Journal of Chemometrics最新文献

{"title":"Infrared Spectroscopy and Machine Learning for Classification of Red Stamp Inks on Questioned Documents","authors":"Yong Ju Lee,&nbsp;Chang Woo Jeong,&nbsp;Mi Jung Choi,&nbsp;Tai-Ju Lee,&nbsp;Hyoung Jin Kim","doi":"10.1002/cem.70056","DOIUrl":"https://doi.org/10.1002/cem.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>This study demonstrates that integrating infrared spectroscopy with machine learning enables highly accurate, nondestructive classification of red-stamp ink manufacturers. We evaluated five classifiers—partial least squares discriminant analysis (PLS-DA), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and a feed-forward neural network (FNN)—across multiple spectral regions. The FNN trained on second-derivative spectra in the 1700–900 cm⁻¹ region achieved perfect test metrics (F1 = 1.000; AUC = 1.000), while PLS-DA and RF also performed robustly (F1 ≥ 0.933). Variable importance in projection (VIP) analysis identified the 1650–1100 cm⁻¹ subrange as most informative, streamlining feature selection and model training. Applied to three unknown samples, the optimized FNN produced high-confidence manufacturer predictions consistent with expected origins. These results confirm that targeted spectral selection combined with derivative preprocessing markedly enhances nondestructive ink classification for forensic applications.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144717052","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":"Predictive QSAR Models Followed by Toxicity, Molecular Docking, and Molecular Dynamics Simulation in Search of Azole Derivatives as AChE Inhibitors for the Treatment of Alzheimer's Disease","authors":"Kajal Gupta,&nbsp;Akshay Kumar,&nbsp;Richa Patel,&nbsp;Piyush Ghode,&nbsp;Himanchal Kumar,&nbsp;Anjali Murmu,&nbsp;Nemdas Sahu,&nbsp;Geeteshwari Verma,&nbsp;Seema Sahu,&nbsp;Sonali Soni,&nbsp;Shakuntala Pal,&nbsp;Jagadish Singh,&nbsp;Partha Pratim Roy,&nbsp;Purusottam Banjare","doi":"10.1002/cem.70049","DOIUrl":"https://doi.org/10.1002/cem.70049","url":null,"abstract":"<div>\u0000 \u0000 <p>The present study aims to find azole-containing acetylcholinesterase (AChE) inhibitors for the treatment of Alzheimer's disease (AD) through a mixed in silico approach. The first step involved the collection of azole derivatives and predictive quantitative structure–activity relationship (QSAR) model development for their AChE inhibition activity, using multiple linear regressions (MLRs) with the genetic algorithm (GA) for feature selection. The developed GA-MLR models were statistically robust enough internally (<i>R</i>²<i>_a_dj</i> = 0.643–0.640, <i>Q</i>²_LOO = 0.616–0.621) as well as externally (<i>R</i>²_pred = 0.626–0.658, <i>R</i>²<i>M</i> = 0.562–0.601). The prediction reliability of the models was assured through the leverage approach of the applicability domain. The most significant models were applied to azole-containing PubChem database compounds, which were classified as active and inactive based on theoretical predictions. The toxicity analysis was also performed for the active compounds by the online web server Protox-II. The less or nontoxic compounds were subjected to molecular docking, along with donepezil as a standard. Docking analysis revealed that the four compounds have better binding affinity (binding energy = −11.6 to −11.2 kcal/mol) as compared to donepezil (binding energy = −11 kcal/mol). Apart from binding energy, donepezil was observed to be toxic by the prediction from the Protox-II. Finally, molecular dynamics (MD) analysis of two compounds (Compound 5, having the lowest IC₅₀, and Compound 25, having the highest IC₅₀ among the top 4 docked compounds) not only differentiated them based on final interactions but also exhibited that the toxicity of donepezil might be due to hydrogen bonding with the active site.