Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Large-scale prediction of collision cross-section with very deep graph convolutional network for small molecule identification","authors":"Ting Xie,&nbsp;Qiong Yang,&nbsp;Jinyu Sun,&nbsp;Hailiang Zhang,&nbsp;Yue Wang,&nbsp;Zhimin Zhang,&nbsp;Hongmei Lu","doi":"10.1016/j.chemolab.2024.105177","DOIUrl":"10.1016/j.chemolab.2024.105177","url":null,"abstract":"<div><p>Ion mobility spectrometry (IMS) is a promising analytical technique for mass spectrometry (MS)-based compound identification by providing collision cross-section (CCS) value as an additional dimension with structural information. Here, GraphCCS was proposed to accurately predict the CCS value and expand the coverage of CCS libraries. A new adduct encoding method was proposed to encode SMILES strings and adduct types of compounds into adduct graphs. GraphCCS extended its predictive capability to ten different adduct types. <strong>A very deep graph convolutional network with up to 40 GC</strong><strong>N layers</strong> was built to predict CCS values from adduct graphs. A curated dataset with 12,775 experimental CCS values was used to train, validate, and test the GraphCCS model. The resulting CCS predictions achieved a median relative error (MedRE) of 0.94 % and a coefficient of determination (R²) of 0.994 on the test set. Results on external test sets showed that GraphCCS outperformed AllCCS2, CCSbase, SigmaCCS, and DeepCCS. Based on the developed GraphCCS method, a large-scale <em>in-silico</em> database was built, including 2,394,468 CCS values. Those CCS values can be used to filter false positives complementary to retention times and tandem mass spectra. Finally, the effectiveness of GraphCCS in assisting compound identification was tested on a mouse adrenal gland lipid dataset with 1,960 lipids. The results demonstrated that the <em>in-silico</em> CCS values combined with MS spectra and retention times can efficiently filter the false positive candidates.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"252 ","pages":"Article 105177"},"PeriodicalIF":3.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622400","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":"Engagement of computerized and electrochemical methods to develop a novel and intelligent electronic device for detection of heroin abuse","authors":"Ali R. Jalalvand ,&nbsp;Sara Chamandoost ,&nbsp;Soheila Mohammadi ,&nbsp;Cyrus Jalili ,&nbsp;Sajad Fakhri","doi":"10.1016/j.chemolab.2024.105176","DOIUrl":"10.1016/j.chemolab.2024.105176","url":null,"abstract":"<div><p>In this work, a novel biosensing platform was fabricated based on modification of a rotating glassy carbon electrode (GCE) with chitosan-ionic liquid (Ch-IL) composite film, electrochemical synthesis of gold palladium platinum trimetallic three metallic alloy nanoparticles (AuPtPd NPs) onto its surface, and electrosynthesis of dual templates molecularly imprinted polymers (MIPs) where morphine (MO) and codeine (COD) used as template molecules. The AuPtPd NPs were synthesized under different electrochemical conditions, and surfaces of electrodes were investigated by digital image processing, and the best electrode was chosen. Effects of experimental variables on response of the biosensor to MO and COD were optimized by a central composite design (CCD), and under optimized conditions (concentration of the phosphate buffered solution (PBS): 0.09 M, pH of the PBS: 3.21–3.2, time of immersion: 204.8–205 s, and rotation rate: 2993.51–3000 rpm) the biosensor responses to MO and COD were individually calibrated (1–20 pM for MO and 0.5–12 pM for COD), three-way calibrated by PARASIAS, PARAFAC2, and MCR-ALS, and validated in the presence of ascorbic acid and uric acid as uncalibrated interference. Finally, performance of the biosensor in simultaneous determination of MO and COD in the presence of ascorbic acid and uric acid as uncalibrated interference in human serum samples were verified and compared with the results of HPLC-UV as the reference method which guaranteed it as a reliable method.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"251 ","pages":"Article 105176"},"PeriodicalIF":3.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566833","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":"Exploratory analysis of hyperspectral imaging data","authors":"Alessandra Olarini ,&nbsp;Marina Cocchi ,&nbsp;Vincent Motto-Ros ,&nbsp;Ludovic Duponchel ,&nbsp;Cyril Ruckebusch","doi":"10.1016/j.chemolab.2024.105174","DOIUrl":"10.1016/j.chemolab.2024.105174","url":null,"abstract":"<div><p>Characterizing sample composition and visualizing the distribution of its chemical compounds is a prominent topic in various research and applied fields. Integrating spatial and spectral information, hyperspectral imaging (HSI) plays a pivotal role in this pursuit. While self-modelling curve resolution techniques, like multivariate curve resolution - alternating least squares (MCR-ALS), and clustering methods, such as K-means, are widely used for HSI data analysis, their effectiveness in complex