Grade prediction of meat quality in Korean Native cattle using neural network

2015 ◽  
Author(s):  
Eunseok Jang ◽  
Hyunhak Cho ◽  
Eun Kyeong Kim ◽  
Sungshin Kim
Keyword(s):  
Neural Network ◽  
Meat Quality ◽  
Grade Prediction ◽  
Native Cattle

scholarly journals Gender-dependent difference in serum paraoxonase 1 levels of Hanwoo, Korean native cattle, and a positive association with meat quality

2019 ◽  
Vol 32 (3) ◽  
pp. 437-441 ◽  
Author(s):  
Jihyun Park ◽  
Jiwoo Kim ◽  
Sungwon Hwang ◽  
Ki Young Chung ◽  
Inho Choi ◽  
...  
Keyword(s):  
Meat Quality ◽  
Positive Association ◽  
Paraoxonase 1 ◽  
Native Cattle ◽  
Serum Paraoxonase

scholarly journals Discrimination study between carcass yield and meat quality by gender in Korean native cattle (Hanwoo)

2020 ◽  
Vol 33 (7) ◽  
pp. 1202-1208
Author(s):  
Do-Gyun Kim ◽  
Joon-Yong Shim ◽  
Byoung-Kwan Cho ◽  
Collins Wakholi ◽  
Youngwook Seo ◽  
...  
Keyword(s):  
Meat Quality ◽  
Function Analysis ◽  
Statistical Significance ◽  
Significance Test ◽  
Carcass Yield ◽  
Yield And Quality ◽  
Quality Grade ◽  
Native Cattle

Objective: The aim of this study was to identify a distribution pattern of meat quality grade (MQG) as a function of carcass yield index (CYI) and the gender of Hanwoo (bull, cow, and steer) to determine the optimum point between both yield and quality. We also attempted to identify how pre- and post-deboning variables affect the gender-specific beef quality of Hanwoo.Methods: A total of 31 deboning variables, consisting of 7 pre-deboning and 24 post-deboning variables from bulls (n = 139), cows (n = 69), and steers (n = 153), were obtained from the National Institute of Animal Science (NIAS) in South Korea. The database was reconstructed to be suitable for a statistical significance test between the CYI and the MQG as well as classification of meat quality. Discriminant function analysis was used for classifying MQG using the deboning parameters of Hanwoo by gender.Results: The means of CYI according to 1+, 1, 2, and 3 of MQG were 68.64±2.02, 68.85±1.94, 68.62±5.88, and 70.99±3.32, respectively. High carcass yield correlated with low-quality grade, while high-quality meat most frequently was obtained from steers. The classification ability of pre-deboning parameters was higher than that of post-deboning parameters. Moisture and the shear force were the common significant parameters in all discriminant functions having a classification accuracy of 80.6%, 71%, and 56.9% for the bull, cow, and steer, respectively.Conclusion: This study provides basic information for predicting the meat quality by gender using pre-deboning variables consistent with the actual grading index.

scholarly journals Automatic Prediction of Meningioma Grade Image Based on Data Amplification and Improved Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hong Zhu ◽  
Qianhao Fang ◽  
Hanzhi He ◽  
Junfeng Hu ◽  
Daihong Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Accuracy ◽  
World Health ◽  
Tumor Type ◽  
Grade Prediction ◽  
Health Organization ◽  
Deep Learning Model ◽  
The Brain

Meningioma is the second most commonly encountered tumor type in the brain. There are three grades of meningioma by the standards of the World Health Organization. Preoperative grade prediction of meningioma is extraordinarily important for clinical treatment planning and prognosis evaluation. In this paper, we present a new deep learning model for assisting automatic prediction of meningioma grades to reduce the recurrence of meningioma. Our model is based on an improved LeNet-5 model of convolutional neural network (CNN) and does not require the extraction of the diseased tissue, which can greatly enhance the efficiency. To address the issue of insufficient and unbalanced clinical data of meningioma images, we use an oversampling technique which allows us to considerably improve the accuracy of classification. Experiments on large clinical datasets show that our model can achieve quite high accuracy (i.e., as high as 83.33%) for the classification of meningioma images.

Determination of Meat Quality by Image Processing and Neural Network Techniques

2000 ◽  
Vol 12 (4) ◽  
pp. 474-479
Author(s):  
Kazuhiko Shiranita ◽  
◽  
Kenichiro Hayashi ◽  
Akifumi Otsubo
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Meat Quality ◽  
Distribution Density ◽  
Marbling Score ◽  
Quality Grading

We study the implementation of a meat-quality grading system, using the concept of the marbling score, and image processing, neural network techniques and multiple regression analysis. The marbling score is a measure of the distribution density of fat in the rib-eye region. We identify five features used for grading meat images. For the evaluation of the five features, we propose a method of image binarization using a three-layer neural network developed based on inputs given by a professional grader and a system of meat-quality grading based on the evaluation of three of five features with multiple regression analysis. Experimental results show that the system is effective.

scholarly journals Meat quality, post-mortem proteolytic enzymes, and myosin heavy chain isoforms of different Thai native cattle muscles

2021 ◽  
Author(s):  
Chanporn Chaosap ◽  
Panneepa Sivapirunthep ◽  
Ronachai Sitthigripong Sitthigripong ◽  
Piyada Tavitchasri ◽  
Sabaiporn Maduae ◽  
...  
Keyword(s):  
Myosin Heavy Chain ◽  
Meat Quality ◽  
Heavy Chain ◽  
Proteolytic Enzymes ◽  
Myosin Heavy Chain Isoforms ◽  
Post Mortem ◽  
Native Cattle

scholarly journals Detection of Allelic and Genotypic Frequencies of Polymorphisms Associated with Meat Quality in the Mediterranean Baladi Cattle

2017 ◽  
Vol 11 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Einav Shor-Shimoni ◽  
Ariel Shabtay ◽  
Rotem Agmon ◽  
Miri Cohen-Zinder
Keyword(s):  
Meat Quality ◽  
Mediterranean Basin ◽  
Cattle Breed ◽  
Meat Tenderness ◽  
Production Traits ◽  
Dual Purpose ◽  
Brahman Cattle ◽  
Southern Mediterranean ◽  
Mediterranean Pasture ◽  
Native Cattle

Baladi, (B taurus; DAGRIS) a native cattle breed found throughout the entire Southern Mediterranean basin, is known for its high disease resistance and hardiness. Baladi cows in Israel and Southern Mediterranean basin are endangered due to the introduction of larger and more productive European breeds in these regions. In order to promote conservation initiatives of Baladi by stakeholders, the yet unexplored production traits, over their well accepted adaptation to the harsh Mediterranean conditions, were sought in the current study. Aiming at locating the genetic potential of Baladi for meat quality, the allelic and genotypic frequencies of four polymorphisms in CAST, CAPN1, DGAT1, and FASN genes, previously reported to be associated with meat quality traits, were compared to four cattle breeds. The other four breeds included Limousine, Holstein, Simmental and Brahman cattle, which represent beef, dairy, dual-purpose and indicine bovine members, respectively. Relative to the four bovine members, Baladi cattle exhibited high frequencies of the increasing alleles and genotypes in all four SNPs associated with meat tenderness or fat deposition. These findings, along with future phenotyping and genomic profiling of meat quality related markers, and the well-established adaptability to the challenging Mediterranean pasture conditions, may promote conservation initiatives of Baladi cattle by stakeholders.

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