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714680","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":"Influence of a Measurement Procedure and Evaluation of Transflectance Sensing System for Quantifying Sunflower Oil Adulterations in Olive Oil. A Proof of Concept","authors":"D. Castro-Reigía,&nbsp;M. Sierra,&nbsp;I. García,&nbsp;S. Sanllorente,&nbsp;L. A. Sarabia,&nbsp;M. C. Ortiz","doi":"10.1002/cem.70054","DOIUrl":"https://doi.org/10.1002/cem.70054","url":null,"abstract":"<p>The development of NIR instruments and/or their modification to adapt the measurements for each problem and improve its performance are crucial steps for the optimal measurement procedures. In this work, it is presented the development of an accessory for cuvettes designed to have the possibility to collect NIR spectra in transflectance mode. In that sense, it is aimed to investigate how different factors in the measurement procedure using this accessory influence both the NIR spectra and the subsequent calibration models for detecting adulterations with sunflower oil in olive oil. The purpose is to show how a proof of concept can be developed using chemometric tools. For that, every measurement condition influencing the spectra was evaluated with ASCA, visualizing how the use of different NIR devices, the sensor arrangement regarding the cuvette, the activation of the internal compensation system of temperature of the sensor, or the concentration levels of the adulterant affected the resulting spectra. Afterwards, every possible combination of the factors was explored through eight different PLS calibration models and their validation to examine if the factors also influenced the calibration models built for quantifying the sunflower oil present in the olive oil. It was found that not only were all factors significant regarding NIR measurements but also when quantifying adulterants. The best results of this proof of concept were obtained by arranging the sensor in a horizontal disposition regarding the cuvette and activating the internal compensation system of temperature. The capability of detection of the method for the particular oils used was 1.4% for probabilities of false positive and false negative of 0.05.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705444","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":"Enhanced Discrimination of Thawed and Nonfrozen Chicken Thighs Using Convex Hull Peeling in Visible Spectral Imaging","authors":"Esmée Versteegen,&nbsp;Mahsa Akbari Lakeh,&nbsp;Anastasia Swanson,&nbsp;Gerjen H. Tinnevelt,&nbsp;Aoife Gowen,&nbsp;Jeroen J. Jansen,&nbsp;Mahdiyeh Ghaffari","doi":"10.1002/cem.70055","DOIUrl":"https://doi.org/10.1002/cem.70055","url":null,"abstract":"<p>Hyperspectral imaging (HSI) combines spectral and spatial data, producing complex 3D datasets that require efficient data reduction methods for improved computational efficiency and prediction accuracy. This study introduces convex hull peeling to enhance the discrimination of thawed and nonfrozen chicken thighs. By removing pixels with noise-dominated spectra and targeting deeper data layers, this method improved model robustness and reduced training time from 426 to 5 s. Essential spectral pixels (ESPs), located on the convex hull in principal component space, effectively preserved critical data, achieving 81% classification accuracy, comparable with using the full dataset. Sensitivity and specificity were 74% and 89%, respectively, demonstrating improved specificity with a slight trade-off in sensitivity. Piece-based accuracy reached 100%, highlighting the potential of this approach for noninvasive food quality assessment. This study underscores the efficiency and adaptability of ESPs and convex hull peeling for complex datasets.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681467","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":"Sparse Twoblock Dimension Reduction: A Versatile Alternative to Sparse PLS2 and CCA","authors":"Sven Serneels","doi":"10.1002/cem.70051","DOIUrl":"https://doi.org/10.1002/cem.70051","url":null,"abstract":"<div>\u0000 \u0000 <p>A method is introduced to perform simultaneous sparse dimension reduction on two blocks of variables. Beyond dimension reduction, it also yields an estimator for multivariate regression with the capability to intrinsically deselect uninformative variables in both independent and dependent blocks. An algorithm is provided that leads to a straightforward implementation of the method. The benefits of simultaneous sparse dimension reduction are shown to carry through to enhanced capability to predict a set of multivariate dependent variables jointly. Both in a simulation study and in two chemometric applications, the new method outperforms its dense counterpart, as well as multivariate partial least squares.