Investigating the impact of spectral data pre-processing to assess honey botanical origin through Fourier transform infrared spectroscopy (FTIR)

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22330%2F23%3A43926146" target="_blank" >RIV/60461373:22330/23:43926146 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.jfca.2023.105276" target="_blank" >https://doi.org/10.1016/j.jfca.2023.105276</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jfca.2023.105276" target="_blank" >10.1016/j.jfca.2023.105276</a>

Result language

angličtina

Original language name

Investigating the impact of spectral data pre-processing to assess honey botanical origin through Fourier transform infrared spectroscopy (FTIR)

Original language description

Honey botanical origin is a parameter affecting its market price as certain origins are related to special organoleptic properties or potential health benefits attracting consumers’ attention. However, identifying honey botanical origin is a challenging task commonly requiring extensive high-end analysis. In this study, to address this challenge, a rapid and non-distractive attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) method was developed and special focus was paid on the spectral data pre-processing and its effect on the performance of chemometric models. Twenty-two different pre-processing methods were tested, namely, scatter correction methods, spectral derivation methods and their combinations. In each occasion, both supervised and non-supervised tools were implemented and the cross-validation parameters were used as an indicator on the efficient projection of fifty-one (n = 51) honey samples originating from 5 different botanical origins (blossom, honeydew, cotton, thyme, citrus). Importantly, combining multiplicative scatter correction followed by Savitzky-Golay first derivation is suggested as the most efficient data pre-processing method. Eventually, this data pre-processing was applied in binary models acquiring excellent recognition (87–100%) and prediction (81–100%) ability. In conclusion, the presented method set light on the undermined effect of spectral data pre-processing before the application of advanced chemometrics. © 2023 Elsevier Inc.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

40500 - Other agricultural sciences

Project

—

Continuities

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Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Food Composition and Analysis

ISSN

0889-1575

e-ISSN

1096-0481

Volume of the periodical

119

Issue of the periodical within the volume

JUN 2023

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

9

Pages from-to

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UT code for WoS article

000955796600001

EID of the result in the Scopus database

2-s2.0-85149823008