Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Functionality of rotational ambiguity in self-modeling methods to signal contribution of chemical components","authors":"Hamideh Bakhshi ,&nbsp;Hamid Abdollahi ,&nbsp;Róbert Rajkó","doi":"10.1016/j.chemolab.2025.105410","DOIUrl":"10.1016/j.chemolab.2025.105410","url":null,"abstract":"<div><div>Self-modeling curve resolution (SMCR) methods are widely acknowledged as potent tool in chemometrics, facilitating the decomposition of bilinear data matrices into chemically interpretable matrices. Nonetheless, these methods frequently yield results with ambiguities, particularly rotational ambiguity, leading to non-uniqueness of outcomes. This study investigates the influence of signal contributions of chemical components on rotational ambiguity. Utilizing simulated data from HPLC-DAD and one real excitation emission fluorescence data, the impacts of signal contributions of chemical components on spectral and concentration profiles are assessed. The findings illustrate that increasing the signal contribution of a chemical component can mitigate rotational ambiguity. Furthermore, the efficacy of employing second-order standard addition in reducing rotational ambiguity and enhancing the accuracy of quantitative analyses is examined.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"262 ","pages":"Article 105410"},"PeriodicalIF":3.7,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887290","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":"Significance determination of individual metabolic abundance changes owing to environmental impacts: Factorial design t-distribution spectral representations","authors":"Gustavo G. Marcheafave ,&nbsp;Leonardo J. Duarte ,&nbsp;Elis D. Pauli ,&nbsp;Ieda S. Scarminio ,&nbsp;Roy E. Bruns","doi":"10.1016/j.chemolab.2025.105413","DOIUrl":"10.1016/j.chemolab.2025.105413","url":null,"abstract":"<div><div>Currently quantitative metabolic analysis is work-intensive and time-consuming normally demanding the use of both chromatography and mass spectrometry. Screening spectral data with principal component analysis is fast and helps identify metabolites. However, it has not been used for quantitative analysis as it cannot determine the statistical significance of individual metabolite abundance changes owing to simulated environmental impacts. Factorial design t univariate spectral representations provide a relatively fast and simple method to determine the statistical significance of individual NMR channels forming peaks and help fill the gap between qualitative and quantitative metabolic analysis of plants suffering environmental impacts. These composite spectral representations, introduced and described for the first time, are univariate statistical t values calculated from factorial design spectra plotted as a function of the analytical channels. They are simple to understand by chemists and biologists with limited statistical knowledge as they only use one basic statistical t distribution equation. We demonstrate their usefulness with factorial design analyses of ¹H NMR spectra of yerba mate samples obtained from different solvent extracts of a mixture design. The spectral representation peak locations are almost the same as those of principal component loadings of ASCA effect matrices although their peak heights are much different and correspond to the statistical significance levels of individual metabolic abundance changes. Spectral representations have ordinates of calculated t values and results for different data sets can be analyzed simultaneously on a common graph whereas this is not possible for loadings that correspond to different PCA coordinate spaces.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105413"},"PeriodicalIF":3.7,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891105","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":"Supervised laser induced breakdown spectroscopy classification for prehistoric chert provenance: A methodological framework","authors":"Julien Le Guirriec ,&nbsp;Jonàs Alcaina-Mateos ,&nbsp;Bruno Bousquet ,&nbsp;François-Xavier Le Bourdonnec ,&nbsp;Marta Sánchez de la Torre","doi":"10.1016/j.chemolab.2025.105411","DOIUrl":"10.1016/j.chemolab.2025.105411","url":null,"abstract":"<div><div>The sourcing of lithic artefacts, and in particular chert, is a key proxy for archaeologist to better understand hunter-gatherer populations. The heterogeneous structure of chert samples, low trace element content, and variability of outcrops make the geochemical characterisation a methodological challenge. Laser Induced Breakdown Spectroscopy (LIBS) can overcome the issues of variability by enabling fast and low-cost and nearly non-destructive analysis of samples. This paper presents a protocol for chert sourcing using LIBS, by comparing several supervised classification models and signal pre-processing with two different approaches: a selection of features or the use of broadband spectra, with an example application on marine flysch cherts from the central Pyrenes (France) presenting similar micropaleontological and textural features.