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Toward more efficient and effective color quality control for the large‐scale offset printing process J. Chemometr. (IF 2.4) Pub Date : 2024-03-16 Pawel Dziki, Lukasz Pieszczek, Michal Daszykowski
This study illustrates at‐line application of hyperspectral imaging in the visible range for quality control of large‐scale offset printing. In particular, the measurement stability of a competitive device is assessed and compared to traditional handheld and desktop spectrophotometers. The performance of the commercially available instruments was assessed based on collected spectra and their corresponding
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Classification of colorectal primer carcinoma from normal colon with mid‐infrared spectra J. Chemometr. (IF 2.4) Pub Date : 2024-03-13 B. Borkovits, E. Kontsek, A. Pesti, P. Gordon, S. Gergely, I. Csabai, A. Kiss, P. Pollner
In this project, we used formalin‐fixed paraffin‐embedded (FFPE) tissue samples to measure thousands of spectra per tissue core with Fourier transform mid‐infrared spectroscopy using an FT‐IR imaging system. These cores varied between normal colon (NC) and colorectal primer carcinoma (CRC) tissues. We created a database to manage all the multivariate data obtained from the measurements. Then, we applied
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Issue Information J. Chemometr. (IF 2.4) Pub Date : 2024-03-11
No abstract is available for this article.
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Developing multifruit global near‐infrared model to predict dry matter based on just‐in‐time modeling J. Chemometr. (IF 2.4) Pub Date : 2024-03-05 Puneet Mishra
Modeling near‐infrared (NIR) spectral data to predict fresh fruit properties is a challenging task. The difficulty lies in creating generalized models that can work on fruits of different cultivars, seasons, and even multiple commodities of fruit. Due to intrinsic differences in spectral properties, NIR models often fail in testing, resulting in high bias and prediction errors. One current solution
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Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM10 concentrations J. Chemometr. (IF 2.4) Pub Date : 2024-03-05 Gökçe Nur Taşağıl Arslan, Serpil Kılıç Depren
The rapid advancement of industrialization and urbanization has led to the global problem of air pollution. Air quality can decrease due to pollutants in the air, including types of gases and particles that are carcinogenic, causing adverse health effects. Therefore, estimating the concentration of air pollutants is of great interest as it can provide accurate information about air quality with proper
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Structured discriminative Gaussian graph learning for multimode process monitoring J. Chemometr. (IF 2.4) Pub Date : 2024-03-03 Jing Wang, Yi Liu, Dongping Zhang, Lei Xie, Jiusun Zeng
Aiming at the actual industrial process background that different modes share the same system configurations and control structure, this article proposes a novel structured discriminant Gaussian graph learning for multimode process monitoring. The proposed method considers not only the sparsity of graph model but also the measurement of data variation based on a mismatched graph and the common node
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Image‐based characterization of flocculation processes through PLS inspired representation learning in convolutional neural networks J. Chemometr. (IF 2.4) Pub Date : 2024-02-20 Andreas Baum, Rayisa Moiseyenko, Simon Glanville, Thomas Martini Jørgensen
Monitoring of flocculation processes such as those used in downstream processing of a fermentation broth is essential for process control. One approach is to apply microscopic imaging combined with image analysis for characterizing the state of the process. In this work, we investigate and compare the use of supervised feedforward convolutional neural network (CNN) architectures to predict the process
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A novel two-phase near-infrared and midinfrared wavelength selection framework for sample classification J. Chemometr. (IF 2.4) Pub Date : 2024-02-17 Juliana Fontes, Michel J. Anzanello, João B. G. Brito, Guilherme B. Bucco
Spectral data describing product samples are typically composed of a large number of noisy and irrelevant wavelengths that tends to undermine the performance of multivariate predictive techniques. This paper proposes a two-phase framework that integrates a preselection wavelength step oriented by wavelength clustering to a wrapper-based strategy. The first phase performs a pruning process in the data
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Issue Information J. Chemometr. (IF 2.4) Pub Date : 2024-02-06
No abstract is available for this article.
