Amino acid composition of biosubstances depending on the method of maral velvet antler extraction

The aim of this study is to determine the most effective method for extracting maral velvet antlers based on the obtained data on amino acid composition. The research involved the production of biosubstances using 4 extraction methods: 1st method: Ultrasound extraction (22 kHz) with a raw material-to-water ratio of 1:10 at a temperature of 45–50 °C; 2nd method: Ultrasound extraction (22 kHz) with a raw material-to-water ratio of 1:10 at 45–50 °C, followed by autoclaving; 3rd method: Ultrasound extraction (37 kHz) with proteolytic enzymes, with a raw material-to-water ratio of 1:10 at 45–50 °C; 4th method: Ultrasound extraction (37 kHz) with microbial enzymes, with a raw material-to-water ratio of 1:10 at 45–50 °C, followed by autoclaving. Amino acid analysis was performed using chromatography on a Shimadzu LC-20 Prominence system equipped with a diode array detector. The calibration curve was constructed using nineteen standard amino acid samples manufactured by Sigma (Germany). The study results indicate that the second and fourth extraction methods are the most effective. These methods yield biosubstances with amino acid concentrations 1.4–2.1 times higher than those obtained using other extraction techniques.

Authors

Institutions

Publication Details

Journal
Bulletin of NSAU (Novosibirsk State Agrarian University)
Published
2025-07-18
DOI
https://doi.org/10.31677/2072-6724-2025-75-2-200-206
Primary Topic
Identification and Quantification in Food
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Amino acid composition of biosubstances depending on the method of maral velvet antler extraction

Anna Korolkova, I. N. Grishaeva, Maria Krotova, Aleksey Nepriyatel' et al.
Bulletin of NSAU (Novosibirsk State Agrarian University)
Identification and Quantification in Food
article

Amino acid composition of biosubstances depending on the method of maral velvet antler extraction

Anna Korolkova, I. N. Grishaeva, Maria Krotova, Aleksey Nepriyatel', I. S. Belozerskyh
article en

Abstract

The aim of this study is to determine the most effective method for extracting maral velvet antlers based on the obtained data on amino acid composition. The research involved the production of biosubstances using 4 extraction methods: 1st method: Ultrasound extraction (22 kHz) with a raw material-to-water ratio of 1:10 at a temperature of 45–50 °C; 2nd method: Ultrasound extraction (22 kHz) with a raw material-to-water ratio of 1:10 at 45–50 °C, followed by autoclaving; 3rd method: Ultrasound extraction (37 kHz) with proteolytic enzymes, with a raw material-to-water ratio of 1:10 at 45–50 °C; 4th method: Ultrasound extraction (37 kHz) with microbial enzymes, with a raw material-to-water ratio of 1:10 at 45–50 °C, followed by autoclaving. Amino acid analysis was performed using chromatography on a Shimadzu LC-20 Prominence system equipped with a diode array detector. The calibration curve was constructed using nineteen standard amino acid samples manufactured by Sigma (Germany). The study results indicate that the second and fourth extraction methods are the most effective. These methods yield biosubstances with amino acid concentrations 1.4–2.1 times higher than those obtained using other extraction techniques.

Bulletin of NSAU (Novosibirsk State Agrarian University)(2)
Altai Scientific Research Institute of Machine Building Technology (RU)
Clean water and sanitation
Openalex Percentile: Top 82%
Identification and Quantification in Food
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.