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深度HSI:工业植物原产地识别的可转移和可扩展的超频谱框架:Pogostemon cablin (Blanco) Benth的案例研究

Xiaqiong Fan¹, Zhengyan Li², Lijin Shang¹

¹School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, PR China.

|February 1, 2026

概括

一种新的DeepHSI方法使用高光谱成像和深度学习来快速,非破坏性地识别Pogostemon的起源. 这种方法有助于质量控制,并防止化品,食品和药物的改.

科学领域:

植物学植物学
分析化学分析化学
计算生物学计算生物学

背景情况:

波戈斯特蒙 (P.cablin) 由于其香味和化学特征,在化品,食品和医药中至关重要.
准确地确定P.cablin的原产地对于质量控制和防止改至关重要.
传统的识别方法通常是缓慢的,劳动密集型,需要复杂的化学分析.

研究的目的:

开发一种快速,通用和非破坏性的方法来识别不同来源的P.cablin.
通过使用代谢学和转录学来验证高光谱成像 (HSI) 对P.cablin起源认证的有效性.

主要方法:

提出了一个深度学习模型,DeepHSI,利用高光谱图像来识别P.cablin的起源.
在多个实验批次中训练并测试了DeepHSI模型,使用来自三个不同的P.cablin来源的HSI数据.
使用转移学习来证明模型的通用性,并采用多源识别模型的融合机制.

主要成果:

通过DeepHSI方法,可以快速准确地识别P.cablin的起源.
代谢学和转录学分析支持HSI对原产地歧视的可行性.
该模型在不同的实验条件中展示了普遍性,展示了转移学习能力.

结论:

DeepHSI提供了一个有希望的,可扩展的解决方案,用于P.cablin.的非破坏性来源识别.
这种方法在使用P.cablin.的行业中加强了质量控制和真实性验证.
这项研究为使用HSI和深度学习的多分类研究提供了一个范式.

关键词:

深度学习是一种深度学习.超光谱图像超光谱图像超光谱图像多个omics的分析分析.原产地标识标识来源波戈塞蒙·卡布林 (Pogostemon Cablin) 是一个著名的卡布林.转移学习转移学习

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