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Affine combination-based over-sampling for imbalanced regression J. Chemometr. (IF 2.4) Pub Date : 2024-02-09 Zhen-Zhen Li, Niu Huang, Lun-Zhao Yi, Guang-Hui Fu
Imbalanced domain prediction analysis is currently one of the hot research topics. Many real-world data mining analyses involve using imbalanced data to obtain predictive models. In the context of imbalance, research on classification problems has been extensive, but research on regression problems is negligible. Rare values rarely occur in imbalanced regression problems, but the focus is on accurately
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Two new methods for the estimation and interpretation of the range of feasible profiles in multivariate curve resolution and their implications to analytical chemistry J. Chemometr. (IF 2.4) Pub Date : 2024-01-30 Alejandro C. Olivieri
Two new models have been recently introduced for studying the remaining rotational ambiguity in the bilinear decomposition of matrix data. One of the models is N-BANDS, which yields two extreme profiles per sample component, corresponding to maximum or minimum signal contribution function or relative component area under its concentration profile. It is highly useful for computing the relative root
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Assessment of adulteration of sage (Salvia sp.) with olive leaves using high-performance thin-layer chromatography, image analysis, and multivariate linear modeling J. Chemometr. (IF 2.4) Pub Date : 2024-01-20 Nina Tomčić, Milica Jankov, Petar Ristivojević, Jelena Trifković, Filip Andrić
According to the study carried out at the University of Bristol, 60% of oregano spices present on the European Union (EU) market are adulterated with olive, myrtle, cistus, and hazelnut leaves. According to the same authors, the sage products are adulterated by similar bulking agents. The aim of this study was to assess possibilities for detection of sage adulteration by olive leaves using high-performance
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Quantitative study on impact damage of honey peaches based on reflection, absorbance, and Kubelka-Munk spectrum combined with color characteristics J. Chemometr. (IF 2.4) Pub Date : 2024-01-20 Bin Li, Jiping Zou, Chengtao Su, Feng Zhang, Yande Liu, Jian Wu, Nan Chen, Yihua Xiao
Impact damage is one of the key factors affecting the quality of honey peaches. Quantitative study of impact damage is of great significance for the sorting of postharvest quality of honey peaches. In order to realize the quantitative prediction of impact damage of honey peaches, the impact damage of honey peaches was quantitatively studied based on the fusion of color characteristics with reflection
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Adaptive deep fusion neural network based soft sensor for industrial process J. Chemometr. (IF 2.4) Pub Date : 2024-01-20 Xiaoping Guo, Jialin Chong, Yuan Li
Deep neural networks have become an important tool for soft sensor modeling. However, common deep autoencoder networks are limited to mining the effective information of each input layer during hierarchical training, ignoring the loss of effective information in the original input data and accumulating it layer-by-layer, resulting in incomplete feature representation of the original input. At the same
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Issue Information J. Chemometr. (IF 2.4) Pub Date : 2024-01-14
No abstract is available for this article.
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Implications of confounding from unmodeled interactions between explanatory variables when using latent variable regression models to make inferences J. Chemometr. (IF 2.4) Pub Date : 2024-01-12 Olav M. Kvalheim, Warren S. Vidar, Tim U. H. Baumeister, Roger G. Linington, Nadja B. Cech
With linear dependency between the explanatory variables, partial least squares (PLS) regression is commonly used for regression analysis. If the response variable correlates to a high degree with the explanatory variables, a model with excellent predictive ability can usually be obtained. Ranking of variable importance is commonly used to interpret the model and sometimes this interpretation guides
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The difference of model robustness assessment using cross-validation and bootstrap methods J. Chemometr. (IF 2.4) Pub Date : 2024-01-11 Rita Lasfar, Gergely Tóth
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Sample selection method using near-infrared spectral information entropy as similarity criterion for constructing and updating peach firmness and soluble solids content prediction models J. Chemometr. (IF 2.4) Pub Date : 2023-12-19 Yande Liu, Cong He, Xiaogang Jiang
When using near-infrared (NIR) techniques for analysis, model construction and maintenance updates are essential. When model construction is performed in machine learning, the sample set is usually divided into the calibration set and the validation set. The representativeness of the calibration set and the reasonable distribution of the validation set affects the accuracy of the established model
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Issue Information J. Chemometr. (IF 2.4) Pub Date : 2023-12-06
No abstract is available for this article.
