Pub Date : 2025-02-02DOI: 10.1016/j.chemolab.2025.105336
Maddina Dinesh Kumar , Dharmaiah Gurram , Se-Jin Yook , C.S.K. Raju , Nehad Ali Shah
Research background and significance
Hybrid nanofluids have garnered significant attention because of their capacity to enhance heat transmission in a range of technical applications; optimising their thermal performance is crucial for improving the efficiency of cooling systems, energy storage devices, and heat exchangers with rotating surfaces.
Present study novelty and methodology
In a present study investigating the heat, velocity and mass diffusion transformation under the effect of the Rossland and magnetic approximations, a ternary hybrid nanofluid is a mixing of more than two characteristics using a base fluid through a spinning disc surface, utilising to speed up the heat transmission rate due to ternary hybrid nanofluid, converting non-linear PDE to ODE in this process dimensional governing equations will convert to dimensionless by using the similarity transformations afterwards with MATLAB inbuilt BVP5C solver has been using for the numeral computation, The quadratic regression model's response surface method (RSM) has been employed to research the impacts of independent parameters on physical parameters; surface plots are drawn through Python programming.
Quantitative evaluation
For the RSM quadratic regression model , it shows the model fit goodness. case-1 including more rate of transmission than case-2, In case-1 with more transmission rate in comparison to case-2, In case-1 Possessing more rate of transmission than case 2.
{"title":"Optimising thermal performance of water-based hybrid nanofluids with magnetic and radiative effects over a spinning disc","authors":"Maddina Dinesh Kumar , Dharmaiah Gurram , Se-Jin Yook , C.S.K. Raju , Nehad Ali Shah","doi":"10.1016/j.chemolab.2025.105336","DOIUrl":"10.1016/j.chemolab.2025.105336","url":null,"abstract":"<div><h3>Research background and significance</h3><div>Hybrid nanofluids have garnered significant attention because of their capacity to enhance heat transmission in a range of technical applications; optimising their thermal performance is crucial for improving the efficiency of cooling systems, energy storage devices, and heat exchangers with rotating surfaces.</div></div><div><h3>Present study novelty and methodology</h3><div>In a present study investigating the heat, velocity and mass diffusion transformation under the effect of the Rossland and magnetic approximations, a ternary hybrid nanofluid is a mixing of more than two characteristics using a base fluid through a spinning disc surface, utilising to speed up the heat transmission rate due to ternary hybrid nanofluid, converting non-linear PDE to ODE in this process dimensional governing equations will convert to dimensionless by using the similarity transformations afterwards with MATLAB inbuilt BVP5C solver has been using for the numeral computation, The quadratic regression model's response surface method (RSM) has been employed to research the impacts of independent parameters on physical parameters; surface plots are drawn through Python programming.</div></div><div><h3>Quantitative evaluation</h3><div>For the RSM quadratic regression model <span><math><mrow><mo>(</mo><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>99.51</mn><mo>%</mo></mrow><mo>)</mo></mrow></math></span>, it shows the model fit goodness. case-1 including more <span><math><mrow><msub><mi>C</mi><mi>f</mi></msub></mrow></math></span> rate of transmission than case-2, In case-1 with more <span><math><mrow><mi>S</mi><mi>h</mi></mrow></math></span> transmission rate in comparison to case-2, In case-1 Possessing more <span><math><mrow><mi>N</mi><mi>u</mi><mi>s</mi></mrow></math></span> rate of transmission than case 2.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105336"},"PeriodicalIF":3.7,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183730","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}
Pub Date : 2025-01-27DOI: 10.1016/j.chemolab.2025.105335
Bader Huwaimel , Saad Alqarni
Poly (lactic-co-glycolic acid) (PLGA) is one of the most commonly used polymers for drug delivery due to its biodegradable property. Production of PLGA particles in nanosized scale would be of great importance to exploit the properties of this polymer for nano-based drug delivery. This work explores machine learning methods for the PLGA regression tasks of particle size (nm) prediction and Zeta potential (mV) in the synthesis process. Utilizing a comprehensive dataset with categorical inputs (PLGA type and anti-solvent type) and numerical inputs (PLGA concentration and anti-solvent concentration), the research incorporates Isolation Forest for outlier detection, Min-Max Normalization, and One-Hot Encoding for preprocessing. Several regression models including LASSO, Polynomial Regression (PR), and Support Vector Regression (SVR) were employed in combination with Bagging Ensemble methods for enhanced predictive performance. Glowworm Swarm Optimization (GSO) was applied for hyperparameter tuning. The results indicate that BAG-SVR attained the highest test R2 of 0.9422 for particle size prediction. For Zeta potential prediction, BAG-PR outperformed other models, achieving a test R2 score of 0.98881.
