Journal of Chemometrics最新文献

Influence of a Measurement Procedure and Evaluation of Transflectance Sensing System for Quantifying Sunflower Oil Adulterations in Olive Oil. A Proof of Concept 测量方法对橄榄油中葵花籽油掺假量的影响及透射传感系统评价。概念验证

IF 2.3 4区化学

Journal of Chemometrics Pub Date : 2025-07-26 DOI: 10.1002/cem.70054

D. Castro-Reigía, M. Sierra, I. García, S. Sanllorente, L. A. Sarabia, M. C. Ortiz

{"title":"Influence of a Measurement Procedure and Evaluation of Transflectance Sensing System for Quantifying Sunflower Oil Adulterations in Olive Oil. A Proof of Concept","authors":"D. Castro-Reigía, M. Sierra, I. García, S. Sanllorente, L. A. Sarabia, M. C. Ortiz","doi":"10.1002/cem.70054","DOIUrl":"https://doi.org/10.1002/cem.70054","url":null,"abstract":"<p>The development of NIR instruments and/or their modification to adapt the measurements for each problem and improve its performance are crucial steps for the optimal measurement procedures. In this work, it is presented the development of an accessory for cuvettes designed to have the possibility to collect NIR spectra in transflectance mode. In that sense, it is aimed to investigate how different factors in the measurement procedure using this accessory influence both the NIR spectra and the subsequent calibration models for detecting adulterations with sunflower oil in olive oil. The purpose is to show how a proof of concept can be developed using chemometric tools. For that, every measurement condition influencing the spectra was evaluated with ASCA, visualizing how the use of different NIR devices, the sensor arrangement regarding the cuvette, the activation of the internal compensation system of temperature of the sensor, or the concentration levels of the adulterant affected the resulting spectra. Afterwards, every possible combination of the factors was explored through eight different PLS calibration models and their validation to examine if the factors also influenced the calibration models built for quantifying the sunflower oil present in the olive oil. It was found that not only were all factors significant regarding NIR measurements but also when quantifying adulterants. The best results of this proof of concept were obtained by arranging the sensor in a horizontal disposition regarding the cuvette and activating the internal compensation system of temperature. The capability of detection of the method for the particular oils used was 1.4% for probabilities of false positive and false negative of 0.05.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Discrimination of Thawed and Nonfrozen Chicken Thighs Using Convex Hull Peeling in Visible Spectral Imaging 利用可见光谱成像技术增强解冻鸡腿与非冷冻鸡腿的识别

IF 2.3 4区化学

Journal of Chemometrics Pub Date : 2025-07-23 DOI: 10.1002/cem.70055

Esmée Versteegen, Mahsa Akbari Lakeh, Anastasia Swanson, Gerjen H. Tinnevelt, Aoife Gowen, Jeroen J. Jansen, Mahdiyeh Ghaffari

{"title":"Enhanced Discrimination of Thawed and Nonfrozen Chicken Thighs Using Convex Hull Peeling in Visible Spectral Imaging","authors":"Esmée Versteegen, Mahsa Akbari Lakeh, Anastasia Swanson, Gerjen H. Tinnevelt, Aoife Gowen, Jeroen J. Jansen, Mahdiyeh Ghaffari","doi":"10.1002/cem.70055","DOIUrl":"https://doi.org/10.1002/cem.70055","url":null,"abstract":"<p>Hyperspectral imaging (HSI) combines spectral and spatial data, producing complex 3D datasets that require efficient data reduction methods for improved computational efficiency and prediction accuracy. This study introduces convex hull peeling to enhance the discrimination of thawed and nonfrozen chicken thighs. By removing pixels with noise-dominated spectra and targeting deeper data layers, this method improved model robustness and reduced training time from 426 to 5 s. Essential spectral pixels (ESPs), located on the convex hull in principal component space, effectively preserved critical data, achieving 81% classification accuracy, comparable with using the full dataset. Sensitivity and specificity were 74% and 89%, respectively, demonstrating improved specificity with a slight trade-off in sensitivity. Piece-based accuracy reached 100%, highlighting the potential of this approach for noninvasive food quality assessment. This study underscores the efficiency and adaptability of ESPs and convex hull peeling for complex datasets.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sparse Twoblock Dimension Reduction: A Versatile Alternative to Sparse PLS2 and CCA 稀疏双块降维：稀疏PLS2和CCA的通用替代方案

