Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Smart portable electronic nose system in combination with machine learning algorithms for the intelligent discrimination of fire debris","authors":"José Luis P. Calle ,&nbsp;Tomasz Dymerski ,&nbsp;Marta Ferreiro-González ,&nbsp;Miguel Palma","doi":"10.1016/j.chemolab.2025.105459","DOIUrl":"10.1016/j.chemolab.2025.105459","url":null,"abstract":"<div><div>The identification and discrimination of ignitable liquid residues (ILRs) in fire debris are crucial in forensic research for determining the intentionality of a fire. This study presents a new method using a portable sensor-based electronic nose (eNose) combined with machine learning (ML) algorithms for automated ILR classification. Six substrates (vinyl, nylon, linoleum, polyester, wood, and cotton) were burned with different ignitable liquids (gasoline, diesel, ethanol, and charcoal starter with kerosene), and samples were collected at intervals from 0 to 48 h after the fire had extinguished. Sensor responses from multiple sensors (SO₂, H₂S, CO, IRR, NO₂, TBM, NH₃, and ethanol) were collected over a duration of 140 s. The data were preprocessed using the first derivative and Savitsky-Golay filter, followed by low-level data fusion. A variable selection using the Boruta algorithm was applied, and both reduced and non-reduced matrices were used to train ML models. For detecting the presence of ILRs, random forest (RF) and support vector machine (SVM) models achieved 100 % accuracy. For discriminating between ILR types, the best performance was achieved by the RF model using the reduced matrix, correctly classifying 94.44 % of the samples. Only four sensors (SO₂, H₂S, CO, IRR) were necessary, indicating the potential for an optimized eNose design. This method offers advantages over traditional techniques, such as faster analysis, lower cost, and greater portability. Additionally, a web application was developed to allow users to automatically characterize fire debris using the algorithms.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"264 ","pages":"Article 105459"},"PeriodicalIF":3.7,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239971","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":"Partial Least Squares models under skew-normal and skew-t settings with applications","authors":"Andrés F. Ochoa-Muñoz ,&nbsp;Javier E. Contreras-Reyes ,&nbsp;Jaime Mosquera ,&nbsp;Rodrigo Salas","doi":"10.1016/j.chemolab.2025.105438","DOIUrl":"10.1016/j.chemolab.2025.105438","url":null,"abstract":"<div><div>In this work, a new Partial Least Square (PLS) model based on skew-normal (SN) and skew-<span><math><mi>t</mi></math></span> (ST) distributions is proposed. This new PLS model may be of interest for applications requiring regression with an asymmetric response variable, heavy-tails, and <span><math><mi>R</mi></math></span> support. Furthermore, like PLS, the PLS-SN and PLS-ST address the multicollinearity problem by finding the PLS components that are orthogonal to each other and maximize the covariance between the response variable and PLS components. Simulation studies were conducted to compare the goodness of fit of PLS-SN and PLS-ST models versus the PLS one, using datasets with different sample sizes. Additionally, two real-world data applications were performed, where more favorable information criteria values were found with the PLS-SN and PLS-ST models compared to the PLS one.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"264 ","pages":"Article 105438"},"PeriodicalIF":3.7,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229862","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":"Application of continuous wavelet transform and a novel hybrid approach based on discrete wavelet transform with principal component analysis and fuzzy inference system for the concurrent spectrophotometric analysis of cardiovascular drugs in biological samples","authors":"Maryam Sharifi Mikal,&nbsp;Mahmoud Reza Sohrabi,&nbsp;Mandana Saber Tehrani,&nbsp;Saeed Mortazavi Nik","doi":"10.1016/j.chemolab.2025.105460","DOIUrl":"10.1016/j.chemolab.2025.105460","url":null,"abstract":"<div><div>In this study, chemometric-assisted UV-spectrophotometric methods using continuous wavelet transform (CWT) and discrete wavelet transform (DWT) integrated with principal component analysis (PCA) and fuzzy inference system (FIS) were developed for the simultaneous determination of losartan (LOS) and diltiazem (DIL) in binary mixtures and urine samples without any separation process. In the CWT, the best zero crossing point was obtained through the Coiflet wavelet with an order of 2 (coif2) at a wavelength of 218 nm for LOS and Daubechies wavelet with an order of 2 (db2) at a wavelength