Journal of Chemometrics最新文献

{"title":"On the Replicability of the Thermodynamic Modeling of Spectroscopic Titration Data in the Nickel(II) En System","authors":"Fenton C. Lawler,&nbsp;Ryan S. Storteboom,&nbsp;Plinio D. Rosales-Lopez,&nbsp;Madison N. Hoogstra,&nbsp;Katherine J. Selvaggio,&nbsp;Trevina Chen,&nbsp;Krista A. Zogg,&nbsp;Dafna L. Heule,&nbsp;Noah J. Pehrson,&nbsp;Aerin E. Baker,&nbsp;Douglas A. Vander Griend","doi":"10.1002/cem.3619","DOIUrl":"https://doi.org/10.1002/cem.3619","url":null,"abstract":"<div>\u0000 \u0000 <p>Characterizing complicated solution phase systems in situ requires advanced modeling techniques to capture the intricate balances between the many chemical species. Due to the error inherent in any scientific measurement, a spectrophotometric titration experiment with nickel(II) and ethylenediamine (en) was repeated six times using an autotitrator to test the replicability of the data and the consistency of the resulting thermodynamic model. All six datasets could be modeled very tightly (<i>R</i>² &gt; 99.9999%) with the following eight complexes: [Ni]²⁺, [Ni₂en]⁴⁺, [Nien]²⁺, [Ni₂en₃]⁴⁺, [Nien₂]²⁺, [Ni₂en₅]⁴⁺, [Nien₃]²⁺, and [Nien₆]²⁺. The logK values for the stepwise associative reactions agree with existing literature values for the majority species ([Nien_{n = 1–3}]²⁺) and matched expectations for the minority species; 95% confidence intervals for each logK value were determined via bootstrapping, which quantifies the variability in the binding constant value that is supported by a given dataset. The repeated experiments, which could not be successfully concatenated together, demonstrate that replication is crucial to capturing all the variability in the logK values. Conversely, bootstrapped confidence intervals across multiple experiments can be readily combined to generate an appropriate range for an experimentally determined binding constant.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861954","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":"Analytical Figures of Merit in Univariate, Multivariate, and Multiway Calibration: What Have We Learned? What Do We Still Need to Learn?","authors":"Alejandro C. Olivieri","doi":"10.1002/cem.3613","DOIUrl":"https://doi.org/10.1002/cem.3613","url":null,"abstract":"<div>\u0000 \u0000 <p>An overview of the status of the research in analytical figures of merit is provided, including all calibration scenarios from univariate to multivariate and multiway analytical protocols. Both linear and nonlinear multivariate models are considered. Starting with the simplest multivariate model, inverse least-squares regression, the basic concepts of sensitivity, sample leverage, and limit of detection are introduced. The extension to other multivariate models is discussed, as well as to nonlinear models based on radial basis functions, kernel partial least-squares, and multilayer feed-forward artificial neural networks. Finally, multiway calibration models are discussed, including multilinear decomposition models such as parallel factor analysis (PARAFAC) and multivariate curve resolution–alternating least-squares (MCR-ALS). In the latter case, recent developments concerning the pervasive phenomenon of rotational ambiguity are discussed. Unfinished works and areas where further research efforts are needed to develop closed-form expressions and to fully understand their meaning are included.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666077","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":"On Hidden Rank Deficiency in MCR Problems","authors":"Tomass Andersons,&nbsp;Mathias Sawall,&nbsp;Martina Beese,&nbsp;Christoph Kubis,&nbsp;Klaus Neymeyr","doi":"10.1002/cem.3608","DOIUrl":"https://doi.org/10.1002/cem.3608","url":null,"abstract":"<p>Pure component decomposition problems in chemometrics can be classified into rank-regular and rank-deficient problems. Rank-deficient problems are characterized by a spectral data matrix that has a lower rank than the number of chemical species. However, it is possible that there exists rank-regular factorization of the spectral data matrix, but none of these solutions can be interpreted chemically, and only a solution of the MCR problem with rank deficiency is chemically meaningful. Then we say that the underlying problem suffers from a hidden rank deficiency. In this paper, MCR problems with hidden rank deficiency are introduced and analyzed with several examples for problems of rank 2 and rank 3. The area of feasible solutions is determined with the help of additional constraints on the solution.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3608","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861953","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":"Paul Geladi (1951–2024) Chemometrician, spectroscopist and pioneer","authors":"Beatriz Galindo-Prieto,&nbsp;Johan Linderholm,&nbsp;Hans Grahn","doi":"10.1002/cem.3614","DOIUrl":"https://doi.org/10.1002/cem.3614","url":null,"abstract":"&lt;p&gt;Prof. Paul Geladi was born the 30&lt;sup&gt;th&lt;/sup&gt; of June of 1951 in Schoten (Belgium) and passed away peacefully on the 18&lt;sup&gt;th&lt;/sup&gt; of May of 2024 in Umeå (Sweden).