Journal of Chemometrics最新文献

{"title":"Comparative Assessment of Similarity Metrics for 1H NMR Spectral Matching: Evidence for the Robustness of the Normalized Square Dot Product","authors":"Fabien Torralba,&nbsp;Guillaume Hoffmann,&nbsp;Emmanuel Cassin,&nbsp;Gwladys Charpentier,&nbsp;Séverine Sechet,&nbsp;Audrey Gaucher,&nbsp;Guillaume Rousselot,&nbsp;Olivier Aroule,&nbsp;Christophe Morell,&nbsp;Asma Bourafai-Aziez","doi":"10.1002/cem.70173","DOIUrl":"https://doi.org/10.1002/cem.70173","url":null,"abstract":"<div>\u0000 \u0000 <p>Automated comparison of ¹H NMR fingerprints is increasingly used for the quality control and authentication of plant extracts; however, there is insufficient guidance on the most reliable similarity metrics for large and heterogeneous spectral datasets. This paper presents a systematic comparison of similarity metrics for ¹H NMR spectra using a large experimental dataset. For this purpose, a large database of 4574 ¹H NMR spectra was constructed (available on request). Spectra were examined both over the full chemical shift range and within three windows: aliphatic (0–3 ppm), aromatic (6–10 ppm) and combined (0–3 + 6–10 ppm). Classical vector-based metrics (Pearson correlation, cosine similarity, Euclidean distance, dot products, normalized area by sum), binary indices (Jaccard, Dice, Russell-Rao) and three recently introduced measures, including the normalized square dot product (NSDP), were applied to all spectrum pairs. Performance was assessed using retrieval statistics (Top 1/Top 10) and bootstrap resampling. Cosine similarity, Pearson correlation, NSDP and a subtraction-based metric yielded high mean similarity values (typically ≥ 0.94), low variance and robust Top 1/Top 10 rankings across all spectral windows, whereas Euclidean distance, raw or squared dot products and binary indices were strongly affected by intensity scaling, threshold choice and peak overlap. The aliphatic region provided the strongest discrimination, whereas combining aliphatic and aromatic regions improved overall robustness of spectral matching. These results support NSDP, cosine similarity, Pearson correlation and subtraction similarity as default metrics for automated ¹H NMR spectral matching in plant extract quality control, dereplication and library-search workflows.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710500","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":"Novelty Assessment Method for Streaming Samples Based on Slope Entropy","authors":"Zhonghai He,&nbsp;Haoxiang Zhang,&nbsp;Dongliang Bai,&nbsp;Xiaofang Zhang","doi":"10.1002/cem.70175","DOIUrl":"https://doi.org/10.1002/cem.70175","url":null,"abstract":"<div>\u0000 \u0000 <p>During spectral detection operations, when the streaming samples under test differ significantly from the modeling samples, the prediction error of the spectral model tends to increase. In such cases, it becomes necessary to incorporate novel samples into the modeling set for model updating. A common approach to assess sample novelty involves calculating the spectral residual, typically using the <i>Q</i> residual. However, existing methods generally employ a single distance scalar to evaluate the novelty of multidimensional spectra. This approach suffers from the issue that residuals at different positions are aggregated into a single value, which is overly integrative. Consequently, variations at different locations may lead to the same novelty value. Therefore, a window-based method that evaluates data in segments is more reasonable. The proposed method segments the residual dimensionality using a windowing approach, discretizes the residual values, and classifies and counts the patterns of residual variations within each window. The slope entropy derived from the residual is then used to represent sample novelty. The effectiveness of the proposed method is demonstrated through both simulated and real-world data.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753565","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":"Reliable Identification and Out-of-Library Detection in Mass Spectra","authors":"Minsu Son,&nbsp;Hyoju Kim,&nbsp;Hyungjun Kim,&nbsp;Youngho Jin,&nbsp;Jaeoh Kim","doi":"10.1002/cem.70174","DOIUrl":"https://doi.org/10.1002/cem.70174","url":null,"abstract":"<p>Accurate molecular identification from mass spectra is essential in analytical workflows, yet conventional library search typically returns the closest match even when the true compound is absent, leading to overconfident false positives. We propose a probabilistic framework that supports both reliable identification of compounds represented in a reference library and model-based flagging of spectra that may not be adequately represented in the reference library. Spectra are modeled within a Bayesian nonparametric method that does not predefine the number of clusters; instead, the model can allocate new clusters when incoming spectra are insufficiently explained by existing references. This property provides a statistical mechanism for flagging potentially unseen compounds while maintaining coherent grouping of known ones. Experiments on large-scale electron ionization libraries demonstrate stable performance under diverse noise conditions, consistent grouping of replicate spectra, and the tendency of spectra absent from the reference database to form new clusters. The framework supports reliable identification while reducing forced matches for spectra that are poorly represented in the reference data.