IDENTIFICATION OF THE FATTY ACID COMPOSITION OF RENDERED FATS

The purpose of the study is to determine the qualitative and quantitative composition of fatty acids in rendered fats obtained from derivatives of hibernating hunting animals by licensed extraction in the territory of the hunting grounds of the Kemerovo Region – Kuzbass. Animal production was carried out during the periods allowed for seizure under licenses for the corresponding animal species in the period from 2019 to 2022. The objects of research are mammals hibernating, primarily brown bear (Ursus arctos), common badger (Meles meles), marmot ( lat. Marmota) and river beaver (Castor fiber). Differences in raw fat of the listed animals were studied in terms of organoleptic (color, smell, appearance) and physicochemical parameters (fat, protein, moisture content, melting point). It is shown that the raw fat of a bear, badger, marmot and beaver differs in the content of fat, protein and moisture. In terms of "melting point", all fats can be classified as low-melting. The fat was rendered by the conductive method at a temperature of 85 ± 2 °C and a duration of 60 minutes. Qualitative indicators were determined in samples of rendered fats (solidification point, refractive index, acid, iodine, peroxide number). It was established that the refractive index can be a marker for the identification of rendered fats. The value of the iodine number indicates a high content of unsaturated fatty acids in rendered fats. The identification of the fatty acid composition was carried out by the chromatographic method with the quantitative determination of the groups of mono- and polyunsaturated fatty acids. From 16 to 27 fatty acids were identified in bear and badger fats, over 50 % of which are polyunsaturated.

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Publication Details

Journal
Bulletin of KSAU
Published
2025-01-22
DOI
https://doi.org/10.36718/1819-4036-2023-6-167-172
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
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article

IDENTIFICATION OF THE FATTY ACID COMPOSITION OF RENDERED FATS

Oksana Kozlova, Elena Vetchomova
Bulletin of KSAU
Advanced Chemical Sensor Technologies
article

IDENTIFICATION OF THE FATTY ACID COMPOSITION OF RENDERED FATS

Oksana Kozlova, Elena Vetchomova
article en

Abstract

The purpose of the study is to determine the qualitative and quantitative composition of fatty acids in rendered fats obtained from derivatives of hibernating hunting animals by licensed extraction in the territory of the hunting grounds of the Kemerovo Region – Kuzbass. Animal production was carried out during the periods allowed for seizure under licenses for the corresponding animal species in the period from 2019 to 2022. The objects of research are mammals hibernating, primarily brown bear (Ursus arctos), common badger (Meles meles), marmot ( lat. Marmota) and river beaver (Castor fiber). Differences in raw fat of the listed animals were studied in terms of organoleptic (color, smell, appearance) and physicochemical parameters (fat, protein, moisture content, melting point). It is shown that the raw fat of a bear, badger, marmot and beaver differs in the content of fat, protein and moisture. In terms of "melting point", all fats can be classified as low-melting. The fat was rendered by the conductive method at a temperature of 85 ± 2 °C and a duration of 60 minutes. Qualitative indicators were determined in samples of rendered fats (solidification point, refractive index, acid, iodine, peroxide number). It was established that the refractive index can be a marker for the identification of rendered fats. The value of the iodine number indicates a high content of unsaturated fatty acids in rendered fats. The identification of the fatty acid composition was carried out by the chromatographic method with the quantitative determination of the groups of mono- and polyunsaturated fatty acids. From 16 to 27 fatty acids were identified in bear and badger fats, over 50 % of which are polyunsaturated.

Bulletin of KSAUVol. 0(6)
Kemerovo State University (RU)
Openalex Percentile: Top 99%
Advanced Chemical Sensor Technologies
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