Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Comparative evaluation of lightweight convolutional neural network and vision transformer models for multi-class brain tumor classification using merged large MRI datasets","authors":"Omneya Attallah ,&nbsp;Ishak Pacal","doi":"10.1016/j.chemolab.2025.105609","DOIUrl":"10.1016/j.chemolab.2025.105609","url":null,"abstract":"<div><div>The accurate classification of brain tumors from MRI scans is important for the timely diagnosis and treatment planning process; however, previous state-of-the-art automatic image classification methods frequently struggle to balance performance with computational cost for clinical applications. In this study, we evaluated twenty lightweight Convolutional Neural Networks (CNN) models and eighteen Vision Transformers (ViT) models for multi-class brain tumor classification using a merged dataset of 17,933 MRI images from 4 categories (glioma, meningioma, pituitary tumors, and healthy brains). The study demonstrated that both groups of architectures can achieve state-of-the-art performance with EfficientNet-b0 (98.36 % accuracy, 4.01 M params) and Tiny-ViT-5M (98.41 % accuracy, 5.07 M params), ranking as the top-performing models for each category. The systematic comparison determined that the proposed lighter models have equivalent or greater performance than established lightweight frameworks, while offering computational advantages, such as MobileViT-xxSmall, which achieved outstanding performance (98.16 % accuracy) with fewer than 1 M parameters. Through benchmarking against fourteen other prior existing frameworks for brain tumor classification, we demonstrated that the top-performing lightweight models of this study maintain stable performances across all evaluation metrics (including precision, recall, and F1 score) and aim to mitigate key weaknesses of prior work, including dataset diversity and model complexity. The findings show very competitive performance across brain tumor classification, highlighting the promise of lightweight architectures to generate accurate and efficient diagnostic support for potential clinical deployment, particularly in low-resource healthcare environments where such efficiencies are vital. Moreover, this work provides useful knowledge that may assist in developing deployable artificial intelligence solutions in neuro-oncology settings.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105609"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733701","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":"A multi-source data integration for soybean differentiation through multiblock data analysis using a novel adaptation of ComDim","authors":"Rodrigo Canarin de Oliveira ,&nbsp;Hector Hernan Hernandez Zarta ,&nbsp;Wargner Alonso Moreno Losada ,&nbsp;Sebastián Javier Caruso ,&nbsp;Hágata Cremasco ,&nbsp;Evandro Bona ,&nbsp;Douglas N. Rutledge ,&nbsp;Diego Galvan","doi":"10.1016/j.chemolab.2025.105621","DOIUrl":"10.1016/j.chemolab.2025.105621","url":null,"abstract":"<div><div>Soybean is a major global commodity. Given its importance, ensuring traceability becomes essential. Genetic, climatic, and soil-related factors influence its chemical composition. Integrating multi-source data using a multiblock analysis represents a powerful approach to differentiating soybeans and monitoring their traceability. This study employed an extension of the ComDim method (also known as Common Components and Specific Weights Analysis, CCSWA) to simultaneously differentiate 20 Brazilian soybean varieties, conventional and transgenic, based on cultivation region and cultivation type. The extension replaced the PCA (Principal Components Analysis) used in classical ComDim by CCA (Common Components Analysis). Forty samples cultivated in Londrina and Ponta Grossa (Paraná, Brazil) were analyzed for their fatty acid, amino acid, isoflavone, and mineral profiles using GC-FID, IEC, HPLC-DAD, and ICP-OES. The CCA-based ComDim results revealed that Common Component 2 (CC2) was primarily responsible for distinguishing the geographical regions of Londrina and Ponta Grossa. The global loadings of CC2 indicated that zinc (Zn), manganese (Mn), oleic acid, arginine, and malonyl genistin were the most influential variables in this component. In contrast, CC3 was associated with differentiating conventional and transgenic cultivars. The global loadings highlighted linoleic acid, oleic acid, α-linolenic acid, malonyl glycitin, malonyl genistin, Fe, Zn, and Mn as the most relevant contributors. The combined CC2 and CC3 plots indicated tendencies toward differentiation of soybean samples by cultivation region and cultivation type. This result highlights the potential of CCA-based ComDim as an effective tool for soybean traceability.