• Asian-Australasian Journal of Animal Sciences
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• v.26 no.11
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• pp.1637-1643
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• 2013

The present work describes the effect of slaughtering age on horse carcass traits and on meat quality. Eighteen male Italian heavy draught horse (IHDH) breed foals were employed in the study. Soon after foaling they were randomly subdivided in 3 groups according to 3 age at slaughtering classes: 6 months old, 11 months old and 18 months old. Live weight, hot carcass weight and dressing percentage of each animal were recorded. After slaughtering, meat samples were collected from Longissimus Dorsi muscle between 13th and 18th thoracic vertebra of each animal and then analyzed. The right half carcass of each animal was then divided in cuts. Each one was subdivided into lean, fat and bones. Then, the classification of the lean meat in first and second quality cuts was performed according to the butchers' customs. Older animals were characterized by a lower incidence of first quality cuts (p<0.01) on carcass. Younger animals showed greater content in protein (p<0.01). Fatty acid profile showed an increasing trend of PUFA connected to the increasing of slaughtering age (p<0.05). The unsaturation index of intramuscular fatty acids was not affected by slaughtering age, confirming that horse meat, if compared to beef, is more suitable from a nutritional point of view. Season influenced reproduction, birth as well as production aspects of this species. The different slaughtering age could represent the way to produce meat of IHDH foals during the entire year without change in the qualitative standard expected by consumers.

• Journal of The Korean Society of Grassland and Forage Science
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• v.21 no.4
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• pp.203-210
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• 2001

To establish the recycling system of animal manure(AM) for environmental preservation and improve the utilization of AM, this study was to investigate the effects of the types and nitrogen application rate of AM on herbage productivity, efficiency of nitrogen utilization, nutritive value and an increase of soil fertility and in mixed grassland. This sudy was arranged in split plot design. Main plots were the types of AM(Cattle feedlot manure, CFM; Pig manure fermented with sawdust, PMFS; cattle sluny, CS) and subplots were the application rate of animal manure, such as 100, 200 and 300kgNiha. I. DM yields of herbage were the highest with CS and decreased by application over ZOOkgNiha AM. 2. Crude protein(CP) ontent was the highest with CFM and followed by CS, and the lowest with PMFS, and increased as application rate of AM increased. 3. Nitrogen(N) yields of CS treatment was higher than that of CFM and CS. and increased significantly as application rate of AM increased(P<0.05). 4. The contents of NDF, ADF and TDN was hardly influenced by the types and application rate of AM. 5. Organic matter(0M) content in the soil was the highest with PMFS and followed by CFM and the lowest with CS. OM content increased significantly as application rate of AM increased(P<0.05). 6. Total nitrogen content of the soil was not affected by the type of AM, but increased significantly as application rate of AM increased(P<0.05). (Key words : Animal manure, Grassland, Cattle feedlot manure, Pig manure fermented with sawdust, Cattle slurry, Soil fertility)

• Food Science of Animal Resources
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• v.42 no.1
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• pp.1-17
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• 2022

Lipids are one of the major macronutrients essential for adequate growth and maintenance of human health. Their structure is not only complex but also diverse, which makes systematic and holistic analyses challenging; consequently, little is known regarding the relationship between phenotype and mechanism of action. In recent years, rapid advancements have been made in the fields of lipidomics and bioinformatics. In comparison with traditional approaches, mass spectrometry-based lipidomics can rapidly identify as well as quantify >1,000 lipid species at the same time, facilitating comprehensive, robust analyses of lipids in tissues, cells, and body fluids. Accordingly, lipidomics is now being widely applied in various fields, particularly food and nutrition science. In this review, we discuss lipid classification, extraction techniques, and detection and analysis using lipidomics. We also cover how lipidomics is being used to assess food obtained from livestock and poultry. The information included herein should serve as a reference to determine how to characterize lipids in animal food samples, enhancing our understanding of the application of lipidomics in the field in animal husbandry.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.1-9
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• 2022