scenarios, where the structure of the data deviates from the models’ assumptions, deserves further investigation. The choice of a data analysis method is most often driven by research question at hand and prior knowledge of the sample. However, overlooking the structure of the investigated data, i.e. linearity, geometry, homogeneity, might lead to erroneous or biased results. Here, we propose an exploratory data analysis approach, based on the geometry of the data points cloud, to investigate the structure of HSI datasets and extract their main characteristics, providing insight into the results obtained by the above-mentioned methods. We employ the principle of essential information to extract archetype (most linearly dissimilar) spectra and archetype single-wavelength images. These spectra and images are then discussed and contrasted with MCR-ALS and K-means clustering results. Two datasets with varying characteristics and complexities were investigated: a powder mixture analyzed with Raman spectroscopy and a mineral sample analyzed with Laser Induced Breakdown Spectroscopy (LIBS). We show that the proposed approach enables to summarize the main characteristics of hyperspectral imaging data and provides a more accurate understanding of the results obtained by traditional data modelling methods, driving the choice of the most suitable one.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"252 ","pages":"Article 105174"},"PeriodicalIF":3.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S016974392400114X/pdfft?md5=fc1e3ebcd612aa27333c2ec8738aca2e&pid=1-s2.0-S016974392400114X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141638994","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":"MacroPARAFAC for handling rowwise and cellwise outliers in incomplete multiway data","authors":"Mia Hubert,&nbsp;Mehdi Hirari","doi":"10.1016/j.chemolab.2024.105170","DOIUrl":"10.1016/j.chemolab.2024.105170","url":null,"abstract":"<div><p>Multiway data extend two-way matrices into higher-dimensional tensors, often explored through dimensional reduction techniques. In this paper, we study the Parallel Factor Analysis (PARAFAC) model for handling multiway data, representing it more compactly through a concise set of loading matrices and scores. We assume that the data may be incomplete and could contain both rowwise and cellwise outliers, signifying cases that deviate from the majority and outlying cells dispersed throughout the data array. To address these challenges, we present a novel algorithm designed to robustly estimate both loadings and scores. Additionally, we introduce an enhanced outlier map to distinguish various patterns of outlying behavior. Through simulations and the analysis of fluorescence Excitation-Emission Matrix (EEM) data, we demonstrate the robustness of our approach. Our results underscore the effectiveness of diagnostic tools in identifying and interpreting unusual patterns within the data.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"251 ","pages":"Article 105170"},"PeriodicalIF":3.7,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566715","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":"Diverse local calibration approaches for chemometric predictive analysis of large near-infrared spectroscopy (NIRS) multi-product datasets","authors":"Xueping Yang ,&nbsp;Fuyu Yang ,&nbsp;Matthieu Lesnoff ,&nbsp;Paolo Berzaghi ,&nbsp;Alessandro Ferragina","doi":"10.1016/j.chemolab.2024.105173","DOIUrl":"https://doi.org/10.1016/j.chemolab.2024.105173","url":null,"abstract":"<div><p>This study aimed to assess the predictive accuracy of Near-Infrared Spectroscopy (NIRS) across a large multi-product library, employing novel local calibration methodologies. Three local strategies were examined: LOCAL Algorithm, Locally Weighted Regression predicted on k-nearest neighbor selection (kNN-LWPLSR), along with a newly proposed algorithm within this study called Hybrid Local. These strategies were applied to an extensive multi-product dataset. When compared with Global PLS models, the results exhibited significant reductions in RMSEP values for all local strategies. Particularly, the kNN-LWPLSR demonstrated proficient prediction for the constituents of ADF and DM. The newly proposed method [Hybrid Local] exhibits comparable performance to the LOCAL Algorithm; however, it notably reduces the prediction time by half compared to the latter, representing a significant advancement for the practical implementation of NIRS technology within industrial processing scenarios.