Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Solving the puzzle: Simulation of multivariate data with control over the structure of columns and rows using the two-sided orthogonal procrustes problem","authors":"Francisco Arteaga ,&nbsp;José Camacho ,&nbsp;Alberto Ferrer","doi":"10.1016/j.chemolab.2025.105556","DOIUrl":"10.1016/j.chemolab.2025.105556","url":null,"abstract":"<div><div>Researchers interested in developing new multivariate statistical methods often need to be able to generate multivariate datasets with specific characteristics to test the effectiveness of their data analysis algorithms under specific conditions.</div><div>In this paper, we present a family of methods for generating multivariate centred datasets by simultaneously controlling features of the cross-product matrices <span><math><mrow><msup><mi>X</mi><mo>⊤</mo></msup><mi>X</mi></mrow></math></span> and <span><math><mrow><mi>X</mi><msup><mi>X</mi><mo>⊤</mo></msup></mrow></math></span>. This provides an interesting trade-off to control for the variance structure in the data, important for the family of algorithms that operate on the data matrix, like, e.g., Principal Component Analysis, and control for the distances among objects, important for algorithms that operate on the distance matrix, like Multidimensional Scaling. The proposed methods form a general framework that can be understood as a jigsaw puzzle, joining pieces obtained from the spectral decomposition of a target covariance matrix and the singular value decomposition of a target data matrix. These methods have in common that they are derived from a two-sided orthogonal Procrustes problem.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105556"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464513","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":"Impact of converting graphs into spanning trees on node and graph classification in Graph Neural Network","authors":"Mohammadmahdi Taheri ,&nbsp;Mahdi Eftekhari ,&nbsp;Gholamreza Aghamollaei","doi":"10.1016/j.chemolab.2025.105562","DOIUrl":"10.1016/j.chemolab.2025.105562","url":null,"abstract":"<div><div>This paper investigates the impact of graph reduction to their spanning trees on Graph Neural Network (GNN) performance in node and graph classification across six GNN architectures. The proposed approach leverages spanning trees for graph sparsification while preserving critical structural information, achieving performance comparable to or better than full-graph models. A theoretical connection is established between edge sampling via determinantal point processes (DPPs) and the transfer-current matrix, showing minimum spanning trees effectively approximate DPP-selected subgraphs. Four edge-weighting schemes are analyzed such that their sparsification trade-offs are revealed. The method consistently reduces memory usage and computation while maintaining accuracy. Findings indicate that spanning tree pruning offers a scalable, theoretically grounded strategy for efficient GNN training without compromising classification accuracy. Experiments on node classification benchmarks (Cora, Citeseer, PubMed, PPI) and graph classification biological and chemical datasets (AIDS, MUTAG, PROTEINS, NCI1, IMDB-BINARY) demonstrate excellent graph classification results, notably 98.27% accuracy on AIDS, with reduced computational overhead.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105562"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464516","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":"Investigation on different strategies of significance testing in ANOVA-simultaneous component analysis (ASCA)","authors":"Faezeh Maddahi ,&nbsp;Mahsa Akbari Lakeh ,&nbsp;Jamile Mohammad Jafari ,&nbsp;Farnoosh Koleini ,&nbsp;Siewert Hugelier ,&nbsp;Paul J. Gemperline ,&nbsp;Hamid Abdollahi","doi":"10.1016/j.chemolab.2025.105573","DOIUrl":"10.1016/j.chemolab.2025.105573","url":null,"abstract":"<div><div>ANOVA Simultaneous Component Analysis (ASCA) integrates analysis of variance with multivariate modelling to quantify how experimental factors and their interactions affect complex multivariate measurements. Statistical significance in ASCA is typically assessed by permutation testing; however, different permutation strategies imply distinct null hypothesis and exchangeability assumptions. In this study, we systematically compare three widely used approaches embedded in popular chemometric software packages where the permutation strategy is often predefined and not always transparent to the user. The restricted permutation method shuffles observations only within experimental strata, preserving the structure of the null hypothesis. The reduced‐model permutation contrasts the full ASCA model with a simplified version in which selected effects are removed. Permutation of marginal design matrices isolates interaction effects by permuting marginal matrices derived from the design matrix. We evaluate these methods on simulated datasets with varying patterns of main effects and interactions, as well as on an experimental study of feral cabbage (Brassica oleracea) under treatment and time factors. Our results show that the restricted permutation method reliably detects main effects, reduced‐model permutation excels at identifying interactions, and permutation of marginal design matrices consistently captures both. By examining the assumptions and performance of each method, we provide practical guidance for selecting the optimal permutation strategy in ASCA-based chemometric analysis, particularly for balanced experimental designs. As a baseline, we additionally assessed unrestricted permutation of the raw data using two test statistics: the sum of squares and the F-ratio. The results demonstrated that when employing the F-ratio, this approach was also capable of accurately detecting statistical significance.