This paper examines the use of artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for accurate identification. Machine learning with large data sets lead to precise identification based on origins. However, clove buds uses small data sets due to lack of metabolites composition and their high cost of extraction. The results show that backpropagation and resilient propagation with one and two hidden layers identifies clove buds origin accurately. The backpropagation with one hidden layer offers 99.91% and 99.47% for training and testing data sets, respectively. The resilient propagation with two hidden layers offers 99.96% and 97.89% accuracy for training and testing data sets, respectively.

中文翻译：

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基于代谢物成分的丁香芽来源人工神经网络方法

本文研究了人工神经网络方法在基于代谢物组成识别丁香芽来源中的应用。通常，大型数据集对于准确识别至关重要。使用大数据集进行机器学习可实现基于来源的精确识别。然而，由于缺乏代谢物成分和提取成本高，丁香芽使用小数据集。结果表明，带有一层和两层隐藏层的反向传播和弹性传播准确地识别了丁香芽的起源。带有一个隐藏层的反向传播分别为训练和测试数据集提供了 99.91% 和 99.47%。具有两个隐藏层的弹性传播分别为训练和测试数据集提供了 99.96% 和 97.89% 的准确率。