{"title":"Design of Poly(lactic-co-glycolic acid) nanoparticles in drug delivery by artificial intelligence methods to find the conditions of nanoparticles synthesis","authors":"Bader Huwaimel , Saad Alqarni","doi":"10.1016/j.chemolab.2025.105335","DOIUrl":"10.1016/j.chemolab.2025.105335","url":null,"abstract":"<div><div>Poly (lactic-co-glycolic acid) (PLGA) is one of the most commonly used polymers for drug delivery due to its biodegradable property. Production of PLGA particles in nanosized scale would be of great importance to exploit the properties of this polymer for nano-based drug delivery. This work explores machine learning methods for the PLGA regression tasks of particle size (nm) prediction and Zeta potential (mV) in the synthesis process. Utilizing a comprehensive dataset with categorical inputs (PLGA type and anti-solvent type) and numerical inputs (PLGA concentration and anti-solvent concentration), the research incorporates Isolation Forest for outlier detection, Min-Max Normalization, and One-Hot Encoding for preprocessing. Several regression models including LASSO, Polynomial Regression (PR), and Support Vector Regression (SVR) were employed in combination with Bagging Ensemble methods for enhanced predictive performance. Glowworm Swarm Optimization (GSO) was applied for hyperparameter tuning. The results indicate that BAG-SVR attained the highest test R<sup>2</sup> of 0.9422 for particle size prediction. For Zeta potential prediction, BAG-PR outperformed other models, achieving a test R<sup>2</sup> score of 0.98881.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105335"},"PeriodicalIF":3.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183733","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}
Pub Date : 2025-01-25DOI: 10.1016/j.chemolab.2025.105334
M.A. Meneses-Nava
This study introduces a spectral analysis method known as Boosted Deconvolution Fitting (BDF) to process spectroscopic data. The BDF method enhances spectral resolution and precisely adjusts spectra by integrating boosted deconvolution for determining band profile parameters, and a multicomponent analysis technique for minor adjustments in band intensity. This technique seeks to address the shortcomings of conventional methods like the Levenberg-Marquardt algorithm (LMA), especially in terms of improving spectral resolution, accurately determining parameters of overlapping bands, and reducing sensitivity to initial conditions. The efficacy of the BDF method is affected by various factors, including the chosen band profile type (Gaussian or Lorentzian), the signal-to-noise ratio (SNR) of the dataset, and the separation and relative intensities of the spectral bands.
