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Model-level weight update domain adaptive dynamic CNN soft sensor for free calcium ion concentration in cement clinker Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-03-13 XiaoYu Zhou, Hui Liu, FuGang Chen, Wei Zheng, Heng Li, XiaoJun Xue
The paramount determinant of clinker quality within the cement clinker production process resides in the precise prediction of F-CAO (free calcium ion) content. Given the intricate nature of cement clinker production, a profound coupling and temporal progression are inherently intertwined between the process data, as working conditions undergo gradual transformations over time. This research suggests
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Estimation of feeding composition content based on novel variable sliding window method and layered data reconciliation with multiple modes Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-03-11 Ningchun Yi, Wenting Li, Yonggang Li, Bei Sun, Weihua Gui
The real-time detection of feeding composition content holds significant importance in process monitoring and control optimization in industrial systems. However, the current feeding composition content is obtained by manual assay, with low detection frequency and significant hysteresis. Moreover, due to adjustment in production plans and adaptation to market demand, multiple operation modes frequently
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A new methodology to robustify an experimental design: Application to the Baranyi model Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-03-04 Alba Muñoz del Río, Víctor Casero-Alonso, Mariano Amo-Salas
A robust experimental design is a desired object for practitioners when there is uncertainty about any of the assumptions necessary to compute the optimal design. For instance, when they use non-linear models, which requires having nominal values of the parameters. Several alternatives have been developed in the literature to obtain robust experimental designs such as adaptive or Bayesian designs,
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ECA-PHV: Predicting human-virus protein-protein interactions through an interpretable model of effective channel attention mechanism Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-03-01 Minghui Wang, Jiali Lai, Jihua Jia, Fei Xu, Hongyan Zhou, Bin Yu
The prediction of human-virus protein-protein interactions (human-virus PPIs) is significant for exploring the mechanisms of viral infection, making their prediction a necessary and practically valuable research topic. Since conventional methods for the determination of human-virus protein-protein interactions are very complex and expensive, the construction of models plays a crucial role. In this
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RadarTSR: A new algorithm for cellwise and rowwise outlier detection and missing data imputation Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-03-01 Alba González-Cebrián, Abel Folch-Fortuny, Francisco Arteaga, Alberto Ferrer
High-dimensional and multivariate data sets often contain missing data and/or cellwise/rowwise outliers. Whereas several solutions have been proposed to deal with each one of these issues independently, the number of suitable techniques that simultaneously confront these phenomena is drastically reduced. In this paper, we introduce RadarTSR, a Robust Adaptation for Data with Anomalous Rows and/or cells
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Analytical chemistry meets art: The transformative role of chemometrics in cultural heritage preservation Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-28 Jordi Riu, Barbara Giussani
This review intends to illustrate the fruitful collaboration between chemometrics and cultural heritage science. It showcases past achievements, aiming to inspire numerous cultural heritage researchers who have yet to incorporate multivariate techniques in analysing their research findings. The goal is to practically discuss applications with examples extracted from the literature. These range from
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ProSpecTool: A MATLAB toolbox for spectral preprocessing selection Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-26 Jokin Ezenarro, Daniel Schorn-García, Olga Busto, Ricard Boqué
This paper introduces the ProSpecTool, a MATLAB toolbox for automated selection of preprocessing methods for obtaining optimal PLS regression models in vibrational spectroscopy. Trial-and-error approaches for preprocessing can be time-consuming, and the success of the process relies on the experience of analysts. The ProSpecTool addresses this challenge by using objective criteria analogous to expert
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Data-driven sensor delay estimation in industrial processes using multivariate projection methods Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-22 Tim Offermans, Bente van Son, Carlo G. Bertinetto, Arjen Bot, Rogier Brussee, Jeroen J. Jansen
