Journal of Chemometrics最新文献

{"title":"From Wet Chemistry to Portable NIR: Robust High-Throughput Compositional Models for Biomass Pretreatment","authors":"Julia M. M. Caetano,&nbsp;Stéphany Ribeiro,&nbsp;Montserrat Fortuny,&nbsp;Marco A. Salle-Filho,&nbsp;Ivan R. de Barros,&nbsp;Luiz P. Ramos,&nbsp;Marcos L. Corazza,&nbsp;Alexandre F. Santos","doi":"10.1002/cem.70124","DOIUrl":"10.1002/cem.70124","url":null,"abstract":"<p>This study demonstrates a robust chemometric framework for high-throughput compositional analysis of lignocellulosic biomass using a portable near-infrared (NIR) spectrometer. Sugarcane bagasse solids produced by pressurized solvent fractionation (PSF) under batch and continuous conditions were analyzed to predict cellulose, hemicellulose, and lignin contents. To enhance calibration representativeness, a matrix-preserving densification strategy was introduced, in which blends of precharacterized samples—rather than synthetic mixtures—were generated, combined with controlled variations in moisture (4–25 wt% d.b.). Spectra (908–1676 nm) were preprocessed using Savitzky–Golay derivatives, standard normal variate, mean centering, and external parameter orthogonalization (EPO) to mitigate scattering and moisture effects. Partial least squares (PLS) regression models were built and validated through SPXY and random subset approaches. Optimized single-constituent models exhibited high predictive accuracy for lignin (RMSEP 3.2–3.5 wt% d.b., <i>R</i>_p² ≈ 0.96), cellulose (3.46–4.1 wt% d.b., <i>R</i>_p² ≈ 0.94), and hemicellulose (1.1–1.5 wt% d.b., <i>R</i>_p² ≈ 0.98), with moderate model complexity (four to six latent variables). EPO significantly reduced moisture-related variance, improving calibration robustness and lowering latent-variable requirements for lignin and hemicellulose. Conversely, multivariate PLS models with multiple dependent variables required higher dimensionality and yielded inferior performance, confirming the superiority of constituent-specific calibrations.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148039951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148086152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Silico Discovery of 1,3,4-Thiadiazole Derivatives as VEGFR-2 Inhibitors for Breast Cancer Liver Metastasis via QSAR modeling, Molecular Docking, ADMET, and DFT Studies","authors":"Pragya Gawande,&nbsp;Balaji Wamanrao Matore,&nbsp;Anjali Murmu,&nbsp;Partha Pratim Roy,&nbsp;Jagadish Singh","doi":"10.1002/cem.70126","DOIUrl":"10.1002/cem.70126","url":null,"abstract":"<div>\u0000 \u0000 <p>Breast cancer liver metastasis (BCLM) represents a highly aggressive stage of breast cancer progression, largely driven by abnormal angiogenesis mediated by vascular endothelial growth factor receptor-2 (VEGFR-2). Consequently, inhibition of VEGFR-2 has emerged as an important therapeutic strategy for suppressing tumor angiogenesis and metastatic progression. In the present study, an integrated ligand- and structure-based drug design approach was employed to identify potential VEGFR-2 inhibitors based on the 1,3,4-thiadiazole (1,3,4-TDA) scaffold. Experimentally reported IC₅₀ data for 1,3,4-TDA derivatives against MCF-7 and HepG2 cancer cell lines were used to develop statistically robust GA-MLR QSAR models exhibiting strong predictive performance (MCF-7: <i>R</i>² = 0.884–0.919, <i>Q</i>²_LOO = 0.853–0.891; HepG2: <i>R</i>² = 0.808–0.815, <i>Q</i>²_LOO = 0.767–0.771). Mechanistic interpretation of molecular descriptors revealed key structural features influencing VEGFR-2 inhibitory activity. Subsequent QSAR-guided virtual screening of a large PubChem library, combined with drug-likeness filtering, identified several promising candidates with predicted sub-micromolar potency. Molecular docking studies against VEGFR-2 (PDB ID: 4ASD) revealed that PubChem CIDs 24647757 and 59818234 were the best binders, exhibiting high LibDock scores (163.017 and 149.055 kcal/mol), strong binding affinities (−11.7 and −12.4 kcal/mol), and forming stable interactions with key residues (ASP1046, CYS1045, and LYS868) in the ATP-binding pocket. ADMET and toxicity analyses suggested favorable pharmacokinetic properties and improved safety profiles relative to sorafenib. In addition, density functional theory (DFT) calculations supported the favorable electronic properties of the identified hits. Overall, these findings highlight 1,3,4-TDA derivatives as promising scaffolds for the development of next-generation VEGFR-2 inhibitors targeting breast cancer liver metastasis.