{"title":"Automatic spectral fitting for LIBS and Raman spectra by boosted deconvolution method","authors":"M.A. Meneses-Nava","doi":"10.1016/j.chemolab.2025.105334","DOIUrl":"10.1016/j.chemolab.2025.105334","url":null,"abstract":"<div><div>This study introduces a spectral analysis method known as Boosted Deconvolution Fitting (BDF) to process spectroscopic data. The BDF method enhances spectral resolution and precisely adjusts spectra by integrating boosted deconvolution for determining band profile parameters, and a multicomponent analysis technique for minor adjustments in band intensity. This technique seeks to address the shortcomings of conventional methods like the Levenberg-Marquardt algorithm (LMA), especially in terms of improving spectral resolution, accurately determining parameters of overlapping bands, and reducing sensitivity to initial conditions. The efficacy of the BDF method is affected by various factors, including the chosen band profile type (Gaussian or Lorentzian), the signal-to-noise ratio (SNR) of the dataset, and the separation and relative intensities of the spectral bands.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105334"},"PeriodicalIF":3.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183653","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}
Pub Date : 2025-01-24DOI: 10.1016/j.chemolab.2025.105333
Min-Hsu Tai, Cheng-Che Hsu
This study presents the development of a generative adversarial network (GAN) to generate high-resolution (HR) spectra from low-resolution (LR) spectra. Plasma emissions with second positive system of nitrogen are used for demonstration. Specair™ is used to generate HR and LR spectra pairs as the training data covering the range of rotational temperatures (Trot) and vibrational temperatures (Tvib) ranging from 300 to 1200 K and 2000 to 6500 K, respectively. Optical emission spectra from low-pressure and atmospheric-pressure plasmas are used as the testing data to show the feasibility of the model for generating HR spectra with spectra acquired using LR spectrometers. Feature matching is used during the training stage to tackle the instability issues. The distributions of the discriminator scores are used as an initial criterion to monitor the training procedure. The results show a weighted coefficient of determination () greater than 0.9999 between the simulated and generated HR spectra. The fitting errors for Trot and Tvib between generated HR spectra and experimental HR spectra acquired from an HR spectrometer are mostly below 5 %. The results indicate that this GAN serves as an efficient approach to obtain HR spectra when HR spectrometers are not available.
本研究介绍了生成对抗网络（GAN）的开发情况，该网络可从低分辨率（LR）光谱生成高分辨率（HR）光谱。等离子体发射的第二正氮系统被用于演示。使用 Specair™ 生成 HR 和 LR 光谱对作为训练数据，涵盖的旋转温度 (Trot) 和振动温度 (Tvib) 范围分别为 300 至 1200 K 和 2000 至 6500 K。低压和大气压等离子体的光学发射光谱被用作测试数据，以显示该模型利用 LR 光谱仪获取的光谱生成 HR 光谱的可行性。在训练阶段使用特征匹配来解决不稳定性问题。判别分数的分布被用作监测训练过程的初始标准。结果显示，模拟和生成的 HR 光谱之间的加权判定系数 (R‾2) 大于 0.9999。生成的心率频谱与从心率频谱仪获取的实验心率频谱之间的 Trot 和 Tvib 拟合误差大多低于 5%。结果表明，在没有 HR 光谱仪的情况下，该 GAN 是获取 HR 光谱的有效方法。
{"title":"Reconstructing spectral shapes with GAN models: A data-driven approach for high-resolution spectra from low-resolution spectrometers","authors":"Min-Hsu Tai, Cheng-Che Hsu","doi":"10.1016/j.chemolab.2025.105333","DOIUrl":"10.1016/j.chemolab.2025.105333","url":null,"abstract":"<div><div>This study presents the development of a generative adversarial network (GAN) to generate high-resolution (HR) spectra from low-resolution (LR) spectra. Plasma emissions with second positive system of nitrogen are used for demonstration. Specair™ is used to generate HR and LR spectra pairs as the training data covering the range of rotational temperatures (T<sub>rot</sub>) and vibrational temperatures (T<sub>vib</sub>) ranging from 300 to 1200 K and 2000 to 6500 K, respectively. Optical emission spectra from low-pressure and atmospheric-pressure plasmas are used as the testing data to show the feasibility of the model for generating HR spectra with spectra acquired using LR spectrometers. Feature matching is used during the training stage to tackle the instability issues. The distributions of the discriminator scores are used as an initial criterion to monitor the training procedure. The results show a weighted coefficient of determination (<span><math><mrow><msup><mover><mi>R</mi><mo>‾</mo></mover><mn>2</mn></msup></mrow></math></span>) greater than 0.9999 between the simulated and generated HR spectra. The fitting errors for T<sub>rot</sub> and T<sub>vib</sub> between generated HR spectra and experimental HR spectra acquired from an HR spectrometer are mostly below 5 %. The results indicate that this GAN serves as an efficient approach to obtain HR spectra when HR spectrometers are not available.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105333"},"PeriodicalIF":3.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183721","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}
Pub Date : 2025-01-23DOI: 10.1016/j.chemolab.2025.105324
Zhaoxuan Pan , Xiaoyu Zhao , Yue Zhao , Lijing Cai , Liang Tong , Zhe Zhai
