{"title":"Past, Present and Future of Research in Analytical Figures of Merit","authors":"Alejandro Olivieri","doi":"10.1002/cem.3616","DOIUrl":"https://doi.org/10.1002/cem.3616","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3616","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665740","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}
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.
{"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}
Beatriz Galindo-Prieto, Johan Linderholm, Hans Grahn
<p>Prof. Paul Geladi was born the 30<sup>th</sup> of June of 1951 in Schoten (Belgium) and passed away peacefully on the 18<sup>th</sup> of May of 2024 in Umeå (Sweden).</p><p>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 >29,000 citations) that shows the extent and vigour of Paul, both in life and work.</p><p>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.</p><p>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 <i>Principal Component Analysis</i> (Wold, Esbensen & 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, <i>Partial least-squares regression: a tutorial</i> (Geladi & 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.</p><p>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 <i>Honorary Doctor of Technology</i> 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.</p><p>His outstanding work related to chemometrics, multivariate c
{"title":"Paul Geladi (1951–2024) Chemometrician, spectroscopist and pioneer","authors":"Beatriz Galindo-Prieto, Johan Linderholm, Hans Grahn","doi":"10.1002/cem.3614","DOIUrl":"https://doi.org/10.1002/cem.3614","url":null,"abstract":"<p>Prof. Paul Geladi was born the 30<sup>th</sup> of June of 1951 in Schoten (Belgium) and passed away peacefully on the 18<sup>th</sup> of May of 2024 in Umeå (Sweden).</p><p>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 >29,000 citations) that shows the extent and vigour of Paul, both in life and work.</p><p>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.</p><p>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 <i>Principal Component Analysis</i> (Wold, Esbensen & 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, <i>Partial least-squares regression: a tutorial</i> (Geladi & 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.</p><p>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 <i>Honorary Doctor of Technology</i> 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.</p><p>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}
Helene Fog Froriep Halberg, Marta Bevilacqua, Åsmund Rinnan
Fluorescence spectroscopy has been applied for analysis of complex samples, such as food and beverages. Parallel factor analysis (PARAFAC) is a well‐known decomposition method for fluorescence excitation–emission matrices (EEMs). When the complexity of the system increases, it becomes considerably more difficult to determine the optimal number of PARAFAC components, especially when the fluorophores of the system are unknown. The two commonly applied diagnostics, core consistency and split‐half analysis, appear to underestimate the model complexity due to covarying components and local minima, respectively. As a more robust alternative, we propose a resampling approach with multiple initializations and submodel comparisons for estimating the optimal number of PARAFAC components in complex data.
{"title":"Resampling as a Robust Measure of Model Complexity in PARAFAC Models","authors":"Helene Fog Froriep Halberg, Marta Bevilacqua, Åsmund Rinnan","doi":"10.1002/cem.3601","DOIUrl":"https://doi.org/10.1002/cem.3601","url":null,"abstract":"Fluorescence spectroscopy has been applied for analysis of complex samples, such as food and beverages. Parallel factor analysis (PARAFAC) is a well‐known decomposition method for fluorescence excitation–emission matrices (EEMs). When the complexity of the system increases, it becomes considerably more difficult to determine the optimal number of PARAFAC components, especially when the fluorophores of the system are unknown. The two commonly applied diagnostics, core consistency and split‐half analysis, appear to underestimate the model complexity due to covarying components and local minima, respectively. As a more robust alternative, we propose a resampling approach with multiple initializations and submodel comparisons for estimating the optimal number of PARAFAC components in complex data.","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"21 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209893","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}
Irene Mariñas‐Collado, Juan M. Rodríguez‐Díaz, M. Teresa Santos‐Martín
This study addresses the complex dynamics of alcohol elimination in the human body, very important in forensic and healthcare areas. Existing models often oversimplify with the assumption of linear elimination kinetics, limiting practical application. This study presents a novel non‐linear model for estimating blood alcohol concentration after multiple intakes. Initially developed for two different alcohol incorporations, it can be straightforwardly extended to the case of more intakes. Emphasising the significance of accurate parameter estimation, the research underscores the importance of precise experimental design, utilising optimal experimental design (OED) methodologies. Sensitivity analysis of model coefficients and the determination of D‐optimal designs, considering correlation structures among observations, reveal a strong linear relationship between support points. This relationship can be used to obtain nearly optimal designs that are highly efficient and much easier to compute.