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"251 ","pages":"Article 105173"},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169743924001138/pdfft?md5=115b1d8cf3d3927fcd4a4da98b29f3e1&pid=1-s2.0-S0169743924001138-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141539174","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":"New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models","authors":"C. Ortiz-Abellán ,&nbsp;E. Aguado-Sarrió ,&nbsp;J.M. Prats-Montalbán ,&nbsp;J. Camps-Herrero ,&nbsp;A. Ferrer","doi":"10.1016/j.chemolab.2024.105171","DOIUrl":"https://doi.org/10.1016/j.chemolab.2024.105171","url":null,"abstract":"<div><p>Currently, magnetic resonance imaging is the most sensitive imaging technique for detecting cancerous processes in early stages. As for breast cancer, due to the tubular structure of the tissue, being formed by ducts, anisotropic diffusion should be considered instead of the general isotropic diffusion. Anisotropic diffusion is studied by applying a technique called Diffusion Tensor Imaging (DTI), where the diffusion gradient is applied by changing the magnetic field in several spatial directions.</p><p>To date, the application of Multivariate Curve Resolution (MCR) models in diffusion sequences has demonstrated its ability to develop cancer biomarkers of easy clinical interpretation in the case of isotropic tissues, such as the prostate. But so far, it has never been applied in the case of anisotropic tissues, as the breast.</p><p>Therefore, the main objective of this work is to obtain easy-to-interpret imaging biomarkers useful for early breast cancer diagnosis from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models. A classification model to identify healthy and tumor affected pixels is also proposed.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"251 ","pages":"Article 105171"},"PeriodicalIF":3.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169743924001114/pdfft?md5=bfa9e402dd60fbdcd42e8d99cb32d250&pid=1-s2.0-S0169743924001114-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606715","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":"Estimation of human bloodstains time since deposition using ATR-FTIR spectroscopy and chemometrics in simulated crime conditions","authors":"Miguel Mengual-Pujante ,&nbsp;Antonio J. Perán ,&nbsp;Antonio Ortiz ,&nbsp;María Dolores Pérez-Cárceles","doi":"10.1016/j.chemolab.2024.105172","DOIUrl":"https://doi.org/10.1016/j.chemolab.2024.105172","url":null,"abstract":"<div><p>Blood in the form of stains is one of the most frequently encountered fluid in crime scene. Estimation of the time since deposition (TSD) is of great importance to guide the police investigation and the clarification of criminal offences. The time elapsed since deposition is usually estimated by modelling the physicochemical degradation of blood biomolecules over time. This work shows an ATR-FTIR spectroscopy and chemometrics study to estimate TSD of bloodstains on various surfaces and under different ambient conditions (indoor and outdoor). For a period from 0 to 212 days, a total of 960 stains were analyzed. Most of the eleven partial least squares regression (PLSR) models obtained showed a good prediction capacity, with a Residual Predictive Deviation (RPD) value higher than 3, and R² higher than 0.90. Models for non-rigid supports showed better predictive capacity than those for rigid ones. A non-rigid surface model including the various non-rigid surfaces and ambient conditions was elaborated, which might be the most useful one from the criminalistic point of view. These results show that this technique can be a rapid, robust, and trustable tool for <em>in situ</em> determination of the TSD of bloodstains at crime scenes.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"251 ","pages":"Article 105172"},"PeriodicalIF":3.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169743924001126/pdfft?md5=12868d33bb0a44826b6ab904bb81dcbd&pid=1-s2.0-S0169743924001126-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487277","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":"Enhancing Hansen Solubility Predictions with Molecular and Graph-Based Approaches","authors":"Darja Cvetković,&nbsp;Marija Mitrović Dankulov,&nbsp;Aleksandar Bogojević,&nbsp;Saša Lazović,&nbsp;Darija Obradović","doi":"10.1016/j.chemolab.2024.105168","DOIUrl":"https://doi.org/10.1016/j.chemolab.2024.105168","url":null,"abstract":"<div><p>The fast and accurate prediction of Hansen solubility benefits many diverse fields such as pharmaceuticals, the food industry, and cosmetics. To estimate the individual HSP values (polar, dispersive, and hydrogen bonding components), we investigated the performance of using Mordred descriptors in multiple linear regressions and XGBoost modeling. For HSP predictions, we also tested a graph-based molecular representation with graph neural network (GNN) modeling. To select the optimal models for final training and predictions, we used nested cross-validation and hyper-parameter optimization. The models with the best predictive performance were selected through internal (<em>R</em>^<em>2</em>_train, RMSE, MEPcv) and external (RMSEP, CCC, MEP, <em>R</em>^<em>2</em>_test, <em>ar</em>²m, Δ<em>r</em>²m) validation metrics using ∼1200 compounds from free-available database <span>https://www.stevenabbott.co.uk</span><svg><path></path></svg>. To confirm the practical reliability, we examined the agreement of experimentally obtained HSP data from the literature for 93 compounds and the data predicted by the created models. The results of GNN modeling showed the best predictive characteristics, which include a coefficient of determination between experimentally obtained and predicted HSP values greater than 0.76 for polar and hydrogen bond forces and greater than 0.66 for dispersive forces. Interpreting the fundamental basis of Hansen solubility using the created MLR equations and XGBoost models, HSP values were found to be influenced by van der Waals volume characteristics, 2D matrix molecular representation, and polarity. We elaborated on the practical benefits of using the selected GNN method through Hansen's solubility sphere as an example. This is the first study to demonstrate the advantages of GNN in predicting individual HSP components, as well as the first study to describe in detail their molecular basis using MLR and XGBoost modeling.