The Competitive Adaptive Re-weighted Sampling (CARS) method, while excelling in feature extraction, encounters several challenges when processing low-quality data, including high computational complexity, intricate parameter settings, and the potential for overfitting. To address these issues, this paper introduces the IWCARS (Initial Weight and Weight, I & W) algorithm, which implements two key methodological enhancements: initial weight selection and weight update strategy. This algorithm, building upon the traditional CARS algorithm and density-based clustering, offers a supplementary tool for data feature selection by computing density and weight, and employs an adaptive model evaluation mechanism to select the most pertinent features, ultimately constructing a model with enhanced predictive capability. IWCARS optimizes model performance by dynamically adjusting the feature set, thereby improving the algorithm's prediction performance and model fit. Furthermore, the IWCARS method, in conjunction with a Partial Least Squares (PLS) model, was applied to measure soil Available Potassium (AK) content using near-infrared spectroscopy. Five pre-processing techniques were conducted on the near-infrared spectrum, with the IWCARS + PLS model constructed using first derivative data, yielding optimal results. The experimental results demonstrated that the model based on 1st Derivative + IWCARS + PLS yielded the best performance. Specifically, the model achieved RC2 of 0.9905, Rp2 of 0.9817, RMSEC of 0.8917, RMSEP of 0.9024, and RPD of 8.5176. Robustness, versatility, and transferability tests demonstrated that the proposed IWCARS algorithm, when integrated into the PLS model, achieved commendable measurement accuracy. While there are limited strategies for concurrently addressing high computational complexity, challenging parameter settings, and overfitting risks, this study aims to mitigate these concerns by reducing the computational complexity of the CARS algorithm, simplifying parameter settings, and preventing overfitting, ultimately enhancing the model's fitting accuracy, training speed, and generalization capability.
{"title":"An enhanced IWCARS method for measuring soil available potassium","authors":"Zhaoxuan Pan , Xiaoyu Zhao , Yue Zhao , Lijing Cai , Liang Tong , Zhe Zhai","doi":"10.1016/j.chemolab.2025.105324","DOIUrl":"10.1016/j.chemolab.2025.105324","url":null,"abstract":"<div><div>The Competitive Adaptive Re-weighted Sampling (CARS) method, while excelling in feature extraction, encounters several challenges when processing low-quality data, including high computational complexity, intricate parameter settings, and the potential for overfitting. To address these issues, this paper introduces the IWCARS (Initial Weight and Weight, I & W) algorithm, which implements two key methodological enhancements: initial weight selection and weight update strategy. This algorithm, building upon the traditional CARS algorithm and density-based clustering, offers a supplementary tool for data feature selection by computing density and weight, and employs an adaptive model evaluation mechanism to select the most pertinent features, ultimately constructing a model with enhanced predictive capability. IWCARS optimizes model performance by dynamically adjusting the feature set, thereby improving the algorithm's prediction performance and model fit. Furthermore, the IWCARS method, in conjunction with a Partial Least Squares (PLS) model, was applied to measure soil Available Potassium (AK) content using near-infrared spectroscopy. Five pre-processing techniques were conducted on the near-infrared spectrum, with the IWCARS + PLS model constructed using first derivative data, yielding optimal results. The experimental results demonstrated that the model based on 1st Derivative + IWCARS + PLS yielded the best performance. Specifically, the model achieved R<sub>C</sub><sup>2</sup> of 0.9905, R<sub>p</sub><sup>2</sup> of 0.9817, RMSEC of 0.8917, RMSEP of 0.9024, and RPD of 8.5176. Robustness, versatility, and transferability tests demonstrated that the proposed IWCARS algorithm, when integrated into the PLS model, achieved commendable measurement accuracy. While there are limited strategies for concurrently addressing high computational complexity, challenging parameter settings, and overfitting risks, this study aims to mitigate these concerns by reducing the computational complexity of the CARS algorithm, simplifying parameter settings, and preventing overfitting, ultimately enhancing the model's fitting accuracy, training speed, and generalization capability.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105324"},"PeriodicalIF":3.7,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183732","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}
Pub Date : 2025-01-21DOI: 10.1016/j.chemolab.2025.105323
Christophe Bajan, Guillaume Lambard
We present MADGUI, Multi-Application Design Graphical User Interface (GUI) using Bayesian Optimization and prediction model for data analysis and optimize process or composition. Its strength is its user-friendly design, which requires no programming knowledge. It is built using the Streamlit library in Python and is divided into three parts, allowing users to select various parameters and fill csv/xlsx files without any coding required. Overall, MADGUI is designed as an optimal experiment design platform with active machine learning, which accelerates the discovery of optimal solutions and provides an intuitive GUI for users with no experience in coding, machine learning, or optimization.