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148039847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Complementary Chemometrics Analysis for Processing Rivers Monitoring Data","authors":"María L. Rolandi,&nbsp;Nora M. Urquiza,&nbsp;Alejandro G. García-Reiriz","doi":"10.1002/cem.70125","DOIUrl":"10.1002/cem.70125","url":null,"abstract":"<div>\u0000 \u0000 <p>This research underscores the value of integrating multiple chemometric techniques to optimize the analysis and interpretation of complex environmental datasets, specifically in the context of river pollution assessment. Applying a complementary suite of methods—principal component analysis (PCA), bilinear multivariate curve resolution with alternating least squares (MCR-ALS) and trilinear MCR-ALS—this study effectively disentangles natural geochemical patterns and diffuse inputs, revealing their spatiotemporal dynamics within Tucumán's river systems. Bimonthly water sampling across 10 sites over 2 years generated a rich multivariate dataset, which was systematically processed and analysed. The combination of methodologies allowed for the identification of different geochemical patterns, including two natural origins—one characterized by bicarbonate and alkaline earth ions linked to lithological weathering and another associated with pH and acid-base equilibria modulated by hydrology—as well as two nutrient enrichment patterns, characterized by nutrients (NO₂⁻, NO₃⁻ and PO₄³⁻) and chlorides, with accumulation downstream in areas influenced by agriculture and urbanization. Riparian vegetation played a mitigating role, with sites in better conservation demonstrating a more effective attenuation of these impacts. The combined use of these chemometric tools underscores their complementary strengths, enhancing understanding of diffuse pollution dynamics and supporting informed environmental management decisions.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148072327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extended Multiplicative Signal Correction of Noisy Raman Spectra","authors":"Evgeniy G. Evtushenko,&nbsp;Ilya N. Kurochkin","doi":"10.1002/cem.70123","DOIUrl":"10.1002/cem.70123","url":null,"abstract":"<div>\u0000 \u0000 <p>Extended multiplicative signal correction (EMSC) is a common toolkit for preprocessing of vibrational spectra in chemometrics. The core of the EMSC is the ordinary least squares procedure, which is known to be affected by regression dilution bias in the presence of random noise in independent variables, i.e., reference spectra. For the simplest EMSC instance containing a single noisy reference spectrum with several baseline components, we introduce the proper statistical model for the EMSC procedure, which shows that the array of processed spectra is divided into two sets with different bias properties. An experimentally assessable parameter <i>α</i>, which defines the magnitude of bias for both sets, was suggested. The validity of several existing estimators together with two newly introduced ones was theoretically evaluated for bias correction. Next, selected estimators were tested using a specially designed experimental data set of sucrose Raman spectra. We believe that our study will serve as a proper introduction to the broad scientific field of regression dilution bias management for EMSC procedures.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148066956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148092707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Support Vector Machine Ensemble for Crude Oil Classification","authors":"Maria Carolina da V. A. Barboza,&nbsp;Márcia Helena Cassago Nascimento,&nbsp;Gabriely Silveira Folli,&nbsp;Junio R. Botelho,&nbsp;Rafael de Queiroz Ferreira,&nbsp;Wanderson Romão,&nbsp;Paulo Roberto Filgueiras","doi":"10.1002/cem.70119","DOIUrl":"10.1002/cem.70119","url":null,"abstract":"<p>The study focuses on proposing a new machine learning approach to classify crude oil samples based on their physicochemical properties, such as sulfur concentration (S), total acidity number (TAN), and American Petroleum Institute (API) gravity. The goal is to overcome the limitations of traditional analysis methods, which are time-consuming and consume large volumes of samples and solvents, using spectroscopic techniques and machine learning models such as support vector machine ensemble (SVM ensemble). The number of 196 crude oil samples with different sulfur content, different TAN, and API gravity were considered. The SVM ensemble is a powerful approach to improve classification performance because it can reduce the variability of individual models, improve robustness against overfitting, and generalize better than a single model. The parameters of sensitivity, specificity, error rate, Matthews correlation coefficient, and accuracy were considered to compare the SVM ensemble with partial least squares-discriminant analysis (PLS-DA) and SVM. The results demonstrated that near infrared spectroscopy (NIR), combined with multivariate classification models, is an efficient and reliable method for simultaneously classifying sulfur content, TAN, and API gravity in crude oils.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70119","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148093260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}