Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Enhancing one-class classification performance through variable selection: A review based on advanced literature search approaches","authors":"Mateus P. Schneider ,&nbsp;Cristina Malegori ,&nbsp;Adriano de A. Gomes ,&nbsp;Paolo Oliveri","doi":"10.1016/j.chemolab.2025.105491","DOIUrl":"10.1016/j.chemolab.2025.105491","url":null,"abstract":"<div><div>Variable selection is a key step in improving One-Class Classification (OCC), especially when applied to high-dimensional datasets common in chemometrics and anomaly detection tasks. This systematic literature review explores how different strategies—filter, wrapper, embedded, and hybrid methods—have been employed to enhance OCC models' accuracy, interpretability, and robustness. A comprehensive search was conducted using Scopus, complemented by AI-powered tools such as Elicit and Litmaps, and visual analytics platforms including VOSviewer and Bibliometrix. The review highlights methodological trends across both chemometric and machine learning domains, revealing a predominance of embedded approaches and a growing interest in hybrid strategies. Embedded methods, particularly LASSO, Elastic Net, and autoencoder-based architectures, were favored for their scalability and model integration. Approximately 69 % of the reviewed studies adopted a rigorous OCC approach—relying solely on target class data—demonstrating a preference for bias-resistant modeling. Additionally, bibliometric analysis revealed a disciplinary division, with chemometric studies emphasizing analytical applications and model interpretability, while computer science-driven studies prioritized automation and scalability. The findings emphasize the need for flexible, domain-adapted variable selection pipelines capable of handling class imbalance and high dimensionality. This work also introduces a reproducible framework combining traditional and AI-assisted literature review tools to support future systematic analyses. The review concludes by identifying emerging trends and suggesting future research directions in OCC and variable selection, with a focus on hybrid modeling, domain adaptability, and performance benchmarking across application fields.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"265 ","pages":"Article 105491"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696527","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":"Deep learning-driven heat transfer prediction in irregular ternary hybrid nanofluid flow over fin geometries via the Adam optimization algorithm","authors":"Maddina Dinesh Kumar ,&nbsp;P. Durgaprasad ,&nbsp;C.S.K. Raju ,&nbsp;Nehad Ali Shah ,&nbsp;Se-Jin Yook","doi":"10.1016/j.chemolab.2025.105489","DOIUrl":"10.1016/j.chemolab.2025.105489","url":null,"abstract":"<div><div>Nanofluids' enhanced thermal and heat transmission qualities have piqued the curiosity of several researchers. Recently, ternary nanoparticles of different shapes have been combined to generate a unique nanofluid with outstanding thermal characteristics; This work examines the Ternary hybrid nanofluid flow dynamics and the effects of radiation, ambient temperature, and natural convection heat transfer on the transient thermal performance of a porous fin that is rectangular, convex, and triangular, Using Darcy's model, this study creates a heat transport equation, Triangular fin exposed, convex, and rectangular are the three case styles considered while assessing thermal performance. Through the use of PDSolve in the Maple 2024 version program using the finite difference technique, the transformed dimensionless partial equations are solved. Deep Neural Network (LSTM with Adam algorithm) was able to predict the heat transfer rate accurately. By using MATLAB software, the present study model represents the accuracy. The study produced groundbreaking findings that the fins' efficiency is increased when a ternary hybrid nanofluid is present. In wet conditions, three fins of different forms have been compared. Compared to convex and triangular fins, the radiative, thermo-geometric, and convective transfer characteristics have more heat in rectangular geometries, The analysis's conclusions greatly impact enhancing heat transmission in industrial processes.