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"251 ","pages":"Article 105168"},"PeriodicalIF":3.7,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487276","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":"Lipid Quant 2.1: Open-source software for identification and quantification of lipids measured by lipid class separation QTOF high-resolution mass spectrometry methods","authors":"Michaela Chocholoušková ,&nbsp;Gabriel Vivó-Truyols ,&nbsp;Denise Wolrab ,&nbsp;Robert Jirásko ,&nbsp;Michela Antonelli ,&nbsp;Ondřej Peterka ,&nbsp;Zuzana Vaňková ,&nbsp;Michal Holčapek","doi":"10.1016/j.chemolab.2024.105169","DOIUrl":"https://doi.org/10.1016/j.chemolab.2024.105169","url":null,"abstract":"<div><p>LipidQuant 2.1 is a software written in Matlab, which is designed for the high-throughput processing of large lipidomic data sets measured by lipid class separation coupled with quadrupole time-of-flight (QTOF) high-resolution mass spectrometry (MS). The software enables the identification of lipid species based on defined mass accuracy. The main focus is on the right lipidomic quantitation using at least one internal standard per lipid class and the implementation of an automated procedure for Type I and Type II isotopic corrections necessary for the determination of accurate molar concentrations, which is not available for the majority of existing software solutions. LipidQuant 2.1 offers three options for peak assignment, visualization of the isotopic pattern, and automated calculation of <em>m/z</em> for various adduct ions. The initial lipidomic database covers 31 lipid classes with more than 2900 lipid species that occur primarily in the human lipidome, but users have the full flexibility to modify and extend the database according to their needs. All algorithms and the detailed user manual are provided. The reliability of LipidQuant 2.1 is demonstrated on a set of more than 250 biological samples measured by ultrahigh-performance supercritical liquid chromatography (UHPSFC) coupled with QTOF-MS.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"251 ","pages":"Article 105169"},"PeriodicalIF":3.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169743924001096/pdfft?md5=9ea2187d616236fadca4f84096ec1816&pid=1-s2.0-S0169743924001096-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487275","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":"Latent variable model inversion for intervals. Application to tolerance intervals in class-modelling situations, and specification limits in process control","authors":"M.S. Sánchez ,&nbsp;M.C. Ortiz ,&nbsp;S. Ruiz ,&nbsp;O. Valencia ,&nbsp;L.A. Sarabia","doi":"10.1016/j.chemolab.2024.105166","DOIUrl":"https://doi.org/10.1016/j.chemolab.2024.105166","url":null,"abstract":"<div><p>The paper deals with the inversion of intervals when a PLS (Partial Least Squares) model is used. However, instead of discretizing the interval, it is proved that the region resulting from the inversion of a PLS model is a convex set bounded by two parallel hyperplanes, each corresponding to the direct inversion of each endpoint of the given interval.</p><p>When the domain of the input variables is a convex set, any feasible solution with predictions within the interval set in the response can be obtained as a convex combination of a point on each of the two hyperplanes. In this way, the new solutions preserve the internal structure of the input variables.</p><p>This methodology can be of interest in several domains where the response under study is defined in terms of an interval of admissible values, such as specifications for a product in an industrial process, or tolerance intervals for computing compliant class-models.</p><p>The inversion of the corresponding fitted model defines a region in the input space (predictor variables) whose predictions fall within the specified interval. Then, estimating and exploring this region will increase the information about the problem under study.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"251 ","pages":"Article 105166"},"PeriodicalIF":3.7,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169743924001060/pdfft?md5=916b6271ac0ec8660781143e8ff364ff&pid=1-s2.0-S0169743924001060-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435151","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}