&lt;/p&gt;&lt;p&gt;Paul Geladi was a brilliant chemometrician and professor specialized in multivariate data analysis (especially, partial least squares methods), multivariate image analysis, multiway analysis, and spectroscopy (near-infrared), as well as a kind and emphatic person with colleagues, students, friends and family. His work trajectory includes, among other, a list of more than 190 publications (with &gt;29,000 citations) that shows the extent and vigour of Paul, both in life and work.&lt;/p&gt;&lt;p&gt;Paul's passion for nature and chemistry awoke in his early years in Schoten, when he was still a very young child, while playing outdoors or experimenting in the attic for hours with the “Chemistry for Beginners” kit that his parents gave him. This was likely the start of a life dedicated to science and research.&lt;/p&gt;&lt;p&gt;After attending Sint-Eduardus in the Londenstraat (Belgium), Paul received his B.Sc. in Chemistry (1974) and his Ph.D. (doctoral degree) in Analytical Chemistry from the University of Antwerp (1979). Afterwards, in the early 1980's, Paul worked in Norway at the non-profit foundation Norwegian Computing Centre, specializing in applied statistics, and accepted a position as Associate Professor in Chemometrics at the Department of Chemistry of Umeå University (Sweden), generating his most cited publication, the tutorial &lt;i&gt;Principal Component Analysis&lt;/i&gt; (Wold, Esbensen &amp; Geladi, 1987). Paul also worked as a visiting Professor at the Department of Chemistry, University of Washington, Seattle, where he wrote his second most cited publication, &lt;i&gt;Partial least-squares regression: a tutorial&lt;/i&gt; (Geladi &amp; Kowalski, 1986). In addition, he also held a position as Associate Professor in Chemometrics and Near Infrared Spectroscopy at the University of Vaasa (Finland) since 2003.&lt;/p&gt;&lt;p&gt;In 2007, Paul was appointed Professor of Chemometrics at the Swedish University of Agricultural Sciences (SLU, Umeå, Sweden), which would be his main institution until his retirement in 2016, when he would become Emeritus Professor at SLU. During the active years, Paul was awarded the title of &lt;i&gt;Honorary Doctor of Technology&lt;/i&gt; by the University of Vaasa (Finland, 2011) in recognition of his esteemed scholarship on Near Infrared Spectroscopy and the international impact of his work. Paul was also External Professor at the Department of Food Science of Stellenbosch University (South Africa) between 2011 and 2014. His work and publications on NIR spectroscopy, multivariate data analysis, hyperspectral imaging, chemometric method development, and their applications in a variety of fields, had a tremendous impact in the scientific community, yielding to numerous invitations to present his work in international conferences and meetings.&lt;/p&gt;&lt;p&gt;His outstanding work related to chemometrics, multivariate c","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3614","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665736","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":"Deep Information Retention Network-Enabled Data Modeling for Key Quality Indicator Prediction in the Chemical Industry","authors":"Jiang Luo,&nbsp;Yalin Wang,&nbsp;Chenliang Liu,&nbsp;Xiaofeng Yuan,&nbsp;Kai Wang","doi":"10.1002/cem.3605","DOIUrl":"https://doi.org/10.1002/cem.3605","url":null,"abstract":"<div>\u0000 \u0000 <p>Deep learning has attracted widespread attention in data modeling and key quality indicator prediction in the chemical industry. However, traditional deep learning networks usually distort the original data distribution due to the superposition effect of multiple layers of nonlinear activation functions. In this case, multivariate statistical learning techniques present an avenue to reveal the intrinsic relationship of the data by combining the linear trends between input and predictor variables. To comprehensively capture data features from multiple perspectives, this study proposes a deep learning-based data modeling network called the information retention unit (IRU). This network combines intrinsic attributes to partial least squares (PLS) and autoencoder (AE) modalities, thus engendering an adaptive response to the complex linear and nonlinear data features. Furthermore, multiple IRUs can be stacked to construct a deep information retention network (DIRN), which enhances the robust extraction of deep data features. Finally, the effectiveness of the proposed network is validated through its prediction application on a dataset obtained from a real-world chemical industrial process. This method combines multivariate statistical learning techniques based on deep learning, providing an innovative and practical solution for data analysis and prediction in the chemical industry.