首页> 外文期刊>Journal of near infrared spectroscopy >Near infrared spectroscopy and hyperspectral imaging for prediction and visualisation of fat and fatty acid content in intact raw beef cuts
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Near infrared spectroscopy and hyperspectral imaging for prediction and visualisation of fat and fatty acid content in intact raw beef cuts

机译:近红外光谱和高光谱成像可预测和可视化完整生牛肉块中的脂肪和脂肪酸含量

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The meat quality grade of a beef carcass is greatly affected by its visible fat content. In premium beef from Japanese Black (Wagyu) cattle, a high fat content is greatly valued. However, the fatty acid composition, which is linked to the properties of the fat, is not considered in grading. In this paper, we describe the feasibility of an evaluation method based on food composition and its distribution. An intact raw beef cut from Wagyu cattle was used as an evaluation target. A total of 90 samples from various parts of three Wagyu cattle were measured by near infrared (NIR) hyperspectral imaging at wavelengths of 1000-2300nm at a spatial resolution of 380 μm pixel~(-1) and were also analysed by conventional physical and chemical methods. The fat and fatty acid content were selected as the objective content, including the proportions of total saturated fatty acid (SFA), total unsaturated fatty acid (UFA) and the main fatty acids: myristic [C14:0, where Cx:y indicates the number of carbon atoms (x) and the number of double bonds (y)], palmitic (C16:0), stearic (C18:0), myristoleic (C14:1), palmitoleic (C16:1), oleic (C18:1) and linoleic (C18:2). The mean spectrum from an area extracted from the hyperspectral image to fit the area analysed by physical and chemical methods was used to develop partial least squares regression models for prediction of fat and fatty acid content. The prediction of total fat, SFA and UFA were satisfactory with r~2, standard error of prediction (SEP) and ratio of prediction to deviation (RPD) values of 0.90, 0.87 and 0.89, 4.81%, 1.69% and 3.41% and 2.84, 2.43 and 2.84, respectively. For individual fatty acids, the r~2 and RPD values ranged from 0.68 to 0.89 and 1.69 to 2.85, respectively. Prediction of fat content for each pixel of the hyperspectral image made using these prediction models yielded spatially distributed visualisations of the content. These results showed the feasibility of a beef evaluation method based on fat content evaluated by NIR hyperspectral imaging.
机译:牛肉car体的肉质等级受可见脂肪含量的影响很大。在日本黑(和牛)牛的优质牛肉中,脂肪含量很高。但是,在分级中不考虑与脂肪的性质有关的脂肪酸组成。在本文中,我们描述了一种基于食物成分及其分布的评估方法的可行性。将和牛牛切下的完整生牛肉用作评估目标。通过近红外(NIR)高光谱成像在空间分辨率为380μmpixel〜(-1)的1000-2300nm波长下对三只和牛牛各部位的90个样品进行了测量,并通过常规的物理和化学方法进行了分析。方法。选择脂肪和脂肪酸含量作为目标含量,包括总饱和脂肪酸(SFA),总不饱和脂肪酸(UFA)和主要脂肪酸的比例:肉豆蔻色[C14:0,其中Cx:y表示碳原子数(x)和双键数(y)],棕榈酸(C16:0),硬脂酸(C18:0),肉豆蔻酸(C14:1),棕榈油酸(C16:1),油酸(C18: 1)和亚油酸(C18:2)。从高光谱图像提取的区域的平均光谱适合通过物理和化学方法分析的区域,用于开发偏最小二乘回归模型来预测脂肪和脂肪酸含量。总脂肪,SFA和UFA的预测值令人满意,r〜2,预测标准误差(SEP)和预测偏差(RPD)值分别为0.90、0.87和0.89、4.81%,1.69%和3.41%和2.84 ,分别为2.43和2.84。对于单个脂肪酸,r〜2和RPD值分别为0.68至0.89和1.69至2.85。使用这些预测模型对高光谱图像每个像素的脂肪含量进行预测,可以得出内容的空间分布可视化。这些结果表明基于NIR高光谱成像评估脂肪含量的牛肉评估方法的可行性。

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