Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Transfer contrastive learning for Raman spectra data of urine: Detection of glucose, protein, and prediction of kidney disorders","authors":"Zhuangwei Shi ,&nbsp;Jiale Wang ,&nbsp;Yunhao Su ,&nbsp;Xiaohong Liang ,&nbsp;Jianchen Zi ,&nbsp;Chenhui Wang ,&nbsp;Hai Bi ,&nbsp;Xia Xiang","doi":"10.1016/j.chemolab.2025.105384","DOIUrl":"10.1016/j.chemolab.2025.105384","url":null,"abstract":"<div><div>Raman spectroscopy, a non-invasive analytical technique, reveals significant potential in clinical diagnosis of kidney disorders by detecting key biomolecules in urine samples, especially glucose and protein. Although machine learning models have been widely applied for efficiently analyzing Raman spectral data, the high-dimensionality, imbalance and sample-scarcity of Raman spectral data still pose challenges to the models in achieving accurate detection. To address these challenges, we propose a novel deep learning model, TCRaman, which integrates transfer learning and contrastive learning for urine detection using Raman spectral data. As contrastive learning is capable of representation learning on imbalanced data, TCRaman first utilizes a pretrained contrastive learning model on a large labeled Raman spectral dataset of bacteria, to enhance the model’s capability to learn meaningful low-dimensional representations from high-dimensional Raman spectral data. Then, the pretrained model is finetuned on clinical urine Raman spectral data. This transfer learning framework is a foundation model that can break through the limitation of sample-scarcity on different downstream tasks. The experiments demonstrate the superiority of TCRaman compared with current state-of-the-art models. The results show that TCRaman achieves 91% accuracy on the detection of both glucose and protein, and 95% accuracy on the prediction of kidney disorders, highlighting the effectiveness of our proposed method in detecting urine Raman spectra. The proposed TCRaman method provides a promising way for accurate, rapid, and cost-effective detection for spectral data of biochemical samples.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"261 ","pages":"Article 105384"},"PeriodicalIF":3.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725394","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":"AQbD approach for the development of an analytical procedure for the separation of cephalosporin drugs and their degradation products","authors":"Paula Beatriz Silva Passarin,&nbsp;Rogerio Takao Okamoto,&nbsp;Fabiane Dörr,&nbsp;Mauricio Yonamine,&nbsp;Felipe Rebello Lourenço","doi":"10.1016/j.chemolab.2025.105389","DOIUrl":"10.1016/j.chemolab.2025.105389","url":null,"abstract":"<div><div>In the pharmaceutical industry, analytical procedures are used to conduct research, development and quality control of drugs and medicines. Given the importance of the Analytical Quality by Design (AQbD) approach in the rational development of analytical procedures, especially by minimizing the need for experiments, acquiring and improving knowledge during development and ensuring the flexibility of the method, this study aimed to develop an analytical procedure, based on AQbD principles, for the identification and quantification of different cephalosporins, as well as to create a tool for defining the method operable design region. The initial screening phase of analytical development was carried out using an <em>in silico</em> tool developed in a previous study. During the analytical development phase, a forced degradation study was performed using Liquid Chromatography Coupled to Mass Spectrometry (LC-MS) to identify cephalosporin degradation products. The developed method, along with its Method Operable Design Region (MODR), was validated, resulting in a robust and flexible analytical procedure for identifying and quantifying different cephalosporins in accordance with AQbD principles. The proposed tool considers multiple chromatographic responses, target uncertainty and desired confidence level, simplifying the verification of compliance with quality requirements defined in Analytical Target Profile, offering a reliable process that adapts to parameter changes, maintaining the quality of results.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"261 ","pages":"Article 105389"},"PeriodicalIF":3.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725395","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":"Research on robust optimization of cement calcination process based on RMODE algorithm","authors":"Xunian Yang,&nbsp;Jieguang Yang,&nbsp;Xiaochen Hao","doi":"10.1016/j.chemolab.2025.105388","DOIUrl":"10.1016/j.chemolab.2025.105388","url":null,"abstract":"<div><div>In the process of cement clinker calcination, the working conditions fluctuate dynamically, and multiple operational indices are interdependent. The inability to monitor key indicators, such as clinker quality and energy consumption, in real time, along with the absence of coordination mechanisms among various operational indicators, results in issues such as product instability, low energy efficiency, and insufficient robustness of the production system. To tackle these challenges under dynamic conditions, this paper proposes a robust optimization method for the cement calcination process (CCP). First, a prediction model for coal consumption and free calcium oxide (f-CaO) content is developed using a Time Series-Based Convolutional Neural Network (TS-CNN), incorporating the multi-time-scale characteristics and significant delays inherent in cement calcination data. Second, a multi-objective optimization model for the CCP is formulated by examining the relationships between process parameters and production indices. Subsequently, the mean effective function of the prediction model is defined as the fitness function, and a robust multi-objective difference algorithm (RMODE) is developed to solve the optimization model, yielding a robust optimal solution with high resistance to disturbances. Finally, comparative experiments are performed using real-world CCP data. The experimental results indicate that, compared to the baseline algorithm, the proposed method enhances system robustness while maintaining product quality and reducing coal consumption.