IF 2.3 4区化学

Journal of Chemometrics Pub Date : 2025-07-22 DOI: 10.1002/cem.70051

Sven Serneels

引用次数: 0

Statistical Validation of Multivariate Treatment Effects in Longitudinal Study Designs 纵向研究设计中多变量治疗效果的统计验证

IF 2.3 4区化学

Journal of Chemometrics Pub Date : 2025-07-19 DOI: 10.1002/cem.70044

Torfinn Støve Madssen, Age Smilde, Jose Camacho, Anders Hagen Jarmund, Johan Westerhuis, Guro F. Giskeødegård

{"title":"Statistical Validation of Multivariate Treatment Effects in Longitudinal Study Designs","authors":"Torfinn Støve Madssen, Age Smilde, Jose Camacho, Anders Hagen Jarmund, Johan Westerhuis, Guro F. Giskeødegård","doi":"10.1002/cem.70044","DOIUrl":"https://doi.org/10.1002/cem.70044","url":null,"abstract":"<div>\u0000 \u0000 <p>Multivariate extensions of repeated measures linear mixed models, such as repeated measures ANOVA simultaneous component analysis (RM-ASCA+) and linear mixed model-principal component analysis (LiMM-PCA), can be used for analyzing longitudinal studies with multivariate outcomes. However, there are no gold standards to assess the statistical validation of the observed effects of such models. Using real and simulated data, we here perform an empirical comparison of different strategies for assessing statistical significance in these frameworks: permutation tests, the global log-likelihood ratio (GLLR) test, and nonparametric bootstrap confidence intervals for the estimated multivariate effects. Power curves were used to examine the statistical power of the different tests in detecting time–treatment interactions with varying effect sizes. Our results show that both the permutation tests and the GLLR test can be used to statistically test the presence of a time–treatment interaction effect for multivariate data; however, the GLLR approach will be sensitive to the number of included principal components in LiMM-PCA. The bootstrap confidence interval approach generally shows good statistical power but has inflated Type 1 error rates under certain conditions. This makes it unsuitable for the purpose of hypothesis testing in its present implementation, although it may still be useful for exploratory purposes. Overall, our results show that the power of the tests for assessing multivariate effects in longitudinal studies is dependent on characteristics of the dataset, and it is important to be aware of the strengths and weaknesses of the different validation procedures.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rapid Determination of Soil Organic Matter by Near-Infrared Spectroscopy With a Novel Double Ensemble Modeling Method 基于双系综模型的近红外光谱快速测定土壤有机质

IF 2.3 4区化学

Journal of Chemometrics Pub Date : 2025-07-17 DOI: 10.1002/cem.70053

Yingxia Li, Jiajing Zhao, Zizhen Zhao, Haiping Huang, Xiaoyao Tan, Xihui Bian

引用次数: 0

Transforming Hyperspectral Images Into Chemical Maps: A Novel End-to-End Deep Learning Approach 将高光谱图像转换为化学图：一种新颖的端到端深度学习方法

IF 2.3 4区化学

Journal of Chemometrics Pub Date : 2025-07-16 DOI: 10.1002/cem.70041

Ole-Christian Galbo Engstrøm, Michela Albano-Gaglio, Erik Schou Dreier, Yamine Bouzembrak, Maria Font-i-Furnols, Puneet Mishra, Kim Steenstrup Pedersen

{"title":"Transforming Hyperspectral Images Into Chemical Maps: A Novel End-to-End Deep Learning Approach","authors":"Ole-Christian Galbo Engstrøm, Michela Albano-Gaglio, Erik Schou Dreier, Yamine Bouzembrak, Maria Font-i-Furnols, Puneet Mishra, Kim Steenstrup Pedersen","doi":"10.1002/cem.70041","DOIUrl":"https://doi.org/10.1002/cem.70041","url":null,"abstract":"<p>Current approaches to chemical map generation from hyperspectral images are based on models such as partial least squares (PLS) regression, generating pixel-wise predictions that do not consider spatial context and suffer from a high degree of noise. This study proposes an end-to-end deep learning approach using a modified version of U-Net and a custom loss function to directly obtain chemical maps from hyperspectral images, skipping all intermediate steps required for traditional pixel-wise analysis. The U-Net is compared with the traditional PLS regression on a real dataset of pork belly samples with associated mean fat reference values. The U-Net obtains a test set root mean squared error of between 9% and 13% lower than that of PLS regression on the task of mean fat prediction. At the same time, U-Net generates fine detail chemical maps where 99.91% of the variance is spatially correlated. Conversely, only 2.53% of the variance in the PLS-generated chemical maps is spatially correlated, indicating that each pixel-wise prediction is largely independent of neighboring pixels. Additionally, while the PLS-generated chemical maps contain predictions far beyond the physically possible range of 0%–100%, U-Net learns to stay inside this range. Thus, the findings of this study indicate that U-Net is superior to PLS for chemical map generation.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spectral Wavelength Selection Method Based on Improved Particle Swarm Optimization Idea and Simulated Annealing Strategy 基于改进粒子群优化思想和模拟退火策略的光谱波长选择方法