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105621"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837308","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":"Chemometrics in Brazil: The early days","authors":"Ieda S. Scarminio ,&nbsp;Roy E. Bruns","doi":"10.1016/j.chemolab.2025.105618","DOIUrl":"10.1016/j.chemolab.2025.105618","url":null,"abstract":"<div><div>A short history of the beginning of chemometric activities in Brazil as well as early international interactions are presented. Details of early research efforts on main frame computers, 8-bit microcomputers and the first 16-bit microcomputers are detailed. A very brief discussion of the rapid growth of chemometrics in Brazil as the result of readily available software is given.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105618"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787023","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":"A hybrid SVM-CPSO-KELM model for the simultaneous detection of methane, ethane, and ethylene via photoacoustic spectroscopy","authors":"Meixuan Zhao,&nbsp;Pengcheng Gu,&nbsp;Yuwang Han","doi":"10.1016/j.chemolab.2025.105620","DOIUrl":"10.1016/j.chemolab.2025.105620","url":null,"abstract":"<div><div>Photoacoustic spectroscopy (PAS) is a powerful technique for detecting trace gas mixtures, with applications spanning industrial safety, environmental monitoring, and energy systems. However, when it is applied to three crucial indicator gases methane (CH₄), ethane (C₂H₆), and ethylene (C₂H₄), strong spectral overlaps introduce cross-interference that complicates accurate concentration retrieval. To address limitations in conventional chemometric and machine learning approaches—such as poor generalization across concentration ranges and vulnerability to interference—this study proposes a hybrid model integrating Support Vector Machine (SVM) classification with Chaotic Particle Swarm Optimization (CPSO)-enhanced Kernel Extreme Learning Machine (KELM). The workflow includes wavelet-based denoising, feature selection via Competitive Adaptive Reweighted Sampling (CARS), dynamic thresholding by SVM to partition samples into high- and low-concentration regimes, and the eventual regression analysis using KELM. The proposed approach significantly improves detection accuracy across a wide concentration range (0.5–500 ppm). Experimental results show that the SVM-CPSO-KELM model achieves an average prediction error of 5.44 %, with maximum error below 14.37 %.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105620"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787027","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":"Integrated monitoring and diagnosis of industrial processes based on causality synergistic and unique decomposition","authors":"Shijie Zhu ,&nbsp;Qi Zhang ,&nbsp;Shuai Li ,&nbsp;Yang Fu ,&nbsp;Dongni Jia ,&nbsp;Yigeng Wang","doi":"10.1016/j.chemolab.2025.105593","DOIUrl":"10.1016/j.chemolab.2025.105593","url":null,"abstract":"<div><div>Causality mining plays a crucial role in monitoring complex industrial processes. However, incomplete extraction of quality related information may lead to a reduced monitoring accuracy rate for quality related faults, while uncertain causal relationships during root variable mining can further result in wrong fault diagnosis outcomes. To address these problems, we decompose the causal relationships between variables into synergistic and unique ones and further propose an integrated monitoring and diagnosis approach for industrial processes based on causality synergistic and unique decomposition. Firstly, we use Granger causality to preliminarily identify quality-related features and enhance the extraction of quality related features via the synergistic effect of causal relationships for addressing their complex interdependence. Secondly, due to the synergistic causality among variables between variable groups, it is necessary to capture and model their dynamic characteristics to ensure monitoring accuracy. We extend quality variable fault monitoring to process variables and further achieve integrated monitoring. Finally, we explore causal uniqueness to identify the fault root cause, which is key to achieving precise and rapid diagnosis in complex and uncertain industrial processes. The feasibility and effectiveness of the proposed method were validated in two scenarios: the benchmark Tennessee Eastman (TE) chemical process and an industrial case study of poor iron ore beneficiation.