Prediction of chevon quality through near infrared spectroscopy and multivariate analyses

Meat Research Pub Date : 2022-12-30 DOI:10.55002/mr.2.6.37
M. Hashem, MR Islam, M. Hossain, A. Alam, M. Khan
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引用次数: 1

Abstract

The aim of this study was to test the ability of near-infrared (NIR) reflectance spectroscopy to predictdry matter, crude protein, ether extract, ash, moisture, cooking loss, and drip loss of chevon. In total, 114 samples were collected from 38 young (two teeth aged) castrated goat carcasses from a local market in Mymensingh district of Bangladesh. For conducting the studyExperimental longissimus dorsi (LD) muscle were sampled from 9th to 13th ribs in the early morning hours. A total of 342 NIRs spectra were collected using the DLP NIRscan Nano Software and average spectrum was 114. Partial least square regression analysis for the calibration and validation models were developed using the Unscrambler X software. Prediction models were satisfactory for dry matter (R2 = 0.75), crude protein (R2 = 0.82), moisture (R2 = 0.75), and drip loss (R2 = 0.83). The most promising model found for ash (R2 = 0.85), and Root Mean Square Errors (RMSE) also very low (0.15). Lowest R2 was found for cooking loss at 0.57. Based on these results, the NIR spectroscopy and multivariate analysis method were reasonably efficient for the rapid assessment of physicochemical traits of ash, drip loss, crude protein, moisture, and dry matter content of chevon.
通过近红外光谱和多变量分析预测雪佛兰的质量
本研究的目的是测试近红外(NIR)反射光谱预测玉米干物质、粗蛋白质、醚提取物、灰分、水分、蒸煮损失和滴漏损失的能力。总共从孟加拉国Mymensingh地区当地市场的38只年轻(两颗牙龄)阉割山羊尸体中收集了114个样本。为了进行研究,实验背最长肌(LD)在清晨从第9至第13肋骨取样。利用DLP NIRscan纳米软件共收集了342张近红外光谱,平均光谱为114张。利用Unscrambler X软件对标定和验证模型进行偏最小二乘回归分析。干物质(R2 = 0.75)、粗蛋白质(R2 = 0.82)、水分(R2 = 0.75)和滴漏损失(R2 = 0.83)的预测模型令人满意。灰分最有希望的模型(R2 = 0.85),均方根误差(RMSE)也很低(0.15)。蒸煮损失的R2最低,为0.57。基于以上结果,采用近红外光谱和多变量分析方法可快速评价玉米的灰分、滴漏损失、粗蛋白质、水分和干物质含量等理化性状。
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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