A key step in preparing industrial data for multivariate statistical modelling of (batch-)continuous processes is the estimation of sensor delays along the production line, to use as a correction for understanding relationships between them. Without such a correction, the measurements collected from the sensors do not all relate to the same portion of material travelling through the plant, which is
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Gaussian–Poisson Mixture Regression model for defects prediction in steelmaking Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-22 Xinmin Zhang, Leqing Li, Xuerui Zhang, Zhihuan Song, Jinchuan Qian
In the steelmaking process, real-time prediction of the occurrence of defects is crucial. To this end, a novel Gaussian–Poisson Mixture Regression (GPMR) model is proposed in this work. GPMR utilizes Poisson and Gaussian distributions to describe input process measurements and the count-type output quality variable, respectively. At the same time, inspired by the idea of finite mixture models, the
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Correlation coefficient distribution and its application in the comparison of chemical data sets Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-22 Jorge Jardim Zacca, Carmen P. Sandoval Jáuregui, Flor M. Villafana Bardales, Johanna Remuzgo Yabar, Julia E. Enciso Soria, Vanessa D. Ayala Caro
This paper presents a comprehensive statistical framework in order to rigorously derive the correlation coefficient distribution between two independent chemical data sets. The novel approach not only allows for unequivocal interpretation of distribution parameters and confidence intervals but also the generalization of previous results for fixed size comparisons into the new and experimentally important
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An extension of PARAFAC to analyze multi-group three-way data Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-21 Marta Rotari, Valeria Fonseca Diaz, Bart De Ketelaere, Murat Kulahci
This paper introduces a novel methodology for analyzing three-way array data with a multi-group structure. Three-way arrays are commonly observed in various domains, including image analysis, chemometrics, and real-world applications. In this paper, we use a practical case study of process modeling in additive manufacturing, where batches are structured according to multiple groups. Vast volumes of
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Quantitative detection of crude protein in brown rice by near-infrared spectroscopy based on hybrid feature selection Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-21 Yujie Tian, Laijun Sun, Hongyi Bai, Xiaoli Lu, Zhongyu Fu, Guijun Lv, Lingyu Zhang, Shujia Li
It is important for rice breeding and quality evaluation to predict the protein content of brown rice rapidly and easily. In this study, near-infrared spectroscopy (NIRS) was utilized to establish a model for detecting crude protein content in brown rice based on 349 samples prepared from three kinds of brown rice, and the performance of the model was evaluated. Improved interval partial least squares
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PLS class modelling using error correction output code matrices, entropy and NIR spectroscopy to detect deficiencies in pastry doughs Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-20 D. Castro-Reigía, M.C. Ortiz, S. Sanllorente, I. García, L.A. Sarabia
Biscuits are a highly demanded product worldwide. Its success makes their manufacture process a challenging task, needing new strategies to maintain the high production levels and a high-quality standard. This is determined by two key processes: the kneading and the rolling. This manuscript aims to reflect the improvements that the application of a novel soft multiclass compliant classification method
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Smartphone-based colorimetric method for decentralized wastewater treatment monitoring by inexperienced users Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-20 Sergei Gusev, Flor Louage, Stijn Van Hulle, Diederik P.L. Rousseau
Rural areas often rely on decentralized wastewater treatment systems, including individual sewage treatment systems (ISTS), for remote houses, farms, and other establishments. For practical and financial reasons, ISTSs are often exempt from routine monitoring of effluent quality. As a result, they cannot guarantee the same level of treatment performance as their large-scale counterparts. A smartphone-based
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Inspection of antimicrobial particles in milk using RGB-laser scattering imaging combined with chemometric procedures Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-14 Samuel Verdú, Cristina Fuentes, Alberto J. Pérez, José M. Barat, Raúl Grau, Alberto Ferrer, J.M. Prats-Montalbán
The immobilisation of phytochemicals on inert particles has demonstrated high potential to develop new formulations of natural antimicrobials for preserving liquid foods such as milk. This work aimed to test the capability of the laser scattering imaging technique combined with chemometric procedures to inspect antimicrobial particles in milk matrices non-destructively. The RGB laser's interactions