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105593"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682909","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 enhanced stacking ensemble learning strategy for product quality estimation in complex industrial processes considering multi-timescale data","authors":"Weihang Sun ,&nbsp;Xin Jin ,&nbsp;Sen Xie ,&nbsp;Rui Wang","doi":"10.1016/j.chemolab.2026.105635","DOIUrl":"10.1016/j.chemolab.2026.105635","url":null,"abstract":"<div><div>The cascaded structure involving multi-process and multi-equipment in complex industrial processes leads to time delays for product quality detection, impacting the optimization and control accuracy of industrial production. To tackle this challenge, an enhanced stacking ensemble learning model for quality estimation in complex industrial processes is developed in this paper. First, features from high-dimensional data are extracted through convolutional neural network (CNN). Subsequently, the base learner is constructed, and the meta-learning model is determined by the optimal weight coefficient selection strategy. In addition, the quality estimation error of the meta-learner is calculated and the error estimation is performed by random forest. Following this, the error compensation is applied to the meta-learner for enhancing the estimation accuracy. Through an actual industrial evaporation case for alumina production, it is demonstrated that, compared with other state-of-the-art estimation models, the estimation model present in this paper not only reduces error level but also significantly improves the estimation reliability, thereby fulfilling the operational requirements of the process industry.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105635"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972956","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 correction of saturated peaks in untargeted GC-MS: a chemometric approach","authors":"Beatriz Quintanilla-Casas,&nbsp;Jesper Løve Hinrich,&nbsp;Paul-Albert Schneide,&nbsp;Rasmus Bro","doi":"10.1016/j.chemolab.2025.105582","DOIUrl":"10.1016/j.chemolab.2025.105582","url":null,"abstract":"<div><div>Gas chromatography (GC) coupled with mass spectrometry (MS) is a widely utilized analytical technique in various research fields, such as food and environmental science to generate untargeted profiles of volatile organic compounds (VOCs). These profiles often contain numerous compounds with diverse functional groups, present in a wide range of concentrations. Despite the multiple advantages of this technique, highly concentrated compounds can lead to problems such as MS detector saturation. This results in a distorted signal and a loss of accurate quantitative information, as the peak area of the saturated ion (<em>m/z</em>) is no longer a reliable estimate of a compound's abundance in a sample. Although detector saturation is a common challenge in chromatography, no robust solution has been developed to effectively address it. Therefore, this study proposes an automated method to detect and correct saturated <em>m/z</em> signals in untargeted GC-MS data, enabling accurate quantification and identification of highly concentrated VOCs. Our approach leverages the fact that saturation in fact manifests as right-censored data in specific masses (<em>m/z</em>), allowing automatic detection and unbiased modelling using tensor decomposition, namely parallel factor analysis 2 (PARAFAC2) models, combined with expectation-maximization imputation to restore saturated <em>m/z</em> values.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105582"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733577","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":"Equivalent and complementary variables screening based on global search mechanism for wavelength optimization in spectral multivariate calibration","authors":"Honghong Wang,&nbsp;Yan Zhang,&nbsp;Anqi Jia,&nbsp;Ting Wu,&nbsp;Yiping Du","doi":"10.1016/j.chemolab.2025.105613","DOIUrl":"10.1016/j.chemolab.2025.105613","url":null,"abstract":"<div><div>Variable selection is a very effective method to improve performance of a multivariate calibration model when using high-dimensional spectral dataset. The newly proposed screening strategy of equivalent variables (EVs) and complementary variables (CVs) is worthy of attention. In the proposed method a local search mechanism was used to select the EVs, and the selection range was limited to the adjacent area of the basic variables (BVs) selected by a variable selection method, while the variables far from the BVs were not effectively screened. Aiming at overcoming the limitation of this strategy, this study proposed a global search mechanism based on full-spectrum scanning to screen EVs and investigate CVs based on EVs. The CVs selected from the EVs screened by the global search can provide