Journal of Chemometrics最新文献

{"title":"Hybrid Best Subset Selection for Bayesian Multivariate Regression","authors":"Shiqiang Jin,&nbsp;Gyuhyeong Goh","doi":"10.1002/cem.70184","DOIUrl":"https://doi.org/10.1002/cem.70184","url":null,"abstract":"<div>\u0000 \u0000 <p>Multivariate linear regression has been widely studied and applied across many scientific fields. The rapid growth of high-dimensional data has increased demand for fast and accurate variable selection methods in multivariate regression. This paper proposes a Bayesian best subset selection approach that efficiently searches for optimal solutions to nonconvex optimization problems arising in high-dimensional multivariate linear regression. The proposed method employs a hybrid search strategy that integrates deterministic local optimization with stochastic global exploration, with an asymptotic global-convergence guarantee for the fixed-size search as the number of stochastic iterations increases. To accelerate implementation, we develop a fast computing strategy that evaluates the marginal posterior distributions of all neighboring models within a single computation. Simulation studies and real data analyses demonstrate the effectiveness and superior performance of the proposed approach.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 11","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176985","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}

{"title":"Biology-Related Research Relies Too Much on the Problematic Method of Significance Testing, Whereas There Are Good Alternatives","authors":"Henk A. L. Kiers,&nbsp;Age K. Smilde,&nbsp;Harro J. Bouwmeester","doi":"10.1002/cem.70180","DOIUrl":"https://doi.org/10.1002/cem.70180","url":null,"abstract":"<p>In biology, as in many other empirical sciences, drawing conclusions is often based on statistical analyses. Still, by far, the most common approach for this is <i>null</i> hypothesis significance testing, despite it falling short seriously on deciding what outcomes are biologically relevant. A similar observation can be made for metabolomics, an area in between analytical chemistry and biology. In the present paper, we discuss alternatives that offer an approach for drawing conclusions that actually assess the biological relevance of outcomes. These are based on replacing testing with estimation to begin with. Next, simple Bayesian procedures are offered for actually assessing probabilities of outcomes being or not being practically relevant.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149145563","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}

{"title":"Integrating Spectral-Spatial Band-Ratio Evaluation and RGB-Hyperspectral Fusion for Damage Detection in Green Peppers","authors":"Xiong Li,&nbsp;Yawen Guo,&nbsp;Mengfei Xie,&nbsp;Hanxiao Jiang,&nbsp;Zhuo Li,&nbsp;Shaohe Jiao,&nbsp;Yande Liu,&nbsp;Ouyang Aiguo","doi":"10.1002/cem.70178","DOIUrl":"https://doi.org/10.1002/cem.70178","url":null,"abstract":"<div>\u0000 \u0000 <p>Rapid and accurate detection of damaged peppers is essential for quality assessment and intelligent sorting in modern agricultural production. This study developed a hyperspectral imaging–based framework for discriminating healthy and damaged green peppers using spectral information acquired within the 372.6–1039.6 nm range. Candidate wavelengths were first selected using UVE-SPA. To identify informative band-ratio features, a comprehensive evaluation framework integrating statistical significance and spatial discriminability was proposed. Based on this framework, three optimal band-ratio combinations were selected to construct a three-channel BR image, enabling enhanced lesion contrast and structural feature representation while reducing the influence of illumination variation. Independently, the MDSR method was employed to select three representative wavelengths for constructing a complementary three-channel spectral image. To further exploit the complementary characteristics of visible and hyperspectral information, a classification-driven fusion strategy was developed to integrate RGB images with BR and MDSR-derived images, respectively. The resulting fusion images effectively combined color, spectral, and spatial information associated with pepper deterioration. Comparative experiments demonstrated that image fusion substantially improved feature representation and classification performance compared with single-source image inputs. Among the evaluated models, ResNet18–MLP achieved the best performance, reaching an overall classification accuracy of 93.5%. The results indicate that the proposed band-ratio evaluation framework and classification-driven fusion strategy effectively enhance damage discrimination and provide a potentially effective approach for hyperspectral image-based defect detection in peppers. This study offers a promising foundation for intelligent postharvest inspection and sorting of agricultural products.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149145964","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}