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681504","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":"Statistical Validation of Multivariate Treatment Effects in Longitudinal Study Designs","authors":"Torfinn Støve Madssen,&nbsp;Age Smilde,&nbsp;Jose Camacho,&nbsp;Anders Hagen Jarmund,&nbsp;Johan Westerhuis,&nbsp;Guro F. Giskeødegård","doi":"10.1002/cem.70044","DOIUrl":"https://doi.org/10.1002/cem.70044","url":null,"abstract":"<div>\u0000 \u0000 <p>Multivariate extensions of repeated measures linear mixed models, such as repeated measures ANOVA simultaneous component analysis (RM-ASCA+) and linear mixed model-principal component analysis (LiMM-PCA), can be used for analyzing longitudinal studies with multivariate outcomes. However, there are no gold standards to assess the statistical validation of the observed effects of such models. Using real and simulated data, we here perform an empirical comparison of different strategies for assessing statistical significance in these frameworks: permutation tests, the global log-likelihood ratio (GLLR) test, and nonparametric bootstrap confidence intervals for the estimated multivariate effects. Power curves were used to examine the statistical power of the different tests in detecting time–treatment interactions with varying effect sizes. Our results show that both the permutation tests and the GLLR test can be used to statistically test the presence of a time–treatment interaction effect for multivariate data; however, the GLLR approach will be sensitive to the number of included principal components in LiMM-PCA. The bootstrap confidence interval approach generally shows good statistical power but has inflated Type 1 error rates under certain conditions. This makes it unsuitable for the purpose of hypothesis testing in its present implementation, although it may still be useful for exploratory purposes. Overall, our results show that the power of the tests for assessing multivariate effects in longitudinal studies is dependent on characteristics of the dataset, and it is important to be aware of the strengths and weaknesses of the different validation procedures.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657735","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":"Rapid Determination of Soil Organic Matter by Near-Infrared Spectroscopy With a Novel Double Ensemble Modeling Method","authors":"Yingxia Li,&nbsp;Jiajing Zhao,&nbsp;Zizhen Zhao,&nbsp;Haiping Huang,&nbsp;Xiaoyao Tan,&nbsp;Xihui Bian","doi":"10.1002/cem.70053","DOIUrl":"https://doi.org/10.1002/cem.70053","url":null,"abstract":"<div>\u0000 \u0000 <p>An intelligent and accurate modeling method is proposed combining near-infrared (NIR) spectroscopy for measuring organic matter content in soil samples. The proposed method uses Monte Carlo (MC) random sampling in the training set, where subsets were randomly selected from the samples and further selected using the butterfly optimization algorithm (BOA) to construct partial least squares (PLS) submodels, named MC-BOA-PLS. Ultimately, the final prediction was obtained by averaging the predictions of these submodels. The parameters of the MC-BOA-PLS model were optimized, including the iteration number of BOA, the number of butterflies, and the number of PLS submodels. Results show that MC-BOA-PLS exhibited superior predictive performance to predict organic matter content in soil compared with PLS and BOA-PLS.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647530","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":"Transforming Hyperspectral Images Into Chemical Maps: A Novel End-to-End Deep Learning Approach","authors":"Ole-Christian Galbo Engstrøm,&nbsp;Michela Albano-Gaglio,&nbsp;Erik Schou Dreier,&nbsp;Yamine Bouzembrak,&nbsp;Maria Font-i-Furnols,&nbsp;Puneet Mishra,&nbsp;Kim Steenstrup Pedersen","doi":"10.1002/cem.70041","DOIUrl":"https://doi.org/10.1002/cem.70041","url":null,"abstract":"<p>Current approaches to chemical map generation from hyperspectral images are based on models such as partial least squares (PLS) regression, generating pixel-wise predictions that do not consider spatial context and suffer from a high degree of noise. This study proposes an end-to-end deep learning approach using a modified version of U-Net and a custom loss function to directly obtain chemical maps from hyperspectral images, skipping all intermediate steps required for traditional pixel-wise analysis. The U-Net is compared with the traditional PLS regression on a real dataset of pork belly samples with associated mean fat reference values. The U-Net obtains a test set root mean squared error of between 9% and 13% lower than that of PLS regression on the task of mean fat prediction. At the same time, U-Net generates fine detail chemical maps where 99.91% of the variance is spatially correlated. Conversely, only 2.53% of the variance in the PLS-generated chemical maps is spatially correlated, indicating that each pixel-wise prediction is largely independent of neighboring pixels. Additionally, while the PLS-generated chemical maps contain predictions far beyond the physically possible range of 0%–100%, U-Net learns to stay inside this range. Thus, the findings of this study indicate that U-Net is superior to PLS for chemical map generation.