of 242 nm for DIL. The linearity range was 1–9 μg/mL for LOS, while for DIL it was 6–18 μg/mL. The LOD was 0.5924 and 1.5416 μg/mL, while the LOQ was 1.7951 and 4.5454 μg/mL for LOS and DIL, respectively. The analysis of laboratory mixtures using CWT demonstrated mean recovery values equal to 98.07 % for LOS and 99.38 % for DIL, where the root mean square error was 0.2376 for LOS and 0.2523 for DIL. In DWT, the decomposition of absorption of mixtures was performed using biorthogonal (bior1.5), db2, and Demeyer (DM) at five levels, and their outputs were reduced via PCA. Their output was dimensionally reduced through PCA to serve as the input of the FIS. The wavelet of DM with mean recovery of 100.12 % and 100.02 %, as well as RMSE of 0.0075 and 0.0105 was selected as the best wavelet for LOS and DIL, respectively. The analysis of LOS and DIL in biological samples using the suggested methods indicated RSD&lt;1.6 % and mean recovery &gt;94 % and their results were compared to HPLC using the ANOVA test. It can be claimed that these suggested chemometrics methods with the help of spectrophotometry are economical, quick, easy, and reliable ways in quality control laboratories as an alternative to available techniques.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"264 ","pages":"Article 105460"},"PeriodicalIF":3.7,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212300","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":"Data fusion strategy for rapid prediction of critical quality attributes in JianWeiXiaoShi extract during pulsed vacuum drying process based on FT-NIR and Vis/NIR-HSI","authors":"Dongyin Yang ,&nbsp;Qing Tao ,&nbsp;Ziqian Wang ,&nbsp;Yuanhui Li ,&nbsp;Xiaorong Luo ,&nbsp;Xinhao Wan ,&nbsp;Mengxin Huang ,&nbsp;Xiang Wang ,&nbsp;Xuecheng Wang ,&nbsp;Zhenfeng Wu","doi":"10.1016/j.chemolab.2025.105451","DOIUrl":"10.1016/j.chemolab.2025.105451","url":null,"abstract":"<div><div>This study explored the feasibility of using two optical sensing methods - Fourier Transform Near-Infrared Spectroscopy (FT-NIR) and Visible/Near-Infrared Hyperspectral Imaging (Vis/NIR-HSI) - to quantitatively predict the critical quality attributes (CQAs) of JianWeiXiaoShi extract during pulsed vacuum drying (PVD) process. Additionally, a data fusion strategy was implemented to integrate the two spectral datasets, aiming to enhance the prediction accuracy and robustness of the quantitative models. Comparative analysis revealed that the FT-NIR model demonstrated higher accuracy in predicting moisture content, narirutin, and hesperidin levels, while the Vis/NIR-HSI model performed better in predicting color changes during the drying process of the extract. In addition to moisture content, the prediction model established by integrating the two spectral datasets through the data fusion strategy demonstrated more accurate predictive performance compared to single-spectrum models. Therefore, integrating FT-NIR and Vis/NIR-HSI spectral datasets through the data fusion strategy for online monitoring of quality changes during PVD of extract represents a rapid, non-destructive, and accurate approach to predict CQAs of materials. This study also provides essential technical support and valuable insights for advancing non-destructive analytical technologies in drying processes.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"264 ","pages":"Article 105451"},"PeriodicalIF":3.7,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222922","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 non-contact methodology based on imaging analysis, chemometrics, and machine learning to predict the lethality of stressors on C. elegans populations in liquid culture","authors":"Samuel Verdú ,&nbsp;Samuel Furones ,&nbsp;Raúl Grau ,&nbsp;José M. Barat ,&nbsp;Alberto Ferrer ,&nbsp;J.M. Prats-Montalbán","doi":"10.1016/j.chemolab.2025.105450","DOIUrl":"10.1016/j.chemolab.2025.105450","url":null,"abstract":"<div><div>This work was centred on developing an objective, reproducible and non-destructive methodology to predict the lethality of <em>C. elegans</em> populations contained in liquid culture mediums, addressing the handicaps presented for imaging analysis in those media types from a numerical point of view, applying chemometric and machine learning procedures on imaging data obtained with a basic image device and processing. The experiment was carried out by taking videos from nematode populations exposed to different conditions of three stressors (hydrogen peroxide, heat and UV radiation). The processed video datasets were used as predictors for different configurations in regression methods. The dimensionality reduction approach improved the prediction capacity of the imaging information compared to the raw dataset. Moreover, the best result was achieved with a super learner model, demonstrating the synergistic effect of combining results from models with lower prediction capacity to develop a meta-model with high prediction capabilities.