Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Leveraging PLS and Lasso in MARS for high-dimensional FTIR data: A hybrid proposed model for antidiabetic activity of schiff base compounds","authors":"Sughra Sarwar,&nbsp;Tahir Mehmood,&nbsp;Muhammad Arfan","doi":"10.1016/j.chemolab.2025.105418","DOIUrl":"10.1016/j.chemolab.2025.105418","url":null,"abstract":"<div><div>In this study, we utilized Fourier Transform Infrared (FTIR) spectral data to create and analyze multiple regression models to predict the anti-diabetic potential of synthesized Schiff bases. Schiff bases are a wide range of compounds characterized by a double bond between the nitrogen and carbon atoms. Their versatility stems from various strategies by which these can be coupled with multiple alkyl or aryl substitutes. The models that were examined consisted of MARS, PLS, SPLS, KPLS, MARS-SPLS, MARS-Kernel-PLS, and an innovative method called MARS-PLS-Lasso, which combines the traditional MARS algorithm with partial least squares and Lasso regularization. To assess the efficacy of the proposed method, we used a high-dimensional spectral data set comprising 19 samples and 1627 predictors. To capture nonlinear interactions in the data, MARS-PLS-Lasso improves the conventional MARS approach by creating adaptive basis functions for each predictor. Lasso regularization was used to choose the most pertinent basis functions and make sure that only the most important predictors were kept. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were used on train and test datasets to evaluate the prediction performance. The MARS-PLS-Lasso model outperformed the typical MARS (RMSE = 30.48, MAE = 23.46) and PLS (RMSE = 14.00, MAE = 11.90) models by achieving the lowest test RMSE of 13.00 and MAE of 10.55. When we performed simulation study, MARS-PLS-LASSO again performed the best among basis-integrated models in terms of both low and high correlated data, with the lowest RMSE (0.4708) and MAE (0.2812) in case of data with dimensions 20, 50 and RMSE (0.685, 0.4806) and MAE (0.1325, 0.3819) using data with dimensions 20, 5000 respectively. These results show that the best way to model complicated relationships in high-dimensional data is to use MARS-PLS-Lasso to improve predictive accuracy.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105418"},"PeriodicalIF":3.7,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071931","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 novel three-dimensional strategy to elucidate the interactions of two fluoroquinolones with DNA using two-dimensional fluorescence maps","authors":"Erdal Dinç ,&nbsp;Asiye Üçer","doi":"10.1016/j.chemolab.2025.105437","DOIUrl":"10.1016/j.chemolab.2025.105437","url":null,"abstract":"<div><div>A novel three-dimensional strategy was introduced to elucidate the interactions of two fluoroquinolones, ciprofloxacin (CIP) and norfloxacin (NOR), with DNA using two-dimensional fluorescence maps (fluorescence excitation and emission measurements). This chemometric strategy is based on decomposing fluorescence excitation-emission matrices to extract detailed excitation, emission, and concentration profiles. By recording the fluorescence spectra after the reactions of CIP and NOR with calf thymus DNA and applying parallel factor analysis (PARAFAC) to the resulting data array, the quantitative estimations of the excitation and emission curves and the relative concentrations of the drugs and their DNA complexes were achieved. This investigation revealed the intricate interactions between CIP and DNA and NOR and DNA, characterized by Stern-Volmer constants (K_sv) and quenching rate constants (K_q). These constants related to the drug-DNA reaction equilibrium were derived from the correlation between actual DNA concentrations and the estimated relative amounts of CIP and NOR. The binding constants obtained through our innovative three-dimensional strategy were rigorously compared with those determined by classic spectrophotometric and spectrofluorometric methods, highlighting the efficacy and accuracy of our proposed approach.