Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"On-the-fly spectral unmixing based on Kalman filtering","authors":"Hugues Kouakou ,&nbsp;José Henrique de Morais Goulart ,&nbsp;Raffaele Vitale ,&nbsp;Thomas Oberlin ,&nbsp;David Rousseau ,&nbsp;Cyril Ruckebusch ,&nbsp;Nicolas Dobigeon","doi":"10.1016/j.chemolab.2024.105252","DOIUrl":"10.1016/j.chemolab.2024.105252","url":null,"abstract":"<div><div>This work introduces an on-the-fly (i.e., online) linear spectral unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the conventional linear mixing model, the proposed approach recasts the linear unmixing problem into a linear state-space estimation framework. Under Gaussian noise and state models, the estimation of the pure spectra can be efficiently conducted by resorting to Kalman filtering. Interestingly, it is shown that this Kalman filter can operate in a lower-dimensional subspace to lighten the computational burden of the overall unmixing procedure. Experimental results obtained on synthetic and real Raman data sets show that this Kalman filter-based method offers a convenient trade-off between unmixing accuracy and computational efficiency, which is crucial for operating in an on-the-fly setting. The proposed method constitutes a valuable building block for benefiting from acquisition and processing frameworks recently proposed in the microscopy literature, which are motivated by practical issues such as reducing acquisition time and avoiding potential damages being inflicted to photosensitive samples. The code associated with the numerical illustrations reported in this paper is freely available online at <span><span>https://github.com/HKouakou/KF-OSU</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105252"},"PeriodicalIF":3.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regression analysis with spatially-varying coefficients using generalized additive models (GAMs)","authors":"Francisco de Asis López ,&nbsp;Javier Roca-Pardiñas ,&nbsp;Celestino Ordóñez","doi":"10.1016/j.chemolab.2024.105254","DOIUrl":"10.1016/j.chemolab.2024.105254","url":null,"abstract":"<div><div>Regression models for spatial data have attracted the attention of researchers from different fields given their widespread application. In this work we analyze the utility of generalized additive models (GAMs) as regression methods with spatially-dependent coefficients. Particularly, three different aspects of the regression analysis were addressed: model definition and estimation, testing spatial heterogeneity, and variable selection. Spatial heterogeneity was addressed through bootstrapping, while and algorithm using the Bayesian Information Criterion (BIC) was implemented for variable selection to reduce computation time. In addition, this study makes a comparison of GAMs with two of the most common methods for regression with spatially-varying coefficients: Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR), using both synthetic and real data.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105254"},"PeriodicalIF":3.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of metrological correlation on the total combined risk in pharmaceutical equivalence evaluations","authors":"Maria Luiza de Godoy Bertanha,&nbsp;Felipe Rebello Lourenço","doi":"10.1016/j.chemolab.2024.105267","DOIUrl":"10.1016/j.chemolab.2024.105267","url":null,"abstract":"<div><div>Pharmaceutical equivalence evaluation requires a multiparametric conformity assessment for both generic and reference medicines. This paper investigates the impact of metrological correlations on the total combined risk in pharmaceutical equivalence evaluations. The study focused on the equivalence between ranitidine hydrochloride tablets, assessed by determining the average weight, the assay of the active pharmaceutical ingredient, and the uniformity of dosage units. The risks of false conformity decisions were evaluated using Monte Carlo method simulations across four scenarios, each reflecting different correlation conditions. The results of the study focus on evaluating pharmaceutical equivalence between ranitidine hydrochloride tablets from two manufacturers. The tablets were tested for three parameters: average weight, active pharmaceutical ingredient (API) assay, and uniformity of dosage units. The measured values were within the regulatory specifications for both medicines A and B. Four scenarios of metrological correlation were assessed: #1 – actual correlation from shared analytical steps, #2 – correlation between parameters within the same medicine, #3 – correlation between generic and reference medicines, and #4 – uncorrelated parameters. The study revealed that correlations significantly affect total and combined risk values. The correlations between different parameters of the same medicine affect the total risk values, while the correlations between generic and reference medicines for a given parameter influence the combined particular risk values. Correlations between parameters of the same medicine affect total risk values, while correlations between generic and reference medicines impact combined particular risk values. Both types of correlations significantly influence combined total risk values, making metrological correlations crucial in pharmaceutical equivalence evaluations. Proper consideration of these correlations ensures the quality, efficacy, and safety of generic and reference medicines.