In this paper, we propose the Deep Learning-Based Companion Animal Abnormal Behavior Detection Service, which using video and sensor data. Due to the recent increase in households with companion animals, the pet tech industry with artificial intelligence is growing in the existing food and medical-oriented companion animal market. In this study, companion animal behavior was classified and abnormal behavior was detected based on a deep learning model using various data for health management of companion animals through artificial intelligence. Video data and sensor data of companion animals are collected using CCTV and the manufactured pet wearable device, and used as input data for the model. Image data was processed by combining the YOLO(You Only Look Once) model and DeepLabCut for extracting joint coordinates to detect companion animal objects for behavior classification. Also, in order to process sensor data, GAT(Graph Attention Network), which can identify the correlation and characteristics of each sensor, was used.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.607-613
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• 2018

The aim of this study is to predict the exact time of parturition from analysis and classification of preliminary behavior based on parturition signals in sows. This study was conducted with 12 crossbred sows (with an average of 3.5 parities). Behavioral characteristics were analyzed for duration and the frequency of different behaviors on a checklist, which includes the duration of the basic behaviors (feeding, standing, lying down, and sitting). The frequency of specific behaviors (investigatory behavior, shame-chewing, scratching, and bar-biting) was also recorded. Image information was collected every two minutes for 24 hours before the first piglets were born. As a result, the basic behavior of a sows' standing time (22.6% of the time after 24 h, 24.9% after 12 h) and time lying down (55.9% after 24 h, 66.3% after 12 h) increased over the 12 h period before parturition, compared with the 24 h period before parturition (p<0.01). Feeding (13.42% after 24 h, 4.38% after 12 h) and sitting (8.2% after 24 h, 4.5% after 12 h) tended to decrease during the 12 h before parturition (p>0.05). The sows' investigatory behavior ($11.44{\pm}1.80$ after 24 h, $55.97{\pm}6.13$ after 12 h), scratching ($3.75{\pm}1.92$ after 24 h, $20.99{\pm}5.81$ after 12 h), and bar-biting ($0.69{\pm}0.15$ after 24 h, $3.71{\pm}1.53$ after 12 h) increased in the 12-hour period before parturition, compared with the 24-hour period before parturition (p<0.01). On the other hand, shame-chewing ($2.20{\pm}1.67$ after 24 h, $0.07{\pm}0.01$ after 12 h) decreased compared to the 12-hour period before parturition (p>0.05). Thus, standing, investigatory behavior, scratching, and bar-biting could be used as behaviors indicative of parturition in sows.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.406-413
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• 2020

In this paper, to improve the classification accuracy of bird and amphibian acoustic sound, we utilize GLU (Gated Linear Unit) and Self-attention that encourages the network to extract important features from data and discriminate relevant important frames from all the input sequences for further performance improvement. To utilize acoustic data, we convert 1-D acoustic data to a log-Mel spectrogram. Subsequently, undesirable component such as background noise in the log-Mel spectrogram is reduced by GLU. Then, we employ the proposed temporal self-attention to improve classification accuracy. The data consist of 6-species of birds, 8-species of amphibians including endangered species in the natural environment. As a result, our proposed method is shown to achieve an accuracy of 91 % with bird data and 93 % with amphibian data. Overall, an improvement of about 6 % ~ 7 % accuracy in performance is achieved compared to the existing algorithms.

• Journal of Animal Science and Technology
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• v.54 no.5
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• pp.375-379
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• 2012

This study was carried out to define the "Wolla" coat color using 376 Jeju registered horses (white patched 142, solid coat color 234). Three major factors related to the white patches i.e ECA3-inversion for Tobiano, EDNRB 2 bp nucleotide substitution for frame Overo, and the KIT intron 16 single nucleotide polymorphism (SNP) for Sabino types of coat color were analyzed. It was found that out of 142 Jeju horses with white patches that have the genotype for ECA3-inversion (To) 140 horses were +/To heterozygous and 2 horses were To/To homozygous all Jeju horses with white patches had ECA3-inversion allele. However, there was no frame Overo or Sabino allele type in EDNRB and KIT intron 16 SNP in Jeju horses with white patches. As for 234 Jeju horses with a solid coat color, there was no ECA3-inversion allele related to the white patches. Thus, it could be considered that Wolla coat color with white patches in Jeju horses might have come from the Tobiano line in the genetic classification by coat color.

