Productive performances of native sheep in Bangladesh

Meat Research Pub Date : 2023-02-28 DOI:10.55002/mr.3.1.43
M. Hossain, Md. Mizanur Rahman, M. Hashem
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引用次数: 2

Abstract

The aim of this study was to test the ability of image technology to predict quality and safety of chicken sausage. Chicken sausages were chosen for image capture. Traits evaluated were color indexes (L*, a*, b*), pH, drip loss, cooking loss, dry matter, moisture, crude protein, ether extract, ash, thiobarbituric acid reactive substances (TBARS), peroxide value (POV), free fatty acid (FFA), total coliform count (TCC), total yeast and mold count (TYMC) and total viable count (TVC). Images were analyzed using the software Matlab (R2015a). Conventional analytical technology i.e., proximate, bio-chemical and microbiological analyses were followed for reference value. Calibration and prediction model were fitted using The Unscrambler X software. Results of this work show that image technology may be a useful tool for prediction of meat quality traits in the laboratory and meat processing industries. The L* value from imaging analysis had medium correlation with a* (r=0.28), b* (r=0.29), pH (r=0.31). A medium correlation found in CP (0.29) with „a*‟ value obtained from imaging analysis. In this experiment we found lower calibration and prediction accuracy in a*, crude protein and ether extract value. From this study it may be recapitulated that image technology has a potentiality to replace analytical technology for meat laboratory and processing units.
孟加拉国本地羊的生产性能
本研究的目的是检验图像技术对鸡肉香肠质量安全的预测能力。我们选择鸡肉香肠来进行图像捕捉。评价的性状包括颜色指标(L*、a*、b*)、pH、滴漏损失、蒸煮损失、干物质、水分、粗蛋白质、粗脂肪、灰分、硫代巴比托酸活性物质(TBARS)、过氧化值(POV)、游离脂肪酸(FFA)、总大肠菌群计数(TCC)、总酵母和霉菌计数(TYMC)和总活菌计数(TVC)。使用Matlab软件(R2015a)对图像进行分析。采用传统的分析技术,即近似分析、生化分析和微生物分析,以提供参考价值。采用The Unscrambler X软件拟合校正和预测模型。这项工作的结果表明,图像技术可能是实验室和肉类加工行业预测肉类品质性状的有用工具。影像分析的L*值与a* (r=0.28)、b* (r=0.29)、pH (r=0.31)有中等相关性。CP与影像学分析得出的A *值有中等相关性(0.29)。在实验中,我们发现a*、粗蛋白质和粗脂肪的校准和预测精度较低。从这项研究中可以总结出,图像技术有可能取代肉类实验室和加工单位的分析技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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