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105411"},"PeriodicalIF":3.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898609","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":"Enhanced data point importance for subset selection in partial least squares regression: A comparative study with Kennard-Stone method","authors":"Mahia V. Solout ,&nbsp;Somaye Vali Zade ,&nbsp;Hamid Abdollahi ,&nbsp;Jahan B. Ghasemi","doi":"10.1016/j.chemolab.2025.105416","DOIUrl":"10.1016/j.chemolab.2025.105416","url":null,"abstract":"<div><div>In multivariate data analysis, the selection of representative subsets of samples is crucial for developing accurate predictive models. This study evaluates the application of the Enhanced Data Point Importance (EDPI) method for subset selection, comparing its performance with the widely-used Kennard-Stone algorithm. The EDPI method ranks all the data points using the DPI and layered convex hull approach, resulting in a ranked sequence of points based on their importance in the dataset, with the most important point being the most informative. Both methods were applied to two distinct datasets, and Partial Least Squares Regression (PLSR) models were developed for each subset to assess predictive performance. The EDPI method demonstrated comparable performance to the Kennard-Stone method across various sample sizes. The EDPI-PLS models achieved lower Root Mean Square Error of Prediction (RMSEP) values with fewer samples, indicating efficient subset selection, and the method is less inclined to select the influential points in the dataset. Moreover, the running time analysis highlighted the computational efficiency of the EDPI method, especially in high-dimensional datasets. These findings suggest that EDPI is a robust and informative strategy for sample subset selection, offering advantages in predictive accuracy and computational efficiency.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105416"},"PeriodicalIF":3.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891104","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":"AI in MRI brain tumor diagnosis: A systematic review of machine learning and deep learning advances (2010–2025)","authors":"Vaidehi Satushe ,&nbsp;Vibha Vyas ,&nbsp;Shilpa Metkar ,&nbsp;Davinder Paul Singh","doi":"10.1016/j.chemolab.2025.105414","DOIUrl":"10.1016/j.chemolab.2025.105414","url":null,"abstract":"<div><div>Brain tumors present critical health challenges due to abnormal tissue growth within the brain, potentially leading to life-threatening conditions if left untreated. MRI stands as the primary diagnostic tool for identifying brain tumors, offering superior resolution and tissue differentiation compared to other imaging modalities. This systematic literature review explores the application of ML and DL techniques in enhancing the diagnosis of brain tumors from MRI scans. ML and DL algorithms, particularly CNNs, have demonstrated significant success in automating the detection and classification of brain tumors by analyzing complex imaging patterns. The review follows PRISMA guidelines, synthesizing findings from studies between 2010 and 2025. Key themes include the utilization of diverse datasets, advanced feature extraction methods, and the computational efficiency of ML and DL models. Despite notable advancements, challenges such as data diversity and model interpretability persist, underscoring the need for ongoing research to optimize these techniques for enhanced clinical outcomes in brain tumor diagnosis. The review discusses the effectiveness of CNNs, SVMs, ensemble methods, transformers and other ML approaches in improving diagnostic accuracy and reliability. It also addresses future research directions aimed at overcoming current limitations and advances in the field.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105414"},"PeriodicalIF":3.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902299","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 new versatile algorithm to extract particle’s features from FESEM images: method evaluation and a case study on rice kernels","authors":"Mattia Sozzi ,&nbsp;Nicola Cavallini ,&nbsp;Alessandro Chiadò ,&nbsp;Gentian Gavoci ,&nbsp;Enrico Cantaluppi ,&nbsp;Filip Haxhari ,&nbsp;Francesco Savorani","doi":"10.1016/j.chemolab.2025.105415","DOIUrl":"10.1016/j.chemolab.2025.105415","url":null,"abstract":"<div><div>Image analysis approaches allow to quickly extract important information from images of diverse nature. Many techniques produce as a result images that contain regular and irregular objects. The ability of automatically extracting the objects and their related morphological features and properties is becoming fundamental, especially when the number of images to analyse is consistent.