• Journal of Animal Science and Technology
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• v.56 no.4
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• pp.15.1-15.10
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• 2014

Background: Morphologically classifying embryos is important for numerous laboratory techniques, which range from basic methods to methods for assisted reproduction. However, the standard method currently used for classification is subjective and depends on an embryologist's prior training. Thus, our work was aimed at developing software to classify morphological quality for blastocysts based on digital images. Methods: The developed methodology is suitable for the assistance of the embryologist on the task of analyzing blastocysts. The software uses artificial neural network techniques as a machine learning technique. These networks analyze both visual variables extracted from an image and biological features for an embryo. Results: After the training process the final accuracy of the system using this method was 95%. To aid the end-users in operating this system, we developed a graphical user interface that can be used to produce a quality assessment based on a previously trained artificial neural network. Conclusions: This process has a high potential for applicability because it can be adapted to additional species with greater economic appeal (human beings and cattle). Based on an objective assessment (without personal bias from the embryologist) and with high reproducibility between samples or different clinics and laboratories, this method will facilitate such classification in the future as an alternative practice for assessing embryo morphologies.

• Journal of Veterinary Science
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• v.21 no.3
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• pp.43.1-43.13
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• 2020

Background: Canine parvovirus (CPV) and feline panleukopenia (FPV) cause severe intestinal disease and leukopenia. Objectives: In Korea, there have been a few studies on Korean FPV and CPV-2 strains. We attempted to investigate several genetic properties of FPV and CPV-2. Methods: Several FPV and CPV sequences from around world were analyzed by Bayesian phylo-geographical analysis. Results: The parvoviruses strains were newly classified into FPV, CPV 2-I, CPV 2-II, and CPV 2-III genotypes. In the strains isolated in this study, Gigucheon, Rara and Jun belong to the FPV, while Rachi strain belong to CPV 2-III. With respect to CPV type 2, the new genotypes are inconsistent with the previous genotype classifications (CPV-2a, -2b, and -2c). The root of CPV-I strains were inferred to be originated from a USA strain, while the CPV-II and III were derived from Italy strains that originated in the USA. Based on VP2 protein analysis, CPV 2-I included CPV-2a-like isolates only, as differentiated by the change in residue S297A/N. Almost CPV-2a isolates were classified into CPV 2-III, and a large portion of CPV-2c isolates was classified into CPV 2-II. Two residue substitutions F267Y and Y324I of the VP2 protein were characterized in the isolates of CPV 2-III only. Conclusions: We provided an updated insight on FPV and CPV-2 genotypes by molecular-based and our findings demonstrate the genetic characterization according to the new genotypes.

• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.87-93
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• 2019

Revision work on the Codex Classification of Foods and Animal Feeds was undertaken in 2007 and presently, revisions for most food groups have been completed. For vegetables, the work was conducted during 2014-2017, and the final draft revision was adopted by the $40^{th}$ Codex Alimentarius Commission (2017). Here, the revised classification of vegetable commodities is introduced in order to be utilized in various food-related fields, in particular, food safety regulation. The revised classification is briefly summarized as follows: Codex classified vegetables into 10 groups (Group 009-018): bulb vegetables (Group 009), Brassica vegetables (except Brassica leafy vegetables) (Group 010), fruiting vegetables, Cucurbits (Group 011), fruiting vegetables, other than Cucurbits (Group 012), leafy vegetables (including Brassica leafy vegetables) (Group 013), legume vegetables (Group 014), pulses (Group 015), root and tuber vegetables (Group 016), stalk and stem vegetables (Group 017) and edible fungi (Group 018). The groups are further divided into a total of 33 subgroups. In the Classification, 430 different commodity codes are assigned to vegetable commodities. Meanwhile, Korea's Ministry of Food and Drug Safety (MFDS) does not include potatoes, beans and mushrooms within a vegetable group. In addition, the MFDS divides one vegetable group into six subgroups including flowerhead Brassicas, leafy vegetables, stalk and stem vegetables, root and tuber vegetables, fruiting vegetables, Cucurbits, and fruiting vegetables other than Cucurbits. Therefore, care is needed in using the Codex Classification.