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105437"},"PeriodicalIF":3.7,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083993","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":"Multiscale transfer learning improves soil calcium carbonate equivalent measurement in data-limited regions using Vis-NIR spectroscopy","authors":"Jingyun Huang ,&nbsp;Geng Leng ,&nbsp;Siyu Liu ,&nbsp;Zeyuan Zhang ,&nbsp;Hongbo Zhang ,&nbsp;Xiangchao Fu ,&nbsp;Yuewu Wang ,&nbsp;Zhenwei Xie ,&nbsp;Junwei Wang","doi":"10.1016/j.chemolab.2025.105436","DOIUrl":"10.1016/j.chemolab.2025.105436","url":null,"abstract":"<div><div>Accurate measurement of soil calcium carbonate equivalent (CCE) is essential for agricultural management and carbon cycle assessments. While Vis-NIR offers a rapid and non-invasive alternative to traditional labor-intensive chemical method, its effectiveness is often constrained by regional variability and the scarcity of local datasets, limiting its applicability in data-scarce regions. Here, we introduce an innovative methodology that leverages large-scale soil spectral datasets and applies transfer learning via transfer component analysis (TCA) to enhance Vis-NIR model performance for measuring cropland soil CCE in data-scarce regions. Our TCA-based transfer models significantly outperformed locally constructed models, achieving an R² of 0.893, RMSE of 19.569, and RPD of 3.17, representing a 64.52 % improvement in accuracy. Remarkably, the proposed transfer learning strategies showcased consistent improvements even with minimal local data (R² = 0.852 and RMSE = 23.077 when only 30 local samples were available), highlighting their robustness and scalability. These findings demonstrate that the integration of transfer learning with Vis-NIR offers a reliable solution for soil CCE measurement in regions lacking sufficient local data, advancing the broader application of spectral analysis in soil science and contributing to more effective agricultural practices.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105436"},"PeriodicalIF":3.7,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932014","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":"MIAUnet++: Multi-inception attention network for MR spine image segmentation","authors":"Lei Li ,&nbsp;Xulong Fu ,&nbsp;Juan Qin,&nbsp;Lianrong Lv,&nbsp;Mengdan Cheng,&nbsp;Biao Wang,&nbsp;Junjie He,&nbsp;Dan Xia,&nbsp;Meng Wang,&nbsp;Haiping Ren,&nbsp;Shike Wang","doi":"10.1016/j.chemolab.2025.105425","DOIUrl":"10.1016/j.chemolab.2025.105425","url":null,"abstract":"<div><div>Accurate segmentation of MR spine images is of great important for the evaluation of spinal diseases. With its unique encoder-decoder symmetric architecture and jump connection design, Unet has become a benchmark model in medical image segmentation since its proposal in 2015. However, the traditional Unet encoder-decoder structure suffers from the problem of semantic information loss during deep feature extraction, and the single-hop connection method is difficult to effectively fuse multi-scale features, resulting in limited segmentation accuracy for complex structures. Responding to these challenges, this study proposes a multi-Inception attention Unet++ (MIAUnet++) model. The model uses different Inception modules to replace the standard convolutional layers of the Unet++, which significantly enhances the multi-scale feature extraction capability by extending the network width and depth. At the same time, multiple attention mechanisms are introduced to further enhance the sensitivity of the network to the boundary information, so that the model can more accurately capture the subtle anatomical structural differences between spine-soft tissues, thus improving the segmentation performance of the network. The experimental results show that the proposed model performs well in the spine image segmentation task with IoU, DSC, TPR and PPV reaching 0.8327, 0.9041, 0.9060 and 0.9068 respectively, outperforming the benchmark method in all metrics. It shows that the proposed method has good performance in MR Spine image segmentation.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105425"},"PeriodicalIF":3.7,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943438","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":"Kernel-based reliability potential to assist QSPR prediction and system transfer of SFC−MS retention time","authors":"Viviana Consonni ,&nbsp;Cristian Rojas ,&nbsp;Jessica Guerrero ,&nbsp;Mateo Mendoza ,&nbsp;Veronica Termopoli ,&nbsp;Davide Ballabio","doi":"10.1016/j.chemolab.2025.105435","DOIUrl":"10.1016/j.chemolab.2025.105435","url":null,"abstract":"<div><div>Quantitative Structure-Property Relationship (QSPR) allows <em>in silico</em> prediction of chromatographic retention time of chemicals from their molecular structure. The QSPR approach relies on the principle that retention time is influenced by molecular