Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Surface-enhanced Raman spectroscopy combined with chemometrics for quantitative analysis and carcinogenic risk estimation of polycyclic aromatic hydrocarbons in water with complex matrix","authors":"Rongling Zhang ,&nbsp;Mengjun Guo ,&nbsp;Maogang Li ,&nbsp;Hongsheng Tang ,&nbsp;Tianlong Zhang ,&nbsp;Hua Li","doi":"10.1016/j.chemolab.2024.105293","DOIUrl":"10.1016/j.chemolab.2024.105293","url":null,"abstract":"<div><div>Polycyclic aromatic hydrocarbons (PAHs) as a kind of persistent organic pollutants have high teratogenic, carcinogenic, mutagenic properties, as well as high octanol/water partition coefficient and sediment/water partition coefficient, causing serious threat to human health and water environment. In this study, the feasibility of Surface-enhanced Raman spectroscopy (SERS) technology combined with chemometrics for quantitative analysis and carcinogenic risk estimation of PAHs in water with complex matrix was explored. Firstly, 36 water samples from lake, tap, and distilled water were prepared, and then nano-silver particles (Ag NPs) were mixed with samples. The integrated strategy of spectral preprocessing was adopted to remove spectral interference, and variable selection algorithm was used to extract the information effectively, thus improving the prediction performance of the random forest (RF) calibration model for PAHs quantitative analysis and carcinogenic risk. The final results indicated that RF combined with spectral preprocessing integration strategy and variable selection had better predictive performance compared with the Raw-RF model. For phenanthrene (Phe) and benzo[<em>a</em>]anthracene (BaA) analysis, the optimal calibration model was WT-SG-SiPLS-VIM-RF (Phe: mean relative error of prediction (MRE_p) = 0.0646, coefficient of determination of prediction (R²_p) = 0.9658; BaA: MRE_p = 0.0949, R²_p = 0.9537). SG-WT-SiPLS-VIM-RF model (MRE_p = 0.0992, R²_p = 0.9551) showed a better predictive performance for fluoranthene (Flu). WT-SG-VIM-RF model (MRE_p = 0.0902, R²_p = 0.9409) showed excellent performance for assessing the carcinogenic risk of PAHs. Therefore, the combination of SERS technology and chemometrics provides a new approach for analyzing PAHs.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105293"},"PeriodicalIF":3.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156198","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":"Exploring NIR spectroscopy data: A practical chemometric tutorial for analyzing freeze-dried pharmaceutical formulations","authors":"Ambra Massei ,&nbsp;Nicola Cavallini ,&nbsp;Francesco Savorani ,&nbsp;Nunzia Falco ,&nbsp;Davide Fissore","doi":"10.1016/j.chemolab.2024.105291","DOIUrl":"10.1016/j.chemolab.2024.105291","url":null,"abstract":"<div><div>Chemometrics tools are of fundamental importance for data analysis in the pharmaceutical field, especially with the increasingly strong assertion of the Process Analytical Technologies (PAT). In fact, analytical technologies such as Near-Infrared or Raman spectroscopies generate a lot of data, the spectra, that must be analyzed in a proper way. Typically, it is quite difficult to deeply understand the information hidden within the raw data. Therefore, careful, and efficient data exploration is needed to highlight the chemical and physical features of the analyzed samples.</div><div>Here, a tutorial on all the fundamental steps and concepts needed to perform a proper data analysis based on a case-study of different freeze-dried formulations in the pharmaceutical field is proposed. The data analysis pipeline begins with the dataset explanation, to better point out the main known differences and similarities among the investigated formulations. After the first step of data preprocessing, Principal Component Analysis (PCA), Partial Least Squares (PLS) for regression, and Partial Least Squares-Discriminant Analysis (PLS-DA) for classification are presented and applied to show how to obtain deep comprehension of the real-case NIR dataset at hand. The experimental results demonstrate that trends related to increasing levels of sucrose and/or arginine, as well as distinct clusters related to the sample type and to the operator who conducted the analysis can be found and modelled in the example data.</div><div>The tutorial aims at providing clear practical steps to conduct a robust data analysis, starting from the extraction and organization of the raw data, up to building more advanced predictive models (regression and classification). At each step some key questions are asked and answered to stimulate critical thinking in the reader. Also, commented MATLAB scripts are provided together with the real-case example NIR data, so that anyone could reproduce the whole data analysis in the tutorial, and try first hand to work with the data.