A method for differentiating the meat of meat-producing animals into quality groups based on measuring the amount of free (crystallizing) water

The problem of identifying meat by quality groups is a crucial task. Various feeding regimes, type of meatproducing animals, cuts, and method and duration of meat storage before sale have a signifi cant impact on its categorization. In the Russian Federation, the meat of meat-producing animals is usually divided into 3 main groups according to quality: NOR — normal quality, with normal post-mortem autolysis, and PSE and DFD —with abnormal autolysis. The research has established that, to a large extent, the classification of meat into one of these groups is associated with the state of intramuscular moisture. The presence of free moisture not associated with hydration shells gives the meat low consumer qualities. As a result of the research, it has been proposed to estimate the amount of free and bound water using the thermal effects that occur when freezing meat. Free water undergoing fi rst-order phase transitions during the freezing process releases latent heat of crystal formation. Based on the amount of such heat, it is proposed to estimate the amount of free water and, accordingly, classify a given batch of meat into one or another quality group. A method and equation for calculating the amount of free (crystallizing) water in meat is proposed. The technique can be used for commodity assessment of the suitability of a specific batch of meat, including for customs control purposes and in the development of specialized diets.

Authors

Publication Details

Journal
Tovaroved prodovolstvennykh tovarov (Commodity specialist of food products)
Published
2024-10-21
DOI
https://doi.org/10.33920/igt-01-2411-01
Primary Topic
Food Industry and Aquatic Biology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A method for differentiating the meat of meat-producing animals into quality groups based on measuring the amount of free (crystallizing) water

Л.Г. Елисеева, П.И. Токарев, Yury Ilich Sidorenko, Сергей Валерьевич Штерман et al.
Tovaroved prodovolstvennykh tovarov (Commodity specialist of food products)
Food Industry and Aquatic Biology
article

A method for differentiating the meat of meat-producing animals into quality groups based on measuring the amount of free (crystallizing) water

Л.Г. Елисеева, П.И. Токарев, Yury Ilich Sidorenko, Сергей Валерьевич Штерман, Л.В. Селезнева, Elvira Ibragimova, A.Yu. Sidorenko, N.I. Murzina
article en

Abstract

The problem of identifying meat by quality groups is a crucial task. Various feeding regimes, type of meatproducing animals, cuts, and method and duration of meat storage before sale have a signifi cant impact on its categorization. In the Russian Federation, the meat of meat-producing animals is usually divided into 3 main groups according to quality: NOR — normal quality, with normal post-mortem autolysis, and PSE and DFD —with abnormal autolysis. The research has established that, to a large extent, the classification of meat into one of these groups is associated with the state of intramuscular moisture. The presence of free moisture not associated with hydration shells gives the meat low consumer qualities. As a result of the research, it has been proposed to estimate the amount of free and bound water using the thermal effects that occur when freezing meat. Free water undergoing fi rst-order phase transitions during the freezing process releases latent heat of crystal formation. Based on the amount of such heat, it is proposed to estimate the amount of free water and, accordingly, classify a given batch of meat into one or another quality group. A method and equation for calculating the amount of free (crystallizing) water in meat is proposed. The technique can be used for commodity assessment of the suitability of a specific batch of meat, including for customs control purposes and in the development of specialized diets.

Tovaroved prodovolstvennykh tovarov (Commodity specialist of food products)(11)
Openalex Percentile: Top 76%
Food Industry and Aquatic Biology
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.