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105573"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464517","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":"Decoding brain tumor patterns in MRI images: Unleashing optimized insights with Progressive Wasserstein generative adversarial network","authors":"G. Jenifa ,&nbsp;B.R. Tapas Bapu ,&nbsp;Vivekanandan M. ,&nbsp;J. Senthil Murugan","doi":"10.1016/j.chemolab.2025.105563","DOIUrl":"10.1016/j.chemolab.2025.105563","url":null,"abstract":"<div><div>Brain tumor (BT) detection and segmentation are of vital importance for accurate diagnosis, but are still difficult because of intricate brain anatomy, non-spherical tumor shapes, and low contrast of MRI images. Conventional manual methods are time-consuming and invasive with observer variability, whereas traditional machine learning (ML) approaches based on handcrafted features tend to miss subtle patterns of tumor areas. Even the deep learning (DL) models like CNNs, despite their effectiveness, have limitations such as high computation expenses, poor generalization to heterogeneous data, and complexity in delineating tumor boundaries accurately, which are subtle. These drawbacks are sought to be overcome by this manuscript, suggesting an innovative technique for automatic BT detection in MRI samples. Initially, the normalized gamma corrected contrast-limited adaptive histogram equalization (NG-CCLAHE) is introduced for enhancing the MRI image quality. Then, the Faster 2D-Otsu Thresholding technique is introduced for segmenting the tumor regions from the MRI samples. Followed by this, the synchroextracting Transform (SET) technique is employed to extract features, which are then optimized with an Improved Ladybug Beetle Optimization Algorithm (ILBOA). The improved features are fed into the PWGAN, allowing for more accurate and effective tumor detection. Experimental assessment using the Br35H Brain Tumor Detection 2020 dataset reflects high-level performance with 98.6 % accuracy, 92 % DSC, 95 % PDR, 23 % classification error, 37.8s computation time, and an F1-score of 98.59 %. These aspects identify the proposed approach's efficiency and competency in brain tumor patterns from MRI images.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105563"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569577","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":"Nondestructive detection of total flavonoids content in daylily using Vis-NIR and NIR hyperspectral imaging: data fusion combined with SHAP for model interpretability","authors":"Xuexia Ma,&nbsp;Na Li,&nbsp;Ruifeng Wang,&nbsp;Jiaxue Ma,&nbsp;Ninghua Zhu,&nbsp;Tingting Li,&nbsp;Zhongxiong Zhang,&nbsp;Haifeng Li,&nbsp;Songlei Wang,&nbsp;Haihong Zhang","doi":"10.1016/j.chemolab.2025.105575","DOIUrl":"10.1016/j.chemolab.2025.105575","url":null,"abstract":"<div><div>Flavonoids, vital bioactive compounds in daylily (a nutritionally and medicinally valuable food), have antioxidant, anti-inflammatory, antibacterial, and antidepressant properties, which boost its nutritional value, health benefits, and quality. Hyperspectral Imaging (HSI) for detecting trace flavonoids usually uses single systems, failing to leverage multispectral complementarity and restricting detection performance. This study integrates a data fusion strategy with two HSI techniques (visible–near-infrared (Vis-NIR) and near-infrared (NIR)) for the non-destructive detection of total flavonoids content (TFC) in daylily. The investigation employed partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) for spectral data. Additionally, data-level and feature-level fusion strategies are implemented for data fusion modeling, while the SHapley Additive exPlanations (SHAP) methodology is used to comprehensively evaluate spectral feature contribution rates. The findings demonstrate that modeling based on the fusion strategy of LS-SVM yields substantially superior results compared to single-system approaches. Notably, the Mid-level fusion model incorporating competitive adaptive reweighted sampling (CARS) and LS-SVM demonstrates optimal performance. The determination coefficient (R²_P), root mean square prediction error (RMSEP) and residual prediction deviation (RPD) of the prediction set were 0.9332, 0.0186 and 3.3560, respectively. This study confirms the feasibility of HSI technology in non-destructively detecting flavonoids in daylily. Furthermore, the collaborative optimization of multi-spectral HSI systems through a data fusion strategy effectively enhances the accuracy of non-destructive flavonoids detection. This study presents innovative technical approaches for non-destructive trace substance detection and agricultural product quality and safety monitoring, thereby providing essential technical support for developing intelligent agricultural product quality and safety monitoring systems.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105575"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569654","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 fluorescence spectroscopy and improved parallel factor framework-clustering analysis for oil spill type identification and concentration quantification","authors":"Peiliang Wu ,&nbsp;Zhiwei Wang ,&nbsp;Yuhan Zhao ,&nbsp;Deming Kong","doi":"10.1016/j.chemolab.2025.105564","DOIUrl":"10.1016/j.chemolab.2025.105564","url":null,"abstract":"<div><div>Oil spills hidden below the sea surface and in a suspended state are known as submerged oil. Determining the source of an oil spill and evaluating the amount of oil spilled can provide a basis for the effective development of oil spill emergency response strategies and policies. Because of this, this paper proposes an oil spill species identification and concentration quantification analytical method based on the combination of time-resolved fluorescence spectroscopy (TRFS) and improved parallel factor framework-clustering analysis (IPFFCA). The IPFFCA model first decomposes the oil TRFS data to extract the loading matrix and reconstructs the landscape maps corresponding to each component based on the loading matrix. Subsequently, the non-negative least squares algorithm was employed to fit the component landscape maps to the unfolded actual spectra, thereby estimating the score matrix of the samples. Building upon this, the score matrix was used as input to develop oil species identification and concentration quantification models via particle swarm optimization support vector machine (PSO-SVM) and extreme gradient boosting (XGBoost), respectively. To verify the effectiveness of the proposed analytical method, six typical submerged oil samples were experimentally prepared, and their TRFS data were collected and analyzed. The experimental results show that the analytical method proposed in this paper achieves 92 % accuracy in the oil species identification task, the average coefficient of determination of the concentration prediction in the validation set of the six types of samples reaches 0.95, and the root mean square error is 0.08, indicating strong predictive performance.