首页> 外文会议>5th European conference on colour in graphics, imaging, and vision and 12th international symposium on multispectral colour science 2010 >Prediction and Visualization of Fat and Fatty Acid Content of Beef Using Near-Infrared Multispectral Imaging
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Prediction and Visualization of Fat and Fatty Acid Content of Beef Using Near-Infrared Multispectral Imaging

机译:牛肉脂肪和脂肪酸含量的近红外多光谱成像预测和可视化

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The beef quality grade is greatly affected by visible fat content. Especially, in Japanese black (Wagyu) cattle, high fat content is typically valued highly. In this paper, we describe the feasibility of beef evaluation by visualizing fat characteristics using near-infrared (NIR) multispectral imaging. An intact raw beef cut from Wagyu cattle was used as an evaluation target. The content of fat and fatty acid, such as the total saturated fatty acid (SFA) content, the total unsaturated fatty acid (UFA) content, myristic acid (C14:0) , palmitic acid (C16:0), stearic acid (C18:0), myristoleic acid (C14:1), palmitoleic acid (C16:1), oleic acid (C18:1), and linoleic acid (C18:2) were estimated and visualized. The total SFA content was calculated as the sum of myristic acid, palmitic acid, and stearic acid. Also, the total UFA content was calculated as the sum of myristoleic acid, palmitoleic acid, oleic acid, and linoleic acid. Reference values for the fat content and fatty acid composition were determined by conventional physical and chemical methods. The fatty acid composition was determined from the extracted lipids by Folch's method, by gas chromatography (GC) using its methyl ester. The fat content was determined by using the Gerhardt SOXTHERM. The NIR multispectral images of the sample were acquired by using the SPECIM Spectral Camera SWIR. It works in the wavelength range of 970-2500 nm with 6.3 nm of bandwidth at 320 pixels resolution in spatial domain. The absorbance spectra of each pixel calculated from pixel intensity of subject and reference white standard was used for constructing the prediction model. In total, 33 samples from various parts of the 2 head of Wagyu cattle were measured. Calibrations were performed by a partial least squares (PLS) regression using mean extracted spectra from each individual sample, limited wavelength range from 1000 to 2300 nm. The coefficients of determination (R~2) were between 0.68 and 0.87. The ranks by evaluation index (EI) were "B (high accuracy)" and "C (slightly high)". The ratios of the standard error of prediction to the standard deviation (RPD) were between 1.74 and 2.74. These results indicate a sufficient feasibility of the prediction except for myristoleic acid content. The visualizations, which show the spatial distribution of fatty acid content, were performed by applying the model to predict the content of each pixel.
机译:牛肉质量等级受可见脂肪含量的影响很大。特别地,在日本黑(和牛)牛中,高脂肪含量通常被高度重视。在本文中,我们通过使用近红外(NIR)多光谱成像可视化脂肪特征来描述牛肉评估的可行性。将和牛牛切成的完整生牛肉用作评估目标。脂肪和脂肪酸的含量,例如总饱和脂肪酸(SFA)含量,总不饱和脂肪酸(UFA)含量,肉豆蔻酸(C14:0),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)估算并显示。 SFA的总含量以肉豆蔻酸,棕榈酸和硬脂酸之和计算。另外,总的UFA含量被计算为肉豆蔻酸,棕榈油酸,油酸和亚油酸的总和。脂肪含量和脂肪酸组成的参考值通过常规的物理和化学方法确定。通过Folch方法由提取的脂质,通过使用其甲酯的气相色谱法(GC)测定脂肪酸组成。脂肪含量通过使用Gerhardt SOXTHERM测定。通过使用SPECIM光谱相机SWIR获取样品的NIR多光谱图像。它在970-2500 nm的波长范围内工作,带宽为6.3 nm,在空间域中的分辨率为320像素。根据被摄体的像素强度和参考白色标准计算出的每个像素的吸收光谱用于构建预测模型。总共测量了来自和牛2头牛不同部位的33个样品。通过使用偏最小二乘(PLS)回归进行校准,使用从每个单独样本中提取的平均光谱(在1000到2300 nm的有限波长范围内)。测定系数(R〜2)在0.68至0.87之间。评估指数(EI)的等级为“ B(高精度)”和“ C(略高)”。预测标准误差与标准偏差(RPD)的比率在1.74和2.74之间。这些结果表明除了肉豆蔻酸含量外,该预测的充分可行性。通过应用模型来预测每个像素的含量,可以实现显示脂肪酸含量的空间分布的可视化。

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