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Review

Use of Chemometrics for the Authentication, Characterization and Detection of Adulteration of Cypriot Products Registered Under EU Quality Schemes: A Review

by
Maria Tarapoulouzi
*,
Ioannis Pashalidis
and
Charis R. Theocharis
*
Department of Chemistry, University of Cyprus, P.O. Box 20537, 1678 Nicosia, Cyprus
*
Authors to whom correspondence should be addressed.
Chemosensors 2025, 13(9), 332; https://doi.org/10.3390/chemosensors13090332
Submission received: 30 May 2025 / Revised: 15 July 2025 / Accepted: 29 August 2025 / Published: 3 September 2025
(This article belongs to the Special Issue Chemometrics Tools Used in Chemical Detection and Analysis)
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Abstract

This review explores the application of chemometric techniques for the authentication, characterization, and adulteration detection of Cypriot agri-food products registered under European Union quality schemes, including Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI). Given the increasing global demand for premium and geographically linked food products, ensuring their authenticity and integrity has become critical. Although Halloumi cheese, wines, and Zivania are the most researched Cypriot products, additional studies are still needed. Chemometrics, when coupled with spectroscopy, offers robust analytical tools for distinguishing genuine products from their imitations by characterizing them. This review provides an overview of certified Cypriot products and focuses on published applications where chemometric approaches have been used to assess product origin, composition, and adulteration. The paper concludes with current challenges, methodological limitations, and future directions for expanding the role of chemometrics in food integrity verification within the context of EU geographical indications.

1. Introduction

Geographical indications (GIs) and other EU quality schemes such as Protected Designation of Origin (PDO), Protected Geographical Indication (PGI), and Traditional Specialty Guaranteed (TSG) serve as intellectual property rights that legally protect the names of products linked to specific regions and traditional production methods [1,2]. These schemes not only safeguard against imitation and misuse but also preserve the integrity and reputation of regional products within the internal market and in international trade [3].
The legal recognition of EU quality schemes ensures that only products genuinely originating from a defined geographical area and meeting precise production criteria can bear the registered name. This protection enhances consumer trust, product traceability, and market differentiation, especially in premium segments where authenticity and provenance are valued [4,5]. Legal framework for the organization and management of agricultural markets in the EU, including provisions on marketing standards, production support, and protection of quality schemes such as PDO and PGI, is preserved by Regulation (EU) No 1308/2013 [6].
Commercially, quality-scheme products often command higher prices and enjoy greater market visibility, contributing significantly to rural development, job creation, and economic resilience in their regions [7,8]. Moreover, these products act as cultural ambassadors, reflecting local heritage, biodiversity, traditional knowledge, and culinary identity [9,10].
Food fraud is the deliberate substitution, addition, tampering, or misrepresentation of food, ingredients, or packaging for economic gain. Common forms include dilution, mislabeling of origin, counterfeit products, and replacing high-value ingredients with inferior or synthetic alternatives [3,11]. The primary motivation behind food fraud is economic, as producers may lower costs or inflate profit margins by compromising product integrity, often at the expense of consumer safety and brand reputation. Such practices can damage the competitiveness of legitimate producers, particularly those following strict standards under EU quality schemes.
Ensuring food authenticity and traceability is critical in maintaining consumer confidence and protecting the value of products under PDO and PGI labels. These certifications depend on a verifiable link between the product’s characteristics and its geographical origin or traditional production method [1,12]. Authenticity assures consumers that the product they purchase meets specific quality standards, while traceability enables tracking through the supply chain, which is vital for verifying origin, detecting fraud, and enforcing food safety regulations [4]. For quality-scheme products, where geographic and cultural identity are central to value, authenticity and traceability systems are not just quality control measures as they are fundamental tools for preserving heritage, fairness in trade, and the economic sustainability of rural communities [7,10].
The integration of analytical techniques with chemometrics has been extensively applied to trace the origin and verify the authenticity of cheese products. Two main analytical strategies are used: targeted approaches, which focus on identifying and quantifying specific pre-selected compounds, and untargeted approaches, which analyze the entire spectral or metabolomic fingerprint of a sample without prior assumptions.
The untargeted approach, often combined with chemometric tools, provides a holistic view of the sample, enhancing its discriminatory power. Several recent studies have demonstrated the effectiveness of this approach. For instance, NMR-based metabolomics successfully distinguished PDO Asiago cheese from pasture-fed versus hay-fed milk, though the model was more reliable for short-ripened varieties [13]. Parmigiano Reggiano was also accurately classified according to geographical origin using NMR and chemometrics, with further studies showing the potential of ATR-FTIR combined with LDA to differentiate grated PDO cheese. Also, an untargeted approach enabled detection across both aqueous and lipid fractions, while a parallel targeted analysis identified key discriminatory compounds, as presented by Maestrello et al. [14].
While the EU’s quality schemes offer valuable frameworks to safeguard the identity and authenticity of traditional agri-food products, their implementation presents distinct challenges in smaller Member States such as Cyprus. Cypriot products often originate from small-scale, family-run operations with fragmented production systems and limited access to advanced analytical infrastructure. Moreover, there is a noticeable gap in peer-reviewed scientific studies that focus specifically on Cypriot PDO/PGI products, which hampers the development of robust, evidence-based authentication methods. These factors underscore the need for targeted research and tailored chemometric approaches that can accommodate the variability and complexity of indigenous cultivars, traditional products, and practices. Addressing these gaps is crucial to enhancing traceability, maintaining consumer trust, and supporting the long-term sustainability of Cyprus’s agri-food sector within the competitive EU market [15].
The objective of this review is to critically examine and synthesize current research studies on the application of chemometric techniques for the authentication and characterization of Cypriot products bearing PDO or PGI status. The review focuses on how multivariate data analysis has been employed with analytical methods, such as spectroscopy, chromatography, and elemental profiling, to verify product authenticity, determine geographical origin, and detect adulteration. By mapping the methodological landscape, highlighting validated approaches, and identifying research gaps, this review aims to support future efforts in food authentication, regulatory compliance, and preserving Cyprus’s gastronomic heritage.