IF 2.3 4区化学

Journal of Chemometrics Pub Date : 2025-07-15 DOI: 10.1002/cem.70050

Ying Dong, Weida Wang, Nanfeng Zhang, Jinming Liu

{"title":"Spectral Wavelength Selection Method Based on Improved Particle Swarm Optimization Idea and Simulated Annealing Strategy","authors":"Ying Dong, Weida Wang, Nanfeng Zhang, Jinming Liu","doi":"10.1002/cem.70050","DOIUrl":"https://doi.org/10.1002/cem.70050","url":null,"abstract":"<div>\u0000 \u0000 <p>Wavelength selection (WS) is an effective means to address the presence of many uncorrelated and collinear variables in high-dimensional spectral data that seriously influence the modeling accuracy and efficiency. Aiming to address too many wavelength variables selected by particle swarm optimization algorithm (PSO) and its premature convergence, this paper proposes a novel spectral WS approach—iPSOSA—based on the improved PSO idea and simulated annealing algorithms (SA) strategy. iPSOSA applies the velocity and position update ideas of PSO to the guided shift evolution process of the binary bits with the value of “1” in the particle and integrates with the perturbation strategy of the SA Metropolis acceptance criterion. It effectively solves the premature convergence of PSO and overcomes the low efficiency of the SA evolution, which has high efficiency in WS. By evaluating the modeling performance of different intelligent WS methods using two public spectral datasets from soil and maize, it was found that the iPSOSA outperforms the full-spectrum and other three comparative algorithms. The best iPSOSA partial least squares regression models for soil organic matter and maize moisture contents have excellent regression performance, with the validation set's coefficient of determination higher than 0.98, relative root mean squared error lower than 1.50%, and residual predictive deviation higher than 8.00. iPSOSA presents better comprehensive performance in WS than traditional intelligent algorithms in terms of modeling performance, variable dimensionality, and searching efficiency, providing a new solution for obtaining high correlation wavelength variables in the spectral modeling process.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Method for Measuring Similarity or Distance of Molecular and Arbitrary Graphs Based on a Collection of Topological Indices 一种基于拓扑指数集合的分子图和任意图相似性或距离度量方法

IF 2.3 4区化学

Journal of Chemometrics Pub Date : 2025-07-15 DOI: 10.1002/cem.70047

Mert Sinan Oz

{"title":"A Method for Measuring Similarity or Distance of Molecular and Arbitrary Graphs Based on a Collection of Topological Indices","authors":"Mert Sinan Oz","doi":"10.1002/cem.70047","DOIUrl":"https://doi.org/10.1002/cem.70047","url":null,"abstract":"<div>\u0000 \u0000 <p>The comparison of graphs using various types of quantitative structural similarity or distance measures has an important place in many scientific disciplines. Two of these are cheminformatics and chemical graph theory, in which the structural similarity or distance measures between molecular graphs are analyzed by calculating the Jaccard/Tanimoto index based on molecular fingerprints. A novel method is proposed to measure the structural similarity or distance for molecular and arbitrary graphs. This method calculates the Jaccard/Tanimoto index based on a collection of topological indices embedded in the entries of a vector. We statistically compare the proposed method with the method for calculating the Jaccard/Tanimoto indices based on five different molecular fingerprints on alkane and cycloalkane isomers. Furthermore, to explore how the method works on non-molecular graphs, we statistically analyze it on the set of all connected graphs with seven vertices. The Jaccard/Tanimoto index values produced by the proposed method cover the value domain. In addition, it provides a discrete similarity distribution with the clustering, which makes the differences clear and provides convenience for comparison. Two outstanding features of the proposed method are its applicability to arbitrary graphs and the computational complexity of the algorithm used in the method is polynomial over the number of graphs and the number of vertices and edges of the graphs.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 7","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MultANOVA Followed by Post Hoc Analyses for Designed High-Dimensional Data: A Comprehensive Framework That Outperforms ASCA, rMANOVA, and VASCA 设计高维数据的事后分析：优于ASCA、rMANOVA和VASCA的综合框架