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Iterative re-weighted multilinear partial least squares modelling for robust predictive modelling J. Chemometr. (IF 2.4) Pub Date : 2023-12-06 Puneet Mishra, Kristian Hovde Liland
Higher order data are commonly encountered in the domain of chemometrics, often generated by advanced analytical instruments. Due to the multilinear nature of the data, higher order data require different regression approaches compared with traditional two-way data for predictive modelling. The main aim is usually to extract the rich multilinear information, which is often lost if the data are simply
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Correction to “A novel eco-friendly methods for simultaneous determination of aspirin, clopidogrel, and atorvastatin or rosuvastatin in their fixed-dose combination using chemometric techniques and artificial neural networks” J. Chemometr. (IF 2.4) Pub Date : 2023-11-14
AlSawy NS, ElKady EF, Mostafa EA. A novel eco-friendly methods for simultaneous determination of aspirin, clopidogrel, and atorvastatin or rosuvastatin in their fixed-dose combination using chemometric techniques and artificial neural networks. Journal of Chemometrics. 2023;37(5):e3474. doi:10.1002/cem.3474 The first letter ‘A’ in the article title was mistakenly added. The updated article title is
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Issue Information J. Chemometr. (IF 2.4) Pub Date : 2023-11-07
No abstract is available for this article.
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Experimental designs for controlling the correlation of estimators in two-parameter models J. Chemometr. (IF 2.4) Pub Date : 2023-11-08 Edgar Benitez, Jesús López-Fidalgo
The state of the art related to parameter correlation in two-parameter models has been reviewed in this paper. The apparent contradictions between the different authors regarding the ability of D-optimality to simultaneously reduce the correlation and the area of the confidence ellipse in two-parameter models were analyzed. Two main approaches were found: (1) those who consider that the optimality
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An antioxidative potential-based comparison of different peanut extraction methods, optimized through response surface methodology J. Chemometr. (IF 2.4) Pub Date : 2023-11-02 Kritamorn Jitrangsri, Amornrut Chaidedgumjorn, Malai Satiraphan
In this research, response surface methodology was employed in order to find the most efficient conditions to produce peanut extracts with the highest antioxidative potential (by DPPH, ABTS, FRAP, and ORAC tests) from ultrasound-assisted (UAE) and microwave-assisted (MAE) extractions. The optimal conditions for UAE were 75 ml of 30%v/v ethanol as extraction solvent, extraction temperature of 65°C,
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Just-in-time latent autoregressive residual generation for dynamic process monitoring J. Chemometr. (IF 2.4) Pub Date : 2023-10-25 Shi Hu, Kuan Chang
With a goal of timely and adaptively exploiting the inconsistency inherited in the monitored samples of current interest, a novel dynamic process monitoring method based on just-in-time latent autoregressive residual generation (JITLAR2G) model is proposed. Different from the mainstream dynamic modeling and monitoring methods which usually train a signature generating mechanism and then repeatedly
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Automatic peak annotation and area estimation of glycan map peaks directly from chromatograms J. Chemometr. (IF 2.4) Pub Date : 2023-10-24 Domen Hudnik, Naja Bohanec, Igor Drobnak, Peter Ernst, Alexander Hanke, Matej Horvat, Franz Innerbichler, Miha Mikelj, Tilen Praper, Vasja Progar, Nika Valenčič, Matjaž Omladič
The present bottleneck in biosimilar bioprocess development has become evaluation of analytical results, due to recent advances in analytics, such as automated sample preparation and development of high-throughput methods. Currently automated chromatogram integration and annotation is only efficient for simple chromatograms. In an ever more competitive field of biosimilars, this represents a serious
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Two-step hybrid modeling for variable selection and estimation: An application to quantitative structure activity relationship study J. Chemometr. (IF 2.4) Pub Date : 2023-10-23 Henrietta Ebele Oranye, Fidelis Ifeanyi Ugwuowo, Kingsley Chinedu Arum
In this study, we developed a simple technique for effective parameter estimation and prediction of the quantitative structure activity relationship studies using a two-step procedure. The first step is to choose the important molecular descriptors using the random forest regression, and the second step is to optimally predict the biological activity of the selected chemical compounds using the following
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Issue Information J. Chemometr. (IF 2.4) Pub Date : 2023-10-05
No abstract is available for this article.