properties, which can be encoded into chemical-structural descriptors and modelled with chemometric techniques. This study focuses on <em>in silico</em> prediction of supercritical fluid chromatography (SFC) retention time. First, we developed a novel QSPR model for predicting retention times measured with high-resolution mass spectrometry (SFC-HRMS); then, the same model was adapted to predict retention times of a different chromatographic system based on low-resolution mass spectrometry (SFC-LRMS). We used a kernel-based approach to account for prediction uncertainties and to leverage the model reliability by defining a structural domain in the chemical space where lower uncertainty is expected. Results demonstrated that the proposed approach can predict retention time across two chromatographic systems when considering the reliability domain established with the kernel approach. The use of the proposed method for estimating the reliability domain can enhance the application of QSPR models to predict and transfer retention times in chromatographic systems similar to those used for the calibration and, consequently, simplify the identification of compounds in untargeted analyses and boost the design, development and optimization of novel chromatographic methods.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105435"},"PeriodicalIF":3.7,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936288","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 sensitive efficient multiple predictable feature extraction and fusion method for complicated industrial fault detection","authors":"Xiaogang Deng,&nbsp;Yujiang Wang,&nbsp;Wenjie Yang","doi":"10.1016/j.chemolab.2025.105423","DOIUrl":"10.1016/j.chemolab.2025.105423","url":null,"abstract":"<div><div>In recent years, graph-based predictable feature analysis (GPFA) has emerged as a potential tool in industrial fault diagnosis. However, the basic GPFA can only extract the linear predictable feature information, which is incompetent in the scenarios of the complicated linear-nonlinear hybrid data characteristic. To handle this issue, an improved fault detection method, called Sensitive Efficient Multiple Graph-based Predictable Feature Analysis (SEM-GPFA), is presented for mining the linear and nonlinear predictable features simultaneously. In this method, a multiple predictable feature fusion framework is constructed to utilize the complementary advantages of linear and nonlinear features fully. The linear predictable features are extracted by the basic GPFA, while the nonlinear predictable features are captured by the nonlinear GPFA model. To address the high computational complexity of traditional kernel methods, a random Fourier mapping method is used to improve the nonlinear feature extraction approach, enhancing operational efficiency. Considering that the specific fault information may be concealed in massive features of the model, a fault-sensitive feature highlighting strategy is designed by assigning relatively large weights to emphasize the influence of significant fault features. Finally, case studies on the Continuously Stirred Tank Reactor (CSTR) process and the Tennessee Eastman (TE) chemical system are conducted to demonstrate the superiority of the proposed method.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105423"},"PeriodicalIF":3.7,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922230","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":"Computing eccentricity based topological indices of silicate network with applications to QSPR/QSAR analysis","authors":"Deepika S.,&nbsp;Arathi P.","doi":"10.1016/j.chemolab.2025.105424","DOIUrl":"10.1016/j.chemolab.2025.105424","url":null,"abstract":"<div><div>Topological indices are one of the useful tools in graph theory provided by chemists, which is a numeric quantity that helps to predict physico-chemical properties of chemical compounds. To classify molecules and model unknown structures the topological representations of molecular structures with the necessary properties can be used. The topological indices of distance-based methods are advanced tools that show effective significance in chemical graph theory (CGT). In recent years, many topological indices (TIs) have been studied and applied in theoretical chemistry and have been widely used to investigate quantitative structure-property relationships (QSPR), and quantitative structure-activity relationship (QSAR) research, and to evaluate networks. In this paper, the various eccentricity-based topological indices were calculated for the silicate network and plotted as 3D graphs for each calculated indices. In contrast, physico-chemical properties such as ‘boiling point (BP), enthalpy (E), molecular weight (MW), complexity (C), molar refractivity (MR), polar surface area (PSA), and refractive index (RI)’ were computed for silicate compounds using a linear regression model and plotted graphs for each properties with the values of correlated coefficients. These results have been used for the development of drug delivery systems.