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105267"},"PeriodicalIF":3.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive soft-sensor update by Latest Sample Targeting Frustratingly Easy Domain Adaptation","authors":"Kaito Katayama ,&nbsp;Kazuki Yamamoto ,&nbsp;Koichi Fujiwara","doi":"10.1016/j.chemolab.2024.105246","DOIUrl":"10.1016/j.chemolab.2024.105246","url":null,"abstract":"<div><div>Soft-sensors are widely used in manufacturing processes to estimate key process variables; however, their performance may deteriorate when process characteristics change. Although Just-In-Time (JIT) modeling techniques have been proposed for adaptive soft-sensor design, they do not always adapt to abrupt changes. Transfer learning (TL) has been suggested as a means to address this issue, with Frustratingly Easy Domain Adaptation (FEDA) being used for soft-sensor design. This study proposes a new TL method called Latest Sample Targeting-FEDA (LST-FEDA) for JIT-based soft-sensor, which can handle both sudden and gradual changes in process characteristics. LST-FEDA updates soft-sensors using a fixed number of latest samples whenever a new sample is obtained. The effectiveness of the proposed method was demonstrated using simulation data from a vinyl acetate monomer (VAM) process and actual operation data from a fluorine-based monomer (FM) process. LST-FEDA accurately estimated objective variables during sudden malfunctions and scheduled maintenance, contributing to efficient and safe process operation.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105246"},"PeriodicalIF":3.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of soil organic carbon content using visible and near-infrared spectroscopy in the Red River Delta, Vietnam","authors":"Nguyen-Xuan Hau ,&nbsp;Nguyen-Thanh Tuan ,&nbsp;Lai-Quang Trung,&nbsp;Tran-Thuy Chi","doi":"10.1016/j.chemolab.2024.105253","DOIUrl":"10.1016/j.chemolab.2024.105253","url":null,"abstract":"<div><div>Accurate estimation of Soil Organic Carbon (SOC) is vital for assessing soil fertility, health, and carbon sequestration. Visible and Near-Infrared (Vis-NIR) spectroscopy has gained popularity worldwide for SOC estimation due to its cost-effectiveness and environmental benefits. However, inconsistencies arise from varying preprocessing techniques and regression models applied across different datasets and regions. Few studies explore combinations of spectral preprocessing, modeling algorithms, and resampling techniques. This study presents the first SOC estimation using Vis-NIR spectroscopy in the Red River Delta, Vietnam. We assessed estimation performances incorporating fifteen preprocessing techniques, four regression models, and three resampling methods to identify the most effective strategies. Standard Normal Variate (SNV) emerged as the top preprocessing technique, while Partial Least Squares Regression (PLSR) demonstrated the highest accuracy with minimal discrepancies between calibration and validation. Regarding resampling methods, repeated cross-validation (repeatedcv) proved most robust, with simple cross-validation as an alternative. By utilizing SNV, PLSR, and repeatedcv, we achieved the first successful Vis-NIR spectroscopy-based SOC estimation in the Red River Delta and Vietnam. This approach satisfied stringent statistical criteria for predictive models, yielding validation performance metrics of R² = 0.740, RMSE = 0.166, RPD = 2.337, and RPIQ = 2.321. Our findings highlight the importance of optimizing preprocessing, regression, and resampling techniques for accurate Vis-NIR spectroscopy-based SOC prediction.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105253"},"PeriodicalIF":3.7,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chemometrics and electrochemistry joined hands to develop a novel and intelligent electronic device for simultaneous determination of malathion and diazinon in fruit juices: A progress in multidisciplinary studies","authors":"Leila Zare ,&nbsp;Ehsan Sadeghi ,&nbsp;Meghdad Pirsaheb ,&nbsp;Maziar Farshadnia ,&nbsp;Ali R. Jalalvand","doi":"10.1016/j.chemolab.2024.105249","DOIUrl":"10.1016/j.chemolab.2024.105249","url":null,"abstract":"<div><div>In this work, chemometrics and electrochemistry connected to each other to open a new way for assisting food industry specialists based on developing a novel electrochemical sensor for simultaneous determination of malathion (MT) and diazinon (DZ) in the presence of patulin (PT) and citrinin (CT) as uncalibrated interference in fruit juices. The sensor was fabricated based on modification of a glassy carbon electrode (GCE) by chitosan-ionic liquid (Ch-IL), electrodeposition of gold nanoparticles (Au NPs), drop-casting of multiwalled carbon nanotubes-IL (MWCNTs-IL), and electrochemical synthesis of dual templates molecularly imprinted polymers (DTMIPs) in which MT and DZ were used as templates. Effects of experimental variables on structure