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Kernel-based mapping of reliability in predictions for consensus modelling Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-13 Viviana Consonni, Roberto Todeschini, Marco Orlandi, Davide Ballabio
Approaches of high-level data fusion, also known as consensus, combine predictions of individual models to increase reliability and overcome limitations of single models. Consensus strategies are frequently applied in the framework of Quantitative Structure - Activity Relationships (QSARs) to reduce the uncertainties in the prediction of molecular activities and provide better accuracy of the model
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Dynamic data reconciliation for enhancing the performance of kernel learning soft sensor models considering measurement noise Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-11 Wangwang Zhu, Mingwei Jia, Zhengjiang Zhang, Yi Liu
In modern industrial processes, data-driven soft sensor models avoiding the limitations of measurement techniques and expensive costs are developed for process monitoring and quality prediction. However, historical datasets usually contaminated by measurement noise reduce the reliability of model prediction. To alleviate the negative effects of measurement noise on process modeling, dynamic data reconciliation
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Variable time delay estimation in continuous industrial processes Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-09 Marco Cattaldo, Alberto Ferrer, Ingrid Måge
Digital sensors and machine learning enable efficiency improvements in production processes, through process monitoring, anomaly detection, soft sensing, and process control. However, the development of such solutions requires several data preprocessing steps. In continuous processes, a crucial part of the data preparation is adjusting for time delays between different sensors. This is necessary to
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Clustering method for the construction of machine learning model with high predictive ability Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-09 H, i, r, o, m, a, s, a, , K, a, n, e, k, o
In the design of molecules, materials, and processes, a mathematical model y = f(x) is constructed to establish a relationship between the explanatory variable x and the objective variable y using a dataset to design x such that y achieves target values. While it is preferable to develop a model with high predictive ability, achieving this becomes unattainable when the relationship between x and y
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Multi source deep learning method for drug-protein interaction prediction using k-mers and chaos game representation Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-05 Hengame Abbasi Mesrabadi, Karim Faez, Jamshid Pirgazi
Identification of drug-protein interactions plays an important role in drug discovery. Development of new calculation methods, which have high accuracy solve the problems related to the previous methods, which were expensive and time-consuming. In this article, a new model for drug-protein interactions, and a new mapping approach to represent drug-protein sequences are proposed. The proposed model
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Multivariate curve resolution of incomplete and partly trilinear multiblock datasets Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-02-01 Aina Queral-Beltran, Marc Marín-García, Silvia Lacorte, Romà Tauler
We extend the application of MCR-ALS to incomplete multiblock datasets, allowing for missing data in some blocks and the application of flexible trilinearity constraints. We test this extension on three experimental datasets: one involving ultraviolet (UV) spectroscopic monitoring of a drug's degradation and its further liquid chromatography coupled to diode array detection and mass spectrometric detection
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Multi-spectral fusion and self-attention mechanisms for Gentiana origin identification via near-infrared spectroscopy Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-29 Sihai Li, Yangyang Wang, Hang Song, Mingqi Liu
Gentiana is rich in Gentiopicroside and strychnine acid with medicinal value. However, the active ingredients of Gentiana from different origins are different, so identifying Gentian's origin is significant. Currently, neural networks such as CNN and GRU are widely used for spectral data analysis, but the modeling effect is easily affected by the spectral preprocessing method, and the long region and
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Multivariate calibration strategies for the simultaneous quantification of aluminium and vanadium in Ti6Al4V alloys Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-27 Federico Belén, Federico Danilo Vallese, David Douglas de Sousa Fernandes, Alisson Silva de Araújo, Adriano de Araújo Gomes, Paula Verónica Messina, Marcelo Fabian Pistonesi