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861311","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":"Next-Gen Crop Monitoring: MTEG-RTU Algorithm and UAV Synergy for Precise Disease Diagnosis","authors":"Hemalatha S,&nbsp;Jai Jaganath Babu Jayachandran","doi":"10.1002/cem.3603","DOIUrl":"https://doi.org/10.1002/cem.3603","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapidly changing climatic scenarios are highly favorable for the rising diseases that lead to increasing threats to food production and supply. Various scholars and scientists make long steps to hasten the process of making innovations in farming for managing these issues. In this context, UAV is applied for the purpose of managing and monitoring plant health. The abiotic stresses available in plant diagnosis through traditional strategies are highly labor-intensive and unfit for large-scale deployment. Conversely, UAVs designed with mobile sensors, multispectral, radar, and so on make them flexible, affordable, and more effective. Thus, this study proposes a novel meta ensemble transfer extreme gradient-based random tactical unit (MTEG-RTU) algorithm for diagnosing crop illnesses precisely. The proposed MTEG-RTU methodology entails three methods such as transfer learning, adaptive boost, and meta-ensemble, and the hyper parameters are tuned using random tactical unit algorithm. Healthier and disordered crop images gained from the crop disease dataset comprise 8000 images and are preprocessed. The more optimal features from the preprocessed images are learned through the ResNet method, and these features enter into the classification phase. Random tactical unit algorithm enhanced the performance by optimizing the hyperparameters of MTEG classifier. The experimental results conducted based on the various assessment components and validation dataset indicate that the developed method outperformed the other chosen models, achieving precision, recall, and accuracy of 98.5%, 97.9%, and 98.6%, respectively. The other achievements made by the model are offering technical guidance for conducting the precise diagnosis and treatment of plant pathologies with less time of 9 s.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860357","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":"Omega-3 Marine Fatty Acid Supplementation to Healthy Subjects Interacts With Moderate Physical Activity to Provide a Cardiovascular Healthier Lipoprotein Subclass Profile","authors":"Olav M. Kvalheim,&nbsp;Tarja Rajalahti","doi":"10.1002/cem.3604","DOIUrl":"https://doi.org/10.1002/cem.3604","url":null,"abstract":"<p>This work investigates the impact of marine omega-3 and physical activity and their interaction on cardiometabolic health as expressed by the serum lipoprotein profile. Using an experimental design that allows for the possibility of interaction, we performed a 6-week intervention on 44 middle-aged women living in Western Norway. The women were randomly divided into four groups: one control group with no intervention, a second group performing sessions of moderate intensity three times per week, a third group taking daily supplements of omega-3 marine fatty acids, and a fourth group combining the interventions for Groups 2 and 3. The difference in the lipoprotein profiles after the intervention from baseline were assessed for statistical significance by comparing groups 2, 3 and 4 with Group 1 using two-tailed t-test corrected for multiple testing and selectivity ratios calculated from the discriminatory component in validated partial least squares discriminant models. The results from the univariate and multivariate analyses were qualitatively equivalent: Only the women combining moderate physical activity and omega-3 supplementation, revealed statistically significant differences in their lipoprotein profile compared to the nonintervention control group. The pattern of change in the lipoprotein profile is associated with improved cardiometabolic health. Use of the design matrix to predict this pattern revealed that the interaction between omega-3 supplementation and physical activity played a major role in inducing this change. The recognition of the influence of this interaction may be a step towards resolving the long-lasting debate of the role played by omega-3 for preventing cardiovascular unhealth.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860301","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":"Industrial Process Fault Detection Based on IGA-Combinatorial Model Decision Mechanism","authors":"Shujuan Wei,&nbsp;Yongsheng Qi,&nbsp;Liqiang Liu,&nbsp;Yongting Li,&nbsp;Xuejin Gao","doi":"10.1002/cem.3602","DOIUrl":"https://doi.org/10.1002/cem.3602","url":null,"abstract":"<div>\u0000 \u0000 <p>To address the challenges of extracting features from complex industrial process data, the reliance of numerous fault detection methodologies on presupposed data distribution types, and the limited generalization capacity of fault detection, this manuscript introduces a sophisticated algorithm for industrial process fault detection. This algorithm harnesses the