{"title":"A Non‐Linear Model for Multiple Alcohol Intakes and Optimal Designs Strategies","authors":"Irene Mariñas‐Collado, Juan M. Rodríguez‐Díaz, M. Teresa Santos‐Martín","doi":"10.1002/cem.3599","DOIUrl":"https://doi.org/10.1002/cem.3599","url":null,"abstract":"This study addresses the complex dynamics of alcohol elimination in the human body, very important in forensic and healthcare areas. Existing models often oversimplify with the assumption of linear elimination kinetics, limiting practical application. This study presents a novel non‐linear model for estimating blood alcohol concentration after multiple intakes. Initially developed for two different alcohol incorporations, it can be straightforwardly extended to the case of more intakes. Emphasising the significance of accurate parameter estimation, the research underscores the importance of precise experimental design, utilising optimal experimental design (OED) methodologies. Sensitivity analysis of model coefficients and the determination of D‐optimal designs, considering correlation structures among observations, reveal a strong linear relationship between support points. This relationship can be used to obtain nearly optimal designs that are highly efficient and much easier to compute.","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"55 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209898","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}
In this paper, we revisit the power curves in ANOVA simultaneous component analysis (ASCA) based on permutation testing and introduce the population curves derived from population parameters describing the relative effect among factors and interactions. The relative effect has important practical implications: The statistical power of a given factor depends on the design of other factors in the experiment and not only of the sample size. Thus, understanding the relative power in a specific experimental design can be extremely useful to maximize our capability of success when planning the experiment. In the paper, we derive relative and absolute population curves, where the former represent statistical power in terms of the normalized effect size between structure and noise, and the latter in terms of the sample size. Both types of population curves allow us to make decisions regarding the number and nature (fixed/random) of factors, their relationships (crossed/nested), and the number of levels and replicates, among others, in an multivariate experimental design (e.g., an omics study) during the planning phase of the experiment. We illustrate both types of curves through simulation.
{"title":"Population Power Curves in ASCA With Permutation Testing","authors":"José Camacho, Michael Sorochan Armstrong","doi":"10.1002/cem.3596","DOIUrl":"https://doi.org/10.1002/cem.3596","url":null,"abstract":"In this paper, we revisit the power curves in ANOVA simultaneous component analysis (ASCA) based on permutation testing and introduce the population curves derived from population parameters describing the relative effect among factors and interactions. The relative effect has important practical implications: The statistical power of a given factor depends on the design of other factors in the experiment and not only of the sample size. Thus, understanding the relative power in a specific experimental design can be extremely useful to maximize our capability of success when planning the experiment. In the paper, we derive relative and absolute population curves, where the former represent statistical power in terms of the normalized effect size between structure and noise, and the latter in terms of the sample size. Both types of population curves allow us to make decisions regarding the number and nature (fixed/random) of factors, their relationships (crossed/nested), and the number of levels and replicates, among others, in an multivariate experimental design (e.g., an omics study) during the planning phase of the experiment. We illustrate both types of curves through simulation.","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"58 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209894","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}
Here, we demonstrate mid‐field 1H NMR spectroscopy combined with chemometrics to be powerful in the classification and authentication of motor oils (MOs). The 1H NMR data were processed with a new algorithm for simultaneous phase and baseline correction, which, for crowded spectra such as those of the refinery products, allowed for more accurate estimation of phase parameters than other literature approaches tested. A principal component analysis (PCA) model based on the unbinned CH3 fingerprint region (0.6–1.0 ppm) enabled the differentiation of hydrocracked and poly‐α‐olefin‐based MOs and was effective in resolving mixtures of these base stocks with conventional base oils. PCA analysis of the 1.0‐ to 1.14‐ppm region enabled the detection of poly (isobutylene) additive and was useful for differentiating between single‐grade and multigrade MOs. Non‐equidistantly binned 1H NMR data were used to detect the addition of esters and to establish discriminant models for classifying MOs by viscosity grade and by major categories of synthetic, semisynthetic, and mineral oils. The performances of four classifiers (linear discriminant analysis [LDA], quadratic discriminant analysis [QDA], naïve Bayes classifier [NBC], and support vector machine [SVM]) with and without PCA dimensionality reduction were compared. In both tasks, SVM showed the best efficiency, with average error rates of ~2.3% and 8.15% for predicting major MO categories and viscosity grades, respectively. The potential to merge spectra collected from different NMR instruments is discussed for models based on spectral binning. It is also shown that small errors in phase parameters are not detrimental to binning‐based PCA models.
