Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Non-invasive diagnosis of common glomerular diseases via Raman spectroscopy and machine learning: an integrated blood and urine analysis approach","authors":"Mengyu Wu ,&nbsp;Yuan Cao ,&nbsp;Ruiyang Wang ,&nbsp;Chongxuan Tian ,&nbsp;Yang Li ,&nbsp;Zunsong Wang","doi":"10.1016/j.chemolab.2026.105630","DOIUrl":"10.1016/j.chemolab.2026.105630","url":null,"abstract":"<div><h3>Background</h3><div>Percutaneous renal biopsy faces three major challenges in clinical management: inherent procedural risks, inability to serially monitor disease activity, and sampling variability. These limitations underscore the demand for safer, repeatable diagnostic tools.</div></div><div><h3>Objective</h3><div>Our objective was to explore the potential of a liquid biopsy strategy utilizing paired blood and urine analysis via Raman spectroscopy and a 1D-CNN to facilitate the differentiation of common glomerular diseases from each other and from healthy individuals.</div></div><div><h3>Methods</h3><div>From January 2021 to January 2025, we collected serum and first-void morning urine from 170 biopsy-confirmed patients (81 membranous nephropathy, 36 IgA nephropathy, 33 diabetic nephropathy, 20 focal segmental glomerulosclerosis) and 21 healthy volunteers. Spectra were acquired on an Attenuated Total Reflection-8300 (ATR-8300) instrument (785 nm excitation) and preprocessed via third-order polynomial baseline correction and 13-point Savitzky–Golay smoothing. A 1D-CNN was trained on the combined spectral data; performance was assessed by accuracy, sensitivity, specificity, and Receiver Operating Characteristic - Area Under the Curve (ROC-AUC).</div></div><div><h3>Results</h3><div>The 1D-CNN model achieved 80.0 % accuracy, 76.2 % sensitivity, and 81.3 % specificity in five-class classification. ROC-AUCs ranged from 0.81 (FSGS) to 0.85 (IgA nephropathy), confirming robust discrimination across disease subtypes and controls. Characteristic Raman bands—e.g. phenylalanine (∼1003 cm⁻¹), Amide I (∼1655 cm⁻¹), and C–H stretching (2800–3000 cm⁻¹)—differed systematically among cohorts, reflecting underlying biochemical alterations.</div></div><div><h3>Conclusions</h3><div>Raman spectroscopy of paired blood and urine, coupled with deep learning, provides a rapid, label-free approach for minimally invasive classification of glomerular diseases. This integrated liquid biopsy strategy may enable early detection and precise stratification in nephrology, reducing reliance on invasive biopsy and informing personalized therapy.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"270 ","pages":"Article 105630"},"PeriodicalIF":3.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075362","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":"Innovative approaches in anti-inflammatory research: QSAR-SVM-PCA, In vivo insights, and molecular docking of Laurus nobilis","authors":"Nassrine Ouafi ,&nbsp;Asma Belkadi ,&nbsp;Mohammed Kebir ,&nbsp;Hichem Tahraoui ,&nbsp;Walid Elfalleh ,&nbsp;Fehmi Boufahja ,&nbsp;Nasir A. Ibrahim ,&nbsp;Nosiba S. Basher ,&nbsp;Amin Mousavi Khaneghah ,&nbsp;Noureddine Nesralah ,&nbsp;Hamza Mousssa ,&nbsp;Kahina Ighilahriz ,&nbsp;Mustapha Mounir Bouhenna ,&nbsp;Yacine Benguerba ,&nbsp;Abdeltif Amrane","doi":"10.1016/j.chemolab.2026.105634","DOIUrl":"10.1016/j.chemolab.2026.105634","url":null,"abstract":"<div><div>This study evaluates the anti-inflammatory potential of the ethanolic extract of <em>Laurus nobilis</em> leaves in chronic (acetic acid-induced ulcerative colitis) and acute (carrageenan-induced paw edema) inflammation models <em>in vivo</em>. Molecular docking studies were performed to investigate interactions between key phenolic compounds, including rutin, quercetin, and β-carotene, and inflammatory mediators such as tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and cyclooxygenase-2 (COX-2). The extract was obtained through cold maceration and analyzed via UPLC-MS/MS, identifying major compounds. In the chronic inflammation model, the <em>Laurus nobilis</em> extract significantly decreased the clinical disease index (weight loss, stool consistency, and rectal bleeding), reduced the colon weight-to-length ratio, and showed significant histological improvements compared to the control group. The acute model demonstrated a 68.84 % reduction in edema, comparable to ibuprofen's effect of 66.14 %. Molecular docking revealed strong binding affinities between β-carotene, quercetin, and rutin with COX-2, suggesting possible inhibition of inflammatory responses.</div><div>In contrast, quercetin and rutin exhibited complex interactions through hydrogen bonding with TNF-α and COX-2 active sites. A QSAR model, developed using Support Vector Machines (SVM) and Principal Component Analysis (PCA), effectively predicted the biological activity of the phenolic compounds, demonstrating high predictive capability and robustness. These findings support the traditional use of <em>Laurus nobilis</em> for managing inflammatory conditions and highlight its potential as an alternative therapeutic agent for inflammatory diseases, warranting further research.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"270 ","pages":"Article 105634"},"PeriodicalIF":3.