and response of the sensor were screened and optimized by Min Run screening and central composite design, respectively. After optimization, the third-order hydrodynamic differential pulse voltammetric (HDPV) data were generated based on changing modulation times and modulation amplitudes as instrumental parameters and modeled by N-PLS/RTL, U-PLS/RTL, U-PCA/RTL, APARAFAC, PARAFAC2 and MCR-ALS to select the best one to assist the sensor for ultra selective simultaneous determination of MT and DZ in the presence of PT and CT as uncalibrated interference in fruit samples. The results confirmed the MCR-ALS was the best assistance for DTMIPs/MWCNTs-IL/Au NPs/Ch-IL/GCE for simultaneous determination of MT and DZ in the presence of PT and CT as uncalibrated interference in both synthetic and real samples. Performance of the sensor assisted by MCR-ALS for ultra selective simultaneous determination of MT (0.1 pM–12.5 pM, LOD = 0.01 pM) and DZ (0.25 pM–8.5 pM, LOD = 0.15 pM) was really admirable which was comparable with HPLC with UV detection while it was faster, simpler and low-cost in comparison to HPLC-UV which motivated us to introduce it as a reliable method to assist food industry specialists for quality assurance purposes.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105249"},"PeriodicalIF":3.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temperature correction of near-infrared spectra of raw milk","authors":"Jose A. Diaz-Olivares ,&nbsp;Stef Grauwels ,&nbsp;Xinyue Fu ,&nbsp;Ines Adriaens ,&nbsp;Wouter Saeys ,&nbsp;Ryad Bendoula ,&nbsp;Jean-Michel Roger ,&nbsp;Ben Aernouts","doi":"10.1016/j.chemolab.2024.105251","DOIUrl":"10.1016/j.chemolab.2024.105251","url":null,"abstract":"<div><div>Accurate milk composition analysis is crucial for improving product quality, economic efficiency, and animal health in the dairy industry. Near-infrared (NIR) spectroscopy can quantify milk composition quickly and nondestructively. However, external factors, such as temperature fluctuations, can alter the molecular vibrations and hydrogen bonding in milk, altering the NIR spectra and leading to errors in predicting key constituents such as fat, protein, and lactose. This study compares the effectiveness of Piecewise Direct Standardization (PDS), Continuous PDS (CPDS), External Parameter Orthogonalization (EPO), and Dynamic Orthogonal Projection (DOP in correcting the impact of temperature-induced variations on predictions in milk long-wave NIR spectra (LW-NIR, 1000–1700 nm).</div><div>A total of 270 raw milk samples were analyzed, collecting both reflectance and transmittance spectra at five different temperatures (20 °C, 25 °C, 30 °C, 35 °C, and 40 °C). The experimental setup ensured precise temperature control and accurate spectral measurements. PLSR models were calibrated at 30 °C to predict milk fat, protein, and lactose content. The performance of these models was assessed before and after applying the temperature correction methods, with a primary focus on reflectance spectra.</div><div>Results indicate that EPO and DOP significantly enhance model robustness and prediction accuracy across all temperatures, outperforming PDS and CPDS, especially for lactose prediction. These orthogonalization methods were compared against PLSR models calibrated with spectra from all temperatures. EPO and DOP showed comparable or superior performance, highlighting their effectiveness without requiring extensive temperature-specific calibration data. These findings suggest that orthogonalization methods are particularly suitable for in-line milk quality measurements under farm conditions where temperature control is challenging. This study highlights the potential of advanced chemometric techniques to improve real-time, on-farm milk composition analysis, facilitating better farm management and enhanced dairy product quality.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105251"},"PeriodicalIF":3.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical modeling of ions adsorption from water/wastewater sources via porous materials: A machine learning-based approach","authors":"Guang Yang ,&nbsp;Nadhir N.A. Jafar ,&nbsp;Rafid Jihad Albadr ,&nbsp;Mariem Alwan ,&nbsp;Zainab Sadeq Yousif ,&nbsp;Suhair Mohammad Husein Kamona ,&nbsp;Safaa Mohammed Ibrahim ,&nbsp;Usama S. Altimari ,&nbsp;Ashwaq Talib Kareem ,&nbsp;Raghu Jettie ,&nbsp;Raaid Alubady ,&nbsp;Ahmed Alawadi","doi":"10.1016/j.chemolab.2024.105250","DOIUrl":"10.1016/j.chemolab.2024.105250","url":null,"abstract":"<div><div>This paper developed the predictive modeling of substance concentration (<em>C</em>) utilizing the input parameters <em>x</em> and <em>y</em>, for analysis of adsorption process. Employing three distinct machine learning models—Multilayer Perceptron (MLP), polynomial regression (PR), and Support Vector Machine (SVM)—the study investigates the efficacy of models in capturing the relationships between the inputs and output. The models are trained from data obtained from mass transfer calculations for removal of solute from solution via porous adsorbent. Furthermore, the hyper-parameters for each model are optimized through the utilization of the Political Optimizer (PO). The Multilayer Perceptron model emerges as a standout performer, showcasing an exceptional R-squared score of 0.9981, indicative of a robust fit to the data. Complemented by impressively low MAE and MSE values (7.94043E-01 and 2.0420E+00, respectively), the MLP model attests to its ability to provide accurate predictions and discern underlying patterns within the dataset. The polynomial regression model, while slightly trailing behind the MLP in terms of R-squared score (0.95929), revealed commendable predictive performance. Support Vector Machine also proves to be a formidable contender, boasting a robust R-squared score of 0.96055.