Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"A new class of unit models with a quantile regression approach applied to contamination data","authors":"Karol I. Santoro ,&nbsp;Yolanda M. Gómez ,&nbsp;Héctor J. Gómez ,&nbsp;Diego I. Gallardo","doi":"10.1016/j.chemolab.2025.105322","DOIUrl":"10.1016/j.chemolab.2025.105322","url":null,"abstract":"<div><div>In this paper, we introduce a new class of unit models defined on the open unit interval. Through the reparameterization of the model, the location parameter can be interpreted as a quantile of the distribution. Furthermore, we can assess the impact of explanatory variables within the conditional quantiles of the dependent variable, offering an alternative to the Kumaraswamy quantile regression model. We engage in quantile regression and apply it to two instances of environmental data. We evaluate the effectiveness of the newly introduced models in scenarios both with and without covariates, drawing comparisons with results yielded by the Kumaraswamy regression model. The proposed method has been implemented in an R package.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105322"},"PeriodicalIF":3.7,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349659","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":"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 ,&nbsp;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 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.</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}

{"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}

{"title":"Reconstructing spectral shapes with GAN models: A data-driven approach for high-resolution spectra from low-resolution spectrometers","authors":"Min-Hsu Tai,&nbsp;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_rot) and vibrational temperatures (T_vib) 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_rot and T_vib 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}

{"title":"An enhanced IWCARS method for measuring soil available potassium","authors":"Zhaoxuan Pan ,&nbsp;Xiaoyu Zhao ,&nbsp;Yue Zhao ,&nbsp;Lijing Cai ,&nbsp;Liang Tong ,&nbsp;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 &amp; 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_C² of 0.9905, R_p² 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}

{"title":"MADGUI: Multi-Application Design Graphical User Interface for active learning assisted by Bayesian optimization","authors":"Christophe Bajan,&nbsp;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}

{"title":"Considerations for missing data, outliers and transformations in permutation testing for ANOVA with multivariate responses","authors":"Oliver Polushkina-Merchanskaya ,&nbsp;Michael D. Sorochan Armstrong ,&nbsp;Carolina Gómez-Llorente ,&nbsp;Patricia Ferrer ,&nbsp;Sergi Fernandez-Gonzalez ,&nbsp;Miriam Perez-Cruz ,&nbsp;María Dolores Gómez-Roig ,&nbsp;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}

{"title":"A multi-output hybrid prediction model for key indicators of wastewater treatment processes","authors":"Xiaoyu Xie,&nbsp;Xin Deng,&nbsp;Linyu Huang,&nbsp;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}

{"title":"Full-spectrum LIBS quantitative analysis based on heterogeneous ensemble learning model","authors":"Xinyue Fang ,&nbsp;Haoyang Yu ,&nbsp;Qian Huang ,&nbsp;Zhaohui Jiang ,&nbsp;Dong Pan ,&nbsp;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}

{"title":"Optimum RBM encoded SVM model with ensemble feature Extractor-based plant disease prediction","authors":"Piyush Sharma,&nbsp;Devi Prasad Sharma,&nbsp;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}