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"265 ","pages":"Article 105489"},"PeriodicalIF":3.7,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680510","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":"Bioelectrochemical metric based on developing a novel and intelligent chemometrics-assisted electrochemical biosensor for multi-enzymatic biosensing of creatinine","authors":"Ali R. Jalalvand","doi":"10.1016/j.chemolab.2025.105490","DOIUrl":"10.1016/j.chemolab.2025.105490","url":null,"abstract":"<div><div>Creatinine (CT) is a breakdown product of creatine phosphate from muscle and protein metabolism. Healthy kidneys filter CT out of the blood. The CT exits body as a waste product in urine. High levels can signal kidney issues. The CT blood test measures the level of CT in the blood. This test is done to see how well the kidneys are working. Therefore, determination of CT in biological fluids such as blood is important. In this work, a novel biosensor was fabricated based on modification of a glassy carbon electrode (GCE) with multiwalled carbon nanotubes-ionic liquid (MWCNTs-IL) and immobilization of three specific enzymes including creatinine amidohydrolase (CNN), creatine amidinohydrolase (CRN), and sarcosine oxidase (SOX) onto its surface for determination of CT. Amperometric responses of the biosensor recorded at optimal conditions found by a central composite design (CCD) as an experimental design approach were modeled for exploiting first-order advantage by partial least squeares-1 (PLS-1), recursive weighted partial least squares (rPLS), least square-support vector machine (LS-SVM), principal component regression (PCR), continuum power regression (CPR), robust continuum regression (RCR), back propagation-artificial neural networks (BP-ANN), wavelet transform-artificial neural networks (WT-ANN), partial robust M-regression (PRM), discrete wavelet transform-artificial neural networks (DWT-ANN), radial basis function-artificial neural networks (RBF-ANN), and radial basis function-partial least squares (RBF-PLS), to select the best algorithm to assist the biosensor for determination of CT in blood samples. The RBF-ANN showed the best performance to assist the CNN-CRN-SOX-MWCNTs-IL/GCE for determination of CT ranging from 0.1 to 18 pg mL⁻¹ with a limit of detection of 0.015 pg mL⁻¹, a limit of quantification of 0.049 pg mL⁻¹ and a sensitivity of 3.81 μA pg⁻¹ mL in blood samples, and its results were in a good accordance with high-performance liquid chromatography (HPLC) as the reference method.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"265 ","pages":"Article 105490"},"PeriodicalIF":3.7,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665672","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":"Lightweight drought recognition model based on feature extraction of soybean multispectral images","authors":"Xiaodan Ma,&nbsp;Zhicheng Gu,&nbsp;Tao Zhang,&nbsp;Haiou Guan","doi":"10.1016/j.chemolab.2025.105488","DOIUrl":"10.1016/j.chemolab.2025.105488","url":null,"abstract":"<div><div>Drought is an important stress factor restricting soybean's high yield and high quality. Rapid detection of soybean drought conditions is of great significance for scientific cultivation management and drought-resistant variety breeding. In view of the complex and diverse phenotypes of soybean canopy, the existing recognition algorithms have high feature dimensions and large amount of calculation, which are difficult to meet the requirements of lightweight models for portable devices to identify soybean drought. Thus, a lightweight soybean drought recognition model based on feature extraction and one-dimensional convolutional neural network is proposed in this paper. Firstly, the multispectral image of soybean canopy was taken as the research object, and ReliefF feature selection method was applied to extract 14 feature vectors from the original 37 phenotypic indicators calculated from soybean canopy image, and the correlation coefficient R² reached 0.886. Finally, based on the selected dataset of soybean canopy phenotypic features, a seven-layer one-dimensional convolutional neural network was constructed to achieve a lightweight recognition model for soybean canopy drought (ReliefF_Conv), with an accuracy of 95.67 % and a inference time of only 0.000009 s. Compared with Back Propagation(BP), Radial Basis Function Network(RBF), Random Forest(RF), Support Vector Machine(SVM), Long Short-Term Memory(LSTM) and MobileNet models, the accuracy of the proposed model is increased by 14.42 %, 8.17 %, 5.05 %, 1.92 %, 14.42 % and 14.42 %, respectively. Compared with the full-variable model (OD_Conv), the accuracy of the proposed model is increased by 9.16 %, the training parameters were reduced by 64.2 %, and the inference efficiency has also increased by 70 %. The results achieved rapid detection of drought traits of soybean, and could provide basis and reference for water-saving irrigation and precise decision-making in drought-resistant varieties breeding, environmental regulation and scientific management.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"265 ","pages":"Article 105488"},"PeriodicalIF":3.