IF 2.3 4区化学

Journal of Chemometrics Pub Date : 2025-07-14 DOI: 10.1002/cem.70039

Benjamin Mahieu, Véronique Cariou

{"title":"MultANOVA Followed by Post Hoc Analyses for Designed High-Dimensional Data: A Comprehensive Framework That Outperforms ASCA, rMANOVA, and VASCA","authors":"Benjamin Mahieu, Véronique Cariou","doi":"10.1002/cem.70039","DOIUrl":"https://doi.org/10.1002/cem.70039","url":null,"abstract":"<p>Analytical platforms generate high-dimensional data, where the number of variables usually exceeds the number of observations. Such data are frequently derived from an experimental design, where samples have been collected to identify potential variation in the factors or interactions of interest. To circumvent issues related to large data sizes when evaluating factor and interaction effects, ANOVA simultaneous component analysis (ASCA), regularized multivariate analysis of variance (rMANOVA), and variable selection ASCA (VASCA) have been proposed previously. However, they require computationally intensive methods to test the effects of factors and interactions. In the present paper, multiple ANOVAs (MultANOVA) is proposed as a simple yet effective alternative to the above methods. MultANOVA has the advantage of being direct and fast, as it does not rely on intensive calculation methods, while incorporating a variable selection strategy. This method entails the execution of multiple ANOVAs, one per variable, with multiple test corrections. Subsequent post hoc analyses are also introduced. These encompass multiple least-squares difference tests (MultLSD) for the pairwise comparison of multivariate least-squares means and diagonal canonical discriminant analysis (DCDA) with approximate confidence ellipses to visualize significant effects. MultANOVA is compared to the aforementioned methods based on simulations, which demonstrate that it holds the nominal alpha risk as opposed to rMANOVA and VASCA, while being more powerful than ASCA and VASCA. Even though MultANOVA is proven less powerful than VASCA for variable selection, it has been demonstrated to hold the nominal risk, whereas VASCA does not. Finally, the MultANOVA framework is illustrated based on metagenomics, metabolomics, and spectroscopic data.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 7","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Classification Limit of Detection: Estimating Sample-Level Classification Uncertainty in Spectroscopy Using Monte Carlo Error Propagation of Spectral Noise 检测的分类极限：利用光谱噪声的蒙特卡罗误差传播估计光谱中样本级分类不确定度

IF 2.3 4区化学

Journal of Chemometrics Pub Date : 2025-07-12 DOI: 10.1002/cem.70048

Helder V. Carneiro, Caelin P. Celani, Karl S. Booksh

{"title":"The Classification Limit of Detection: Estimating Sample-Level Classification Uncertainty in Spectroscopy Using Monte Carlo Error Propagation of Spectral Noise","authors":"Helder V. Carneiro, Caelin P. Celani, Karl S. Booksh","doi":"10.1002/cem.70048","DOIUrl":"https://doi.org/10.1002/cem.70048","url":null,"abstract":"<div>\u0000 \u0000 <p>This study presents a novel Monte Carlo–based methodology for estimating classification uncertainty in chemometric models by propagating spectral measurement noise. Unlike traditional approaches that treat classification as deterministic, this method simulates realistic noise structures, both independent and correlated, captured from multiple spectrum measurements to quantify sample-specific uncertainty. The technique is applicable to both linear and non-linear models, including partial least squares discriminant analysis (PLS-DA) and various support vector machine (SVM) kernels. The methodology was validated using three datasets: synthetic 2D simulations for controlled model geometry, X-ray fluorescence (XRF) spectra from colored glass rods, and laser-induced breakdown spectroscopy (LIBS) data from <i>Dalbergia</i> wood species. Results revealed that uncertainty increases with spectral similarity and perpendicular alignment between noise structures and decision boundaries. In real-world applications, classification metrics alone proved insufficient to assess model reliability. The inclusion of uncertainty intervals enabled identification of ambiguous predictions even in cases of perfect classification accuracy. This work advances chemometric analysis by linking measurement uncertainty to classification outcomes, offering a robust framework for decision-making in high-stakes analytical contexts.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 7","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0