<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study tested the possibility of using non-targeted analysis of volatile organic compounds by headspace solid-phase microextraction-gas chromatography-mass spectrometry coupled with chemometrics as a potential tool for differentiating leg meat of oat- and wheat-fed (<italic>ad libitum</italic>) White Kołuda geese. Thirty-six classification models were obtained for which the correct classification rate and classification accuracy for oatfed and wheat-fed geese were calculated based on a seven-fold cross-validation. Generally, the most advantageous method of the sample preparation was the high-temperature heat treatment version, whereas the highest correct classification rate was obtained when the chemometric analysis was carried out on the female, then male, and finally male + female variant of group comparisons (P&lt;0.01). Furthermore, log-transformation appeared to be a slightly better data preprocessing technique in comparison to systematic ratio normalization. The obtained classification models can potentially differentiate the meat of oat-fattened from wheat-fattened White Kołuda geese.</p>
</abstract>

This study tested the possibility of using non-targeted analysis of volatile organic compounds by headspace solid-phase microextraction-gas chromatography-mass spectrometry coupled with chemometrics as a potential tool for differentiating leg meat of oat- and wheat-fed (ad libitum) White Kołuda geese. Thirty-six classification models were obtained for which the correct classification rate and classification accuracy for oatfed and wheat-fed geese were calculated based on a seven-fold cross-validation. Generally, the most advantageous method of the sample preparation was the high-temperature heat treatment version, whereas the highest correct classification rate was obtained when the chemometric analysis was carried out on the female, then male, and finally male + female variant of group comparisons (P<0.01). Furthermore, log-transformation appeared to be a slightly better data preprocessing technique in comparison to systematic ratio normalization. The obtained classification models can potentially differentiate the meat of oat-fattened from wheat-fattened White Kołuda geese.

Non-targeted analysis of VOCs by HS-SPME-G C/MS coupled with chemometrics as a potential tool for authentication of White Kołuda oat goose

Annals of Animal Science

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study tested the possibility of using non-targeted analysis of volatile organic compounds by headspace solid-phase microextraction-gas chromatography-mass...

Non-targeted analysis of VOCs by HS-SPME-G C/MS coupled with chemometrics as a potential tool for authentication of White Kołuda oat goose

Pubblicato online: 29 ott 2022

Pagine: 1401 - 1410

Ricevuto: 29 set 2021

Accettato: 03 ago 2022

DOI: https://doi.org/10.2478/aoas-2022-0060

Parole chiave
White Kołuda goose, food authentication, volatile organic compounds, chemometrics, Fisher ratio, systematic ratio normalization

© 2022 Krzysztof Wojtycza et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Non-targeted analysis of VOCs by HS-SPME-G C/MS coupled with chemometrics as a potential tool for authentication of White Kołuda oat goose

Pubblicato online: 29 ott 2022

Pagine: 1401 - 1410

Ricevuto: 29 set 2021

Accettato: 03 ago 2022

DOI: https://doi.org/10.2478/aoas-2022-0060

Parole chiaveWhite Kołuda goose, food authentication, volatile organic compounds, chemometrics, Fisher ratio, systematic ratio normalization

© 2022 Krzysztof Wojtycza et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
White Kołuda goose, food authentication, volatile organic compounds, chemometrics, Fisher ratio, systematic ratio normalization