</div><div>In this context, a new algorithm able to extract a series of morphological features from FESEM images was developed. Starting from a case study on 54 varieties of rice kernels, 220 images were acquired, and the algorithm was coded with the aim of extracting information from the round-shaped starch particles naturally present in rice kernels. The algorithm constitutes of different steps to segment the images and identify the object shapes and boundaries. Once those objects are identified, the algorithm extracts their morphological features, the number of identified objects and the amount of empty spaces among those objects.</div><div>The developed algorithm is suitable for a rapid and automated analysis of several images, with the aim of extracting object-related morphological features and information about the general objects space disposition. The use of adaptive thresholds and correction steps allow to analyse images of different natures containing also defective and non-representative objects that will be automatically removed from the features calculation. In addition, to evaluate the algorithm performances, a Design of Experiment approach was developed to determine the effect of the input parameters choice on the algorithm output results, highlighting which parameters show a stronger effect on the output.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105415"},"PeriodicalIF":3.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887709","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":"Classification of maize leaf diseases with deep learning: Performance evaluation of the proposed model and use of explicable artificial intelligence","authors":"Feyyaz Alpsalaz ,&nbsp;Yıldırım Özüpak ,&nbsp;Emrah Aslan ,&nbsp;Hasan Uzel","doi":"10.1016/j.chemolab.2025.105412","DOIUrl":"10.1016/j.chemolab.2025.105412","url":null,"abstract":"<div><div>Maize leaf diseases pose significant threats to global agricultural productivity, yet traditional diagnostic methods are slow, subjective, and resource-intensive. This study proposes a lightweight and interpretable convolutional neural network (CNN) model for accurate and efficient classification of maize leaf diseases. Using the ‘Corn or Maize Leaf Disease Dataset’, the model classifies four disease categories Healthy, Gray Leaf Spot, Common Rust, and Northern Leaf Blight with 94.97 % accuracy and a micro-average AUC of 0.99. With only 1.22 million parameters, the model supports real-time inference on mobile devices, making it ideal for field applications. Data augmentation and transfer learning techniques were applied to ensure robust generalization. To enhance transparency and user trust, Explainable Artificial Intelligence (XAI) methods, including LIME and SHAP, were employed to identify disease-relevant features such as lesions and pustules, with SHAP achieving an IoU of 0.82. The proposed model outperformed benchmark models like ResNet50, MobileNetV2, and EfficientNetB0 in both accuracy and computational efficiency. Robustness tests under simulated environmental challenges confirmed its adaptability, with only a 2.82 % performance drop under extreme conditions. Comparative analyses validated its statistical significance and practical superiority. This model represents a reliable, fast, and explainable solution for precision agriculture, especially in resource-constrained environments. Future enhancements will include multi-angle imaging, multimodal inputs, and extended datasets to improve adaptability and scalability in real-world conditions.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"262 ","pages":"Article 105412"},"PeriodicalIF":3.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874963","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":"Evaluation of n-component surrogate mixtures formulated for jet fuel physicochemical property predictions","authors":"Abhinav Abraham ,&nbsp;Hadis Anahideh ,&nbsp;Eric Mayhew ,&nbsp;Kenneth Brezinsky ,&nbsp;Patrick T. Lynch","doi":"10.1016/j.chemolab.2025.105409","DOIUrl":"10.1016/j.chemolab.2025.105409","url":null,"abstract":"<div><div>Compression ignition (CI) engines operating on aviation fuels are sensitive to fuel property variation but can maintain robust ignition with inline, feedforward sensing and control. Due to the expensive and time-consuming nature of conventional fuel property testing methods, alternate methods like Quantitative Structure Property Relationship (QSPR) models have been developed, which link fuel properties to their molecular structures, but many fail to account for the complex intermolecular interactions in multicomponent mixtures like jet fuels. Recent studies have employed machine learning (ML) models trained on surrogate mixtures of jet fuels to predict fuel properties like the derived cetane number, density, and viscosity; and they show good performance. These surrogate mixtures, composed of hydrocarbons representing real fuel components, facilitate dataset generation for ML model training. However, the sufficiency of such datasets has not been studied exhaustively. This study investigates the sufficiency of datasets composed of jet fuel surrogate mixtures in predicting fuel properties for fuels beyond the training set. While