{"title":"MADGUI: Multi-Application Design Graphical User Interface for active learning assisted by Bayesian optimization","authors":"Christophe Bajan, Guillaume Lambard","doi":"10.1016/j.chemolab.2025.105323","DOIUrl":"10.1016/j.chemolab.2025.105323","url":null,"abstract":"<div><div>We present MADGUI, Multi-Application Design Graphical User Interface (GUI) using Bayesian Optimization and prediction model for data analysis and optimize process or composition. Its strength is its user-friendly design, which requires no programming knowledge. It is built using the Streamlit library in Python and is divided into three parts, allowing users to select various parameters and fill csv/xlsx files without any coding required. Overall, MADGUI is designed as an optimal experiment design platform with active machine learning, which accelerates the discovery of optimal solutions and provides an intuitive GUI for users with no experience in coding, machine learning, or optimization.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105323"},"PeriodicalIF":3.7,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183720","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}
Pub Date : 2025-01-17DOI: 10.1016/j.chemolab.2025.105320
Oliver Polushkina-Merchanskaya , Michael D. Sorochan Armstrong , Carolina Gómez-Llorente , Patricia Ferrer , Sergi Fernandez-Gonzalez , Miriam Perez-Cruz , María Dolores Gómez-Roig , José Camacho
Multifactorial experimental designs allow us to assess the contribution of several factors, and potentially their interactions, to one or several responses of interests. Following the principles of the partition of the variance advocated by Sir R.A. Fisher, the experimental responses are factored into the quantitative contribution of main factors and interactions. A popular approach to perform this factorization in ANOVA and related factorizations like ASCA(+) is through General Linear Models. Subsequently, different inferential approaches can be used to identify whether the contributions are statistically significant or not. Unfortunately, the performance of inferential approaches in terms of Type I and Type II errors can be heavily affected by missing data, outliers and/or the departure from normality of the distribution of the responses, which are commonplace problems in modern analytical experiments. In this paper, we study these problems and suggest good practices of application.