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Normalization approaches for extracellular vesicle-derived lipidomic fingerprints – A human milk case study Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-24 Isabel Ten-Doménech, Victoria Ramos-Garcia, Abel Albiach-Delgado, Jose Luis Moreno-Casillas, Alba Moreno-Giménez, María Gormaz, Marta Gómez-Ferrer, Pilar Sepúlveda, Máximo Vento, Guillermo Quintás, Julia Kuligowski
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Sensing performance enhancement of plasmonic waveguide sensor using a bimodal strategy with digital Gaussian filter Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-18 Guiqiang Wang, Xiaoxue Xu, Jiao Ren, Pengpeng Xie, Rui Li
In this paper, a novel bimodal strategy with digital Gaussian filter was proposed to improve the sensing performance of plasmonic waveguide sensor (PWG). The central angle of the bimodal spectrum is adopted rather than the traditional resonance angle. Compared with conventional noise reduction algorithm, the proposed digital Gaussian filter would transform resonance dip into bimodal spectrum and then
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Detection of variety and wax bloom of Shaanxi plum during post-harvest handling Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-20 Hanchi Liu, Jinrong He, Xuanping Fan, Bin Liu
The detection of plum variety and wax bloom has extensive applications in the fields of fruit classification and fruit quality assessment. By automating the detection and identification of plum varieties and wax bloom, it is possible to enhance the efficiency and accuracy of variety identification and quality assessment, and reduce manual intervention and misjudgment, thereby improving the market competitiveness
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Using hierarchical information-theoretic criteria to optimize subsampling of extensive datasets Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-15 Belmiro P.M. Duarte, Anthony C. Atkinson, Nuno M.C. Oliveira
This paper addresses the challenge of subsampling large datasets, aiming to generate a smaller dataset that retains a significant portion of the original information. To achieve this objective, we present a subsampling algorithm that integrates hierarchical data partitioning with a specialized tool tailored to identify the most informative observations within a dataset for a specified underlying linear
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Noninvasive system for weight estimation in cactus crops: A YOLOv5-decision tree approach based on interval type-2 fuzzy sets Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-13 José L. Rodríguez-Álvarez, Jorge L. García-Alcaraz, Rita Puig i Vidal, Raúl Cuevas-Jacques, José R. Díaz-Reza
This study proposes a noninvasive system to estimate the weight of a prickly pear cactus (Opuntia ficus-indica), which combines a model based on deep learning to detect and estimate its area and a model based on supervised learning and developed under a type-2 interval fuzzy sets approach to estimate its weight. YOLOv5 was used for detection, which achieved results with an accuracy of 0.999, recall
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MSident: Straightforward identification of chemical compounds from MS-resolved spectra Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-11 Carlos Perez-Lopez, Antoni Ginebreda, Joaquim Jaumot, Flavia Yoshie Yamamoto, Damia Barcelo, Roma Tauler
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Does the fish rot from the head? Hyperspectral imaging and machine learning for the evaluation of fish freshness Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-07 Mike Hardy, Bernadette Moser, Simon A. Haughey, Christopher T. Elliott
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Discrimination of Thai melon seeds using near-infrared spectroscopy and adaptive self-organizing maps Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-06 Sureerat Makmuang, Tirayut Vilaivan, Simon Maher, Sanong Ekgasit, Kanet Wongravee
Melon (Cucumis melo L.) is a popular fruit consumed around the world. It has significant economic value as a crop, export product, and source of essential nutrients. Thus, using high-quality, authentic seed varieties is the first step toward achieving impactful agricultural production. Unfortunately, distinguishing between seed varieties using only human perception can be difficult because of their
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Instance transfer partial least squares for semi-supervised adaptive soft sensor Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-06 Zhijun Zhao, Gaowei Yan, Rong Li, Shuyi Xiao, Fang Wang, Mifeng Ren, Lan Cheng
In order to deal with the unknown data drift problem caused by the change of working conditions in the process of industrial soft sensor modeling, this paper proposes a semi-supervised soft sensor modeling algorithm based on instance transfer learning, named instance transfer partial least squares. Under the framework of nonlinear partial least squares, the algorithm introduces the instance transfer