dataset sufficiency does depend on the population and the property modeled, as a heuristic, we emphasize spanning the chemical functional groups present in likely mixtures and doing this with low correlation and high diversity in the chemical functional groups. A large dataset of surrogate mixtures was generated spanning the ranges of UNIversal Functional Activity Coefficients (UNIFAC) functional groups present in real jet fuels. This study establishes criteria for dataset sufficiency, suggesting a minimum dataset size for developing a robust ML model. We also report the tools and codes developed for determining the minimum number of surrogate mixtures needed for the purpose of training ML models given user defined UNIFAC functional group compositions and a palette of suitable neat hydrocarbon components. This research contributes to developing efficient, accurate ML models for predicting the properties of complex fuel mixtures, with applications in fuel formulation, sensing, and control.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105409"},"PeriodicalIF":3.7,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895185","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":"Visualized correlation and distance preserving dimensionality reduction method","authors":"Zhonghai He ,&nbsp;Zhanbo Feng ,&nbsp;Haoxiang Zhang ,&nbsp;Xiaofang Zhang","doi":"10.1016/j.chemolab.2025.105406","DOIUrl":"10.1016/j.chemolab.2025.105406","url":null,"abstract":"<div><div>A large of existing dimensionality reduction methods are aimed at preserving some properties of data, which cannot take label information into account. With the aim of reduced low-dimensional coordinate is used as tool for timing judgment of model updating, the concentration information should be incorporated into dimensionality reduction procedure, which is presented and named as Visualized Correlation and Distance Preserving dimensionality reduction method. To address the difficulty of 2D coordinate and 1D label correlation computation, pairwise distance matrices in both the subspace and label space are computed and the strictly lower triangular parts of these matrices are extracted and vectorized in column-major order, resulting in two vectors so that correlation can be computed. Distance preservation term is included as sub-objective function to ensure the low distance dissimilarity between high and low coordinates. To reduce structural loss caused by sequential dimensionality reduction method, the projection matrix is concatenated to vector then optimized to ensure projection vectors are optimized synchronously. PCA transformation is continued to adjust the reduced coordinates to better suited for visual judgment.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"262 ","pages":"Article 105406"},"PeriodicalIF":3.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850369","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":"Generalized continuum regression (GCR): An advanced multivariate method for precise dimensionality reduction and efficient regression modeling","authors":"Yang Chen ,&nbsp;Chonghui Dan ,&nbsp;Yao He,&nbsp;Xiaoyuan Zheng","doi":"10.1016/j.chemolab.2025.105407","DOIUrl":"10.1016/j.chemolab.2025.105407","url":null,"abstract":"<div><div>The collinearity inherent in high-dimensional, low-sample-size (HDLSS) data critically undermines the accuracy of chemometric regression. We have proven that when predictor matrix <span><math><mrow><mi>X</mi></mrow></math></span> has full row rank, the optimal dimensionality of <em>X</em>-block latent variables (LVs) in multivariate linear regression equals <span><math><mrow><mi>r</mi><mi>a</mi><mi>n</mi><mi>k</mi><mrow><mo>(</mo><mi>Y</mi><mo>)</mo></mrow></mrow></math></span>. Based on this theoretical foundation, we develop generalized continuum regression (GCR), an advanced multivariate regression method rooted in continuum canonical correlation (CCC). GCR's core innovation lies in the extension of CCC's scalar parameter <span><math><mrow><mi>α</mi></mrow></math></span> to a vector form for precise dimension reduction in multivariate regression. We also develop an efficient numeric algorithm for computational speed. Real-world implementations on two spectroscopic datasets confirm that GCR adopts LVs with dimensionality equal to the rank of <span><math><mrow><mi>Y</mi></mrow></math></span>, validating its precise dimensionality reduction. When compared to CCC regression (CCCR), GCR exhibits superior performance with: (1) a 7.28 %–43.70 % reduction in mean-squared error for validation (MSEV) when utilizing two or three latent variables (LVs); and (2) a 30 to 55-fold increase in solution speed. These findings highlight GCR's potential as a valuable tool for dimensionality reduction and regression modeling in chemometrics, specifically for HDLSS analysis.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"262 ","pages":"Article 105407"},"PeriodicalIF":3.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878346","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}