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752760","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":"Snake Optimization Algorithm for Variable Selection in Spectral Quantitative Analysis of Complex Samples","authors":"Yingxia Li,&nbsp;Jiajing Zhao,&nbsp;Huan Xue,&nbsp;Nuo Han,&nbsp;Xihui Bian","doi":"10.1002/cem.70172","DOIUrl":"https://doi.org/10.1002/cem.70172","url":null,"abstract":"<div>\u0000 \u0000 <p>The discretized snake optimization algorithm was first proposed as a variable selection method to reduce irrelevant variables and enhance the prediction accuracy of complex samples. In discretized snake optimization (SO), the positions of the snakes were updated, and three transfer functions, V-shaped, arctangent and sigmoid functions, were introduced and compared for discretization. The partial least squares (PLS) model was built using the spectral variables selected by discretized SO. The performance of snake population, transfer functions in SO, and distribution of selected variables for different methods are investigated. To verify the feasibility of SO-PLS, the predictive accuracy of SO-PLS was compared with full-spectrum PLS, uninformative variable elimination-PLS (UVE-PLS), Monte Carlo UVE-PLS (MCUVE-PLS), randomization test-PLS (RT-PLS), grey wolf optimizer-PLS (GWO-PLS), and whale optimization algorithm-PLS (WOA-PLS) models on four complex sample datasets. The results indicate that the V-shaped function is the best transfer function. Compared with the other variable selection methods, SO-PLS uses the least number of variables and gets the best prediction accuracy. Furthermore, compared to PLS, the SO-PLS model reduced the root mean squared error of prediction (RMSEP) by 52%, 43%, 38%, and 23% for predicting protein, sugar, alcohol, and fat in wheat, orange juice, wine, and cocoa bean dataset, respectively. The conresponding correlation coefficients (<i>R</i>) increased from 0.8942, 0.7375, 0.9984, and 0.8121 to 0.9782, 0.8935, 0.9996, and 0.8920, respectively.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752618","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":"Supervised Restricted Data Fusion With Common, Local, and Distinct Components","authors":"Fred T. G. White,&nbsp;Geert Roelof van der Ploeg,&nbsp;Anna Heintz-Buschart,&nbsp;Lemeng Dong,&nbsp;Harro J. Bouwmeester,&nbsp;Age K. Smilde,&nbsp;Johan A. Westerhuis","doi":"10.1002/cem.70171","DOIUrl":"https://doi.org/10.1002/cem.70171","url":null,"abstract":"<p>In multi-block data, the dominant sources of variation are not always most relevant to a response of interest, meaning that purely exploratory decompositions may fail to recover subtle but important response-associated structure. We introduce PESCAR, a supervised extension of <i>Penalised Exponential Simultaneous Component Analysis</i> (PESCA) that incorporates response information directly into the estimation of common, local and distinct (CLD) structure across multiple data blocks. This allows simultaneous multiblock decomposition and response variable-influenced recovery of latent structure. Through simulation studies, we show that PESCAR can detect weak response-related components across a range of settings, including different noise levels and model-rank mis-specification. Applied to a real multi-omics dataset, PESCAR recovers biologically meaningful response-associated patterns and retains interpretable block structure. We further demonstrate that sparsity in the fitted loading matrices admits a hypergraph-based interpretability layer, summarising overlapping support patterns across components and blocks. These results show that direct incorporation of response information into multiblock decomposition can improve detection of subtle relevant signal and facilitate interpretation in complex systems.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148647689","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":"Gaussian Mixture Modelling and DBSCAN for Reclassifying Legacy pXRF Compositional Data: A Chemometric Workflow Applied to Archaeological Ceramics","authors":"Meligkotsidou Loukia,&nbsp;Liritzis Ioannis","doi":"10.1002/cem.70170","DOIUrl":"https://doi.org/10.1002/cem.70170","url":null,"abstract":"<div>\u0000 \u0000 <p>Legacy portable X-ray fluorescence (pXRF) compositional datasets in archaeometry are often interpreted using principal component analysis and hierarchical clustering, which may incompletely resolve internal structure or outlier behaviour. Here, we reanalyse a published pXRF dataset of 125 archaeological ceramics and experimental