2. Overview of Certified Cypriot Products

As of 2025, Cyprus boasts 29 certified products under the European Union’s quality schemes, as shown in Table 1, in terms of PDO and PGI. These certifications serve to protect the authenticity, traditional methods, and regional identity of Cypriot agri-food products, while enhancing their value and competitiveness in both local and international markets. These belong to 3 categories, i.e., food products, wines, and spirit drinks, with 14, 13, and 2 representatives, respectively, in each category.
Moreover, it must be noted that Table 1 highlights the ongoing efforts to protect the authenticity and heritage of local produce through the registration of these products, ensuring that they are recognized for their geographical and cultural significance. The registrations are spread across various dates, the most recent being in 2024 for Agros Rosewater and Cyprus red soil potatoes. The mix of registered and published statuses reflects the ongoing process of securing these recognitions, which serve not only to preserve local traditions but also to provide economic benefits through the promotion of Cypriot specialties in both domestic and international markets.

3. Chemometric Approaches in Food Authentication

Chemometrics is the application of mathematical and statistical techniques to analyze complex data, which is particularly useful in food authentication. It allows for the effective analysis of large and multidimensional datasets, helping to ensure the quality, safety, and authenticity of food products. By employing advanced analytical techniques, chemometrics helps detect adulteration, verify product authenticity, and confirm compliance with regulatory standards [16,17].
The pre-processed training dataset is subjected to a modeling procedure. Pattern recognition techniques are broadly classified into two main categories: unsupervised and supervised methods. Within supervised methods, a further distinction is made between discriminative and modeling approaches. Modeling techniques can target either a single class (one class) or multiple classes and may follow either a strict (rigorous) or flexible (compliant) strategy, as illustrated in Figure 1. The first category, unsupervised methods, acts like tools for preliminary or exploratory analysis, serving as an initial step before developing supervised models. The second category, supervised methods, encompasses very commonly used approaches in food analysis literature [16,18].
Robustness is another key factor in chemometric approaches, as presented in Figure 2. A robust model can handle variability in the data, such as differences in environmental conditions, measurement instruments, or sample composition. A model that is sensitive to small changes in the data may not perform reliably in real-world applications, so robustness ensures the model remains accurate and stable across a variety of conditions. Furthermore, a robust model should be generalizable, meaning that it performs well not only on the data it was trained on but also on new, unseen data. This ability to generalize is crucial for ensuring the model’s effectiveness in real-world food authentication scenarios, where data may vary over time or across different geographic regions [16,21,22].
By considering both the interpretability and robustness of chemometric models, food authentication can become more reliable, transparent, and applicable to real-world conditions. This integrated approach not only enhances the scientific credibility of the results but also builds trust among stakeholders, ultimately benefiting both producers and consumers by ensuring food safety, protecting product authenticity, and supporting regulatory compliance [16,22].