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105291"},"PeriodicalIF":3.7,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155126","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":"Lactose prediction in dry milk with hyperspectral imaging: A data analysis competition at the “International Workshop on Spectroscopy and Chemometrics 2024”","authors":"Maria Frizzarin ,&nbsp;Vicky Caponigro ,&nbsp;Katarina Domijan ,&nbsp;Arnaud Molle ,&nbsp;Timilehin Aderinola ,&nbsp;Thach Le Nguyen ,&nbsp;Davide Serramazza ,&nbsp;Georgiana Ifrim ,&nbsp;Agnieszka Konkolewska","doi":"10.1016/j.chemolab.2024.105279","DOIUrl":"10.1016/j.chemolab.2024.105279","url":null,"abstract":"<div><div>In April 2024, the Vistamilk SFI Research Centre organized the fourth edition of the “International Workshop on Spectroscopy and Chemometrics — Spectroscopy meets modern Statistics”. Within this event, a data challenge was organized among workshop participants, focusing on hyperspectral imaging (HSI) of milk samples.</div><div>Milk is a complex emulsion comprising of fats, water, proteins, and carbohydrates. Due to the widespread prevalence of lactose intolerance, precise lactose quantification in milk samples became necessary for the dairy industry.</div><div>The dataset provided to the participants contained spectral data extracted from HSI, without the spatial information, obtained from 72 samples with reference laboratory values for lactose concentration [mg/mL]. The winning strategy was built using ROCKET, a convolutional-based method that was originally designed for time series classification, which achieved a Pearson correlation of 0.86 and RMSE of 9.8 on the test set. The present paper describes the approaches and statistical methods adopted by all the participants to analyse the data and develop the lactose prediction models.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105279"},"PeriodicalIF":3.7,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419059","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":"Sparse attention regression network-based soil fertility prediction with UMMASO","authors":"RVRaghavendra Rao ,&nbsp;U Srinivasulu Reddy","doi":"10.1016/j.chemolab.2024.105289","DOIUrl":"10.1016/j.chemolab.2024.105289","url":null,"abstract":"<div><div>The challenge of imbalanced soil nutrient datasets significantly hampers accurate predictions of soil fertility. To tackle this, a new method is suggested in this research, combining Uniform Manifold Approximation and Projection (UMAP) with Least Absolute Shrinkage and Selection Operator (LASSO). The main aim is to counter the impact of uneven data distribution and improve soil fertility models' predictive precision. The model introduced uses Sparse Attention Regression, effectively incorporating pertinent features from the imbalanced dataset. UMAP is utilised initially to reduce data complexity, unveiling hidden structures and essential patterns. Following this, LASSO is applied to refine features and enhance the model's interpretability. The experimental outcomes highlight the effectiveness of the UMAP and LASSO hybrid approach. The proposed model achieves outstanding performance metrics, reaching a predictive accuracy of 98 %, demonstrating its capability in accurate soil fertility predictions. It also showcases a Precision of 91.25 %, indicating its adeptness in accurately identifying fertile soil instances. The Recall metric stands at 90.90 %, emphasizing the model's ability to capture true positive cases effectively.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105289"},"PeriodicalIF":3.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156199","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":"Multivariate image analysis for assessment of textural attributes in transglutaminase-reconstituted meat","authors":"Samuel Verdú ,&nbsp;Ignacio García ,&nbsp;Carlos Roda ,&nbsp;José M. Barat ,&nbsp;Raúl Grau ,&nbsp;Alberto Ferrer ,&nbsp;J.M. Prats-Montalbán","doi":"10.1016/j.chemolab.2024.105280","DOIUrl":"10.1016/j.chemolab.2024.105280","url":null,"abstract":"<div><div>The control of sensorial textural attributes has high interest to the meat industry focused on the recovery of the value of meat by-products by developing reconstituted meat pieces with added sensory and nutritional values. Sensorial analysis of foods is still a quite subjective methodology, highly dependent of a well-trained team of inspectors, which is simulated by textural analysis in order to measure objective physical properties. This work presents a non-destructive and contactless experimental methodology to predict the physical properties of a reconstituted meat product, based on integrating multispectral imaging and multivariate image analysis (MIA). The experiment was based on reconstituting grounded meat with different concentrations of transglutaminase (0.1, 1, 3, 6 and 10 %), from which textural properties and multispectral imaging data were measured. Multispectral images (UV, VIS and NIR wavelengths) were processed with chemometric procedures to obtain the distribution maps and score images, from which different blocks of features were extracted to generate feature vectors (basic statistics and co-occurrence matrix) for each image. The obtained regression models built with these features predicted all physical properties of the meat with Q² &gt; 0.90, after feature selection using VIPs. These results evidenced the capacity of multispectral imaging, combined with chemometric procedures, to capture the variability of physical properties induced by transglutaminase in a derivate meat product. It could represent the base of a potential contactless application for a meat industrial inspection, where work environments have strong hygienic requirements.