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"261 ","pages":"Article 105388"},"PeriodicalIF":3.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735065","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 general and unified class of gamma regression models","authors":"Marcelo Bourguignon ,&nbsp;Diego I. Gallardo","doi":"10.1016/j.chemolab.2025.105382","DOIUrl":"10.1016/j.chemolab.2025.105382","url":null,"abstract":"<div><div>The usual mean linear regression provides the average relationship between a response variable and explanatory variables, but it is not always the best metric for modeling right-skewed data in regression. In this paper, we extend the usual mean gamma regression model using a general and unified parameterization of this distribution that is indexed by some central tendency measure. Unlike the traditional gamma regression model, which focuses on the arithmetic mean, this new parameterization accommodates different measures of central tendency, including the median, mode, and geometric mean, harmonic mean along with a precision parameter. We consider a regression structure for both components. The model provides a robust framework for regression, allowing for greater adaptability to different data characteristics. Estimation is performed by maximum likelihood. Furthermore, we discuss residuals. A Monte Carlo experiment is conducted to evaluate the performances of these estimators and residuals in finite samples with a discussion of the obtained results. The methods developed are applied to two real data sets from minerals and nutrition.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"261 ","pages":"Article 105382"},"PeriodicalIF":3.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704584","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":"Hybrid factors latent Gaussian process modeling with wasserstein distance for soft sensing of extruder processes","authors":"Yun Dai ,&nbsp;Chao Yang ,&nbsp;Zhixiang Gu ,&nbsp;Yuan Yao ,&nbsp;Yi Liu","doi":"10.1016/j.chemolab.2025.105387","DOIUrl":"10.1016/j.chemolab.2025.105387","url":null,"abstract":"<div><div>In the domain of polymer production, twin-screw extruders are crucial, necessitating precise monitoring of key quality variables. However, challenges arise due to time-variant and intricate production processes, as well as high-dimensional and hybrid screw configuration data, hindering efficient quality inference. To overcome these hurdles, a latent variable-based online soft sensing approach, termed just-in-time hybrid factors Gaussian process latent variable regression (GPLVR) via the Wasserstein metric, is proposed for twin-screw extruders. Specifically, composite variables are firstly constructed that combine both quantitative and qualitative factors, ensuring a comprehensive representation of the process. Subsequently, the just-in-time method is adopted to ensure continuous updating of the model. To describe the mixed-type dataset of extrusion process data including both continuous and discrete elements. The Wasserstein distance is utilized to select historical samples that closely match the distribution of online query samples. Furthermore, the GPLVR is used to address the curse of dimensionality associated with high-dimensional screw configuration elements. The effectiveness of our proposed method has been verified through its application in the production process of polypropylene, demonstrating its potential to improve the accuracy and reliability of quality inference in twin-screw extruder operations.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"261 ","pages":"Article 105387"},"PeriodicalIF":3.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681812","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":"Improvement of hyperspectral imaging signal quality using filtering technique","authors":"Jiasheng Zhou,&nbsp;Te Ma,&nbsp;Satoru Tsuchikawa,&nbsp;Tetsuya Inagaki","doi":"10.1016/j.chemolab.2025.105386","DOIUrl":"10.1016/j.chemolab.2025.105386","url":null,"abstract":"<div><div>This paper proposes to improve signal quality in hyperspectral imaging (HSI) on the basis of noise analysis and filtering method. HSI technology enables nondestructive and precise analysis in agriculture and food industries by acquiring high-resolution images over multiple wavelengths, but the identification and removal of noise in the signal is a challenge. In this study, HSI measurement data of sucrose solution samples of different concentrations were used as experimental subjects. The outliers outside the three-fold standard deviation range of all data were identified as noise and a filtering method using noise mask and Wavelet transform was proposed. By evaluating the effect of the filtering method on noise reduction, we conducted qualitative and quantitative analysis and comparison, mainly through statistical methods and the limits of detection (LOD), LODmin and LODmax. The experiment results show that the proposed method is useful in removing noise, reducing the detection limit when applying Partial Least Squares (PLS) and improving the HSI signal quality. This is expected to improve the accuracy of nondestructive analysis using HSI data.