4. Applications of Chemometrics in Studies Related to Cypriot Food and Beverage Products with Quality Schemes

This section explores the studies that took place and combine chemometrics to study any GI-protected Cypriot products, which are summarized in Table 2. The small volume of this table shows that research on Cyprus’s GI foodstuffs and beverages is scarce and needs to be enriched in the future. The earliest study dates to 2003 and focused on Zivania. This was followed by research on Commandaria and other wines, then Halloumi cheese, and most recently, Cyprus potatoes.
Despite the valuable insights provided by the studies summarized in Table 2, a few common limitations can be identified. These include the use of relatively small sample sizes, which may limit the statistical power and generalizability of the findings, and the frequent absence of external validation, which is essential to confirm the robustness of the developed models; however, some of them were preliminary studies. Additionally, there is limited standardization in chemometric workflows across studies, making it difficult to compare results or replicate methodologies. Addressing these issues in future research will be crucial to strengthening the reliability and applicability of chemometric tools for the authentication of Cypriot PDO/PGI products.
Other studies, related to chemometrics and focused on Cyprus’ fruits like prickly pears [33,34], carobs [35,36,37,38], anari cheese [29], kefalotyri cheese [30,39], honey [40,41], olive oil [42,43] and olives [44] are also important, but their Cyprus foodstuffs have not yet been registered as PDO/PGI. In addition, another study by Kastanos et al. [45] related to milk species’ origin in Halloumi cheese took place through triplex-PCR, but without using chemometrics.