clay briquettes using two complementary chemometric classification approaches: Gaussian mixture modelling (GMM) with Bayesian information criterion (BIC) selection, and density-based spatial clustering of applications with noise (DBSCAN). Element/Si ratios were log-transformed prior to analysis. GMM on the first six principal components (retaining 77% of total variance) identified two statistically supported clusters (BIC = −120.7), with probabilistic allocation of samples. DBSCAN (eps = 20, minPts = 18) independently confirmed a two-cluster structure and further distinguished two chemically meaningful outlier subsets, primarily separated by log(S/Si) and log(P/Si) ratios. Both methods show that compositional variability is compatible with local clay sources, clay mixing and firing-temperature effects, rather than non-local imports. Crucially, experimental briquettes co-cluster with archaeological specimens, and firing-induced shifts (e.g., DS8 at 700°C vs. 900°C) are captured as outlier movement. The study demonstrates that probabilistic model-based clustering and density-based unsupervised learning can extract structured technological variability from legacy pXRF data beyond conventional exploratory methods. This chemometric workflow is transferable to other semi-quantitative compositional datasets where new measurements are infeasible.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148617383","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":"Local Asymmetric Least Squares (LAsLS) for Noisy and Complex Baseline Correction","authors":"Adrián Gómez-Sánchez,&nbsp;Berta Torres-Cobos,&nbsp;Rodrigo Rocha de Oliveira","doi":"10.1002/cem.70169","DOIUrl":"https://doi.org/10.1002/cem.70169","url":null,"abstract":"<p>The asymmetric least squares (AsLS) method is commonly used and works well when baseline characteristics are relatively uniform, but can be less accurate for non-uniform, complex, or noisy baselines. Here, we present local AsLS (LAsLS), a local extension of AsLS that preserves the original penalized least-squares framework while allowing asymmetric weights and smoothing penalties to vary across user-defined regions of the signal. This local parameterization provides more accurate baseline estimation for heterogeneous baselines. The method is versatile and can be applied to a variety of techniques, such as chromatography, Raman spectroscopy, and mid-infrared spectroscopy. To test the method under controlled conditions, we compared LAsLS with traditional AsLS using simulated chromatogram-like signals that mimic complex baseline scenarios. Across four cases with heterogeneous baselines, AsLS baseline rRMSE was 2.6–5.7 times higher than LAsLS, and peak quantification rRMSE was 1.4–3.5 times higher. In a controlled PLSR quantitation example, LAsLS also reduced external prediction rRMSE compared with AsLS (11.3% vs. 17.9%). We provide open-source MATLAB implementations with a graphical interface to facilitate method adoption.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616549","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":"EPSpec: An Evidence-Guided, Prior-Retrieval Agent for Near-Infrared Spectral Band Selection","authors":"Shenghao Gu,&nbsp;Mingjian Hong","doi":"10.1002/cem.70167","DOIUrl":"https://doi.org/10.1002/cem.70167","url":null,"abstract":"<div>\u0000 \u0000 <p>To address band selection in high-dimensional continuous near-infrared spectra for quantitative analysis, the evidence-guided, prior-retrieval spectral interval selection (EPSpec) framework is proposed. The method partitions the full spectrum along the wavelength into a set of contiguous candidate intervals and computes multidimensional statistical indicators for each interval to construct a structured evidence table. At the same time, it introduces mechanistic prior knowledge based on the vibrational band evidence index (VIBEX) functional group–band prior library and task-relevant mapping. Subsequently, under the joint guidance of interval evidence and mechanistic prior knowledge, a large language model ranks the candidate intervals by importance and outputs interpretable rationales. Finally, band selection and modeling are completed through Top-K subspectrum search and nested cross-validation without information leakage. Experimental results on three near-infrared regression datasets show that EPSpec combined with partial least squares regression (EPSpec+PLSR) consistently improves upon full-spectrum PLSR while substantially reducing the number of input wavelength variables and maintaining relatively low model complexity. Specifically, on the shootout, soil, and corn datasets, EPSpec+PLSR increases the coefficient of determination (<i>R</i>²) from 0.9298, 0.9428, and 0.8092 to 0.9372, 0.9657, and 0.9273, respectively, and reduces the root mean square error of prediction (RMSEP) from 4.4856, 2.5369, and 0.2833 to 4.2272, 1.9538, and 0.1671, respectively. Compared with traditional variable selection methods, EPSpec+PLSR achieves overall comparable or locally slightly better results. In particular, on the soil dataset, it achieves the best result while retaining only about 85/1050 wavelengths on average. Ablation experiments further show that interval-level statistical evidence and mechanistic priors are key to stable performance gains, and that sliding-window partitioning can further improve predictive performance.