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"261 ","pages":"Article 105386"},"PeriodicalIF":3.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681808","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":"A comprehensive tutorial on the SOM-RPM toolbox for MATLAB","authors":"Sarah E. Bamford ,&nbsp;Wil Gardner ,&nbsp;Davide Ballabio ,&nbsp;Brian Oslinker ,&nbsp;David A. Winkler ,&nbsp;Benjamin W. Muir ,&nbsp;Paul J. Pigram","doi":"10.1016/j.chemolab.2025.105383","DOIUrl":"10.1016/j.chemolab.2025.105383","url":null,"abstract":"<div><div>We present the SOM-RPM Toolbox for MATLAB, which is an interactive command line implementation of the self-organizing map with relational perspective mapping (SOM-RPM) algorithm. SOM-RPM has shown considerable utility for the interpretation of complex hyperspectral data. In essence, it provides a means for interactively exploring similarities between pixels (based on their spectral information) through the so-called similarity map. This manuscript provides an overview of the theoretical underpinnings of SOM-RPM, followed by a detailed description of the SOM-RPM toolbox structure. We supplement these sections with a demonstrative case study using time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data as the subject of the analysis. This case study emphasizes the interactive nature of the toolbox and the method itself, which allow for exploration of the data based on the SOM-RPM model. It also highlights the analytical potential of the approach. Our primary aim is to make the SOM-RPM method more accessible to the broader scientific community. This manuscript provides sufficient content for a non-expert in machine learning to be able to utilize SOM-RPM for exploratory analysis of their hyperspectral data. The toolbox, and associated documentation, is available through the linked data repository.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"261 ","pages":"Article 105383"},"PeriodicalIF":3.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681811","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":"The Raman spectroscopy combined with selective state-space algorithm for constructing a rapid disease diagnosis model","authors":"Andong Chen ,&nbsp;Jun Yu ,&nbsp;Chenjie Chang ,&nbsp;Xiaoyi Lv ,&nbsp;Xuguang Zhou ,&nbsp;Yuxuan Guo ,&nbsp;Enguang Zuo ,&nbsp;Min Li ,&nbsp;Yujia Ren ,&nbsp;Shengquan Liu ,&nbsp;Chen Chen ,&nbsp;Xiantao Ai ,&nbsp;Cheng Chen","doi":"10.1016/j.chemolab.2025.105375","DOIUrl":"10.1016/j.chemolab.2025.105375","url":null,"abstract":"<div><div>In recent years, how to construct a rapid disease diagnosis model is still the focus of research in the artificial medical field. Raman spectroscopy is widely used in the medical diagnostic field because of its non-invasive, rapid and highly sensitive properties. However, in Raman spectroscopy, the resonance of multiple functional groups and compounds can result in identical characteristic peaks within the spectrum, which affects the accuracy of this technique in the field of disease diagnosis. Existing studies often focus solely on capturing either local or global information from Raman spectra, potentially causing models to overlook interactions between characteristic peaks or the intricate details within individual peaks of a single spectrum. To address these issues, this paper proposes a medical Raman spectroscopy model, MRSMamba, based on the selective state-space algorithm. The spectral data is first encoded into labeled sequences through a Patch module, which are then input into the Mamba block of the selective state-space algorithm. This model leverages the unique features of selective state-space algorithms to capture detailed local information within each labeled segment while preserving global spectral characteristics, thereby constructing a rapid disease diagnosis model. For the first time, the selective state-space algorithm is applied to the field of medical Raman spectroscopy, with modifications tailored for Raman data. During the encoding phase, the paper also introduces an innovative sequence labeling module designed specifically for the Mamba framework. Experiments using the proposed MRSMamba model were conducted on multiple disease datasets, including thyroid benign and malignant tumor datasets, cancer datasets, and autoimmune disease datasets. We evaluated MRSMamba on a binary classification task involving 99 cases of benign and malignant thyroid tumors, achieving an Accuracy of 0.9286, a recall of 0.9286, a Specificity of 0.9285, and an F1-score of 0.9286. MRSMamba demonstrated a 3.57 % higher accuracy compared to the MLP model. Additionally, the model was tested on a four-class cancer classification task, achieving an Accuracy of 0.7813, a Recall of 0.7042, a Specificity of 0.9165, and an F1-score of 0.7381. MRSMamba outperformed the standalone encoding module PACE by 6.25 % in terms of accuracy. Furthermore, the model was evaluated on an autoimmune disease classification task, achieving an accuracy of 0.9813 and an F1-score of 0.9793. These results highlight the exceptional performance of MRSMamba in the field of rapid disease diagnosis using Raman spectroscopy, demonstrating significant practical application potential.