5. Challenges, Limitations, and Future Perspectives

Chemometric techniques, when coupled with spectroscopic and chromatographic analyses, have shown significant potential in verifying the authenticity and quality of GI food products and beverages [46,47]. This section discusses the key limitations and emerging perspectives critical for advancing the application of chemometrics in Cypriot GI product authentication.
One of the most significant scientific challenges in applying chemometric techniques is the inherent variability in food matrices. Cypriot quality-scheme products, often produced using traditional and region-specific methods, exhibit high intra-class variability due to differences in raw material composition, seasonal fluctuations, terroir effects, microflora, and artisanal production techniques. For instance, milk composition in Halloumi varies depending on whether it is made from sheep, goat, or a mixture of milks (as well as the animals’ feeding regimes), and this variation directly affects fat, protein, and moisture levels, parameters critical in spectral signatures [30,31]. Such complexity complicates the development of robust and generalizable chemometric models. Matrix effects can mask or distort the signals of interest, leading to poor model performance or overfitting. Traditional multivariate statistical approaches like PCA, PLS-DA, or LDA may not sufficiently account for this heterogeneity without careful data pre-treatment and model optimization. To overcome these challenges, it is necessary to adopt comprehensive experimental designs that capture a wide range of natural variability during model training. Advanced signal processing and variable selection techniques can help isolate the most informative features in complex datasets [48,49,50].
Moreover, the absence of harmonized analytical and computational protocols represents a major bottleneck in translating chemometric research into standardized quality control workflows. Currently, methodologies vary widely across research groups and laboratories regarding sample preparation, instrumental settings (e.g., resolution, scanning range), spectral pre-processing (e.g., baseline correction, normalization), and chemometric modeling strategies. These inconsistencies hinder the reproducibility and comparability of results, impeding cross-validation and technology transfer [51,52,53].
For Cypriot PDO products, where authentication must often comply with both local and EU regulatory frameworks, standardized protocols are essential to ensure legal robustness and acceptance. Developing harmonized protocols requires interdisciplinary collaboration among chemists, data scientists, food technologists, and regulatory agencies. Such protocols should include standardized sample collection procedures, minimum data quality requirements, recommended spectral pre-processing pipelines, and criteria for model validation and performance assessment. Initiatives like the METROFOOD-RI project offer useful templates for establishing such protocols [54,55].
Despite their scientific merit, chemometric methods still face limited acceptance in official control laboratories and regulatory contexts. This is primarily due to the perception of these models as “black boxes” lacking transparency and legal defensibility. Regulators typically require analytical methods to meet stringent validation criteria such as accuracy, repeatability, reproducibility, specificity, and robustness. However, chemometric models, especially those based on non-targeted analysis, often struggle to meet these parameters in a legally defensible way when model outputs are probabilistic rather than deterministic [54,56]. The development of decision-support tools that integrate chemometrics with conventional targeted analysis (e.g., GC-MS, HPLC) may provide a more balanced solution, where chemometrics serves as a rapid screening tool that flags suspicious samples for further confirmatory testing [54].
Furthermore, robust chemometric models rely heavily on comprehensive reference datasets that encompass the full range of variability in authentic and fraudulent samples. Datasets collected with different instruments, under varied conditions, or using different protocols are often incompatible, limiting model portability and scalability. Another related challenge is model transferability. A model developed on one spectroscopic device may not perform well on another, even if both are nominally the same model. This instrument-to-instrument variability, often caused by differences in optics, light sources, or calibration routines, can lead to significant degradation in model accuracy. Techniques such as calibration transfer, domain adaptation, and standardization methods (e.g., piecewise direct standardization) can help mitigate these issues, but they are not yet routinely implemented. Establishing shared, open access spectral databases, supported by consistent metadata, is essential for enhancing the robustness, transparency, and scalability of chemometric authentication models [47,53].
The evolution of machine learning (ML) and deep learning (DL) algorithms offers exciting possibilities for enhancing the analytical power of chemometric models [50,57]. Unlike traditional linear models, these advanced methods can capture complex, non-linear relationships in high-dimensional datasets, enabling improved discrimination between authentic and adulterated samples [15,52]. However, the application of ML/DL also introduces new challenges, including the need for large, well-annotated training datasets and high computational resources. Overfitting remains a significant risk, especially when the number of samples is small relative to the number of variables. Furthermore, interpretability continues to be a concern in regulatory contexts, where explainable AI (XAI) tools will be crucial for model acceptance. Despite these limitations, ML/DL approaches are likely to play a pivotal role in the future of food authentication, particularly when combined with portable or online sensing systems [58,59].
The emergence of portable and miniaturized spectrometers, such as handheld NIR, Raman, and FTIR devices, has opened up new possibilities for in situ food authentication. These tools can bring chemometric screening capabilities directly to farms, factories, markets, and border control points, allowing real-time decision-making without the need for sophisticated lab infrastructure [60,61,62,63], as shown in Figure 3 For Cypriot quality-scheme products, especially those exported to international markets, field-based screening could offer an effective first line of defense against fraud and lower quality. On the other hand, the practical deployment of portable devices faces limitations, including lower spectral resolution, reduced signal-to-noise ratios, and sensitivity to environmental conditions such as temperature, humidity, and light exposure. The chemometric models used on portable devices must be highly optimized and validated under real-world conditions. Model updates and cloud-based connectivity may help address these issues by enabling continuous learning and remote support. Future developments should focus on integrating portable sensors with mobile apps and cloud analytics to ensure user-friendliness, traceability, and scalability [61,64,65,66].
The integration of chemometric data with blockchain technology offers a powerful approach to strengthening food integrity and supply chain transparency. In a blockchain-based system, analytical results from chemometric authentication tests can be securely recorded and linked to specific product batches via QR codes or digital certificates. This creates a tamper-proof audit trail that can be accessed by stakeholders across the supply chain, including producers, certifiers, retailers, and consumers. For Cypriot quality-scheme products, which often rely on traditional and decentralized production systems, blockchain could help ensure compliance with geographical origin and production specifications. However, challenges include the cost and complexity of blockchain implementation, the need for digital literacy among producers, and the development of interfaces that integrate seamlessly with existing laboratory information management systems (LIMS). Future research should explore hybrid solutions that combine blockchain with other digital traceability tools, including Internet of Things (IoT) sensors and geolocation data [67,68,69,70].
Finally, the successful implementation of chemometric approaches in food authentication depends on collaborative efforts across disciplines and sectors. Policymakers, researchers, regulatory authorities, producer cooperatives, and technology providers must work together to establish clear frameworks for method validation, data sharing, training, and policy development. In Cyprus, national and regional authorities can play a critical role in promoting chemometric applications and AI by funding pilot studies, creating certification pathways, and facilitating stakeholder engagement [15].
European initiatives such as the EU Food Fraud Network, the Joint Research Centre (JRC), and several COST Actions provide ideal platforms for building such collaborations. Capacity-building programs, especially those targeting SMEs and artisanal producers, will be essential for ensuring inclusive adoption [71,72,73]. Looking ahead, the creation of an EU-recognized roadmap for chemometric-based food authentication, incorporating Cypriot products as case studies, could significantly advance both scientific understanding and practical implementation.
Future studies should adopt rigorous validation protocols, including cross-validation, external validation, and independent test sets, to ensure model robustness. Collaboration with producers and regulatory bodies is also essential to translate analytical findings into traceability tools and certification support. Finally, integrating chemometrics with other emerging technologies (e.g., sensor networks, portable devices, and AI-based classification) could enhance real-time quality control and strengthen the global competitiveness of Cypriot products. Future studies should focus on developing shared spectral libraries and benchmarking datasets to support cross-laboratory reproducibility.