多因素实验设计使我们能够评估多个因素及其相互作用对一个或多个相关反应的影响。根据 R.A. Fisher 爵士倡导的方差分配原则，实验反应被分解为主要因素和交互作用的定量贡献。在方差分析和相关因子分析（如 ASCA(+)）中进行这种因子分析的常用方法是通用线性模型。随后，可以使用不同的推论方法来确定贡献是否具有统计意义。遗憾的是，推论方法在 I 类和 II 类误差方面的表现会受到缺失数据、异常值和/或响应分布偏离正态性的严重影响，而这些都是现代分析实验中常见的问题。在本文中，我们将对这些问题进行研究，并提出良好的应用实践建议。
{"title":"Considerations for missing data, outliers and transformations in permutation testing for ANOVA with multivariate responses","authors":"Oliver Polushkina-Merchanskaya , Michael D. Sorochan Armstrong , Carolina Gómez-Llorente , Patricia Ferrer , Sergi Fernandez-Gonzalez , Miriam Perez-Cruz , María Dolores Gómez-Roig , José Camacho","doi":"10.1016/j.chemolab.2025.105320","DOIUrl":"10.1016/j.chemolab.2025.105320","url":null,"abstract":"<div><div>Multifactorial experimental designs allow us to assess the contribution of several factors, and potentially their interactions, to one or several responses of interests. Following the principles of the partition of the variance advocated by Sir R.A. Fisher, the experimental responses are factored into the quantitative contribution of main factors and interactions. A popular approach to perform this factorization in ANOVA and related factorizations like ASCA(+) is through General Linear Models. Subsequently, different inferential approaches can be used to identify whether the contributions are statistically significant or not. Unfortunately, the performance of inferential approaches in terms of Type I and Type II errors can be heavily affected by missing data, outliers and/or the departure from normality of the distribution of the responses, which are commonplace problems in modern analytical experiments. In this paper, we study these problems and suggest good practices of application.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105320"},"PeriodicalIF":3.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183719","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}
Pub Date : 2025-01-17DOI: 10.1016/j.chemolab.2025.105316
Xiaoyu Xie, Xin Deng, Linyu Huang, Qian Ning
The fluctuating working conditions in wastewater treatment processes, influenced by various factors, result in highly nonlinear characteristics in online monitoring data. This presents challenges for accurately estimating water quality. Addressing the issue of single-model performance degradation under changing data distributions, this paper proposes a two-stage hybrid prediction scheme based on clustering. Firstly, historical data is divided and features are extracted and clustered based on different time periods. Subsequently, distinct prediction models are applied to data within each working mode, thereby enhancing overall prediction performance. The selection and combination of two classical models with different characteristics, namely the partial least squares random weight neural network (PLS-RWNN) and the multi-output correlation vector machine (MRVM), enable better adaptation to the complex wastewater treatment data source. The proposed approach is validated using the wastewater treatment platform BSM2. On average, clustering modeling combined with models provides better predictions for all three variables. The comprehensive index RMSSD of the mixed model is 0.6189, which is 42.17 % higher than that of a single model used before clustering. Results indicate that the proposed network architecture significantly improves prediction performance, highlighting its effectiveness in dealing with the nonlinear and fluctuating nature of wastewater treatment data.
{"title":"A multi-output hybrid prediction model for key indicators of wastewater treatment processes","authors":"Xiaoyu Xie, Xin Deng, Linyu Huang, Qian Ning","doi":"10.1016/j.chemolab.2025.105316","DOIUrl":"10.1016/j.chemolab.2025.105316","url":null,"abstract":"<div><div>The fluctuating working conditions in wastewater treatment processes, influenced by various factors, result in highly nonlinear characteristics in online monitoring data. This presents challenges for accurately estimating water quality. Addressing the issue of single-model performance degradation under changing data distributions, this paper proposes a two-stage hybrid prediction scheme based on clustering. Firstly, historical data is divided and features are extracted and clustered based on different time periods. Subsequently, distinct prediction models are applied to data within each working mode, thereby enhancing overall prediction performance. The selection and combination of two classical models with different characteristics, namely the partial least squares random weight neural network (PLS-RWNN) and the multi-output correlation vector machine (MRVM), enable better adaptation to the complex wastewater treatment data source. The proposed approach is validated using the wastewater treatment platform BSM2. On average, clustering modeling combined with models provides better predictions for all three variables. The comprehensive index RMSSD of the mixed model is 0.6189, which is 42.17 % higher than that of a single model used before clustering. Results indicate that the proposed network architecture significantly improves prediction performance, highlighting its effectiveness in dealing with the nonlinear and fluctuating nature of wastewater treatment data.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105316"},"PeriodicalIF":3.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183718","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}
Laser-induced breakdown spectroscopy (LIBS) technology is widely used in fields such as analytical chemistry, materials science, and environmental monitoring. Modeling the quantitative relationship between component contents and spectral data is a key step in LIBS analysis. However, traditional regression methods commonly use individual regression model, which are difficult to comprehensively and reasonably utilize the information in the spectra, resulting in limitations in full-spectrum multicomponent regression. This paper proposes a heterogeneous ensemble learning (HEL) model, selecting four heterogeneous sub-models: CNN, Lasso, Boosting, and FNN, for full-spectrum LIBS quantitative regression analysis. HEL can fully leverage the strengths of different models by using Bayesian weighting strategy, thereby improving the performance of LIBS quantitative analysis. Experimental results show that the proposed HEL regression model has better accuracy and stability compared to the existing models.