richer and more accurate feature information to improve the performance of the model. Three variable selection algorithms, stability competitive adaptive reweighted sampling (SCARS), competitive adaptive reweighted sampling (CARS) and Monte Carlo and uninformative variable elimination (MC-UVE), were used to screen EVs and CVs. This strategy is applied to three datasets (corn and tablet NIR dataset, UV–visible dataset). In corn dataset, compared with the model established by the combination of CVs and BVs that used the local search mechanism to screen SCARS from the EVs of CARS and MC-UVE, the performance of the model constructed by 30 CVs combined with BVs based on the global search mechanism was significantly improved, RMSEC and RMSEP decreased from 0.0365 and 0.0590 to 0.0305 and 0.0496, respectively. Similarly, the RMSEP of the model prediction results constructed by the CVs of CARS and MC-UVE combined with BVs obtained by the global search decreased from 0.0625 and 0.0505 to 0.0555 and 0.0403, respectively. Similar results were obtained for other datasets.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105613"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733700","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 efficient automated deep spatio-temporal feature learning framework for industrial soft sensing","authors":"Xiaogang Deng ,&nbsp;Ziheng Wang ,&nbsp;Lumeng Huang ,&nbsp;Ping Wang","doi":"10.1016/j.chemolab.2025.105623","DOIUrl":"10.1016/j.chemolab.2025.105623","url":null,"abstract":"<div><div>Deep learning neural networks have been widely adopted for developing quality prediction models in industrial processes. Despite their strong capability of nonlinear intrinsic features, the existing models have some notable drawbacks, such as insufficient capturing of local spatio-temporal features, high computational complexity of model training, difficult determination of deep model structure, and lack of model interpretability. To address these issues, this paper presents an efficient automated deep spatio-temporal feature learning framework for dynamic industrial process soft sensing, named Deep Convolutional Partial Least Squares (DeCPLS). The proposed approach introduces the convolutional Partial Least squares (CPLS) model as a basic feature extraction unit and stacks multiple CPLS layers to construct an efficient deep dynamic feature learning model. A layerwise training mechanism is presented to facilitate the automated determination of model structures and hyperparameters, thereby reducing the computational complexity. Furthermore, a model prediction error explanation mechanism is introduced to analyze prediction outcomes effectively. Compared to classical deep neural networks, the proposed method demonstrates the advantage of efficiently capturing local spatio-temporal features while maintaining acceptable computational complexity. Finally, the superiority of the proposed method is validated through a simulated industrial case study and a real-world industrial application.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105623"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920783","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":"Distributed learning of deep residual principal component analysis for large-scale industrial process monitoring","authors":"Ouguan Xu ,&nbsp;Zeyu Yang ,&nbsp;Zhiqiang Ge","doi":"10.1016/j.chemolab.2026.105629","DOIUrl":"10.1016/j.chemolab.2026.105629","url":null,"abstract":"<div><div>Distributed principal component analysis (PCA) has been widely used for monitoring large-scale industrial processes in the past years, with lots of improved forms and extension counterparts. This paper introduces a deep residual form of PCA into the distributed modeling framework, in order to improve the monitoring performance for large-scale industrial processes. While deep residual PCA model is developed for feature engineering in each separated block of the process, those augmented features extracted in different blocks are combined together in the second level for construction of an additional deep residual PCA model. By further augmenting the extracted features from different layers of the deep residual model, the final process monitoring scheme can be formulated for large-scale industrial processes. Based on two industrial case studies, the monitoring performance has been improved more than 20 % by the proposed distributed deep learning model, while at the same time the computation burden of the new method has been kept in a low level.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105629"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920786","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}