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"264 ","pages":"Article 105450"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204724","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":"Multifactorial analysis of fluorescence detection for soil total petroleum hydrocarbons using random forest and multiple linear regression","authors":"Gaoyong Shi ,&nbsp;Ruifang Yang ,&nbsp;Nanjing Zhao ,&nbsp;Gaofang Yin ,&nbsp;Wenqing Liu","doi":"10.1016/j.chemolab.2025.105444","DOIUrl":"10.1016/j.chemolab.2025.105444","url":null,"abstract":"<div><div>This study combined random forest (RF) and multiple linear regression (MLR) approaches to analyze the influence of various factors on the fluorescence detection of total petroleum hydrocarbons (TPH) in soil. We considered the effects of soil moisture, organic matter, and minerals, and tested samples of three common soil types and varying concentrations of soil petroleum hydrocarbons using a self-developed fluorescence imaging technology. The fluorescence signals are greatly influenced by moisture, organic matter, and minerals, exhibiting distinct effects depending on the soil types and hydrocarbon concentrations. The RF model improves accuracy and consistency by constructing decision trees, making it appropriate for non-linear and high-dimensional data scenarios, although its underperformance in our study. The MLR model provides a comprehensive understanding of the linear relationships between variables, displaying better statistical performance and consistency in most cases of our experiment, with a coefficient of determination (R²) above 0.8, and Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) all lower than those of the RF. Our research provides an important scientific basis for monitoring, evaluating, and managing soil petroleum hydrocarbon pollution, aiding in the formulation of effective soil pollution prevention strategies, and offers a foundation for further research into environmental risk assessment and soil remediation.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"264 ","pages":"Article 105444"},"PeriodicalIF":3.7,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212466","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":"Chemometric analysis of UV–visible spectral data for the differentiation of Dalbergia latifolia and Dalbergia sissoo woods","authors":"Rohit Sharma,&nbsp;Rakesh Kumar","doi":"10.1016/j.chemolab.2025.105448","DOIUrl":"10.1016/j.chemolab.2025.105448","url":null,"abstract":"<div><div><strong><em>Dalbergia latifolia</em></strong> and <strong><em>Dalbergia sissoo</em></strong> woods are economically valuable due to their high-quality timber. However, the overexploitation of <strong><em>D. latifolia</em></strong> has led to the inclusion of <strong><em>D. sissoo</em></strong> along with <strong><em>D. latifolia</em></strong> in the <strong>CITES</strong> (Convention on International Trade in Endangered Species of Wild Fauna and Flora) <strong>list,</strong> which mandates regulated trade. Traditional wood identification methods, such as anatomical analysis, often fail to distinguish between these species. This study investigates the use of UV–visible spectroscopy combined with chemometric techniques - specifically principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) for the rapid and accurate differentiation of these two species. UV–visible spectral analysis of methanol extracts revealed distinct absorption peaks that facilitated the differentiation. The PCA, PLS-DA and LDA models demonstrated the effectiveness of this approach in distinguishing the two species' woods. This method offers a promising alternative for these <em>Dalbergia</em> species differentiation, providing a balance between speed, cost, and reliability. It is particularly valuable in situations where DNA barcoding or other high-precision techniques are impractical. The findings highlight the potential of UV–visible spectroscopy combined with multivariate analysis for timber differentiation and trade monitoring, contributing to conservation efforts.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"264 ","pages":"Article 105448"},"PeriodicalIF":3.7,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204725","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":"Editorial of the XI Colloquium Chemiometricum Mediteraneum (CCM2023)","authors":"D. Ballabio,&nbsp;P. Facco,&nbsp;F. Marini","doi":"10.1016/j.chemolab.2025.105447","DOIUrl":"10.1016/j.chemolab.2025.105447","url":null,"abstract":"","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105447"},"PeriodicalIF":3.