Advanced Chemometric Methods for Analytical Applications

A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Analytical Methods, Instrumentation and Miniaturization".

Deadline for manuscript submissions: 15 February 2027 | Viewed by 2839

Special Issue Editors


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Guest Editor
Department of Chemistry, Faculty of Sciences, University of Burgos, 09001 Burgos, Spain
Interests: sensory polymers; advanced functional polymer materials; smart materials; chemical sensors; biosensors; colorimetric sensing; fluorimetric sensing; bioanalytical applications; functional materials for sensing
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Guest Editor
Department of Chemistry, University of Pavia, 27100 Pavia, PV, Italy
Interests: colorimetric sensors; smart polymers; food freshness monitoring; environmental analysis; bioanalytical applications digital imaging colorimetry; chemometrics; design of experiments; multivariate analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid growth of data-driven technologies and advanced instrumentation has transformed the way we approach chemical detection and analysis. In this context, chemometric tools have become essential for reliably extracting meaningful information from complex, high-dimensional datasets. By integrating advanced statistical methods, machine learning algorithms, and optimization strategies, chemometrics contributes to enhancing the sensitivity, selectivity, and interpretability of chemical measurements across diverse applications.

The application of chemometrics in fields as varied as food quality control, environmental monitoring, pharmaceutical research, and clinical diagnostics is driving a new era of intelligent, adaptive chemical analysis. Moreover, the use of multivariate models and rigorous validation strategies helps ensure the traceability, reproducibility, and practical applicability of analytical results, supporting high-confidence, evidence-based decision-making.

This Special Issue thus aims to collect recent advances and innovative approaches in the application of chemometric tools for chemical detection and analysis, covering both experimental studies in laboratory settings and validation efforts in real-world environments, with the goal of promoting their technological transfer and adoption in practical scenarios.

Dr. Marta Guembe-García
Dr. Lisa Rita Magnaghi
Guest Editors

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Keywords

  • chemometrics
  • multivariate analysis
  • analytical chemistry
  • chemical sensors
  • data-driven chemical analysis

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Published Papers (3 papers)

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Research

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18 pages, 2642 KB  
Article
Design and Validation of a Chemometric-Assisted Methodology for the Simultaneous Measurement of Flunixin Meglumine and Florfenicol in Veterinary Formulations: Appraisal of Eco-Friendliness and Functionality
by Mona A. Abdel Rahman, Hazim Mohammed Ali, Mohammed Gamal, Lobna Mohammed Abd Elhalim, Mai Mohamed Abd El-Aziz and Rehab Moussa Tony
Chemosensors 2026, 14(5), 103; https://doi.org/10.3390/chemosensors14050103 - 30 Apr 2026
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Abstract
Multivariate calibration methods have proven to be helpful in interpreting complex spectral data, particularly in the simultaneous analysis of pharmaceutical mixtures. In this study, three chemometric-assisted spectrophotometric methods were developed and validated for the simultaneous assessment of flunixin meglumine (FM) and florfenicol (FF), [...] Read more.
Multivariate calibration methods have proven to be helpful in interpreting complex spectral data, particularly in the simultaneous analysis of pharmaceutical mixtures. In this study, three chemometric-assisted spectrophotometric methods were developed and validated for the simultaneous assessment of flunixin meglumine (FM) and florfenicol (FF), namely, multivariate curve resolution–alternating least squares (MCR-ALS), artificial neural networks (ANNs), and partial least squares (PLS). These methods were successfully utilized to address the significant spectral overlap between FM and FF in their combined dose form, enabling simultaneous quantification without prior chromatographic separation. Statistical analysis was conducted to compare the performance of the proposed methods to that of a published HPLC method, and the results showed no significant variation in trueness or precision. The proposed methods were validated according to ICH guidelines, showing high sensitivity, low LOD and LOQ, and excellent precision (%RSD < 2.0%). Furthermore, they were evaluated for environmental sustainability using the analytical greenness (AGREE) metric and the complex modified green analytical procedure index (Complex MoGAPI), which provided a greenness score of 0.7 and a total sustainability score of 80. These results demonstrate the applicability of the proposed chemometric methods as straightforward, effective, and ecologically beneficial substitutes for regular quality control analysis. Full article
(This article belongs to the Special Issue Advanced Chemometric Methods for Analytical Applications)
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Review

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28 pages, 1687 KB  
Review
Multi-Way Data Analysis Nowadays: Taking Advanced Chemometric Tools to Everyday Analytical Chemistry Applications
by Marta Guembe-Garcia, Lisa Rita Magnaghi, Guglielmo Emanuele Franceschi, Antonio Bova and Raffaela Biesuz
Chemosensors 2026, 14(2), 37; https://doi.org/10.3390/chemosensors14020037 - 2 Feb 2026
Cited by 1 | Viewed by 1725 | Correction
Abstract
Multi-way analysis has become one of the most powerful and versatile chemometric approaches for dealing with the increasing complexity of data generated in modern analytical chemistry. Advances in instrumentation, the widespread use of hyphenated techniques, and the inherently multidimensional nature of many experimental [...] Read more.
Multi-way analysis has become one of the most powerful and versatile chemometric approaches for dealing with the increasing complexity of data generated in modern analytical chemistry. Advances in instrumentation, the widespread use of hyphenated techniques, and the inherently multidimensional nature of many experimental designs require methods capable of preserving structural relationships within datasets. In this context, multi-way tools such as Tucker 3, PARAFAC, or other supervised variants provide rigorous and interpretable descriptions of variability across multiple modes (samples, variables, conditions), enabling the extraction of meaningful patterns, improved noise handling, and enhanced robustness, compared with traditional bilinear approaches. This review offers a critical overview of the most commonly applied multi-way algorithms and their practical use in fields such as environmental chemistry, food science, clinical diagnostics, industrial process monitoring, and pharmaceutical analysis. The essential steps of the workflow, from data acquisition and preprocessing to model selection and interpretation, are discussed, highlighting their impact on model reliability. A dedicated section summarizes the software environments available for performing multi-way analyses, guiding readers in selecting the most suitable tools for their needs. Overall, this review emphasizes how multi-way chemometrics is becoming increasingly crucial for converting complex, high-dimensional data into reliable and actionable chemical knowledge. Full article
(This article belongs to the Special Issue Advanced Chemometric Methods for Analytical Applications)
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2 pages, 506 KB  
Correction
Correction: Guembe-Garcia et al. Multi-Way Data Analysis Nowadays: Taking Advanced Chemometric Tools to Everyday Analytical Chemistry Applications. Chemosensors 2026, 14, 37
by Marta Guembe-Garcia, Lisa Rita Magnaghi, Guglielmo Emanuele Franceschi, Antonio Bova and Raffaela Biesuz
Chemosensors 2026, 14(4), 87; https://doi.org/10.3390/chemosensors14040087 - 3 Apr 2026
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Abstract
Error in Figure 7a [...] Full article
(This article belongs to the Special Issue Advanced Chemometric Methods for Analytical Applications)
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