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635096","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":"Spectral Wavelength Selection Method Based on Improved Particle Swarm Optimization Idea and Simulated Annealing Strategy","authors":"Ying Dong,&nbsp;Weida Wang,&nbsp;Nanfeng Zhang,&nbsp;Jinming Liu","doi":"10.1002/cem.70050","DOIUrl":"https://doi.org/10.1002/cem.70050","url":null,"abstract":"<div>\u0000 \u0000 <p>Wavelength selection (WS) is an effective means to address the presence of many uncorrelated and collinear variables in high-dimensional spectral data that seriously influence the modeling accuracy and efficiency. Aiming to address too many wavelength variables selected by particle swarm optimization algorithm (PSO) and its premature convergence, this paper proposes a novel spectral WS approach—iPSOSA—based on the improved PSO idea and simulated annealing algorithms (SA) strategy. iPSOSA applies the velocity and position update ideas of PSO to the guided shift evolution process of the binary bits with the value of “1” in the particle and integrates with the perturbation strategy of the SA Metropolis acceptance criterion. It effectively solves the premature convergence of PSO and overcomes the low efficiency of the SA evolution, which has high efficiency in WS. By evaluating the modeling performance of different intelligent WS methods using two public spectral datasets from soil and maize, it was found that the iPSOSA outperforms the full-spectrum and other three comparative algorithms. The best iPSOSA partial least squares regression models for soil organic matter and maize moisture contents have excellent regression performance, with the validation set's coefficient of determination higher than 0.98, relative root mean squared error lower than 1.50%, and residual predictive deviation higher than 8.00. iPSOSA presents better comprehensive performance in WS than traditional intelligent algorithms in terms of modeling performance, variable dimensionality, and searching efficiency, providing a new solution for obtaining high correlation wavelength variables in the spectral modeling process.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635052","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":"A Method for Measuring Similarity or Distance of Molecular and Arbitrary Graphs Based on a Collection of Topological Indices","authors":"Mert Sinan Oz","doi":"10.1002/cem.70047","DOIUrl":"https://doi.org/10.1002/cem.70047","url":null,"abstract":"<div>\u0000 \u0000 <p>The comparison of graphs using various types of quantitative structural similarity or distance measures has an important place in many scientific disciplines. Two of these are cheminformatics and chemical graph theory, in which the structural similarity or distance measures between molecular graphs are analyzed by calculating the Jaccard/Tanimoto index based on molecular fingerprints. A novel method is proposed to measure the structural similarity or distance for molecular and arbitrary graphs. This method calculates the Jaccard/Tanimoto index based on a collection of topological indices embedded in the entries of a vector. We statistically compare the proposed method with the method for calculating the Jaccard/Tanimoto indices based on five different molecular fingerprints on alkane and cycloalkane isomers. Furthermore, to explore how the method works on non-molecular graphs, we statistically analyze it on the set of all connected graphs with seven vertices. The Jaccard/Tanimoto index values produced by the proposed method cover the value domain. In addition, it provides a discrete similarity distribution with the clustering, which makes the differences clear and provides convenience for comparison. Two outstanding features of the proposed method are its applicability to arbitrary graphs and the computational complexity of the algorithm used in the method is polynomial over the number of graphs and the number of vertices and edges of the graphs.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 7","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624520","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}