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105424"},"PeriodicalIF":3.7,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916804","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":"Assessing soil texture classification accuracy based on VNIR lab spectroscopy","authors":"Ternikar Chirag Rajendra ,&nbsp;Cécile Gomez ,&nbsp;Subramanian Dharumarajan ,&nbsp;D. Nagesh Kumar","doi":"10.1016/j.chemolab.2025.105419","DOIUrl":"10.1016/j.chemolab.2025.105419","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Soil texture is an important soil parameter controlling various physical, chemical and biological soil properties. Visible Near-Infrared (VNIR) spectroscopy has garnered attention due to its simplicity, non-destructive nature, absence of hazards and rapidity. Due to its popularity, numerous studies employ this technique without adhering to the unity constraint on predicted fractions. This study aims to assess the accuracy of soil texture classification in the USDA textural triangle through laboratory VNIR spectra. Five different approaches were evaluated in this study: i) four approaches (&lt;em&gt;A1-A4&lt;/em&gt;), defined as regression-assisted classification techniques, were based on the Partial Least Squares Regression (PLSR) method to predict quantitative fractions followed by a texture classification based on the USDA texture triangle and ii) one approach (&lt;em&gt;A5&lt;/em&gt;), defined as a direct classification method, was based on the Partial Least Squares Discriminant Analysis (PLS-DA) classifier to classify soil texture using spectra directly. Each regression-assisted classification approach varies in predicting fractions and ensuring the unity constraint on the predicted fractions. In approach &lt;em&gt;A1&lt;/em&gt;, the clay, silt and sand fractions predicted by PLSR for each sample were normalized to ensure sum-to-unity. In approach &lt;em&gt;A2&lt;/em&gt;, the silt content was derived as residual from the clay and sand contents predicted by PLSR for each sample, ensuring unity. In Approach &lt;em&gt;A3&lt;/em&gt;, the clay, silt and sand fractions were simultaneously predicted using a multi-output variant of PLSR. Approach &lt;em&gt;A4&lt;/em&gt; employed PLSR on log-ratio transformed (LRT) fractions, enabling simultaneous prediction and inherently ensuring sum-to-unity. Approach &lt;em&gt;A4&lt;/em&gt; via LRT utilizes information about the relative fractions of soil texture instead of the absolute fractions. For the regression-based fraction predictions, approaches (&lt;em&gt;A1-A4&lt;/em&gt;) achieved similar performances, with mean coefficients of determination (R&lt;sup&gt;2&lt;/sup&gt;) of 0.88–0.90 for clay (RMSE: 4.2–4.4 %), 0.82–0.84 for sand (RMSE: 6.1–6.5 %), but lower (R&lt;sup&gt;2&lt;/sup&gt; = 0.29–0.38) for silt (RMSE: 3.8–4.1 %). Approach &lt;em&gt;A2&lt;/em&gt;, which infers silt as a residual, yielded poorer silt predictions. Despite these quantitative differences, the resulting classification accuracies in the USDA texture triangle were high with overall accuracy of 71–71.8 %, average accuracy of 62.4–65.3 % and Cohen's Kappa of 0.61–0.62 for &lt;em&gt;A1&lt;/em&gt;-&lt;em&gt;A4&lt;/em&gt; while &lt;em&gt;A5&lt;/em&gt;, attained only 56.4 % overall accuracy and Cohen's Kappa of 0.42. Among the regression-assisted methods, Approach &lt;em&gt;A4&lt;/em&gt; using log-ratio transformations of clay, silt, and sand simultaneously enforced compositional constraints and matched the best classification performances (OA = 71.4 %, AA = 65.3 %, K = 0.62) while requiring fewer models. This work highlighted that i) the four regression-assisted classification approaches provided comparabl","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105419"},"PeriodicalIF":3.