2021
DOI: 10.1002/jsfa.11176
|View full text |Cite
|
Sign up to set email alerts
|

An attempt to classify the botanical origin of honey using visible spectroscopy

Abstract: BACKGROUND The production of honey, and especially the unifloral varieties, is limited by factors such as weather conditions or the availability of nectar flow and honeydew. This results in a deficit in supply leading to the adulteration of honey. If they are not properly labeled, customers cannot distinguish artificial / synthetic products from real honey. Currently, the basic, commonly used method for determining the varieties of honey (botanical origin) is palynological analysis. However, this procedure is … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
1
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
3
1
1

Relationship

0
5

Authors

Journals

citations
Cited by 6 publications
(3 citation statements)
references
References 29 publications
0
1
0
Order By: Relevance
“…Previously, using a multivariate analysis, ATR-FTIR spectroscopy could also discriminate Anatolian honey samples of different botanical origins [125]. The classification of the botanical origin of honey was achieved, as for other transparent or semitransparent products, using the visible light spectra transmitted through a relatively thin layer of honey samples [126]. Additionally, chemometric methods (PCA and PLS) and an artificial neural network were applied with Raman spectroscopy as a rapid method for the quantification of glucose, fructose, sucrose, and maltose contents in honey samples [34].…”
Section: Honey Characterization Through Spectroscopic Techniquesmentioning
confidence: 99%
“…Previously, using a multivariate analysis, ATR-FTIR spectroscopy could also discriminate Anatolian honey samples of different botanical origins [125]. The classification of the botanical origin of honey was achieved, as for other transparent or semitransparent products, using the visible light spectra transmitted through a relatively thin layer of honey samples [126]. Additionally, chemometric methods (PCA and PLS) and an artificial neural network were applied with Raman spectroscopy as a rapid method for the quantification of glucose, fructose, sucrose, and maltose contents in honey samples [34].…”
Section: Honey Characterization Through Spectroscopic Techniquesmentioning
confidence: 99%
“…It involves spectral, rapid measurement with data post-processing and AI classification [20][21][22][23][24]. Classifying samples' biological origins through spectral data analysis is now a trend, i.e., honey-type classification [25] or whether an egg comes from an MS-infected chicken or healthy chicken [26][27][28].…”
Section: Introductionmentioning
confidence: 99%
“…It involves spectral, rapid measurement with data post-processing and AI classification. Classifying samples' biological origins through spectral data analysis is now a trend, i.e., honey types classification [16] or whether the egg comes from MSinfected chicken or healthy chicken [17][18][19].…”
mentioning
confidence: 99%