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"261 ","pages":"Article 105375"},"PeriodicalIF":3.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682060","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 selective genetic algorithm - PLS-DA approach based on untargeted LC-HRMS: Application to complex biomass samples","authors":"Ian Ramtanon ,&nbsp;Marion Lacoue-Nègre ,&nbsp;Alexandra Berlioz-Barbier ,&nbsp;Agnès Le Masle ,&nbsp;Jean-Hugues Renault","doi":"10.1016/j.chemolab.2025.105381","DOIUrl":"10.1016/j.chemolab.2025.105381","url":null,"abstract":"<div><div>Supervised multivariate analyses (MVA) are commonly used to extract valuable information, classify samples and highlight data trends from complex LC-HRMS datasets. However, traditional MVA methods, such as partial least-squares discriminant analysis (PLS-DA), may not provide sufficient class discrimination in the case of complex mixtures due to the presence of confounders, high-dimensionality and multicollinearity. In this regard, variable selection methods such as genetic algorithms can reduce datasets by retaining explanatory information while discarding non-informative features. The hyphenation of these selective heuristic methods and highly interpretable PLS-DA has shown significant potential for sample discrimination in spectroscopic techniques but has never been applied to complex LC-HRMS data.</div><div>Therefore, this work investigated the potential of combining variable selection by GA and classification by PLS-DA for identifying key chemical descriptors in these intricate LC-HRMS datasets. The determination of low reactivity discriminants (<em>i.e.,</em> potential enzymatic inhibitors) in complex biomass samples was chosen as a case study and is presented in this work. Initially, PLS-DA was applied to an LC-ESI(±)-HRMS dataset containing 2167 features. However, the resulting model suffered from overfitting and yielded sub-optimal classification for low reactivity samples. Subsequently, GA was employed in triplicate, resulting in 768 final models. The most predictive model retained only 418 variables (<em>i.e.,</em> an 81 % decrease in the dataset) and was subjected to PLS-DA. The new supervised model avoided overfitting and demonstrated significantly improved classification for low reactivity samples, achieving sensitivity, precision, and accuracy increases of 45 %, 25 %, and 38 %, respectively. Ultimately, an original strategy based on combining the variables’ importance in projection (VIP), GA-selection frequency (GA-SF) and cosine similarity was used to pinpoint species associated with low enzymatic reactivity. This study marks the first application of GA-PLS-DA for LC-HRMS data analysis. This approach effectively streamlined feature selection and enhanced class discrimination, successfully identifying 10 potential inhibitory features within the biomass samples. This methodology offers significant potential for studies on complex mixtures that encounter limited classification and/or overfitting issues with traditional supervised MVA methods.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"261 ","pages":"Article 105381"},"PeriodicalIF":3.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637471","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":"Stacking density estimation and its oversampling method for continuously imbalanced data in chemometrics","authors":"Xin-Ru Zhao ,&nbsp;Lun-Zhao Yi ,&nbsp;Guang-Hui Fu","doi":"10.1016/j.chemolab.2025.105366","DOIUrl":"10.1016/j.chemolab.2025.105366","url":null,"abstract":"<div><div>Continuously imbalanced data means that the target variable is continuous and its distribution is uneven. This kind of data is widespread in many practical application areas. However, methods to effectively handle continuously imbalanced data have been relatively scarce, and there is an urgent need to establish corresponding imbalance regression methods to enhance the capability of handling continuously imbalanced data. Firstly, we propose a Stacking-based density estimation (SDE) method to solve the density estimation problem of continuously imbalanced target variables. SDE links density estimation with the Ensemble learning algorithm called Stacking, and its core concept is the “fusion of multiple perspectives for accurate capture”. Performing SDE enhances the model’s understanding of complex data structures and makes it more sensitive and accurate in identifying rare values. Subsequently, we investigate an SDE-based oversampling technique (SDE-OS). SDE-OS uses SDE to synthesize new rare instances in the rare-value region, achieving fine-tuned customization of rare-value additions. In a series of numerical experiments, SDE has been estimated more accurately than the kernel density estimation method on ANLL. SDE-OS outperforms conventional sampling methods such as SMOGN and SMOTER in various metrics. Therefore, the proposed SDE and SDE-OS are highly competitive and effective tools for addressing the imbalanced regression problem.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"261 ","pages":"Article 105366"},"PeriodicalIF":3.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628369","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}