{"title":"Chemometric Classification of Motor Oils Using 1H NMR Spectroscopy With Simultaneous Phase and Baseline Optimization","authors":"A. Olejniczak, J. P. Łukaszewicz","doi":"10.1002/cem.3598","DOIUrl":"https://doi.org/10.1002/cem.3598","url":null,"abstract":"Here, we demonstrate mid‐field <jats:sup>1</jats:sup>H NMR spectroscopy combined with chemometrics to be powerful in the classification and authentication of motor oils (MOs). The <jats:sup>1</jats:sup>H NMR data were processed with a new algorithm for simultaneous phase and baseline correction, which, for crowded spectra such as those of the refinery products, allowed for more accurate estimation of phase parameters than other literature approaches tested. A principal component analysis (PCA) model based on the unbinned CH<jats:sub>3</jats:sub> fingerprint region (0.6–1.0 ppm) enabled the differentiation of hydrocracked and poly‐α‐olefin‐based MOs and was effective in resolving mixtures of these base stocks with conventional base oils. PCA analysis of the 1.0‐ to 1.14‐ppm region enabled the detection of poly (isobutylene) additive and was useful for differentiating between single‐grade and multigrade MOs. Non‐equidistantly binned <jats:sup>1</jats:sup>H NMR data were used to detect the addition of esters and to establish discriminant models for classifying MOs by viscosity grade and by major categories of synthetic, semisynthetic, and mineral oils. The performances of four classifiers (linear discriminant analysis [LDA], quadratic discriminant analysis [QDA], naïve Bayes classifier [NBC], and support vector machine [SVM]) with and without PCA dimensionality reduction were compared. In both tasks, SVM showed the best efficiency, with average error rates of ~2.3% and 8.15% for predicting major MO categories and viscosity grades, respectively. The potential to merge spectra collected from different NMR instruments is discussed for models based on spectral binning. It is also shown that small errors in phase parameters are not detrimental to binning‐based PCA models.","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"3 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209895","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}
Many data analysis methods actually combine optimization of several criteria. In this paper, a framework is offered for categorizing such multi-criteria methods. In particular, it categorizes multiset and three-way analysis methods as well as penalized methods and combinations thereof. The framework aims to stimulate critical evaluation of methods and reflection on the purpose of methods and, by signaling gaps, to help the development of new data analysis methods.
{"title":"Some Views on Multi-criteria Methods for Data Analysis","authors":"Henk A. L. Kiers, Marieke E. Timmerman","doi":"10.1002/cem.3597","DOIUrl":"10.1002/cem.3597","url":null,"abstract":"<p>Many data analysis methods actually combine optimization of several criteria. In this paper, a framework is offered for categorizing such multi-criteria methods. In particular, it categorizes multiset and three-way analysis methods as well as penalized methods and combinations thereof. The framework aims to stimulate critical evaluation of methods and reflection on the purpose of methods and, by signaling gaps, to help the development of new data analysis methods.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3597","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209923","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}
Diabetes is a common and serious global disease that damages blood vessels in the eye, leading to vision loss. Early and accurate diagnosis of this issue is crucial to reduce the risk of visual impairment. The typical deep learning (DL) methods for diabetic retinopathy (DR) grading are often time-consuming, resulting in unsatisfactory detection performance due to inadequate representation of lesion features. To overcome these challenges, this research proposes a new automated mechanism for detecting and classifying DR, aiming to identify DR severities and different stages. To figure out and capture feature characteristics from DR samples, a conjugated attention mechanism and vision transformer are utilized within a collective net model, which automatically generates feature maps for diagnosing DR. These extracted feature maps are then fused through the feature fusion function in a fused attention net model, calculating attention weights to produce the most powerful feature map. Finally, the DR cases are identified and discriminated using the kernel extreme learning machine (KELM) model. For evaluating DR severity, our work utilizes four different benchmark datasets: APTOS 2019, MESSIDOR-2 dataset, DiaRetDB1 V2.1, and DIARETDB0 datasets. To illuminate data noise and unwanted variations, two preprocessing steps are carried out, which include contrast enhancement and illumination correction. The experimental results evaluated using well-known indicators demonstrate that the suggested method achieves a higher accuracy of 99.63% compared to other baseline methods. This research contributes to the development of powerful DR screening techniques that are less time-consuming and capable of automatically identifying DR severity levels at a premature level.
