论文标题
使用基于光纤的光学相干断层扫描，对肉内肌内脂肪含量进行实时评估
Towards real time assessment of intramuscular fat content in meat using optical fibre-based optical coherence tomography
论文作者
论文摘要
我们考虑使用光学相干断层扫描（OCT）成像来预测肉质的质量。我们发现，肌内脂肪（IMF）吸收的红外光大约比肌肉强9倍，这使我们能够估计完整的肉样品中的脂肪含量。通过使用主成分分析（PCA）从OCT生成的三维高分辨率图像中提取相关信息，从而使该方法非常有效。然后将主要组件用作支持向量回归（SVR）预测模型的回归器。发现SVR模型可以稳定，准确地预测IMF含量，R^2值为0.94。我们的研究为肉类样品质量的自动，无接触，无损，实时分类铺平了道路。
We consider the use of optical coherence tomography (OCT) imaging to predict the quality of meat. We find that intramuscular fat (IMF) absorbs infrared light about nine times stronger than muscle, which enables us to estimate fat content in intact meat samples. The method is made very efficient by extracting relevant information from the three-dimensional high-resolution images generated by OCT using principal component analysis (PCA). The principal components are then used as regressors into a support vector regression (SVR) prediction model. The SVR model is found to predict IMF content stably and accurately, with an R^2 value of 0.94. Our study paves the way for automated, contact-less, non-destructive, real time classification of the quality of meat samples.