{"title":"Geographical Origin Tracing of Dendrobium officinale in Different Spatial Regions Based on a Multispectral Data Strategy Combined With Deep Learning","authors":"Guangyao Li,&nbsp;Zhili Duan,&nbsp;Yuanzhong Wang","doi":"10.1002/cem.70182","DOIUrl":"https://doi.org/10.1002/cem.70182","url":null,"abstract":"<div>\u0000 \u0000 <p>The environment in which plants are grown significantly affects their quality, particularly for species with both medicinal and edible value, such as <i>Dendrobium officinale</i> (<i>D. officianle</i>). This study proposes a method of tracking the geographical origin of <i>D. officinale</i> in different regions using a combination of 3D multispectral data and deep learning. Using 3D MIR and NIR spectral data, the study compares and analyzes the spatial representativeness of geographic information between single-spectral and data fusion techniques. By integrating deep learning models, we have achieved 100% accuracy in tracking the geographical origin of <i>D. officinale</i> in different spatial regions. This research provides new technical means for tracking the origin of plants and ensuring the traceability of agricultural products, particularly Chinese herbal medicines with unique regional characteristics, such as <i>D. officinale</i>. It offers new ideas for origin tracing and quality assurance.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149145740","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}

{"title":"Artificial Neural Network–Based Prediction of Instability-Prone Operating States in Routine HPLC Systems Using Pressure and Reinjection Data","authors":"İkbal Demet Nane","doi":"10.1002/cem.70183","DOIUrl":"https://doi.org/10.1002/cem.70183","url":null,"abstract":"<div>\u0000 \u0000 <p>Typically, there is inherent vulnerability to gradual, nondestructive operational instability in routine high-performance liquid chromatography (HPLC) systems under continuous pharmaceutical production. Although traditional approaches to monitoring involve either past-due alerts to failures or binary thresholds, all of them are unable to identify the onset of multivariate process instability. In this paper, an intelligent solution driven by an artificial neural network (ANN) is proposed for forecasting operational instability (in terms of stable, warning, and unstable conditions) based on routine HPLC parameters including column pressure, mobile phase pH, organic modifier composition, flow rate, analytical production load, and reinjection correction history. To overcome the limitations of pure modeling approaches, this study explicitly links operational data to underlying physical degradation mechanisms such as localized hydrodynamic friction, stationary phase mechanical collapse, and mass transfer kinetics. For modeling the complex nonlinear operational process dynamics and improving interpretability, Explainable artificial intelligence (XAI) by means of permutation feature importance was considered. The refined ANN model showed a very good ability to classify data points on the testing dataset in an independent test setting with an overall accuracy rate of 92.6%, an F1 score of 0.91, and a multiclass area under the receiver operating characteristic curve (ROC-AUC) score of 0.97, surpassing the gradient boosting, random forest, and support vector machine (SVM) models, as well as a traditional multinomial logistic regression mathematical model. According to the results of the XAI analysis, the most important predictors of system degradation included column pressure differential (Δ<i>P</i>), process protocol (mobile phase aging), and the number of historical correction injections performed. This result proves the correctness of the paradigm shift in the use of historical correction injections to predict future system failures. The presented approach demonstrates a successful transition from reactive problem solving to proactive intelligent system prediction.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146821","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}