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680464","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":"Quantitative prediction of soil AS content based on variational auto-encoder generated samples coupled with machine learning","authors":"Chengbiao Fu ,&nbsp;Qingyuan Zhuang ,&nbsp;Anhong Tian","doi":"10.1016/j.chemolab.2025.105486","DOIUrl":"10.1016/j.chemolab.2025.105486","url":null,"abstract":"&lt;div&gt;&lt;div&gt;This study aims to enhance the prediction accuracy of soil arsenic content, which is currently constrained by limited sample data. To address this limitation, we propose a method that employs variational auto-encoder (VAE) to generate additional samples for augmenting the original training dataset. The proposed approach was validated using contaminated farmland soil samples collected from Yunnan Province as the research object. We applied Savitzky-Golay (SG) smoothing and Standard Normal Variate (SNV) to preprocess the hyperspectral data and feature bands were extracted through Successive Projections Algorithm (SPA). In terms of modelling, four machine learning models (PLSR, SVR, RBF, GBM) were used to establish prediction models for soil arsenic (As) content. The predictive ability of the models was evaluated by three indices: coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;), root mean square error (RMSE) and ratio of the performance to deviation(RPD). The results show that after augmenting the real training dataset with samples generated by VAE, the predictive capabilities of the four models improved to varying degrees, and the models' overfitting problems were effectively alleviated. The RPD value of the PLSR model ameliorated from 1.682 to 2.226 after using the generated sample. Meanwhile the RPD values of the remaining three machine learning models (SVR, RBF, GBM) are raised above 3.000. Notably, the GBM model demonstrated the most significant performance improvement, with its RPD value increasing from 1.566 to 3.326. What's more, the number of generated samples affects the prediction accuracy of the model. On the one hand, too few generated samples make the prediction accuracy of the model unsatisfactory. On the other hand, too many generated samples will lead to a decline in the prediction performance of the model. When the VAE network is at the 16000th iteration, the generated samples are highly similar to the real training data set. The average structural similarity index measure and average peak-signal-to-noise ratio obtained are 0.972 and 20.558 dB respectively, and the Pearson correlation coefficient is 0.861. The generated samples and real samples have significantly strong correlations. After the training data set was increased, the model with the best prediction performance was SVR. The R&lt;sup&gt;2&lt;/sup&gt;, RMSE, and RPD of the validation set were 0.923, 72.187 mg kg&lt;sup&gt;−1&lt;/sup&gt;, and 3.611 respectively. The number of extracted feature bands was 25, and the expansion included an additional 5 samples. In the meantime, the model with the largest improvement in predictive performance is GBM whose validation set R&lt;sup&gt;2&lt;/sup&gt; improves by 0.318, RMSE decreases by 88.044 mg kg&lt;sup&gt;−1&lt;/sup&gt;, and RPD improves by 1.760. This study proves that the data augmentation method based on VAE can effectively improve the feasibility of machine learning algorithms in predicting soil heavy metal arsenic content, and provides a new idea for improving mod","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"265 ","pages":"Article 105486"},"PeriodicalIF":3.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686221","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":"Marker genes identification and prediction of Parkinson's disease by integrating blood-based multi-omics data","authors":"Jisha Augustine,&nbsp;A.S. Jereesh","doi":"10.1016/j.chemolab.2025.105478","DOIUrl":"10.1016/j.chemolab.2025.105478","url":null,"abstract":"<div><div>Parkinson's disease (PD) is a rapidly progressing neurodegenerative disease marked by a combination of motor and non-motor symptoms. The molecular mechanism of PD remains unexplained, and there is currently no genetic risk factor with clinically proven reliability. Therefore, diagnosing PD has relied chiefly on analyzing brain images and clinical tests. Understanding the molecular-level mechanism of PD is challenging, primarily due to the complexities involved in sampling the posterior brains of both typical individuals and those with PD; however, several independent research have recently produced and assessed extensive omics data obtained from blood samples, making the diagnosis cheap and less invasive. Therefore, developing diagnostic or predictive methods for PD utilizing these data is necessary. In addition, integrating