糖尿病是一种常见的全球性严重疾病，它会损害眼部血管，导致视力下降。对这一问题进行早期准确诊断对于降低视力损伤风险至关重要。用于糖尿病视网膜病变（DR）分级的典型深度学习（DL）方法往往耗时较长，而且由于病变特征的表征不充分，导致检测性能不尽如人意。为了克服这些挑战，本研究提出了一种新的自动检测和分级 DR 的机制，旨在识别 DR 的严重程度和不同阶段。为了找出并捕捉 DR 样本的特征，在一个集合网模型中利用了共轭注意力机制和视觉转换器，自动生成用于诊断 DR 的特征图。然后，通过融合注意力网络模型中的特征融合功能将这些提取的特征图进行融合，计算注意力权重以生成最强大的特征图。最后，使用核极端学习机（KELM）模型识别和区分 DR 病例。为了评估 DR 的严重程度，我们的工作使用了四个不同的基准数据集：APTOS 2019、MESSIDOR-2 数据集、DiaRetDB1 V2.1 和 DIARETDB0 数据集。为了消除数据噪声和不必要的变化，进行了两个预处理步骤，包括对比度增强和光照校正。使用知名指标评估的实验结果表明，与其他基线方法相比，建议的方法达到了 99.63% 的较高准确率。这项研究有助于开发功能强大的 DR 筛选技术，这种技术耗时少，能够自动识别过早出现的 DR 严重程度。
{"title":"A Novel Automated System for Early Diabetic Retinopathy Detection and Severity Classification","authors":"Santoshkumar S Ainapur, Virupakshappa Patil","doi":"10.1002/cem.3593","DOIUrl":"https://doi.org/10.1002/cem.3593","url":null,"abstract":"<div>\u0000 \u0000 <p>Diabetes is a common and serious global disease that damages blood vessels in the eye, leading to vision loss. Early and accurate diagnosis of this issue is crucial to reduce the risk of visual impairment. The typical deep learning (DL) methods for diabetic retinopathy (DR) grading are often time-consuming, resulting in unsatisfactory detection performance due to inadequate representation of lesion features. To overcome these challenges, this research proposes a new automated mechanism for detecting and classifying DR, aiming to identify DR severities and different stages. To figure out and capture feature characteristics from DR samples, a conjugated attention mechanism and vision transformer are utilized within a collective net model, which automatically generates feature maps for diagnosing DR. These extracted feature maps are then fused through the feature fusion function in a fused attention net model, calculating attention weights to produce the most powerful feature map. Finally, the DR cases are identified and discriminated using the kernel extreme learning machine (KELM) model. For evaluating DR severity, our work utilizes four different benchmark datasets: APTOS 2019, MESSIDOR-2 dataset, DiaRetDB1 V2.1, and DIARETDB0 datasets. To illuminate data noise and unwanted variations, two preprocessing steps are carried out, which include contrast enhancement and illumination correction. The experimental results evaluated using well-known indicators demonstrate that the suggested method achieves a higher accuracy of 99.63% compared to other baseline methods. This research contributes to the development of powerful DR screening techniques that are less time-consuming and capable of automatically identifying DR severity levels at a premature level.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666139","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}
Klaus Neymeyr, Martina Beese, Hamid Abdollahi, Mathias Sawall
In MCR analyses, the similarity of pairs of spectra or concentration profiles can be measured in terms of the acute angle that is enclosed by the representing vectors. Acute angles between vectors can be generalized to pairs of subspaces. So-called canonical angles, also called principal angles, measure the mutual orientation of a pair of subspaces. This work discusses how angles and canonical angles can support multivariate curve resolution analyses. A canonical angle analysis (CAA) can help to detect changes of the chemical composition during a chemical reaction in a way comparable, but different to the evolving factor analysis (EFA).
{"title":"Can Angle Measures Be Useful in MCR Analyses?","authors":"Klaus Neymeyr, Martina Beese, Hamid Abdollahi, Mathias Sawall","doi":"10.1002/cem.3582","DOIUrl":"10.1002/cem.3582","url":null,"abstract":"<p>In MCR analyses, the similarity of pairs of spectra or concentration profiles can be measured in terms of the acute angle that is enclosed by the representing vectors. Acute angles between vectors can be generalized to pairs of subspaces. So-called canonical angles, also called principal angles, measure the mutual orientation of a pair of subspaces. This work discusses how angles and canonical angles can support multivariate curve resolution analyses. A canonical angle analysis (CAA) can help to detect changes of the chemical composition during a chemical reaction in a way comparable, but different to the evolving factor analysis (EFA).</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209896","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}
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