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105280"},"PeriodicalIF":3.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745558","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":"Robust adaptive control for nonlinear discrete-time systems based on DE-GMAW","authors":"Jing Wang ,&nbsp;Engang Tian ,&nbsp;Huaicheng Yan ,&nbsp;Fanrong Qu","doi":"10.1016/j.chemolab.2024.105274","DOIUrl":"10.1016/j.chemolab.2024.105274","url":null,"abstract":"<div><div>Gas Metal Arc Welding (GMAW) is a critical process in manufacturing, known for its efficiency and versatility. The double-electrode GMAW (DE-GMAW) technique further enhances these attributes, offering superior welding speed and improved melting effects. However, controlling the DE-GMAW process effectively remains a complex challenge due to the nonlinear and dynamic nature of the system. The process involves intricate interactions between electrical, thermal, and mechanical phenomena, resulting in highly nonlinear behavior. Variations in material properties, environmental conditions, and external disturbances can adversely affect the welding process. Moreover, traditional control methods often fail to account for unmodeled dynamics and modeling errors, leading to performance degradation and potential instability. To address these challenges, this paper introduces a robust adaptive control scheme tailored for DE-GMAW systems, which combines online projection estimation identification and pole placement strategy at the same time to compensate for parameter uncertainties, external disturbances, and unmodeled dynamics. Simulation examples in welding process are carried out to demonstrate the effectiveness of the proposed robust adaptive control scheme.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105274"},"PeriodicalIF":3.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719741","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 satellite image resolution with a residual network and correlation filter","authors":"Ajay Sharma ,&nbsp;Bhavana P. Shrivastava ,&nbsp;Praveen Kumar Tyagi ,&nbsp;Ebtasam Ahmad Siddiqui ,&nbsp;Rahul Prasad ,&nbsp;Swati Gautam ,&nbsp;Pranshu Pranjal","doi":"10.1016/j.chemolab.2024.105277","DOIUrl":"10.1016/j.chemolab.2024.105277","url":null,"abstract":"<div><div>This study addresses the predominant challenge of very low-resolution satellite images in remote sensing applications, a common issue in satellite image-based surveillance. Existing satellite image recognition algorithms struggle with such low-resolution images, and traditional Super-Resolution (SR) techniques fall short for very low-resolution cases. We propose the Progressive Satellite Image Super-Resolution (PSISR) model to bridge this gap. Unlike current learning-based SR methods, the PSISR model specifically targets very low-resolution satellite images. In satellite image super-resolution, problems with feature fusion that result in image noise, blind spots, poor perceptual quality, and checkboard artifacts are encountered during the reconstruction process. Current models try to improve perceptual quality, but they frequently show challenges in attaining acceptable outcomes because of losses during reconstruction. Using a combined loss function, correlation filters, and a loss-aware upscaling network layer, the PSISR model presents a revolutionary methodology. The model adopts a cascading structure with dense skip connections, sequentially upscaling images by factors of <span><math><mrow><mn>2</mn><mo>×</mo></mrow></math></span>, <span><math><mrow><mn>4</mn><mo>×</mo></mrow></math></span>, and <span><math><mrow><mn>8</mn><mo>×</mo></mrow></math></span> through three modules. To validate the model's superiority, a study is conducted, confirming its effectiveness compared to baseline models and also training the other models using the available dataset to prove the effectiveness of the model. The PSISR model effectively addresses the challenge of extracting more features with minimal losses, resulting in high magnification during reconstruction. Our method outperforms state-of-the-art techniques, including Swin2-MoSE, MambaFormer, SRFBN and RCAN, with a PSNR improvement of up to 0.4 dB and a 0.003 SSIM enhancement across various datasets. This demonstrates the effectiveness of our approach in producing high-quality outputs, achieving a 99.25 % correlation efficiency between the generated and original images.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105277"},"PeriodicalIF":3.