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首页> 外文会议>Sensing for Agriculture and Food Quality and Safety VIII >Food inspection using hyperspectral imaging and SVDD
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Food inspection using hyperspectral imaging and SVDD

机译：使用高光谱成像和SVDD进行食品检查

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摘要

Nowadays food inspection and evaluation is becoming significant public issue, therefore robust, fast, and environmentally safe methods are studied instead of human visual assessment. Optical sensing is one of the potential methods with the properties of being non-destructive and accurate. As a remote sensing technology, hyperspectral imaging (HSI) is being successfully applied by researchers because of having both spatial and detailed spectral information about studied material. HSI can be used to inspect food quality and safety estimation such as meat quality assessment, quality evaluation of fish, detection of skin tumors on chicken carcasses, and classification of wheat kernels in the food industry. In this paper, we have implied an experiment to detect fat ratio in ground meat via Support Vector Data Description which is an efficient and robust one-class classifier for HSI. The experiments have been implemented on two different ground meat HSI data sets with different fat percentage. Addition to these implementations, we have also applied bagging technique which is mostly used as an ensemble method to improve the prediction ratio. The results show that the proposed methods produce high detection performance for fat ratio in ground meat.
机译：如今，食品检查和评估已成为重要的公共问题，因此，人们在研究健壮，快速且对环境安全的方法，而不是人工视觉评估。光学传感是具有非破坏性和精确性的潜在方法之一。作为一种遥感技术，高光谱成像（HSI）已被研究人员成功应用，因为它具有有关所研究物质的空间和详细光谱信息。 HSI可用于检查食品质量和安全性评估，例如肉类质量评估，鱼的质量评估，鸡car体皮肤肿瘤的检测以及食品行业中小麦粒的分类。在本文中，我们暗示了一项通过支持向量数据描述检测碎肉中脂肪比率的实验，该向量是针对HSI的一种高效且强大的一类分类器。实验已在两个具有不同脂肪百分比的不同碎肉HSI数据集上进行。除了这些实现之外，我们还应用了套袋技术，该技术主要用作集成方法以提高预测率。结果表明，所提出的方法对碎肉中的脂肪比率具有很高的检测性能。

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