Chemometrics and Intelligent Laboratory Systems最新文献

{"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":"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}

{"title":"VAE-SIMCA — Data-driven method for building one class classifiers with variational autoencoders","authors":"Akam Petersen,&nbsp;Sergey Kucheryavskiy","doi":"10.1016/j.chemolab.2024.105276","DOIUrl":"10.1016/j.chemolab.2024.105276","url":null,"abstract":"<div><div>The paper proposes a new method for building one class classifiers based on variational autoencoders (VAE). The classification decision is built on a linear combination of two squared distances: computed for the original and the reconstructed image as well as for the representation of the original image inside the latent space formed by VAE. Because both distances are well approximated by scaled chi-square distribution, the decision boundary is computed using the theoretical quantile function for this distribution and the predefined probability for Type I error, ⍺. Thereby the boundary does not require any specific optimization and is solely based on the model outcomes computed for the training set.</div><div>The original idea of the proposed method is inherited from another OCC approach, Data Driven Soft Independent Method for Class Analogies, where singular value decomposition is employed for building the latent space. In this paper we show how this idea can be adopted to be used with VAE for detection of anomalies on images. The paper describes the theoretical background, introduces the main outcomes as well as tools for visual exploration of the classification results, and shows how the method works on several simulated and real datasets.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105276"},"PeriodicalIF":3.7,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719850","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":"Sound uncertainty-based strategy for oil spill source identification","authors":"Ana Catarina Rocha ,&nbsp;Carla Palma ,&nbsp;Ricardo J.N. Bettencourt da Silva","doi":"10.1016/j.chemolab.2024.105275","DOIUrl":"10.1016/j.chemolab.2024.105275","url":null,"abstract":"<div><div>Oil spills are frequent and a major environmental threat, whether they are massive or small. Therefore, authorities and experts have developed analytical chemistry tools to identify spill sources and address these illegal acts by comparing oil patterns obtained by Gas Chromatography-Mass Spectrometry analysis of the spill (Sp) and suspected sources (SS) samples. Several methodologies have proposed different balances between data processing complexity and reliability. Supported by the accessibility and validity of Microsoft Excel spreadsheets, an alternative, accurate, and user-friendly tool was developed for spill source identification based on Monte Carlo Method (MCM) simulation of correlated oil components expressed by abundance ratio (<em>DR</em>). However, the statistical control of various <em>DR</em> and the degree of similarity of samples' compositions, at defined confidence levels, impact the probability of true and false composition equivalence claim of Sp and SS becoming a challenge to recognise the offender. This work not only compares the MCM and the conventional approaches allowing to highlight the limitations that result in evidence with greater uncertainty, but also offers a statistically sound strategy that manages the probabilities of a compositional equivalence claim assessing the ability to distinguish competing spill sources and reporting the most likely polluting source with reduced uncertainty. A decision chart proposed, based on objective and statistically sound criteria, indicates the performance of consecutive <em>DR</em> comparison trials if necessary. The target values established for the probability of compositional equivalence claim of the Sp and the first and second most likely SS (≥95.0 % and ≤0.50 %, respectively) provide to forensic experts’ sound evidence to be presented in court (likelihood ratio ≥190). This work represents a significant breakthrough in comparing complex chemical oil patterns.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105275"},"PeriodicalIF":3.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719851","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 flame image soft sensor for oxygen content prediction based on denoising diffusion probabilistic model","authors":"Yi Liu ,&nbsp;Angpeng Liu ,&nbsp;Shuang Gao","doi":"10.1016/j.chemolab.2024.105269","DOIUrl":"10.1016/j.chemolab.2024.105269","url":null,"abstract":"<div><div>High-precision oxygen content measurement is crucial for statistical analysis of combustion chemical reaction. Deep learning based soft sensor is a new class of intelligent tools for monitoring combustion oxygen content. But in the actual production, data for sensors are often insufficient. A new soft sensing model is proposed to display the excellent performance of denoising diffusion probabilistic model (DDPM) in data generation. Firstly, a UNet based soft sensor is designed by integrating self-attention mechanism into the convolution layers. Then, a denoising loss function is designed to link the feature extraction process of soft sensor model with the reverse denoising process of DDPM, and the noise prediction neural network of DDPM is used to improve the feature extractability of the soft sensor model. Finally, the proposed model is compared with common models. The effectiveness and superiority of the proposed soft sensing model for oxygen content prediction, especially in the case with a small sample size, are both confirmed by the results.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105269"},"PeriodicalIF":3.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652924","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":"Prediction of potential antitumor components in Ganoderma lucidum: A combined approach using machine learning and molecular docking","authors":"Qi Yang ,&nbsp;Lihao Yao ,&nbsp;Fang Jia ,&nbsp;Guiyuan Pang ,&nbsp;Meiyu Huang ,&nbsp;Chengxiang Liu ,&nbsp;Hua Luo ,&nbsp;Lili Fan","doi":"10.1016/j.chemolab.2024.105271","DOIUrl":"10.1016/j.chemolab.2024.105271","url":null,"abstract":"<div><div>The objective of this study is to develop a reliable predictive model for antitumour activity and to identify potential antitumour components in <em>Ganoderma lucidum</em>. Four machine learning models, including Random Forest, were employed to train predictive models for antitumour activity, utilising Morgan fingerprints as molecular descriptors. The most effective model was then employed to predict the chemical composition of <em>Ganoderma lucidum</em>, identifying the four most probable compounds for molecular docking with known TNF-α-related targets. The findings of the study indicate that a Support Vector Machine (SVM) model exhibits an accuracy, F1 score, AUC, and sensitivity of 0.7638, 0.7638, 0.8332, and 0.7621, respectively. The model demonstrated an 80 % accuracy rate in predicting the antitumour activity of 10 FDA-approved drugs. Besides, the model identified 11 components in <em>Ganoderma lucidum</em>, including 3-nitroanisole, with a probability of antitumour activity exceeding 0.5, indicating their potential as antitumour agents. The results of the molecular docking procedure indicated that the four most promising antitumour compounds derived from <em>Ganoderma lucidum</em> exhibited a favourable binding affinity with the TNF-α target. In conclusion, this study incorporated a machine learning prediction step prior to molecular docking, thereby enhancing the reliability of the latter. Furthermore, it identified previously unreported compounds in <em>Ganoderma lucidum</em> with potential antitumour activity, such as 3-nitroanisole.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105271"},"PeriodicalIF":3.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652927","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":"Spectra data calibration based on deep residual modeling of independent component regression","authors":"Junhua Zheng ,&nbsp;Zeyu Yang ,&nbsp;Zhiqiang Ge","doi":"10.1016/j.chemolab.2024.105270","DOIUrl":"10.1016/j.chemolab.2024.105270","url":null,"abstract":"<div><div>Independent component regression (ICR) has recently become quite popular in spectra data calibration, due to its advantages in non-Gaussian data modeling and high-order statistics feature extraction. Inspired by the idea of deep learning, this paper extends the basic ICR model to the deep form by introducing a layer-wise residual learning strategy. Based on the residual information generated from last layer of the deep learning model, more and more different patterns of independent components can be extracted layer-by-layer. Then, a further information compression step is taken to combine and also to condense those independent components obtained from different layers of the deep model. Two detailed benchmark case studies are implemented to evaluate the calibration performance of the develop model, based on which the effectiveness of both layer-by-layer component extraction and further information compression are well confirmed.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105270"},"PeriodicalIF":3.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652891","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}