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Assessment of subvisible particles in biopharmaceuticals with image feature extraction and machine learning Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-04 Ravi Maharjan, Jae Chul Lee, Johan Peter Bøtker, Ki Hyun Kim, Nam Ah Kim, Seong Hoon Jeong, Jukka Rantanen
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Aging prediction in single based propellants using hybrid strategy of machine learning and genetic algorithm Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2024-01-02 Faizan Khalid, Muhammad Nouman Aslam, Muhammad Abdaal Ghani, Nouman Ahmad, , Khurram Sattar
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Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-12-29 Vinay Kumar, Arkaprava Banerjee, Kunal Roy
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Non-destructive detection of fusarium head blight in wheat kernels and flour using visible near-infrared and mid-infrared spectroscopy Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-12-28 Muhammad Baraa Almoujahed, Aravind Krishnaswamy Rangarajan, Rebecca L. Whetton, Damien Vincke, Damien Eylenbosch, Philippe Vermeulen, Abdul M. Mouazen
Fusarium head blight (FHB) is one of the most severe fungal diseases that reduces yield of cereal crops and degrades kernel quality with mycotoxins, which are harmful to human and animal health. The majority of FHB identification at post-harvest stage is through lab-based analysis, whilst effective it is a time consuming, expensive, and laborious process. Hence, a non-destructive, rapid, accurate,
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Insights into the influence of matrix fragments on FT-IR-based meat and bone meal species-specific analysis Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-12-17 Bing Gao, Xiaodong Xu, Qingyu Qin, Lujia Han, Xian Liu
This study proposed, verified and analyzed a potential reason (matrix composition complexity) for the subpar performance of FT-IR-based meat and bone meal (MBM) species discrimination. In order to address the aforementioned issue, the MBM matrix was modeled as a binary mixture comprising meat fragment (MF) and bone fragment (BF). BF and MF samples derived from four species were prepared, and MBM samples
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DOE-based multi-criteria optimization of starch/gly/CMC films’ composition and preparation procedure by casting deposition Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-12-19 Lisa Rita Magnaghi, Marta Guembe-Garcia, Vitiana Cerone, Paola Perugini, Giancarla Alberti, Raffaela Biesuz
Bioplastic materials represent a hot topic in the recent literature, with a particular focus on starch-based materials; despite the huge composite films proposed, bioplastics' weak points are still limiting their large-scale applications. In this work, we propose a DOE-based multi-criteria optimization of starch/gly/CMC films targeted to improve both film composition and lab-scale preparation. Film
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Impedimetric biofilm characterization with microelectrode arrays using equivalent electrical circuit features and ensemble classifiers Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-12-20 Maxime Van Haeverbeke, Charlotte Cums, Thijs Vackier, Dries Braeken, Michiel Stock, Hans Steenackers, Bernard De Baets
Electrochemical impedance spectroscopy has proven to be a promising technique for detecting bacterial biofilms. However, its potential for microbial identification has yet to be thoroughly investigated. In this work, we explore the classification of bacterial biofilms at both the strain and species level using commercial microelectrode arrays. Here, we built predictive decision tree ensemble classifiers
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Applications of Rasch modeling in chemometrics: Binary data analysis and analytical platform selection Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-12-18 Andrea Jr Carnoli, Petra oude Lohuis, Lutgarde M.C. Buydens, Jeroen J. Jansen, Gerjen H. Tinnevelt
Often researchers make use of Principal Component Analysis and Partial Least Squares Regression, an unsupervised and a supervised method, respectively, to extract the chemical information in the shape of one or more latent variables. However, when the research question is qualitative and requires a figure of merit, these two models will primarily focus on the quantitative and continuous information
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An improved Symmetric Chaotic War strategy optimization algorithm for efficient Scanning electron microscopy image segmentation: Calcium oxide catalyst case Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-12-11 Amdjed Abdennouri, Emna Zouaoui, Hana Ferkous, Amir Hamza, Morad Grimes, Abdelkrim Boukabou
The Scanning Electron Microscopy (SEM) device is a useful tool that enables scientists to obtain microscopic images that aid in noticing the changes happening on the surface of the catalyst. Manual inspection of these images by eye. However, might not be adequate to obtain a better understanding. The implementation of the segmentation method is a crucial step in streamlining image representation and