6. Conclusions

Authentication, characterization, and adulteration detection of Cypriot agri-food products protected under EU quality schemes were the primary focus of this review. Since 2003, significant progress has been made, particularly in studies on Halloumi cheese, Commandaria wine, and Zivania. However, there remains a clear need for broader research encompassing a wider range of products and the application of more advanced analytical approaches. Addressing current challenges, such as limited sample availability, complex food matrices, and the need for methodological standardization, will be essential to developing robust and reliable chemometric models.
Halloumi cheese has recently become the subject of some analytical studies aimed at verifying its authenticity. Chemometric workflows have been applied to spectroscopic data to classify Halloumi samples based on milk composition (cow vs. goat/sheep), production practices, and compliance with PDO specifications. Principal component analysis (PCA) has been used for initial data exploration, followed by discriminant analysis. These models achieved high sensitivity and specificity; however, there is a need for larger sample sets and further validation.
Future efforts should prioritize the integration of emerging technologies, expansion of comprehensive databases, and enhanced collaboration among researchers, producers, and regulatory bodies to effectively safeguard the authenticity and reputation of Cypriot quality-scheme products in local as well as global markets. To realize the full potential of chemometric tools in GI product authentication, Cyprus must invest in interdisciplinary research hubs, international collaborations, and digital infrastructure supporting traceability and consumer transparency.

