Journal of Chemometrics最新文献

{"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}

{"title":"Topics in Chemometrics TIC 2023 in Rostock, Germany","authors":"Klaus Neymeyr,&nbsp;Mathias Sawall","doi":"10.1002/cem.3612","DOIUrl":"https://doi.org/10.1002/cem.3612","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862352","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":"Evaluation of Classical Least Squares Discriminant Analysis (CLS-DA) as a Novel Supervised Pattern Recognition Technique","authors":"Somaye Vali Zade,&nbsp;Hamid Abdollahi","doi":"10.1002/cem.3609","DOIUrl":"https://doi.org/10.1002/cem.3609","url":null,"abstract":"<div>\u0000 \u0000 <p>Multivariate calibration techniques and machine learning algorithms are inextricably linked within the realm of chemometrics and data analysis. Classical least squares (CLS) modeling, a fundamental multivariate regression approach, has traditionally been utilized for quantitative analysis tasks, establishing relationships between predictor variables (e.g., spectroscopic data) and response variables (e.g., chemical concentrations). However, a unique feature of CLS is its ability to handle scenarios with partial knowledge of the independent variable matrix, making it an intriguing candidate for qualitative pattern recognition and discriminant analysis applications. This study proposes a novel approach, Classical Least Squares Discriminant Analysis (CLS-DA), which combines the principles of CLS modeling with discriminant analysis objectives. The performance of CLS-DA is comprehensively evaluated using two real-world datasets: chemical analysis of three wine cultivars and mid-infrared spectroscopy of minced meat samples (pork, chicken, and turkey). The results are compared against the well-established Partial Least Squares Discriminant Analysis (PLS-DA) method, a widely adopted technique for classification tasks in chemometrics. For both sets of experimental data, CLS-DA and PLS-DA showed comparable efficiency. For the classification of three types of wine, the accuracy of the proposed method was 94.3%, while the accuracy of the reference method was 98.1%. For the classification of minced meat samples, the accuracies of CLS-DA and PLS-DA were 97.2% and 94%, respectively for all three groups. The findings demonstrate the potential of CLS-DA as a straightforward and interpretable supervised pattern recognition technique, exhibiting comparable classification performance to PLS-DA. The study highlights the advantages of CLS-DA, including its ability to operate within the original data space and its flexibility in accommodating partial knowledge scenarios. The proposed CLS-DA approach presents a promising alternative for discriminant analysis, offering new perspectives on the applications of classical least squares modeling in chemometrics.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862344","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":"Near-Infrared Spectroscopy and Aquaphotomics in Cancer Research: A Pilot Study","authors":"Anastasiia Surkova,&nbsp;Ekaterina Boichenko,&nbsp;Olga Bibikova,&nbsp;Viacheslav Artyushenko,&nbsp;Jelena Muncan,&nbsp;Roumiana Tsenkova","doi":"10.1002/cem.3600","DOIUrl":"https://doi.org/10.1002/cem.3600","url":null,"abstract":"<div>\u0000 \u0000 <p>Currently, the majority of methods to monitor cancer treatment through the analysis of body fluids are based on a highly selective detection of single molecules or cells. In this study, we are considering the analysis of the aqueous medium of liquid samples, that is, water, itself, using aquaphotomics and near-infrared spectroscopy (NIR) for spectral data acquisition and processing, within cancer research. Water, as a molecular system, is a rich source of information about the current state of a patient, which can be extracted from near-infrared spectra of liquid samples via simple algorithms based on multivariate data analysis. The reported results, obtained ex vivo of body fluids, demonstrate the potential of aquaphotomics in cancer research.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862257","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":"Resampling as a Robust Measure of Model Complexity in PARAFAC Models","authors":"Helene Fog Froriep Halberg,&nbsp;Marta Bevilacqua,&nbsp;Åsmund Rinnan","doi":"10.1002/cem.3601","DOIUrl":"10.1002/cem.3601","url":null,"abstract":"<p>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.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3601","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Non-Linear Model for Multiple Alcohol Intakes and Optimal Designs Strategies","authors":"Irene Mariñas-Collado,&nbsp;Juan M. Rodríguez-Díaz,&nbsp;M. Teresa Santos-Martín","doi":"10.1002/cem.3599","DOIUrl":"10.1002/cem.3599","url":null,"abstract":"<p>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.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3599","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}