{"title":"Machine Learning–Based Optimization of Phthalate Extraction Conditions Using HS-INME-GC/MS Data","authors":"Hyeyoung Jung,&nbsp;Sunyoung Bae,&nbsp;Jooyoung Kim","doi":"10.1002/cem.70181","DOIUrl":"https://doi.org/10.1002/cem.70181","url":null,"abstract":"<p>Optimization of extraction conditions in GC/MS analysis remains challenging because experimental variables can interact in complex and nonlinear ways. In this study, a machine learning based chemometric workflow was applied to optimize headspace in-needle microextraction GC/MS (HS-INME-GC/MS) conditions for four phthalates: dimethyl phthalate (DMP), diethyl phthalate (DEP), dibutyl phthalate (DBP), and di(2-ethylhexyl) phthalate (DEHP). A dataset comprising 75 experimental data points was used with saturation temperature, adsorption time, and desorption time as input variables. Ten regression models were evaluated, and five candidate models were further examined using feature importance, partial dependence, Pareto front analysis, desirability functions, and supporting robustness evaluation. Feature importance and partial dependence analyses showed that adsorption time and desorption time were generally more influential than saturation temperature. Among the candidate models, SVR and XGBoost were retained for the final confirmatory interpretation because their predicted conditions were close to the condition evaluated in the confirmatory experiments. Confirmatory experiments at 50.0°C saturation temperature, 50.0-min adsorption time, and 5.0-min desorption time showed good agreement between predicted and observed responses. These findings suggest that the proposed workflow provides a complementary chemometric approach for model comparison, response visualization, and balanced optimization of analytical extraction conditions.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048358","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}

{"title":"Handling Experimental Data in FACPACK: Noise Effects on Duality Relations and Algorithmic Parameter Selection","authors":"Mathias Sawall,&nbsp;Christoph Kubis,&nbsp;Alejandro Olivieri,&nbsp;Steven J. Roeters,&nbsp;Klaus Neymeyr","doi":"10.1002/cem.70179","DOIUrl":"https://doi.org/10.1002/cem.70179","url":null,"abstract":"<p>The area of feasible solutions (AFS) quantifies factor ambiguity in multivariate curve resolution (MCR). Although AFS computation is well-defined for noise-free data, experimental datasets require constraint relaxation and parameter tuning, which influence the resulting feasible bands. This work addresses the computation of the AFS in the presence of noise. We propose a statistically motivated strategy for control parameter estimation based on Savitzky–Golay filtering, introduce a two-stage preprocessing approach for baseline-distorted data, and analyze the effect of noise in the dual space. The methods are demonstrated on model data and experimental FT-IR datasets.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994452","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}

{"title":"A Novel FULS-TSVM-Based Model for Non-Destructive Detection of Heavy Metals in Mussels via Near-Infrared Spectroscopy","authors":"Ming Li,&nbsp;Xiangli Meng,&nbsp;Yao Liu,&nbsp;Zhongyan Liu","doi":"10.1002/cem.70177","DOIUrl":"https://doi.org/10.1002/cem.70177","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rising global demand for seafood, mussels have become an important commercial product. They are located in estuarine environments and are prone to heavy metal accumulation. This poses a threat to the safety of mussel products for human consumption. Traditional detection methods are accurate, but limited by complex sample pretreatment, high costs, and slow processing speed. Near-infrared spectroscopy (NIRS) can achieve non-destructive and rapid detection, but the detection results are easily disrupted by imbalanced sample datasets, individual outliers, and low-concentration contamination in samples. To solve these issues, this study first applied a fuzzy universum least squares twin support vector machine (FULS-TSVM) algorithm to the detection of heavy metals in mussels. By integrating universum data to incorporate prior distribution knowledge and assigning fuzzy memberships to handle outlier samples and class imbalance, this model effectively extracts weak spectral differences caused by heavy metals. This study focused on mussels contaminated by cadmium (Cd). We conducted comparative experiments in imbalanced datasets, with outlier samples, and under high and low contamination concentrations. We also expanded the test objects to lead (Pb) and zinc (Zn) contaminated mussels to verify the model's generalization ability. The results show that compared to LS-TSVM, FLS-TSVM, and ULS-TSVM models, the FULS-TSVM has overall better detection accuracy and robustness. It maintains reliable detection performance even in extreme imbalanced datasets, numerous outliers, and low-concentration contamination scenarios. This research proves that combining NIRS technology with the FULS-TSVM algorithm provides an efficient, low-cost way to quickly screen heavy metal contamination in mussels. The method has great application value for daily safety monitoring of marine aquatic food.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148974991","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}