Author Contributions

Conceptualization, C.R.T.; resources, C.R.T.; writing—original draft preparation, M.T.; writing—review and editing, I.P. and C.R.T.; supervision, I.P. and C.R.T.; project administration, C.R.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Chemosensors 13 00332 g001
Figure 1. A range of approaches to pattern recognition. Moreover, interpretability is an important consideration in food authentication, as it helps ensure that chemometric models are transparent and understandable. For non-experts, such as food producers or regulatory bodies, the models must be interpretable to facilitate trust and decision-making. Understanding which features are most influential in making predictions can provide valuable insights into why certain products are classified as authentic or adulterated. Additionally, interpretability allows better transparency in the decision-making process, which is essential for regulatory compliance [19,20].
Figure 1. A range of approaches to pattern recognition. Moreover, interpretability is an important consideration in food authentication, as it helps ensure that chemometric models are transparent and understandable. For non-experts, such as food producers or regulatory bodies, the models must be interpretable to facilitate trust and decision-making. Understanding which features are most influential in making predictions can provide valuable insights into why certain products are classified as authentic or adulterated. Additionally, interpretability allows better transparency in the decision-making process, which is essential for regulatory compliance [19,20].
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Figure 2. A proper validation strategy applied to analytical data (e.g., spectral signatures) can yield robust models.
Figure 2. A proper validation strategy applied to analytical data (e.g., spectral signatures) can yield robust models.
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Figure 3. Portable and miniaturized spectrometers, such as handheld NIR devices, have opened up new possibilities for in situ food authentication.
Figure 3. Portable and miniaturized spectrometers, such as handheld NIR devices, have opened up new possibilities for in situ food authentication.
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Table 1. Cyprus’ 29 certified products under the European Union’s quality schemes, listed from the most recent to the oldest, as of May 2025.
Table 1. Cyprus’ 29 certified products under the European Union’s quality schemes, listed from the most recent to the oldest, as of May 2025.
#Product Type/CategoryFile NumberNameStatusDate of Registration
1Food/RosewaterPGI-CY-02862Rodostagma Agrou/Agros RosewaterRegistered4 October 2024
2Food/VegetablePGI-CY-02906Kypriaki patata kokkinoyis/Cyprus red soil potatoPublished20 September 2024
3Food/PastaPGI-CY-02762TertziellouthkiaRegistered10 July 2024
4Food/CheesePGI-CY-02872Halitzia TilliriasRegistered7 December 2023
5WinePGI-CY-A1620-AM01LarnacaApplied20 September 2022
6WinePGI-CY-A1621-AM01NicosiaApplied20 September 2022
7Food/PastaPGI-CY-02763ΜMakaronia tis SmilasMakaronia tou SklinitziouRegistered22 July 2022
8Food/CheesePDO-CY-01243Halloumi/HellimRegistered13 April 2021
9Food/SausagePGI-CY-02369Loukaniko PitsiliasRegistered10 February 2021
10Food/Processed or preserved meatPGI-CY-02367Lountza PitsiliasRegistered10 February 2021
11Food/Processed or preserved meatPGI-CY-02368Hiromeri PitsiliasRegistered8 October 2020
12Food/VegetablePDO-CY-01309Kolokasi Sotiras/Kolokasi-Poulles SotirasRegistered3 August 2016
13Food/Preserved fruits and flowers in syrupPGI-CY-01310Glyko Triantafyllo AgrouRegistered21 June 2016
14Food/SausagePGI-CY-1244Pafitiko LoukanikoRegistered20 October 2015
15Food/ConfectioneryPGI-CY-0800Koufeta Amygdalou GeroskipouRegistered3 March 2012
16Spirit drinkPGI-CY-01942Ζιβάνα/ZivaniaRegistered13 February 2008
17Spirit drinkPGI-CY + GR-01828OuzoRegistered13 February 2008
18Food/ConfectioneryPGI-CY-0454Loukoumi GeroskipouRegistered15 December 2007