{"title":"Full-spectrum LIBS quantitative analysis based on heterogeneous ensemble learning model","authors":"Xinyue Fang , Haoyang Yu , Qian Huang , Zhaohui Jiang , Dong Pan , Weihua Gui","doi":"10.1016/j.chemolab.2025.105321","DOIUrl":"10.1016/j.chemolab.2025.105321","url":null,"abstract":"<div><div>Laser-induced breakdown spectroscopy (LIBS) technology is widely used in fields such as analytical chemistry, materials science, and environmental monitoring. Modeling the quantitative relationship between component contents and spectral data is a key step in LIBS analysis. However, traditional regression methods commonly use individual regression model, which are difficult to comprehensively and reasonably utilize the information in the spectra, resulting in limitations in full-spectrum multicomponent regression. This paper proposes a heterogeneous ensemble learning (HEL) model, selecting four heterogeneous sub-models: CNN, Lasso, Boosting, and FNN, for full-spectrum LIBS quantitative regression analysis. HEL can fully leverage the strengths of different models by using Bayesian weighting strategy, thereby improving the performance of LIBS quantitative analysis. Experimental results show that the proposed HEL regression model has better accuracy and stability compared to the existing models.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105321"},"PeriodicalIF":3.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155129","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}
Pub Date : 2025-01-11DOI: 10.1016/j.chemolab.2025.105319
Piyush Sharma, Devi Prasad Sharma, Sulabh Bansal
In agricultural technology, accurate and speedy plant disease identification is essential to maintain the optimum crop quality and output. This research proposed a system that can automatically diagnose diseases in apple fruit and apple trees using machine learning (ML) image processing. Thus, this research offers a novel approach for accurate plant disease prediction by combining an Ensemble Feature Extractor with an Optimum Restricted Boltzmann Machine (RBM) Encoded Support Vector Machine (SVM) model. The model uses RBM-encoded features and SVM classification, and several feature extraction techniques enhance it. The experiments across the PDD271 dataset with 220,592 images and 271 categories demonstrate the model's outstanding classification performance, stressing its potential to develop agricultural technology and enable early disease diagnosis for better crop management. Consequently, with respective values of 98 %, 98 %, 89.7 %, and 97.8 %, the model may give more successful outcomes regarding accuracy, precision, recall, and F1 Score.
{"title":"Optimum RBM encoded SVM model with ensemble feature Extractor-based plant disease prediction","authors":"Piyush Sharma, Devi Prasad Sharma, Sulabh Bansal","doi":"10.1016/j.chemolab.2025.105319","DOIUrl":"10.1016/j.chemolab.2025.105319","url":null,"abstract":"<div><div>In agricultural technology, accurate and speedy plant disease identification is essential to maintain the optimum crop quality and output. This research proposed a system that can automatically diagnose diseases in apple fruit and apple trees using machine learning (ML) image processing. Thus, this research offers a novel approach for accurate plant disease prediction by combining an Ensemble Feature Extractor with an Optimum Restricted Boltzmann Machine (RBM) Encoded Support Vector Machine (SVM) model. The model uses RBM-encoded features and SVM classification, and several feature extraction techniques enhance it. The experiments across the PDD271 dataset with 220,592 images and 271 categories demonstrate the model's outstanding classification performance, stressing its potential to develop agricultural technology and enable early disease diagnosis for better crop management. Consequently, with respective values of 98 %, 98 %, 89.7 %, and 97.8 %, the model may give more successful outcomes regarding accuracy, precision, recall, and F1 Score.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105319"},"PeriodicalIF":3.7,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183731","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}
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