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928202","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":"Identification of Monteverdia ilicifolia by fourier-transform mid-infrared spectroscopy associated with chemometrics and machine learning","authors":"Ahmad Kassem El Zein ,&nbsp;Alexandre de Fátima Cobre ,&nbsp;Raul Edison Luna Lazo ,&nbsp;Kevin Alves Antunes ,&nbsp;Jane Manfron ,&nbsp;Luana Mota Ferreira ,&nbsp;Roberto Pontarolo","doi":"10.1016/j.chemolab.2025.105420","DOIUrl":"10.1016/j.chemolab.2025.105420","url":null,"abstract":"<div><div><em>Monteverdia ilicifolia</em> (Mart. Ex Reissek) Biral, a member of the Celastraceae botanical family, is widely recognized for its broad-spectrum therapeutic effects in South America, particularly in Brazil, where it is commonly referred as “espinheira-santa”. This study aimed to develop a chemometric and machine learning-based method for to accurately identify and differentiate <em>M. ilicifolia</em> from morphologically similar species used as adulterants. Fourier transform mid-infrared spectrometry (MIR-FTIR) was used to analyze leaves (n = 6 species, 3000 spectra), powders (n = 6 species, 3000 spectra) and extracts samples (n = 6 species, 600 spectra). The spectral datasets were predicted by Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). The PLS-DA model was challenged with samples of other common plant species (n = 3) and commercial available <em>M. ilicifolia</em> (n = 10) to evaluate its predictive capability. PCA successfully distinguished between the plant species. PLS-DA achieved superior performance with extract samples, exhibiting sensitivity, specificity and accuracy of 94, 100 and 99 %, respectively. Machine learning algorithms were developed to better represent the leaves and powder samples through Random Forest and 10-fold validation methodology. The model yielded high accuracy in all sample types, with low false positive rate and excellent performance across the metrics of accuracy, recall, precision, F1 Score, Kappa index and Matthews Correlation Coefficient (MCC). PCA and PLS-DA models presented limitations over the complexity of leaves and powders samples. Machine learning algorithms showed robustness and flexibility, proving to be effective in the detection and discrimination of <em>M. ilicifolia</em>.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105420"},"PeriodicalIF":3.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922229","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":"Comparative analysis of continuous similarity measures for compound identification in mass spectrometry-based metabolomics","authors":"Hunter Dlugas ,&nbsp;Xiang Zhang ,&nbsp;Seongho Kim","doi":"10.1016/j.chemolab.2025.105417","DOIUrl":"10.1016/j.chemolab.2025.105417","url":null,"abstract":"<div><div>In mass spectrometry (MS)-based metabolomics, the most straightforward and efficient approach for compound identification is the comparison of similarity scores between experimental spectra and reference spectra. Among various single and composite similarity measures, the Cosine Correlation is favored due to its simplicity, efficiency, and effectiveness. Recently, the Shannon Entropy Correlation has shown superior performance over several other measures, including the Cosine Correlation, in LC-MS-based metabolomics, particularly concerning receiver operating characteristic (ROC) curves and false discovery rates. However, previous comparisons did not consider the weight factor transformation, which is critical for achieving higher accuracy with the cosine correlation. This study conducted a comparative analysis of the Cosine Correlation and Shannon Entropy Correlation, incorporating the weight factor transformation during preprocessing. Additionally, we developed a novel entropy correlation measure, the Tsallis Entropy Correlation, which offers greater versatility than the Shannon Entropy Correlation. Our accuracy-based results indicate that the weight factor transformation is essential for achieving higher identification performance in both LC-MS and GC-MS-based compound identification. Although the Tsallis Entropy Correlation outperforms the Shannon Entropy Correlation in terms of accuracy, it comes with higher computational expense. In contrast, the Cosine Correlation, when combined with the weight factor transformation, achieves the highest accuracy and the lowest computational expense, demonstrating both robustness and efficiency in MS-based compound identification.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"263 ","pages":"Article 105417"},"PeriodicalIF":3.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916803","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}