19WinePDO-CY-A1628Krasochoria of LemesosRegistered17 February 2006
20WinePDO-CY-A1627PitsiliaRegistered17 February 2006
21WinePDO-CY-A1626Laona AkamaRegistered17 February 2006
22WinePDO-CY-A1625Vouni Panagias—AmpelitisRegistered17 February 2006
23WinePDO-CY-A1624Krasochoria of Lemesos—LaonaRegistered17 February 2006
24WinePDO-CY-A1623Krasochoria of Lemesos—AfamesRegistered17 February 2006
25WinePDO-CY-A1622CommandariaRegistered17 February 2006
26WinePGI-CY-A1621NicosiaRegistered26 January 2006
27WinePGI-CY-A1620LarnacaRegistered26 January 2006
28WinePGI-CY-A1619LemesosRegistered26 January 2006
29WinePGI-CY-A1618PaphosRegistered26 January 2006
Table 2. Chronological overview of studies on Cypriot food and beverage products with geographical indications.
Table 2. Chronological overview of studies on Cypriot food and beverage products with geographical indications.
Food or Beverage ProductAnalytical Method(s)Target(s)Chemometric Method(s)Reference
ZivaniaICP-OESType of distilled alcoholic beverages and geographical originCDA, CBT, PCA, PCOKokkinofta et al. [23]
ZivaniaHPLC-RID, GC-FID, 1H NMR, and ICP-OESType of distilled alcoholic beverages and geographical originPCA, CART, RDA, DendrogramKokkinofta and Theocharis [24]
Zivania1H NMR spectroscopyType of distilled alcoholic beveragesCDA, CBTPetrakis et al. [25]
CommandariaFTIRType of sweet wine and geographical originPCA, CART, RDA, DendrogramIoannou-Papayianni et al. [26]
WinesSNIF-NMR, IR-MS, ICP-OESGeographical originPCA, CART, RDA, DendrogramKokkinofta et al. [27]
WinesPCRGeographical originLDAKamilari et al. [28]
Halloumi cheeseFTIR spectroscopyMilk’s animal originOPLS-DATarapoulouzi and Theocharis [29]
Halloumi cheese1H NMR and FTIR spectroscopyMilk’s animal originOPLS-DATarapoulouzi et al. [30]
Halloumi cheeseNIR and HSIMilk’s animal originPCA and HCATarapoulouzi et al. [31]
PotatoIRMS, SNIF-NMR, ICP-OESGeographical originOPLS-DAIoannou-Papayianni et al. [32]
CART: classification and regression trees, CBT: classification binary trees, CDA: canonical discriminant analysis, HCA: hierarchical cluster analysis, 1H NMR: proton nuclear magnetic resonance, HPLC-RID: high-performance liquid chromatography with refractive index detection, HSI: hyperspectral imaging, ICP-OES: inductively coupled plasma–optical emission spectroscopy, IR-MS: isotope ratio–mass spectrometry, FTIR spectroscopy: Fourier transform infrared spectroscopy, GC-FID: gas chromatography with flame–ionization detection, LDA: linear discriminant analysis, NIR spectroscopy: near-infrared spectroscopy, PCA: principal component analysis, PCO: principal coordinate analysis, PCR: polymerase chain reaction, RDA: regularized discriminant analysis, SNIF-NMR: site-specific natural isotopic fractionation-nuclear magnetic resonance.
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Tarapoulouzi, M.; Pashalidis, I.; Theocharis, C.R. Use of Chemometrics for the Authentication, Characterization and Detection of Adulteration of Cypriot Products Registered Under EU Quality Schemes: A Review. Chemosensors 2025, 13, 332. https://doi.org/10.3390/chemosensors13090332

AMA Style

Tarapoulouzi M, Pashalidis I, Theocharis CR. Use of Chemometrics for the Authentication, Characterization and Detection of Adulteration of Cypriot Products Registered Under EU Quality Schemes: A Review. Chemosensors. 2025; 13(9):332. https://doi.org/10.3390/chemosensors13090332

Chicago/Turabian Style

Tarapoulouzi, Maria, Ioannis Pashalidis, and Charis R. Theocharis. 2025. "Use of Chemometrics for the Authentication, Characterization and Detection of Adulteration of Cypriot Products Registered Under EU Quality Schemes: A Review" Chemosensors 13, no. 9: 332. https://doi.org/10.3390/chemosensors13090332

APA Style

Tarapoulouzi, M., Pashalidis, I., & Theocharis, C. R. (2025). Use of Chemometrics for the Authentication, Characterization and Detection of Adulteration of Cypriot Products Registered Under EU Quality Schemes: A Review. Chemosensors, 13(9), 332. https://doi.org/10.3390/chemosensors13090332

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