{"title":"Effect of Modeling and Transformation Approaches on Predictability and Error Metrics of Design of Experiment-Based Optimization","authors":"Aiman Mahmood,&nbsp;Muhammad Usama Bukhari,&nbsp;Hafsa Afzal,&nbsp;Misbah Sultana,&nbsp;Fatima Rasool,&nbsp;Muzaffar Abbas,&nbsp;Nadeem Irfan Bukhari","doi":"10.1002/cem.70176","DOIUrl":"https://doi.org/10.1002/cem.70176","url":null,"abstract":"<div>\u0000 \u0000 <p>Design of Experiment (DoE) is widely employed for formulation and process optimization. Current project studied the effect of different models and response data transformations, beyond recommended by DoE tool on prediction. DoE tool was used on a case study of central composite design-based multifactor response surface methodology having three factors (X1, X2, and X3) and two responses (Y1 and Y2). Artificial neural network (ANN) software, InForm Version 5, was employed to empirically compare findings. DoE tool recommended quadratic and linear models for prediction, without data transformation for Y1 and Y2. Nevertheless, for Y1 and Y2, natural and base-10 log transformations improved the model performance, as indicated by reduced SSE by 6.0% (Y1) and 3.7% (Y2) and RMSE by 16.1% (Y1) and 2.5% (Y2). With the above transformations, model re-evaluation indicated that DoE recommended quadratic and linear models, respectively for Y1 and Y2 were best suited. With recommended and data driven models, the pairs of quadratic-log and linear-base-10 log- transformations improved model predictability; increased <i>R</i>² by 3.6%, reduced root mean squared error (RMSE) by 16.1% and decreased predicted residual error squares (PRESS) by 19.7% (Y1) and improved <i>R</i>² by 0.4%, reduced RMSE by 2.5%, and decreased sum of squared error by 3.7% (Y2). Optimized factor levels generated with above model-transformation pairs for Y1 and Y2 were close to ANN-predicted levels. Indeed, DoE recommendation for data transformation (if required) and model selection must stand out from nonrecommended ones, but present results indicated otherwise warranting enhancement of features in DoE tools.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848860","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}

{"title":"Evaluation of ATR-FTIR–Based Asphalt Binder Classification Models Under Sample-Level and Cross-Source Conditions","authors":"Juntao Jiao,&nbsp;Erhu Yan,&nbsp;Huisen Xia,&nbsp;Yan Gong,&nbsp;Xinyue Xu,&nbsp;Tingting Xie","doi":"10.1002/cem.70166","DOIUrl":"https://doi.org/10.1002/cem.70166","url":null,"abstract":"<div>\u0000 \u0000 <p>ATR-FTIR spectroscopy combined with chemometric modelling is increasingly used for asphalt binder classification, but model reliability can be overestimated when repeated spectra, related parent samples, and instrument/source differences are not controlled during validation. This study evaluates ATR-FTIR–based asphalt binder classification models under sample-level and cross-source conditions using a multi-source spectral database organized into measurement records, parent samples, and final split units. Binary classification of base/non-SBS and SBS-modified binders was used as the case task. Candidate preprocessing-model pipelines were evaluated at the parent sample–level, and two cross-source validation directions were used to test stability across source conditions. Random spectrum-level splitting produced optimistic performance estimates. SNV+LinearSVM achieved the highest internal main BA (0.9482), but its minimum cross-source BA decreased to 0.5621. In contrast, the selected SNV+PLS-DA–style pipeline achieved comparable internal performance (main BA = 0.9428) while maintaining a higher minimum-direction BA (0.9554) and a smaller direction gap BA (0.0237). Diagnostic experiments showed that cross-source stability depends on both sufficient training sample support and adequate source representation. Latent-variable analysis, coefficient/VIP spectra, and window masking indicated that the model used multiple spectral regions rather than a single SBS marker peak. Source-predictability analysis further showed that source effects were attenuated but not eliminated. The results support a sample-level and cross-source evaluation strategy for reproducible ATR-FTIR chemometric classification.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148783710","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}