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On problematic practice of using normalization in self-modeling/multivariate curve resolution (S/MCR) Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-12-05 Róbert Rajkó
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New resolution independent approach to noise estimation in Minimum Noise Fraction denoising of tissues measured with Infrared Imaging Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-12-05 Danuta Liberda-Matyja, Tomasz P. Wrobel
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Extended least squares (ELS) and generalized least squares (GLS) for clutter suppression in hyperspectral images: A theoretical description Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-11-30 Neal B. Gallagher, Reaha Goyetche, José Manuel Amigo, Sergey Kucheryavskiy
Hyperspectral imaging (HSI) has been demonstrated to be useful for estimating spatial distributions of different chemical constituents on surfaces and in imaged scenes, as well as for detecting anomalies and known targets that may be present. In target detection, the objective is to find pixels that contain significant signal attributable to a known “target” spectrum. Unfortunately, measured images
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Averaging a local PLSR pipeline to predict chemical compositions and nutritive values of forages and feed from spectral near infrared data Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-11-29 Matthieu Lesnoff
Partial least squares regression (PLSR) is a reference method in chemometrics. In agronomy, it is used for instance to predict components of chemical composition (response variables y) of vegetal materials from spectral near infrared (NIR) data X collected from spectrometers. The principle of PLSR is to reduce the dimension of the spectral data X by computing vectors that are then used as latent variables
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PARAFACM: A second-order calibration algorithm for handling data with missing values Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-11-18 Ming-Yue Dong, Hai-Long Wu, Tong Wang, Kun Huang, Hang Ren, Ru-Qin Yu
In the process of collecting chemical data, missing values often occur due to some reasons. The missing values will destroy the multi-linear structure of the data, thus making the traditional multi-way calibration algorithm unable. Therefore, this work proposed a novel second-order calibration algorithm, PARAFAC for missing values (PARAFACM), which can directly handle the three-way data array with
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Enhancing standardization through score-augmented projection-based calibration transfer Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-11-08 Mahdiyeh Ghaffari, Hamid Abdollahi
Reconstructing statistical models for novel instrumentation entails substantial time and financial investments. To obviate the necessity for such model reconstruction, standardization techniques are widely employed. In this context, we introduce a pioneering standardization method termed Score-Augmented Projection-Based Standardization (SA-PBS). Central to SA-PBS is the extraction of spectral sub-spaces
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Multi-modal hybrid modeling strategy based on Gaussian Mixture Variational Autoencoder and spatial–temporal attention: Application to industrial process prediction Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-11-11 Haifei Peng, Jian Long, Cheng Huang, Shibo Wei, Zhencheng Ye
The industrial process is characterized by its multi-modal nature and complex spatial and temporal correlations. Despite the fact that several multi-modal methods have been proposed, few of them can effectively extract deep multi-modal representations and the highly intricate spatial and temporal relationships. In this paper, a novel multi-modal hybrid modeling strategy (GMVAE-STA) is proposed for
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Chemometric enhancement for blind signal resolution from non-invasive spatially offset Raman spectra Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-11-07 Alejandra Arroyo-Cerezo, Miriam Medina-García, Luis Cuadros-Rodríguez, Douglas N. Rutledge, Ana M. Jiménez-Carvelo
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Deep Tutti Frutti: Exploring CNN architectures for dry matter prediction in fruit from multi-fruit near-infrared spectra Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-11-05 Dário Passos, Puneet Mishra
Convolutional Neural Networks (CNNs) have proven to be a valuable Deep Learning (DL) algorithm to model near-infrared spectral data in Chemometrics. However, optimizing CNN architectures and their associated hyperparameters for specific tasks is challenging. In this study, we explore the development of 1D-CNN architectures for the task of fruit dry matter (DM) estimation, testing various designs and
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Extension of adjacency matrix in QSPR analysis Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-11-04 Parikshit Das, Sourav Mondal, Biswajit Some, Anita Pal