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Chromatographic method development for simultaneous determination of serotonin, melatonin, and L-tryptophan: Mass transfer modeling, chromatographic separation factors, and method prediction by artificial neural network J. Chemometr. (IF 2.4) Pub Date : 2023-10-03 Dipshikha Tamili, Susovan Jana, Paramita Bhattacharjee
This work endeavored to develop a high performance liquid chromatography (HPLC) method for simultaneous quantification of three important biotherapeutic molecules namely, L-tryptophan, serotonin, and melatonin, present in low amounts in agro-commodities. In the first approach, using pure chemical standards of the same in a mixture, chromatogram separation parameters such as peak sharpness, peak shouldering
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Malt quality profile of barley predicted by near-infrared spectroscopy using partial least squares, Bayesian regression, and artificial neural network models J. Chemometr. (IF 2.4) Pub Date : 2023-10-01 Oyeyemi O. Ajayi, Lanre Akinyemi, Sikiru Adeniyi Atanda, Jason G. Walling, Ramamurthy Mahalingam
Due to the significant cost and time involved in identifying barley lines with superior malting quality, the malting industry is searching for accurate and rapid methods to expedite the selection of superior barley lines that meet breeder's goals. The aim of this study is to compare partial least squares regression (PLSR) with advanced statistical models (Bayesian and machine learning) and reliably
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Principal balances of compositional data for regression and classification using partial least squares J. Chemometr. (IF 2.4) Pub Date : 2023-09-21 V. Nesrstová, I. Wilms, J. Palarea-Albaladejo, P. Filzmoser, J. A. Martín-Fernández, D. Friedecký, K. Hron
High-dimensional compositional data are commonplace in the modern omics sciences, among others. Analysis of compositional data requires the proper choice of a log-ratio coordinate representation, since their relative nature is not compatible with the direct use of standard statistical methods. Principal balances, a particular class of orthonormal log-ratio coordinates, are well suited to this context
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Issue Information J. Chemometr. (IF 2.4) Pub Date : 2023-09-11
No abstract is available for this article.
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Chemical identification of microfossils from the 1.88-Ga Gunflint chert: Towards empirical biosignatures using laser ablation ionization mass spectrometer J. Chemometr. (IF 2.4) Pub Date : 2023-09-11 Rustam A. Lukmanov, Marek Tulej, Niels F. W. Ligterink, Coenraad De Koning, Andreas Riedo, Valentine Grimaudo, Anna Neubeck, David Wacey, Peter Wurz
The article from this special issue was previously published in Journal of Chemometrics, Volume 35, Issue 10, 2021. For completeness we are including the title page of the article below. The full text of the article can be read in Issue 35:10 on Wiley Online Library: https://doi.org/10.1002/cem.3370
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Detection of the storage time of light bruises in yellow peaches based on spectrum and texture features of hyperspectral image J. Chemometr. (IF 2.4) Pub Date : 2023-09-14 Bin Li, Ji-ping Zou, Hai Yin, Yan-de Liu, Feng Zhang, Ai-guo Ou-yang
Yellow peaches are soft, and they bruise easily; the bruised areas of them are prone to breed bacteria and molds, so the consumption and the safety of related products of yellow peaches are affected by the difference in the storage time of light bruises in them. In order to accurately distinguish of the storage time of light bruises in yellow peaches, the spectra of the sample bruised region were combined
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ASER: Adapted squared error relevance for rare cases prediction in imbalanced regression J. Chemometr. (IF 2.4) Pub Date : 2023-09-08 Ying Kou, Guang-Hui Fu
Many real-world data mining applications involve using imbalanced datasets to obtain predictive models. Imbalanced data can hinder the model performance of learning algorithms in rare cases. Although there are many well-researched classification task solutions, most of them cannot be directly applied to regression task. One of the challenges in imbalanced regression is to find a suitable evaluation
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On the complementary nature of ANOVA simultaneous component analysis (ASCA+) and Tucker3 tensor decompositions on designed multi-way datasets J. Chemometr. (IF 2.4) Pub Date : 2023-08-30 Farnoosh Koleini, Siewert Hugelier, Mahsa Akbari Lakeh, Hamid Abdollahi, José Camacho, Paul J. Gemperline
The complementary nature of analysis of variance (ANOVA) Simultaneous Component Analysis (ASCA+) and Tucker3 tensor decompositions is demonstrated on designed datasets. We show how ASCA+ can be used to (a) identify statistically sufficient Tucker3 models; (b) identify statistically important triads making their interpretation easier; and (c) eliminate non-significant triads making visualization and
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Distributed statistical process monitoring based on block-wise residual generator J. Chemometr. (IF 2.4) Pub Date : 2023-08-23 Chudong Tong, Xinyan Zhou, Kai Qian, Xin Xu, Jiongting Jiang
The increasing scale of modern chemical plants keeps popularizing investigation as well as application of distributed process monitoring approaches. With a goal of directly quantifying the normal relations between different blocks divided from the whole process, a novel multi-block modeling strategy called block-wise residual generator is proposed, which trains a residual generator for each block through
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Issue Information J. Chemometr. (IF 2.4) Pub Date : 2023-08-07
No abstract is available for this article.
