Classification and authentication of spices and aromatic herbs by means of HPLC-UV and chemometrics

Pages-Rebull, Josep; Pérez‐Ràfols, Clara; Serrano, Núria; Valle, Manel del; Dı́az-Cruz, José Manuel

doi:10.1016/j.fbio.2023.102401

Cited by 15 publications

(6 citation statements)

References 18 publications

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“…Similarly, Pages-Rebull et al [26] developed statistical models for the characterization, identification and authentication of cinnamon, oregano, thyme, sesame, bay leaf, clove, cumin, and vanilla samples based on sesamol, eugenol, thymol, carvacrol, salicylaldehyde and vanillin along with multivariate statistics such as PCA, PLS-DA, and soft independent modelling of class analogy (SIMCA). PLS-DA classified samples with a high prediction rate (>99%) in agreement with present results.…”

Section: Discussionmentioning

confidence: 99%

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Typification of Green Coffee and Selected Spices Based on the Analysis of Volatile Compounds and Supervised Learning

Lazaridis,

Kokkosi,

Mylonaki

et al. 2024

Preprint

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The present study comprises the second part of our previous work that dealt mainly with the phytochemical and physicochemical characterization of green coffee, clove, cinnamon/clove and nutmeg ethanolic extracts of grape origin. In the present study, we focused on producing a discriminating model concerning green coffee, clove, cinnamon, cinnamon and clove, and nutmeg fine powders based on multivariate analysis of variance and supervised learning on data of volatile compounds analysis, carried out with solid phase dynamic extraction in combination with gas chromatography/mass spectrometry. Results showed that 7 volatile compounds namely ethylene, methanol, 3-methylpentane, ethyl acetate, 9-hexadecen-1-ol, toluene, and methyl acetate could differentiate the investigated samples resulting in a 100% classification rate using the cross-validation method of linear discriminant analysis. Results were further confirmed using partial least squares regression analysis. The study contributes to the typification of green coffee, cinnamon, clove, cinnamon and clove and nutmeg, based on selected volatile compounds, and provides further support to the literature by means of possible adulteration given the statistical models developed.

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Section: Discussionmentioning

confidence: 99%

“…Finally, it is quite evident from the literature that metrics data of foods (foodometrics) [12] along with the most recent advances/trends in the analytical methodologies can comprise a decisive tool for the authentication and quality/adulteration control of beverages, herbs and spices [25][26][27][28].…”

Section: Discussionmentioning

confidence: 99%

Typification of Green Coffee and Selected Spices Based on the Analysis of Volatile Compounds and Supervised Learning

Lazaridis,

Kokkosi,

Mylonaki

et al. 2024

Preprint

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“…Sun et al (2023) used non-targeted HPLC-FLD fingerprinting for Guizhou paprika, highlighting its effectiveness in detecting fraud. Pages-Rebull et al (2023) applied HPLC coupled with ultraviolet (UV)-Vis detection for spices, aiding adulterant identification. Deng et al (2023) utilized LC-MS to detect chicken-derived components in collagencontaining foods, demonstrating its specificity.…”

Section: Analytical Approaches For Food Authenticationsmentioning

confidence: 99%

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Haider,

Iqbal,

Bhatti

et al. 2024

Comp Rev Food Sci Food Safe

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Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high‐risk animal‐derived goods, in which molecular biomarker‐based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

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“…Pages-Rebull et al [111] evaluated 87 samples, 16 of which were cinnamon, by HPLC-UV coupled with chemometrics (PCA, SIMCA, and PLS-DA), being compared in terms of sensitivity, specificity, and misclassification. Both methods provided similar results, with some sensitivity and specificity values below 1, and an overall error of 0.75% and 0.82% for PLS-DA and SIMCA.…”

Section: Chromatographicmentioning

confidence: 99%

Differentiating True and False Cinnamon: Exploring Multiple Approaches for Discrimination

Feltes,

Ballen,

Steffens

et al. 2023

Micromachines

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This study presents a comprehensive literature review that investigates the distinctions between true and false cinnamon. Given the intricate compositions of essential oils (EOs), various discrimination approaches were explored to ensure quality, safety, and authenticity, thereby establishing consumer confidence. Through the utilization of physical–chemical and instrumental analyses, the purity of EOs was evaluated via qualitative and quantitative assessments, enabling the identification of constituents or compounds within the oils. Consequently, a diverse array of techniques has been documented, encompassing organoleptic, physical, chemical, and instrumental methodologies, such as spectroscopic and chromatographic methods. Electronic noses (e-noses) exhibit significant potential for identifying cinnamon adulteration, presenting a rapid, non-destructive, and cost-effective approach. Leveraging their capability to detect and analyze volatile organic compound (VOC) profiles, e-noses can contribute to ensuring authenticity and quality in the food and fragrance industries. Continued research and development efforts in this domain will assuredly augment the capacities of this promising avenue, which is the utilization of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in conjunction with spectroscopic data to combat cinnamon adulteration.

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Classification and authentication of spices and aromatic herbs by means of HPLC-UV and chemometrics

Cited by 15 publications

References 18 publications

Typification of Green Coffee and Selected Spices Based on the Analysis of Volatile Compounds and Supervised Learning

Typification of Green Coffee and Selected Spices Based on the Analysis of Volatile Compounds and Supervised Learning

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Differentiating True and False Cinnamon: Exploring Multiple Approaches for Discrimination

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