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616624","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":"Temporal Chemometric Modelling of Result-Level Proficiency-Testing Risk in AQUACHECK Water-Chemistry Data","authors":"Selman Kander","doi":"10.1002/cem.70165","DOIUrl":"https://doi.org/10.1002/cem.70165","url":null,"abstract":"<div>\u0000 \u0000 <p>Recurrent proficiency-testing (PT) and external quality-assessment (EQA) records are temporally ordered, mixed-type and clustered chemical-laboratory data because the same laboratories reappear across rounds, analytes, matrices and methods. Using LGC AQUACHECK ‘Nutrients and other components’ core-water records, this study evaluated whether strictly prior PT information can support result-level risk ranking under temporal validation. The modelling cohort comprised 27,123 scored submissions from 18 rounds and 1293 laboratories. Two binary endpoints were analysed: problematic performance (|<i>z</i>| &gt; 2, where <i>z</i>-scores are standardized deviations from assigned values) and unsatisfactory performance (|<i>z</i>| ≥ 3). CatBoost (a gradient-boosted decision tree framework) was compared with a full feature-parity logistic-regression baseline, a supplementary numeric-subset generalized estimating equations (GEE) baseline and a numeric-subset partial least squares discriminant analysis (PLS-DA) comparator. CatBoost gave the highest discrimination, but its gain over logistic regression was modest: Test-set area under the receiver operating characteristic curve (ROC-AUC)/area under the precision-recall curve (PR-AUC) were 0.756/0.295 versus 0.723/0.252 for problematic performance and 0.758/0.221 versus 0.731/0.190 for unsatisfactory performance. Against no-skill precision-recall baselines equal to endpoint prevalence (0.115 and 0.080), CatBoost PR-AUC values corresponded to 2.57- and 2.76-fold enrichment, indicating modest but above-chance retrieval. Feature-block ablation showed that laboratory-specific history was the dominant signal source, whereas population-level analytical priors and static context were weaker. No configuration exceeded approximately ROC-AUC 0.76, indicating a bounded prediction ceiling under the available data. Isotonic calibration was essential for the unsatisfactory endpoint, reducing the Brier score from 0.172 to 0.068. Performance fell for laboratories absent from training, and one expanding-window fold collapsed to chance-level discrimination. Recurrent PT records therefore contain usable but bounded chemometric signal for within-scheme review prioritization, not standalone failure prediction or cross-scheme generalization without external validation, recalibration and prospective monitoring.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615811","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":"L-Probe: New Approach for Spike Removal in Raman Hyperspectral Imaging Based on Local Prominence Peak Filtering","authors":"Leonardo Francisco Rafael Lemes,&nbsp;Victor Klassen de Oliveira,&nbsp;Frederico Luis Felipe Soares","doi":"10.1002/cem.70164","DOIUrl":"https://doi.org/10.1002/cem.70164","url":null,"abstract":"<p>Raman hyperspectral imaging is an indispensable analytical technique that can provide simultaneous chemical and spatial information of a sample, enabling a higher comprehension and sampling of the analyzed material. However, a known challenge in processing large hyperspectral Raman datasets is the effective removal of spurious, high-intensity peaks caused by cosmic rays (<i>cosmic spikes</i>). Correcting cosmic spikes in complex hyperspectral images is particularly difficult due to the sheer volume of data, the inherent chemical heterogeneity of pixels, variations in background intensity, and the shape of authentic Raman bands. To address this, we propose a novel cosmic spike correction algorithm specifically engineered for Raman hyperspectral imaging. The L-probe algorithm employs the concept of local prominence for the reliable identification of suspicious peaks and spike removal based on the assumption of chemical composition homogeneity across adjacent pixels, allowing targeted and localized correction. The algorithm was rigorously evaluated using a factorial design of experiments (DoE) applied to an artificial hyperspectral image to establish its parameters. Final validation was successfully performed using real hyperspectral images acquired from complex, phase-separated samples, such as cosmetic emulsions, demonstrating the robustness of the algorithm.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"40 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533583","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}