KPCA yielded the best feature extraction, achieving 96.88 % average accuracy; and the BPNN ensemble learning evaluation model delivered the highest accuracy of 99.6 %. In addition to verifying the benefits of KPCA in feature extraction and the robustness of the model, this study conducted a comparative test with traditional principal component analysis (PCA) methods and introduced various types and intensities of noise into the test set. The study found that the model can effectively evaluate the aging state of transformer insulating oil and has high anti-interference capabilities, providing a new method for improving transformer operating status monitoring.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"266 ","pages":"Article 105514"},"PeriodicalIF":3.8,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144894755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}