omics data can serve as a valuable asset for a comprehensive understanding of the disease. This research devised a computational approach to predict PD by integrating gene expression and DNA methylation datasets. The significant challenges were the high dimensionality and heterogeneous data sources. A two-level statistical approach is proposed to identify Differentially expressed and Methylated Genes. Archimedes Optimization Algorithm, a meta-heuristic algorithm, selects 17 optimal genes and 18 mapping CpG sites. A clustering-based method is proposed to integrate the heterogeneous omics data. Predictions of PD and healthy samples are performed using the Tabnet classification model. The proposed approach demonstrated an ROC-AUC of 0.7615 and an F1-score of 0.7325 on test data. The significance of our work is supported by biological analysis and assessment metrics.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"265 ","pages":"Article 105478"},"PeriodicalIF":3.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580343","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":"Uncertainty of predictions in absorption spectroscopy: Modelling with quantile regression forest","authors":"Alexandre M.J.-C. Wadoux ,&nbsp;Leonardo Ramirez-Lopez","doi":"10.1016/j.chemolab.2025.105473","DOIUrl":"10.1016/j.chemolab.2025.105473","url":null,"abstract":"<div><div>Machine learning modelling is becoming popular for estimating agricultural and environmental properties from their infrared spectra. Commonly in modelling with machine learning and in commercial software applications, however, uncertainty estimates of the prediction are seldom reported. Uncertainty quantification of variables predicted with infrared spectroscopy is yet highly relevant in a number of applications, such as in uncertainty propagation analyses studies or for drug exposure detection. In this paper, we report on the development and application of quantile regression forest to predict properties from infrared spectroscopic data along with a sample-specific estimate of the uncertainty. Quantile regression forest is a machine learning algorithm that builds on random forest and provides estimate of the mean but also of the full conditional distribution of the predicted variable. We illustrate the algorithm with two chemometric applications and evaluate the modelling approach for its ability for predict the variable of interest and quantify the uncertainty. Evaluation involved usual validation statistics but also the validation of the uncertainty with the prediction interval coverage probability calculated for various interval widths. We tested prediction and prediction uncertainty quantification of two soil properties (cation exchange capacity and total organic carbon) as well as the dry matter of mango. The results confirm the potential of quantile regression forests for prediction and uncertainty quantification of properties predicted from infrared spectroscopy data. In all cases, the predictions were accurate and sample-specific estimates of the uncertainty were obtained. Validation of the uncertainty showed that the interval width was too large, thus overestimating the uncertainty for most intervals. Nevertheless, we recommend its use for operational applications as well as in future software developments, in particular when the data inferred by the spectroscopic model are used in other applications.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"265 ","pages":"Article 105473"},"PeriodicalIF":3.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144569895","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":"One criterion, two merits: A single-criterion-based sample selection method for informativeness and diversity","authors":"Zhongjiang He ,&nbsp;Zhonghai He ,&nbsp;Xiaofang Zhang","doi":"10.1016/j.chemolab.2025.105477","DOIUrl":"10.1016/j.chemolab.2025.105477","url":null,"abstract":"<div><div>In streaming batch-mode active learning process for data, sample selection typically involves two stages: informativeness measurement and similarity measurement. By analyzing the expression of model performance improvement induced by new samples, we identify a linear relationship between the performance gradient and the sample's vectors. Based on this finding, we propose a streaming batch active learning sample selection method, named One Criterion Two Merits (OCTM), which integrates informativeness and diversity measurement using a single criterion—the model improvement gradient. First, the model update gradient is computed for each incoming sample. Then, the magnitude of this gradient is used as an informativeness measure. Finally, the minimum angle between the new sample and buffer samples is calculated to quantify diversity. The threshold used for real-time decisions is critical in data stream scenarios, which traditionally relies on the assumption of a known threshold distribution. To address this issue, we propose a distribution-free threshold estimation method that determines the threshold based on the distribution of labeled samples. By sorting the measurement values and setting a confidence level, the threshold can be effectively computed.