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Methodology adjusting for least squares regression slope in the application of multiplicative scatter correction to near-infrared spectra of forage feed samples J. Chemometr. (IF 2.4) Pub Date : 2023-08-03 Mewa S. Dhanoa, Secundino López, Ruth Sanderson, Sue J. Lister, Ralph J. Barnes, Jennifer L. Ellis, James France
Scatter corrections are commonly applied to refine near-infrared (NIR) spectra. The aim of this study is to assess the impact of measurement errors when using ordinary least squares (OLS) for multiplicative scatter correction (MSC). Any measurement errors attached to the set-mean spectrum may attenuate the OLS slope and that in turn will affect the estimate of the intercept and the adjustment of the
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Selection of principal variables through a modified Gram–Schmidt process with and without supervision J. Chemometr. (IF 2.4) Pub Date : 2023-07-29 Joakim Skogholt, Kristian H. Liland, Tormod Næs, Age K. Smilde, Ulf G. Indahl
In various situations requiring empirical model building from highly multivariate measurements, modelling based on partial least squares regression (PLSR) may often provide efficient low-dimensional model solutions. In unsupervised situations, the same may be true for principal component analysis (PCA). In both cases, however, it is also of interest to identify subsets of the measured variables useful
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Application of ultramicrotomy and infrared imaging to the forensic examination of automotive paint J. Chemometr. (IF 2.4) Pub Date : 2023-07-19 Haoran Zhong, Elizabeth Donkor, Lisa Whitworth, Collin G. White, Kaushalya Sharma Dahal, Ayuba Fasasi, Thomas M. Hancewicz, Franklin Uba, Barry K. Lavine
In several previously published studies, Lavine and coworkers have demonstrated that infrared (IR) spectra from all layers of an intact multilayered automotive paint chip can be collected in a single analysis by scanning across each layer of a cross sectioned paint chip using a Fourier transform IR imaging microscope. Applying alternating least squares to the spectral data, the IR spectrum of each
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Smoothing and differentiation of data by Tikhonov and fractional derivative tools, applied to surface-enhanced Raman scattering (SERS) spectra of crystal violet dye J. Chemometr. (IF 2.4) Pub Date : 2023-07-11 Nelson H. T. Lemes, Taináh M. R. Santos, Camila A. Tavares, Luciano S. Virtuoso, Kelly A. S. Souza, Teodorico C. Ramalho
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Planetary and space science special issue J. Chemometr. (IF 2.4) Pub Date : 2023-07-11 Emmanuel A. Lalla, Menelaos Konstantinidis
Today more than ever, space science is a vibrant and exciting field. The Mars 2020 Perseverance Rover took off and landed at the height of the Covid-19 pandemic, and the scientific results of its payload are already paying dividends to the scientific community. Meanwhile on Earth, scientific development, far from having halted, remains as active as ever before, albeit with some hiccups over the last
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Issue Information J. Chemometr. (IF 2.4) Pub Date : 2023-07-06
No abstract is available for this article.
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Application of stable consistency wavelength in optimizing gasoline RON near-infrared analysis model transfer J. Chemometr. (IF 2.4) Pub Date : 2023-07-08 Hong-hong Wang, Hui Yuan, Yun-chao Hu, Zhi-xin Xiong, Zhi-jian Liu, Ying Wang, Hao-ran Huang
The purpose of model transfer is to solve the problem that multivariate calibration models cannot be shared among different near-infrared spectrometers. Taking gasoline as the research object, the transfer analysis of its octane number model was carried out. The gasoline samples collected by two near-infrared spectrometers of the same type were used as the research object. The screening wavelengths
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Moisture content prediction of semen ziziphi spinosae based on hyperspectral images coupled with convolutional neural networks and subregional voting J. Chemometr. (IF 2.4) Pub Date : 2023-06-28 Xiong Li, Yande Liu, Liangfeng Liu, Xiaogang Jiang, Guantian Wang
Deep learning algorithms represented by convolutional neural networks bring new opportunities for spectral analysis technology. Convolutional neural networks are more straightforward than traditional chemometric algorithms for detecting the quality of agricultural products, reducing the procedures of spectral preprocessing and band selection, and with higher prediction accuracy. However, there are
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Shift-invariant tri-linearity—A new model for resolving untargeted gas chromatography coupled mass spectrometry data J. Chemometr. (IF 2.4) Pub Date : 2023-06-26 Paul-Albert Schneide, Rasmus Bro, Neal B. Gallagher
Multi-way data analysis is popular in chemometrics for the decomposition of, for example, spectroscopic or chromatographic higher-order tensor datasets. Parallel factor analysis (PARAFAC) and its extension, PARAFAC2, are extensively employed methods in chemometrics. Applications of PARAFAC2 for untargeted data analysis of hyphenated gas chromatography coupled with mass spectrometric detection (GC-MS)