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745557","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 soft sensor approach for industrial quality prediction based TCN with spatial and temporal attention","authors":"Lei Zhang ,&nbsp;Guofeng Ren ,&nbsp;Shanlian Li ,&nbsp;Jinsong Du ,&nbsp;Dayong Xu ,&nbsp;Yinhua Li","doi":"10.1016/j.chemolab.2024.105272","DOIUrl":"10.1016/j.chemolab.2024.105272","url":null,"abstract":"<div><div>The complex industrial process is often characterized by strong multivariate coupling and nonlinear dynamic changes, which pose great challenges to modeling and prediction. Traditional deep learning methods are difficult to effectively capture spatiotemporal characteristics of industrial processes, resulting in poor prediction accuracy. To tackle this issue, we propose a novel end-to-end method named STA-TCN, which utilizes a temporal convolutional network (TCN) with both spatial and temporal attention mechanisms. The TCN uses causal and dilated convolutions to capture long temporal patterns in time series data. The spatial attention identifies the significance of different features, while the temporal attention focuses on crucial time steps. This design assigns adaptive weights to different features and emphasizes key moments to improve the accuracy of dynamic processes. We conduct experiments on two industrial datasets and show that the proposed STA-TCN method achieves significantly improved predictive performance compared to TCN for quality prediction of industrial processes. The results validate the effectiveness and robustness of the proposed method.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105272"},"PeriodicalIF":3.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156194","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":"GATNM: Graph with Attention Neural Network Model for Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds","authors":"Agung Surya Wibowo ,&nbsp;Osphanie Mentari Primadianti ,&nbsp;Hilal Tayara ,&nbsp;Kil To Chong","doi":"10.1016/j.chemolab.2024.105265","DOIUrl":"10.1016/j.chemolab.2024.105265","url":null,"abstract":"<div><div><em>Mycobacterium tuberculosis</em> cell wall has complexity and unusual organization. These conditions make the nutrients and antibiotics difficult to penetrate this wall which affects the low activity of several antimycobacterial drugs in mycobacteria cells. Based on this information, the cell wall permeability prediction in some compounds becomes important and would help develop novel antitubercular drugs. Recently, there have been many predictions helped by computational technology using the Simplified Molecular Input Line Entry System (SMILES) input drug compounds. In this study, we applied computational technology to predict the permeability of cell walls to some compounds or drugs. We evaluated several common machine learning models for their ability to predict cell wall permeability. However, none of these models achieved satisfactory performance. We investigated a Graph with Attention Neural Network (GATNN) model to address this challenge. In the case of permeability detection, to the best of our knowledge, the GATNN model is considered a new approach to improve the prediction performance of the penetration ability of some compounds to the cell wall of the mycobacterial. Additionally, we optimized the accuracy value to get the best hyperparameter and the best model by Optuna. After getting the optimal model, by using the benchmark dataset, this model has slightly increased the performance over the previous model in accuracy and specificity to 78.9% and 81.5%. As a complementary, we also provided an ensemble model and generated the interpretability of the model. The code and materials of all experiments in this paper can be accessed freely at this link: <span><span>https://github.com/asw1982/MTbPrediction</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105265"},"PeriodicalIF":3.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719739","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 review of quantitative structure-activity relationship: The development and current status of data sets, molecular descriptors and mathematical models","authors":"Jianmin Li ,&nbsp;Tian Zhao ,&nbsp;Qin Yang ,&nbsp;Shijie Du ,&nbsp;Lu Xu","doi":"10.1016/j.chemolab.2024.105278","DOIUrl":"10.1016/j.chemolab.2024.105278","url":null,"abstract":"<div><div>Developing Quantitative Structure-Activity Relationship (QSAR) models applicable to general molecules is of great significance for molecular design in many disciplines. This paper reviews the development and current status of molecular QSAR research, including datasets, molecular descriptors, and mathematical models. A representative bibliometric analysis reveals the evolutionary trends in this field in the past decade. Based on the discussion of the advantages and shortcomings of existing methods, the requirements and possible approaches for developing a widely applicable QSAR model were put forward. This goal poses a series of challenges to QSAR, including: (1) Having a sufficient number of structure-activity relationship instances as training data to cope with the complexity and diversity of molecular structures and action mechanisms; (2) Developing and using precise molecular descriptors to avoid the situation of ‘garbage in, garbage out’, while balancing descriptor dimensions and computational costs; and (3) Using powerful and flexible mathematical models, such as deep learning models, to learn complex functional relationships between descriptors and activity. With the emergence of larger and higher-quality data sets, more accurate molecular descriptors and deep learning methods, predictive ability, interpretability and application domain of QSAR models will continue to improve, and it will play a more important role in various fields of molecular design.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105278"},"PeriodicalIF":3.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719740","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}