information gain adaptive (IGA) technique for feature selection and a synergistic model decision mechanism. Initially, the process involves the computation of information gain via decision trees, coupled with the determination of the \u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow>\u0000 <annotation>$$ k $$</annotation>\u0000 </semantics></math> value through cross-validation. This strategy enables the adaptive selection of features, thereby facilitating data dimensionality reduction and effective feature extraction. The subsequent phase introduces a ternary statistical measure monitoring group for the detection of linear faults, while autoencoders and one-class SVM methodologies are applied for the monitoring of nonlinear faults. The culmination of this approach is the development of an innovative weighted decision mechanism, designed to amalgamate the findings from both linear and nonlinear detection avenues, yielding more dependable detection results. The validation of this algorithm employs datasets from the water chillers process and Tennessee Eastman (TE) process, demonstrating the IGA-combined model's superior performance over isolated linear or nonlinear detection algorithms in terms of detection accuracy and robustness. Notably, the efficacy of this method is not contingent upon specific assumptions regarding data distribution, rendering it a versatile and efficacious tool for the fault detection in industrial processes.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860415","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":"Spatial–Temporal Deviation Analysis for Multivariate Statistical Process Monitoring","authors":"Meng Wang,&nbsp;Chudong Tong,&nbsp;Feng Xu,&nbsp;Lijia Luo","doi":"10.1002/cem.3611","DOIUrl":"https://doi.org/10.1002/cem.3611","url":null,"abstract":"<div>\u0000 \u0000 <p>Given that an effective process monitoring implementation should take both the spatial and temporal variations into account, a novel online process monitoring scheme based on a newly formulated algorithm titled as spatial–temporal deviation analysis (STDA) is proposed. Different from the mainstream process monitoring methods that focus on characterizing the spatial and/or temporal variation in the historical normal samples, the proposed STDA algorithm is designed to adaptively and timely train a pair of projecting vectors to uncover potential deviation in the spatial–temporal variation of online monitored samples, so as to guarantee consistently enhanced monitoring performance. Instead of utilizing a fixed projecting framework trained offline, the STDA algorithm is repeatedly executed once a newly measured sample become available for online monitoring. Therefore, the proposed STDA-based method could consistently ensure its effectiveness for online fault detection, because a projecting framework targeted to revealing deviation in spatial–temporal variation is dynamically determined for different online monitoring samples in a timely manner. Finally, the salient monitoring performance achieved by the proposed STDA-based approach is evaluated through comparisons with other counterparts.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859960","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 Simulation Study of the Effects of Additive, Multiplicative, Correlated, and Uncorrelated Errors on Principal Component Analysis","authors":"Edoardo Saccenti,&nbsp;Marieke E. Timmerman,&nbsp;José Camacho","doi":"10.1002/cem.3595","DOIUrl":"https://doi.org/10.1002/cem.3595","url":null,"abstract":"<p>Measurement errors are ubiquitous in all experimental sciences. Depending on the particular experimental platform used to acquire data, different types of errors are introduced, amounting to an admixture of additive and multiplicative error components that can be uncorrelated or correlated. In this paper, we investigate the effect of different types of experimental error on the recovery of the subspace with principal component analysis (PCA) using numerical simulations. Specifically, we assessed how different error characteristics (variance, correlation, and correlation structure), loading structures, and data distributions influence the accuracy to estimate an error-free (true) subspace from sampled data with PCA. Quality was assessed in terms of the mean squared reconstruction error and the congruence to the error-free loadings, using the pseudorank and adjusting for rotational ambiguity. Analysis of variance reveals that the error variance, error correlation structure, and their interaction with the loading structure are the factors mostly affecting quality of loading estimation from sampled data. We advocate for the need to characterize and assess the nature of measurement error and the need to adapt formulations of PCA that can explicitly take into account error structures in the model fitting.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3595","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859945","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}