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024965","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":"Noise-robust contrastive ensemble learning for flotation process monitoring","authors":"Mingxi Ai ,&nbsp;Jin Zhang ,&nbsp;Zhaohui Tang ,&nbsp;Yongfang Xie","doi":"10.1016/j.chemolab.2026.105649","DOIUrl":"10.1016/j.chemolab.2026.105649","url":null,"abstract":"<div><div>Froth flotation is a widely used mineral beneficiation technique, where effective process monitoring is essential for optimizing mineral separation. However, in practical industry, manual labeling suffers from noises, leading to a significant portion of incorrectly labeled data. Though deep learning monitoring models are powerful in capturing complex visual patterns, their high capacity makes them vulnerable to overfitting noisy labels, hindering robust model development. To address this challenge, this study proposes a noise-robust contrastive ensemble learning method for practical industrial process monitoring. The method first constructs multiple diverse monitoring models in distinct representation spaces using a novel disparity contrastive learning strategy. Then, clean and mislabeled data for each sub-model are distinguished by measuring the inter-model consensus and intra-model uncertainty of its peer models. Finally, a structure-consistency-based semi-supervised learning strategy is proposed to refine these sub-models by treating mislabeled data as unlabeled, encouraging representation-aligned predictions through mutual information maximization. Through iterative noisy-label identification and semi-supervised refinement, robust monitoring model are obtained even with heavily corrupted training data. Extensive experiments on industrial froth flotation data demonstrate the effectiveness and advantages of the proposed method compared to existing state-of-the-art noise-robust learning techniques.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"270 ","pages":"Article 105649"},"PeriodicalIF":3.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074867","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":"Evaluating calibration models in isotope geochemistry: Lessons from carbonates and sulfides","authors":"Alban Petitjean ,&nbsp;Olivier Musset ,&nbsp;Ludovic Duponchel ,&nbsp;Christophe Thomazo","doi":"10.1016/j.chemolab.2026.105640","DOIUrl":"10.1016/j.chemolab.2026.105640","url":null,"abstract":"<div><div>Geology routinely employs isotopic geochemistry with the main objective of measuring radiogenic or stable isotopic compositions to reconstruct the history of the Earth. A critical aspect of this analytical process lies in verifying the accuracy and reliability of the measurements performed. To this end, standards or reference materials are repeatedly analyzed enabling calibration or adjustment of experimental instruments. In order to ensure a strong correlation between the reference values and the averaged measurements, a linear regression is the most widely adopted. Among the available methodologies, this work advocates for the use of models compliant with the ISO 28037:2010 standard, which is specifically designed to perform linear regression in a statistically robust manner. The guidelines established by this standard are, regrettably, not always implemented correctly, and the statistical nature of the measurements is frequently overlooked. This study provides a detailed examination of the methodologies advocated by the standard, with the objective of facilitating their application to geochemical problems specifically, issues related to isotopic measurement by revisiting the underlying theoretical principles, assumptions, and the respective advantages and limitations inherent to each approach. To facilitate implementation and respect recommendations, we propose a software application developed in Python 3.14. This computational tool has been tested and validated using experimental datasets obtained from isotopic analyses of carbon, oxygen, and sulfur elements of fundamental interest in geological studies. The objective of this study is therefore to clearly and practically illustrate the challenges involved in geochemical calibration and adjustment.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"270 ","pages":"Article 105640"},"PeriodicalIF":3.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075361","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":"Automated spectral peak detection with machine learning: Parameter optimization and effective parameter space analysis with SciPy’s find_peaks","authors":"Thanakrit Yoongsomporn,&nbsp;Sivakorn Kanharattanachai,&nbsp;Pongsapak Lueangratana,&nbsp;Tsubasa Yamashita,&nbsp;Chia Hsiu Chen,&nbsp;Mitsuru Irie,&nbsp;Chaiyanut Jirayupat","doi":"10.1016/j.chemolab.2026.105651","DOIUrl":"10.1016/j.chemolab.2026.105651","url":null,"abstract":"<div><div>We propose a machine learning approach for automating parameter selection in the “find_peaks” function from Python’s SciPy module, a widely-used threshold-based tool for peak detection. Our method determines optimal detection parameters by analyzing the unique characteristics of each spectrum, eliminating the need for manual parameter tuning. The model, trained and cross-validated on 2000 generated spectra with diverse characteristics, achieved an average F1-score of 0.940 for peak identification. Moreover, the peak detection performance of our proposed method was validated using experimental spectra, delivering F1-scores of 0.952, 0.946, and 0.893 for XRD, GC–MS, and Raman spectra respectively, significantly outperforming both default parameter configurations and CNN-based detection approaches. Our analysis revealed that optimal parameters exist within ranges called “effective parameter spaces” that vary based on each spectrum’s characteristics. This finding confirms that our model effectively captures the relationship between spectral properties and their corresponding effective parameter spaces, resulting in consistently high performance across diverse spectral data types.