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Deep electron cloud-activity and field-activity relationships J. Chemometr. (IF 2.4) Pub Date : 2023-06-22 Lu Xu, Qin Yang
Chemists have been pursuing general mathematical laws to explain and predict molecular properties for a long time. However, most of the traditional quantitative structure-activity relationship (QSAR) models have limited application domains; for example, they tend to have poor generalization performance when applied to molecules with parent structures different from those of the trained molecules. This
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Utilization of ultraviolet-visible spectrophotometry in conjunction with wrapper method and correlated component regression for nitrite prediction outside the Beer–Lambert domain J. Chemometr. (IF 2.4) Pub Date : 2023-06-22 Meryem NINI, El Mati Khoumri, Omar Ait Layachi, Mohamed Nohair
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Advancements in multivariate analysis of variance J. Chemometr. (IF 2.4) Pub Date : 2023-06-15 Ingrid Måge, Federico Marini
The Journal of Chemometrics is pleased to announce a special issue focused on multivariate analysis of data from designed experiments. ANOVA (Analysis of Variance) is the standard method for analyzing data from experimental designs. The classical ANOVA methods are however univariate and do not handle multiple collinear response variables. Designed experiments with multivariate outputs are prevalent
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Issue Information J. Chemometr. (IF 2.4) Pub Date : 2023-06-12
No abstract is available for this article.
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New editors on Journal of Chemometrics J. Chemometr. (IF 2.4) Pub Date : 2023-05-30 Cyril Ruckebusch
I am greatly honored to have been selected as the new Editor-in-Chief of Journal of Chemometrics. I am pleased and enthusiastic to contribute to the history of a journal that has been around since the very early days of chemometrics, and I will do my best to drive it into new developments. I would like to express my warm and sincere thanks to the outgoing Editor-in-Chief, Age Smilde, for his contribution
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Issue Information J. Chemometr. (IF 2.4) Pub Date : 2023-05-16
No abstract is available for this article.
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Dedicated to Edmund R. Malinowski. Secondary, model-based examination of model-free analysis results: Making the most of soft-modelling outcomes J. Chemometr. (IF 2.4) Pub Date : 2023-05-13 Marcel Maeder
Edmund R. Malinowski is well known for his factor analysis-based work; he is clearly less well known for his chemical model-based analyses of chemical data. In this contribution, we discuss the, at the time innovative, idea of subjecting the primary model-free analysis results to a secondary quantitative model-based evaluation. Two examples from his research serve as illustrations: the complexation
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Is core consistency a too conservative diagnostic? J. Chemometr. (IF 2.4) Pub Date : 2023-04-24 Helene Fog Froriep Halberg, Marta Bevilacqua, Åsmund Rinnan
Fluorescence spectroscopy combined with parallel factor analysis (PARAFAC) has successfully been applied for the analysis of food and beverages containing numerous autofluorescent compounds. For the decomposition of such data, it is crucial to establish the PARAFAC model complexity. This is not a trivial matter, especially when the sample complexity increases. Diagnostics are available for assisting
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limpca: An R package for the linear modeling of high-dimensional designed data based on ASCA/APCA family of methods J. Chemometr. (IF 2.4) Pub Date : 2023-04-16 Michel Thiel, Nadia Benaiche, Manon Martin, Sébastien Franceschini, Robin Van Oirbeek, Bernadette Govaerts
Many modern analytical methods are used to analyze samples issued from an experimental design, for example, in medical, biological, chemical, or agronomic fields. Those methods generate most of the time, highly multivariate data like spectra or images, where the number of variables (descriptor responses) tends to be much larger than the number of experimental units. Therefore, multivariate statistical
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Issue Information J. Chemometr. (IF 2.4) Pub Date : 2023-04-10
No abstract is available for this article.
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Research progress on the application of hyperspectral imaging techniques in tea science J. Chemometr. (IF 2.4) Pub Date : 2023-04-12 Dongxia Liang, Qiaoyi Zhou, Caijin Ling, Liyang Gao, Xiaoting Mu, Zhencheng Liao
Hyperspectral imaging technology combines two-dimensional imaging and spectral technology, which can simultaneously obtain spatial and spectral information of the object to be measured and is an advanced technical method. With the development of science and technology, the detection of tea has also been continuously improved, and it has developed in the direction of being nondestructive, fast, real-time