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212361","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":"Comparison of colour and texture feature extraction methods to predict anthocyanins content in Sangiovese grapes","authors":"Camilla Menozzi ,&nbsp;José Manuel Prats-Montalbán ,&nbsp;Rosalba Calvini ,&nbsp;Alessandro Ulrici","doi":"10.1016/j.chemolab.2025.105446","DOIUrl":"10.1016/j.chemolab.2025.105446","url":null,"abstract":"<div><div>Colour and texture are the two main sources of information contained in RGB images of food products. Different image-level approaches are available to analyse the image properties based on the extraction of colour and texture features, and the selection of the most appropriate method is a critical point, since it could significantly impact the outcomes. The present study has three main objectives. Firstly, we propose an innovative data dimensionality reduction method to extract and codify the texture features of an RGB image into a one-dimensional signal, named texturegram (TXG). Then, TXG approach is compared with different image-level feature extraction methods, such as colourgrams (CLG), Soft Colour Texture Descriptors (SCTD) and Grey Level Co-occurrence Matrices (GLCM). These techniques were used to analyse a benchmark dataset of RGB images already considered in a previous study to build Partial Least Squares (PLS) models and relate the image features with anthocyanins content of red grape samples. We also investigated the possible advantages of combining the colour and texture information brought by the different image-level techniques using data fusion. PLS models were calculated considering different partitions of the RGB image dataset into training and test set. The performances of the different models were statistically evaluated by means of Analysis of Variance (ANOVA) and Principal Component Analysis (PCA). Overall, the results suggested an interesting, even if slight, improvement of the model performances when fusing CLG and TXG, but also highlighted the hybrid nature of TXG to simultaneously explore colour and texture properties.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105446"},"PeriodicalIF":3.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170238","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":"Applications of artificial intelligence and machine learning in combination with surface-enhanced Raman spectroscopy (SERS)","authors":"Hashim Jabbar ,&nbsp;Inass Abdulah Zgair ,&nbsp;Kamran Heydaryan ,&nbsp;Shaymaa Awad Kadhim ,&nbsp;Saeideh Mehmandoust ,&nbsp;Vahid Eskandari ,&nbsp;Hossein Sahbafar","doi":"10.1016/j.chemolab.2025.105445","DOIUrl":"10.1016/j.chemolab.2025.105445","url":null,"abstract":"<div><div>Surface-enhanced Raman spectroscopy (SERS) offers exceptional sensitivity for identifying and detecting a wide range of compounds by greatly enhancing Raman signals from molecules on metal surfaces. SERS application has been further transformed by the integration of artificial intelligence (AI) and machine learning (ML), which automates spectrum interpretation, enhances identification accuracy, and optimizes experimental settings. This paper examines current developments in the synergistic use of AI and ML with SERS in a variety of disciplines, including environmental monitoring, food safety, pathogen detection, and disease diagnosis. Studies that have been published show that these models can distinguish between analytes such as bacteria, viruses, cancer cells, and chemical substances with above 95 % accuracy. The promise of these methods is shown by the fact that some research even showed 100 % accuracy in sample identification. Food safety, environmental monitoring, and clinical diagnostics might all be revolutionized by SERS-ML techniques because of their great sensitivity, specificity, and reliability. Future research should focus on extending clinical applications, enhancing substrate capabilities and detection limitations, incorporating sophisticated machine learning techniques, and increasing the application broadness. In order to improve the robustness and practicality of these methodologies, further validation in larger cohorts and real-world contexts is also emphasized. The study demonstrates how combining AI/ML with SERS offers the potential to fundamentally change the fields of materials research, environmental monitoring, diagnostics, and other related fields.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105445"},"PeriodicalIF":3.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185730","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}