A chemometric approach for the differentiation of 15 monofloral honeys based on physicochemical parameters.
honey
monofloral honeys
multi-discriminant analysis (MDA)
multivariate analysis
physicochemical parameters
principal component analysis (PCA)
Journal
Journal of the science of food and agriculture
ISSN: 1097-0010
Titre abrégé: J Sci Food Agric
Pays: England
ID NLM: 0376334
Informations de publication
Date de publication:
15 Jan 2022
15 Jan 2022
Historique:
revised:
05
05
2021
received:
16
09
2020
accepted:
31
05
2021
pubmed:
1
6
2021
medline:
20
11
2021
entrez:
31
5
2021
Statut:
ppublish
Résumé
Although the main method for authentication of monofloral honey is pollen analysis, other classification approaches have been also applied. However, the majority of the existing classification models so far have utilized a few honey types or a few honey samples of each honey type, which can lead to inaccurate results. Aiming at addressing this, the goal of the present study was to create a classification model by analysing in total 250 honey samples from 15 different monofloral honey types in ten physicochemical parameters and then, multivariate analysis [multivariate analysis of variance (MANOVA), principal component analysis (PCA) and multi-discriminant analysis (MDA)] was applied in an effort to distinguish and classify them. Electrical conductivity and colour were found to have the highest discriminative power, allowing the classification of monofloral honey types, such as oak, knotgrass and chestnut honey, as well as the differentiation between honeydew and nectar honeys. The classification model had a high predictive power, as the 84.4% of the group cases was correctly classified, while for the cases of chestnut, strawberry tree and sunflower honeys the respective prediction was correct by 91.3%, 95% and 100%, allowing further determination of unknown honey samples. It seems that the characterization of monofloral honeys based on their physicochemical parameters through the proposed model can be achieved and further applied on other honey types. The results could contribute to the development of methodologies for the determination of honey's botanical origin, based on simple techniques, so that these can be applied for routine analysis. © 2021 Society of Chemical Industry.
Sections du résumé
BACKGROUND
BACKGROUND
Although the main method for authentication of monofloral honey is pollen analysis, other classification approaches have been also applied. However, the majority of the existing classification models so far have utilized a few honey types or a few honey samples of each honey type, which can lead to inaccurate results. Aiming at addressing this, the goal of the present study was to create a classification model by analysing in total 250 honey samples from 15 different monofloral honey types in ten physicochemical parameters and then, multivariate analysis [multivariate analysis of variance (MANOVA), principal component analysis (PCA) and multi-discriminant analysis (MDA)] was applied in an effort to distinguish and classify them.
RESULTS
RESULTS
Electrical conductivity and colour were found to have the highest discriminative power, allowing the classification of monofloral honey types, such as oak, knotgrass and chestnut honey, as well as the differentiation between honeydew and nectar honeys. The classification model had a high predictive power, as the 84.4% of the group cases was correctly classified, while for the cases of chestnut, strawberry tree and sunflower honeys the respective prediction was correct by 91.3%, 95% and 100%, allowing further determination of unknown honey samples.
CONCLUSION
CONCLUSIONS
It seems that the characterization of monofloral honeys based on their physicochemical parameters through the proposed model can be achieved and further applied on other honey types. The results could contribute to the development of methodologies for the determination of honey's botanical origin, based on simple techniques, so that these can be applied for routine analysis. © 2021 Society of Chemical Industry.
Substances chimiques
Plant Nectar
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
139-146Informations de copyright
© 2021 Society of Chemical Industry.
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