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"264 ","pages":"Article 105477"},"PeriodicalIF":3.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549547","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":"Graph residual based method for molecular property prediction","authors":"Kanad Sen ,&nbsp;Saksham Gupta ,&nbsp;Abhishek Raj ,&nbsp;Alankar Alankar","doi":"10.1016/j.chemolab.2025.105471","DOIUrl":"10.1016/j.chemolab.2025.105471","url":null,"abstract":"<div><div>Machine learning-driven methods for chemical property prediction have been of deep interest. However, much work remains to be done to improve the generalization ability, accuracy, and inference time of critical applications. Traditional machine learning models predict properties based on the features extracted from the molecules, which are often not readily available. In this work, a novel deep learning method, the Edge Conditioned Residual Graph Neural Network (ECRGNN), has been applied, allowing us to predict properties directly only the Graph-based structures of the molecules. SMILES (Simplified Molecular Input Line Entry System) representation of the molecules has been used in the present study as input data format, which has been further converted into a graph database, constituting the training data. This article highlights a detailed description of the novel GRU (Gated Recurrent Unit) - based methodology, ECRGNN, to map the inputs that have been used. Emphasis is placed on highlighting both the regressive property and the classification efficacy of the same. A detailed description of the Variational Autoencoder (VAE) and the end-to-end learning method used for multi-class multi-label property prediction has also been provided. The results have been compared with standard benchmark datasets and some newly developed datasets. All performance metrics that have been used have been clearly defined, and their reason for choice.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"265 ","pages":"Article 105471"},"PeriodicalIF":3.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633096","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":"Open Raman spectral library for biomolecule identification","authors":"Marcelo Terán ,&nbsp;José Javier Ruiz ,&nbsp;Pablo Loza-Alvarez ,&nbsp;David Masip ,&nbsp;David Merino","doi":"10.1016/j.chemolab.2025.105476","DOIUrl":"10.1016/j.chemolab.2025.105476","url":null,"abstract":"<div><div>Raman spectroscopy combined with Multivariate Curve Resolution (MCR) analysis is widely used in biomedical applications. However, assignation of biomolecules to the components extracted by MCR can be challenging due to the absence of an open Raman spectral library for biomolecules. Raman experts typically identify unmixed component spectra as biomolecules by comparing them with reference spectra from the literature. This process can be time-consuming and subject to human bias. In this work, we created an open Raman spectral database with 140 biomolecules by implementing an algorithm to digitalize the spectra plots and most relevant peaks from articles available in the literature. Additionally, we implemented two search algorithms. The first one uses the spectral linear kernel or cosine similarity on the full spectra. The second algorithm is based on peak matching, and relies on the intersection over the union of the matched peaks with a defined tolerance for peak matching. Our experimental validation showed 100 % top 10 accuracy in molecule identification (e.g. collagen) and 100 % accuracy in molecule type identification (e.g. protein) in both pure biomolecule measurements and also when replicating results from prior studies. Objectively narrowing the identification to the top 10 ranked candidates and providing type identification can significantly reduce both the time required for visual identification and the need to purchase reference component samples. We publish our spectral library as an open-source tool so it can be expanded collaboratively by the research community. It is available at: <span><span>https://github.com/mteranm/ramanbiolib</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"264 ","pages":"Article 105476"},"PeriodicalIF":3.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557410","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}