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105564"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464514","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":"SigmaCCS2: Collision cross section prediction using molecular topological and geometric information with graph neural network","authors":"Yuxuan Liao,&nbsp;Ruixi Chen,&nbsp;Ting Xie,&nbsp;Chuanxiu Yu,&nbsp;Jiongyu Wang,&nbsp;Zhimin Zhang,&nbsp;Hongmei Lu","doi":"10.1016/j.chemolab.2025.105588","DOIUrl":"10.1016/j.chemolab.2025.105588","url":null,"abstract":"<div><div>Collision cross section (CCS) values are crucial for compound characterization in ion mobility spectrometry (IMS), yet their experimental determination is often resource-intensive. To overcome this, we introduce SigmaCCS2, a graph neural network (GNN)-based method that predicts CCS values by integrating both molecular topological and geometric information. By representing molecules as dual graphs, where molecular graphs encode topological structures and line graphs encode molecular geometries, SigmaCCS2 captures comprehensive structural features for accurate predictions. This approach achieves highly competitive and robust predictive performance, generally superior or comparable to state-of-the-art methods on external test sets. SigmaCCS2 demonstrates robust versatility across different instruments and molecular superclasses. Furthermore, a user-friendly web server is provided, enhancing accessibility for researchers. This advancement in CCS prediction enhances compound identification in various fields, including metabolomics, lipidomics, and drug discovery, by improving CCS coverage in databases.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105588"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614663","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":"2025-12-15","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":"When just-in-time learning meets deep learning: An industrial quality prediction practice on deep partial least squares model","authors":"Junhua Zheng ,&nbsp;Zeyu Yang ,&nbsp;Zhiqiang Ge","doi":"10.1016/j.chemolab.2025.105555","DOIUrl":"10.1016/j.chemolab.2025.105555","url":null,"abstract":"<div><div>While deep learning has made great progresses in various application domains, the nature of computational expensive and reliance on large-scale data makes it inefficient or even impossible for small data modeling, particularly under the just-in-time learning framework. Effective combination of deep learning and just-in-time learning may explore great potentials for both two learning paradigms, thus should be attractive and beneficial to the research community. In this paper, an improved form of the lightweight deep partial least squares (PLS) model is developed under the framework of Just-in-time learning. Without complicated backpropagation and time-consuming parameter tuning algorithms, deep PLS provides a transparent model structure which also works well for small training data. As a result, fusion of those two learning strategies makes the new proposed method as a very promising predictive modeling tool in industrial soft sensor applications, the performance of which is evaluated and confirmed through a real industrial example.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105555"},"PeriodicalIF":3.8,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412394","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":"Chemometric modelling of anticancer drugs using CatBoost regression and graphical derivatives","authors":"Yingxuan Huang ,&nbsp;Muhammad Farhan Hanif ,&nbsp;Eiman Maqsood ,&nbsp;Mudassar Rehman","doi":"10.1016/j.chemolab.2025.105551","DOIUrl":"10.1016/j.chemolab.2025.105551","url":null,"abstract":"<div><div>In this work, a chemometric methodology based on graph topology descriptors and CatBoost regression is proposed for predicting the physicochemical properties of anticancer drugs. Molecular structures were encoded as graphs, and degree-based topological descriptors were derived to capture their complexity. These descriptors were used in the construction of regression models predicting boiling point, molar refractivity, and polarizability. The first statistical analysis with linear and cubic regression demonstrated that models of order higher than unity were able to take into account the non-linear dependence of descriptors vs. molecular properties. CatBoost regression model was used for improved predictability and better interpretability. This model exhibits a coefficient of determination <span><math><mrow><mo>(</mo><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></math></span> of 0.997 for the prediction of boiling point and superior performance across all the other two properties, with average absolute errors lower than 2%. Of importance, we identified several graph descriptors as important predictors, which confirmed their chemometric relevance. The method may contribute with useful information as a complementary method to current machine learning-based models used for prediction of drug properties in chemoinformatics or pharmaceutical drug development, it integrates chemical graph theory with intelligent reasoning and modeling for a more fault tolerant and generalized 1 solution to drug property prediction.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105551"},"PeriodicalIF":3.8,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145358697","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}