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首页> 外文期刊>International Journal of Technology >Development of Rapid and Accurate Method to Classify Malaysian Honey Samples using UV and Colour Image
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Development of Rapid and Accurate Method to Classify Malaysian Honey Samples using UV and Colour Image

机译：开发快速准确的利用紫外线和彩色图像对马来西亚蜂蜜样品进行分类的方法

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The purpose of this paper is to classification of three main types of Malaysian honey (Acacia, Kelulut and Tualang) according to their botanical origin using UV–Vis Spectroscopy and digital camera. This paper presented the classification of the honey based on two characteristics from three (3) types of local honey, namely the antioxidant contents and colour variations. The former uses the UV spectroscopy of selected wavelength range, and the latter using RGB digital camera. Principal Component Analysis (PCA) was used for both methods to reduce the dimension of extracted data. The Support Vector Machine (SVM) was used for the classification of honey. The assessment was done separately for each of the methods, and also on the fusion of both data after features extraction and association. This paper shows that classification of the fusion method improved significantly compared to single modality Honey classification based on the fusion method was able to achieve 94% accuracy. Hence, the proposed methods have the ability to provide accurate and rapid classification of honey products in terms of origin. The proposed system can be applied in Malaysia honey industry and further improve the quality assessment and provide traceability.
机译：本文的目的是使用UV-Vis光谱仪和数码相机根据植物来源来对三种主要类型的马来西亚蜂蜜（相思，克卢鲁特和图朗）进行分类。本文基于三（3）种本地蜂蜜的两种特征，即抗氧化剂含量和颜色变化，对蜂蜜进行了分类。前者使用选定波长范围的紫外光谱，而后者使用RGB数码相机。两种方法都使用主成分分析（PCA）来减小提取数据的维数。支持向量机（SVM）用于蜂蜜的分类。对每种方法分别进行评估，也对特征提取和关联后的两种数据融合进行评估。本文表明，与基于融合方法的单模式Honey分类相比，融合方法的分类显着提高，准确度达到94％。因此，所提出的方法具有根据来源对蜂蜜产品进行准确，快速分类的能力。提出的系统可以应用于马来西亚蜂蜜行业，并进一步改善质量评估并提供可追溯性。

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