The utilization of extended adjacency matrices to investigate various combinatorial properties of graph is one of the most recent research directions in the spectral graph theory. This work proposes a useful modification to the classical adjacency matrix, as well as associated spectral radius and energy. When compared to existing descriptors, the novel energy and Estrada index are established to be
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Can we gain insight about the ductile behavior of materials by using polymer informatics? Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-11-07 Fiorella Cravero, Ignacio Ponzoni, Mónica F. Díaz
Predicting the ductile behavior of thermoplastic materials is a significant challenge for both industry and research. In this study, we present a predictive model that classifies polymers based on their ductility degree, which is a relationship created in the present work to approach this problem. It comes from relating two critical and measurable properties from the tensile test. This target was discretized
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Domain adaptation graph convolution network for quality inferring of batch processes Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-11-07 Jialiang Zhu, Mingwei Jia, Ying Zhang, Weihua Zhou, Hongying Deng, Yi Liu
Developing a reliable quality inference model in batch processes remains a challenge. The performance of the model tends to decline due to batch-to-batch variations. Additionally, the spatial coupling relationships among multiple variables need to be mined to explain the model behavior. To this end, a domain adaptation graph convolution network is developed for quality prediction of batch processes
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Determination of microplastics by FTIR spectroscopy based on quaternion parallel feature fusion and support vector machine Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-10-29 Ailing Tan, Jing Zhao, Yong Zhao, Xiaohang Li, Haijie Su
This study proposed a FTIR spectroscopy combing with quaternion parallel feature extraction methodology for the first time to address the qualitative and quantitative analysis of microplastics. For the international available microplastic FTIR dataset, the original, the first derivative and the second derivative FTIR spectra of polyethylene(PE), PE + fouling and polypropylene(PP) samples were first
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Extended atom-based and bond-based group contribution descriptor and its application to melting point prediction of energetic compounds Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-11-01 Dingling Kong, Yue Luan, Xiaowei Zhao, Yanhua Lu, Wei Li, Qingyou Zhang, Aimin Pang
17817 compounds were collected from the Bradley open melting point data set, including eight elements: C, H, O, N, F, S, Cl, Br, and I. An extended atom-based and bond-based group contribution descriptor was suggested to represent these compounds, which consists of a one-dimensional descriptor based on the molecular formula, a two-dimensional group contribution descriptor based on atoms and bonds,
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SEBP_HNHC: Stacking Ensemble-based Bi-level Predictor for Human Non-Histone Crotonylation combining with iterative feature representation strategy Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-10-31 Qiao Ning, Yue Wang, Zedong Qi
Accurate identification of crotonylation on human non-histone sites plays a crucial role in human biological research. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods are more and more popular in recent years. Most of previous research methods are only for the prediction of crotonylation sites of histones, and there is a lack of targeted
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Application of improved Stacking ensemble learning in NIR spectral modeling of corn seed germination rate Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-10-31 Xiaojin Hao, Zhengguang Chen, Shujuan Yi, Jinming Liu
Stacking ensemble learning is one of the most effective integration technologies and is increasingly applied to near-infrared spectroscopy combined with chemometrics methods. The prediction accuracy of Stacking is primarily affected by the selection of different models. However, many current studies are mainly artificial selection models' combinations. It affects the model's prediction accuracy and
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OCPMDM 2.0: An intelligent solution for materials data mining Chemometr. Intell. Lab. Systems (IF 3.9) Pub Date : 2023-10-29 Dongping Chang, Pengcheng Xu, Minjie Li, Wencong Lu
Machine learning methods have played a significant role in materials design. With the rapid development of the Materials Genome Initiative (MGI) and data science, material researchers are confronted with the requirements to conduct sophisticated data analytics in modeling the property of materials. To make it more convenient for material researchers to design new materials using machine learning methods