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105250"},"PeriodicalIF":3.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a new method by MIR-FTIR and chemometrics for the early diagnosis of leprosy and evaluation of the treatment effect","authors":"Andrea Cristina Novack ,&nbsp;Alexandre de Fátima Cobre ,&nbsp;Dile Pontarolo Stremel ,&nbsp;Luana Mota Ferreira ,&nbsp;Michel Leandro Campos ,&nbsp;Roberto Pontarolo","doi":"10.1016/j.chemolab.2024.105248","DOIUrl":"10.1016/j.chemolab.2024.105248","url":null,"abstract":"<div><h3>Objective</h3><div>Develop a new method for diagnosing leprosy and monitoring the pharmacological treatment effect of patients.</div></div><div><h3>Material and methods</h3><div>Plasma samples from patients diagnosed with leprosy (n = 211) who had not yet received any pharmacological treatment were collected at a basic health unit in Brazil. After treatment, samples were collected from the same patients (n = 125). Plasma samples from healthy volunteers were also collected (n = 179) and used as a control group. All samples were analyzed by Fourier transform mid-infrared spectrophotometry (MIR-FTIR). The spectral data of the samples were subjected to chemometric analysis. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to predict diagnosis and monitor pharmacological treatment.</div></div><div><h3>Results</h3><div>The PCA model successfully distinguished among three sample classes: healthy individuals, pre-treatment leprosy patients, and post-treatment leprosy patients. The PLS-DA algorithm accurately classified healthy, treated, and diseased samples, facilitating both reliable diagnosis and treatment monitoring for leprosy. The model achieved a sensitivity of 97 %–100 %, specificity of 100 %, and accuracy ranging from 99 % to 100 %. Furthermore, when tested on plasma samples from patients with other conditions—renal failure (n = 1032), hypertriglyceridemia (n = 100), hypercholesterolemia (n = 100), and mixed dyslipidemia (n = 100)—the model correctly classified these as negative for leprosy, with diagnostic specificity between 93 % and 96 %.</div></div><div><h3>Conclusion</h3><div>The MIR-FTIR technique combined with PLS-DA analysis proved to be a highly effective tool for screening leprosy patients and monitoring treatment outcomes. Given its low cost, this method could be easily implemented in laboratories across emerging and low-income countries.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"254 ","pages":"Article 105248"},"PeriodicalIF":3.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LTFM: Long-tail few-shot module with loose coupling strategy for mineral spectral identification","authors":"Youpeng Fan ,&nbsp;Yongchun Fang","doi":"10.1016/j.chemolab.2024.105247","DOIUrl":"10.1016/j.chemolab.2024.105247","url":null,"abstract":"<div><div>In recent years, deep learning methods have exhibited superior performance in mineral identification when especially compared with conventional machine learning methods such as Support Vector Machine (SVM) and Partial Least Squares (PLS). Nevertheless, almost all of these deep learning methods pay more attention to improving and designing network structures, while neglecting the phenomenon of long-tail distribution in spectral data due to the inconsistency of ore distribution and the scarcity of several natural minerals. To alleviate the interference of majority categories on minority categories, we propose <strong>L</strong>ong-<strong>T</strong>ail <strong>F</strong>ew-shot <strong>M</strong>odule (LTFM) which is inspired by rethinking the fashionable decoupling strategy that conducts primary representation learning and further classifier retrained on mineral spectral data. In particular, LTFM serves as a multi-expert mode, where these experts are respectively specialized in improving feature representation learning, mitigating the long-tail effect, and alleviating the interference of few shots. Additionally, the loose coupling learning strategy is introduced to facilitate primary representation learning and the subsequent additional experts to inherit this knowledge. Experiments on two publicly available spectral datasets show that the proposed LTFM significantly outperforms existing methods. In the end, extensive ablation studies are conducted to investigate the effectiveness, correctness, and robustness of our proposal.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"254 ","pages":"Article 105247"},"PeriodicalIF":3.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}