The scientific and systematic classification of cultivation age is important for preventing age falsification and ensuring the quality of ginseng. Therefore, we applied deep learning to classify the cultivation age of ginseng. Deep learning, which is based on an artificial neural network, is one of the new class of models for machine learning, and is state-of-the-art. It is a powerful tool and has been used to solve complex problems in many fields. In the present study, powdered samples of 4-, 5-, and 6-year-old ginseng were measured using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy. NMR data were analyzed with deep learning and partial least-squares discriminant analysis (PLS-DA) to improve accuracy. The accuracy of the PLS-DA was 87.1% and the accuracy of the deep learning model was 93.9%. NMR spectroscopy with deep learning can be a useful tool for discrimination of ginseng cultivation age.

中文翻译：

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使用¹ H-NMR光谱进行机器学习以快速判别人参栽培年龄

对耕种年龄进行科学和系统的分类对于防止年龄伪造和确保人参质量很重要。因此，我们应用深度学习对人参的栽培年龄进行分类。深度学习基于人工神经网络，是机器学习的新型模型之一，并且是最先进的。它是一个功能强大的工具，已用于解决许多领域中的复杂问题。在本研究中，使用高分辨率魔角旋转核磁共振（HR-MAS NMR）光谱仪测量了4、5和6岁人参的粉末状样品。NMR数据通过深度学习和偏最小二乘判别分析（PLS-DA）进行分析，以提高准确性。PLS-DA的准确性为87.1％，深度学习模型的准确性为93.9％。