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"270 ","pages":"Article 105651"},"PeriodicalIF":3.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170886","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":"Predictive modeling of Oil–Water interfacial tension using biosurfactant parameters and machine learning approaches","authors":"Mustafa Abdullah ,&nbsp;Raed Alfilh ,&nbsp;Tariq Abdulkader Alrihaim ,&nbsp;Raghavendra Rao P S ,&nbsp;Abinash Mahapatro ,&nbsp;Karthikeyan A ,&nbsp;Harjot Singh Gill ,&nbsp;Yashwant Singh Bisht ,&nbsp;Siya Singla ,&nbsp;Tabib Shahzada","doi":"10.1016/j.chemolab.2026.105632","DOIUrl":"10.1016/j.chemolab.2026.105632","url":null,"abstract":"<div><div>Oil–water interfacial tension (IFT) governs many industrial phenomena in petroleum recovery and environmental remediation, yet its experimental determination under complex biosurfactant–crude oil–water conditions is laborious and resource-intensive. This study aimed to develop a robust machine learning framework capable of accurately predicting IFT from physicochemical descriptors of biosurfactant and crude oil systems, thereby reducing empirical dependency through data-driven modeling. A dataset containing 1480 laboratory measurements was compiled from peer-reviewed sources and characterized by eight explanatory variables, Head charge, Molecular Weight, hydroxyl and carboxyl group counts (OH, COOH), biosurfactant concentration, oil API gravity, oil–air interfacial tension, and acid number. Models including Decision Tree, Random Forest, AdaBoost, Ensemble Learning, Support Vector Regression (SVR), Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP-ANN) were optimized and evaluated using five-fold cross-validation with R², MSE, and AARE % metrics. Results evidenced superior performance for ensemble-based algorithms, particularly Random Forest (R² = 0.957, MSE = 0.46) and AdaBoost (R² = 0.978, MSE = 0.24), exhibiting high stability and minimal prediction error. SHAP (SHapley Additive exPlanations) analysis identified COOH and oil compositional variables (API and acid number) as the most influential predictors, aligning with theoretical expectations regarding polarity and molecular orientation at the oil–water interface. Overall findings demonstrate that properly tuned ensemble learning provides a physically interpretable, highly accurate surrogate to laboratory IFT measurement, revealing clear structural–functional dependencies across biosurfactant systems and supporting its broader integration into predictive material design and green petroleum applications.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"270 ","pages":"Article 105632"},"PeriodicalIF":3.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146169988","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":"Integrative chemometric and intelligent modeling approaches for pollutant adsorption: Synergistic insights from experimental design, artificial intelligence (AI), and DFT applied to bisphenol A, β-naphthol, and eriochrome black","authors":"Taoufiq Bouzid,&nbsp;Abdelali Grich,&nbsp;Aicha Naboulsi,&nbsp;Hicham Yazid,&nbsp;Ali Elbasraoui,&nbsp;Abdelmajid Regti,&nbsp;Mamoune El Himri,&nbsp;Mohammadine El Haddad","doi":"10.1016/j.chemolab.2026.105639","DOIUrl":"10.1016/j.chemolab.2026.105639","url":null,"abstract":"<div><div>Water pollution has emerged as one of the most pressing environmental challenges in recent years. Various solutions have been investigated to mitigate this issue, and among them, adsorption has proven to be an innovative and efficient technique for removing pollutants from water. In this review, we highlight the combined use of Artificial Intelligence (AI), experimental design, and Density Functional Theory (DFT) calculations three powerful tools whose integration in adsorption studies has not yet been reported in the literature.</div><div>Within the scope of AI, we explore the selection and evaluation of different models applied to adsorption processes. Specifically, we discuss Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Random Forests (RF), and Linear Regression. Overall, ANN emerged as the most effective approach, as it can account for multiple factors influencing adsorption simultaneously. In this section, we also define AI concepts, describe the models, explain data collection strategies, and outline validation methods to ensure accurate prediction of adsorption performance.</div><div>Regarding experimental design, we compare two approaches: the full factorial design and the central composite design. We demonstrate the advantages of the central composite design in optimizing adsorption conditions. This part also details how experimental plans are structured, validated, and how outputs such as 3D surface plots and other response analyses can be leveraged to extract valuable insights.</div><div>Finally, we examine the role of DFT calculations, emphasizing their ability to provide a deeper understanding of adsorption mechanisms at the molecular level.</div><div>To illustrate the practical application of these integrated methodologies, we present case studies on three representative pollutants: Bisphenol A, β-naphthol, and Eriochrome Black T, which exemplify the diversity of contaminants that can be addressed through this approach.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"270 ","pages":"Article 105639"},"PeriodicalIF":3.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025044","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":"MGD-CNN: An end-to-end convolutional neural network model for collaborative preprocessing of Raman spectra","authors":"Yuyan Liao ,&nbsp;Zilong Wang ,&nbsp;Yunfeng Li ,&nbsp;Yuting Li ,&nbsp;Pei Liang","doi":"10.1016/j.chemolab.2026.105655","DOIUrl":"10.1016/j.chemolab.2026.105655","url":null,"abstract":"<div><div>This paper introduces an end-to-end convolutional neural network—Morphology-Gaussian Guided Dual-Branch Convolutional Neural Network (MGD-CNN)—for Raman spectral preprocessing, designed to integrate baseline estimation and signal denoising through a collaborative training mechanism. This approach enhances signal quality while preserving key spectral features. The method employs a dual-module co-training scheme that unifies baseline estimation and denoising into a single convolutional network, utilizing a customized deep convolutional architecture to automatically learn spectral characteristics, enabling fully automated signal processing. In the comparative evaluation of preprocessing performance, the proposed model achieves a spectral signal-to-noise ratio (SSNR) of 562.93, a coefficient of determination (R²) of 0.9572, and a root mean square error (RMSE) of 0.0242, significantly outperforming conventional methods and setting a new benchmark for these core metrics. Furthermore, in downstream classification tasks, the preprocessed spectra improve the classification accuracy to 99.15 %, underscoring the method's exceptional ability to preserve discriminative spectral information. This method provides a high-precision, efficient, and adaptive preprocessing solution for Raman spectral analysis.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"270 ","pages":"Article 105655"},"PeriodicalIF":3.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170887","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":"Time-resolved simulation of hybrid nano-milk flow in an electromagnetic vibration channel with parabolic thermal ramping: A Python AI approach","authors":"Sanatan Das ,&nbsp;Poly Karmakar","doi":"10.1016/j.chemolab.2026.105647","DOIUrl":"10.1016/j.chemolab.2026.105647","url":null,"abstract":"<div><div>This research paper explores the innovative application of artificial intelligence (AI) in understanding the behaviors of silver and magnesium oxide nanoparticles within milk flow. This study utilizes a specially designed vibrating electromagnetic channel to observe the effects under controlled parabolic thermal ramping and oscillatory pressure variations. This framework couples essential physical mechanisms-radiative emission, thermal sinks, and porous matrix interactions-where Darcy's law quantifies the permeability-driven viscous drag. The mechanics of milk flow through an electromagnetically activated channel are meticulously formulated and solved using mathematical and computational methods, with the Laplace transform (LT) technique facilitating a streamlined solution to the equations. The analysis concentrates on flow metrics, presenting results through detailed graphical representations. Significant findings comprise the enhancement of thermal conductivity and flow viscosity due to the nanoparticles, which improve heat transport efficiency and modify flow patterns. The operational control of milk flow dynamics shows dual dependencies-momentum amplification via electromagnetic intensity (Hartmann number) versus suppression through electrode spacing, while thermal management reveals frequency-dependent shear stress (SS) augmentation and rate of heat transfer (RHT) enhancement through optimized heat uptake parameter. An artificial neural network (ANN) is calibrated to emulate the LT solver's outputs for wall SS and RHT. The ANN achieves high fidelity <span><math><mrow><mo>(</mo><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>&gt;</mo><mn>0.99</mn></mrow><mo>)</mo></mrow></math></span> in predicting these metrics across the parameter space explored in the LT simulations, but its generalization to experimental or real dairy systems remains unvalidated and is a focus of future work. The key findings demonstrate the potential of integrating advanced materials and AI technologies to improve product characteristics and processing efficiency.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"270 ","pages":"Article 105647"},"PeriodicalIF":3.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075363","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":"Corrigendum to “An enhanced stacking ensemble learning strategy for product quality estimation in complex industrial processes considering multi-timescale data” [Chemometr. Intellig. Lab. Syst. 269 (2026) 105635]","authors":"Weihang Sun,&nbsp;Xin Jin,&nbsp;Sen Xie,&nbsp;Rui Wang","doi":"10.1016/j.chemolab.2026.105657","DOIUrl":"10.1016/j.chemolab.2026.105657","url":null,"abstract":"","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"270 ","pages":"Article 105657"},"PeriodicalIF":3.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146169975","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}