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相关概念视频

Data Validation01:15

Data Validation

124
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
124
Sampling Methods: Overview01:06

Sampling Methods: Overview

229
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
229
Stratified Sampling Method01:16

Stratified Sampling Method

11.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.6K
Random Sampling Method01:09

Random Sampling Method

10.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
10.9K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.3K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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相关实验视频

Updated: May 10, 2025

HPLC Coupled with Chemical Fingerprinting for Multi-Pattern Recognition for Identifying the Authenticity of Clematidis Armandii Caulis
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小样本真实性识别和品种分类的Anoectochilus roxburghii (墙) 的. 林德尔. 林德尔. 林德尔. 林德尔. 林德尔. 林德尔. 林德尔. 林德尔. 林德尔. 使用高光谱成像和机器学习.

Yiqing Xu1, Haoyuan Ding1, Tingsong Zhang1

  • 1College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China.

Plants (Basel, Switzerland)
|April 26, 2025
PubMed
概括

超光谱成像和机器学习能够准确地从假冒中识别出真正的金线 (Anoectochilus roxburghii). 支持矢量机和CNN模型实现了基于叶子光谱数据区分植物物种的100%准确性.

关键词:
安诺科奇勒斯·罗克斯堡希 (瓦尔兰) (英语:Anoectochilus roxburghii) 是一种葡萄牙的植物. 林德尔. 林德尔. 林德尔. 林德尔. 林德尔. 林德尔. 林德尔. 林德尔. 林德尔.认证真实性的标识.超光谱成像技术的使用.机器学习是机器学习.品种分类,品种分类.

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科学领域:

  • 农业科学 农业科学
  • 计算机科学 计算机科学
  • 植物学 植物学

背景情况:

  • 准确的鉴定药用植物,如金线 (Anoectochilus roxburghii) 是至关重要的.
  • 假冒物种在草药和贸易中构成了重大挑战.
  • 超光谱成像为植物分析提供详细的光谱信息.

研究的目的:

  • 开发和评估用于验证Aneoctochilus roxburghii的机器学习模型.
  • 通过使用超光谱数据,将Goldthread与其假冒物种区分开来.
  • 探索各种机器学习算法和光谱融合技术的有效性.

主要方法:

  • 收集了来自九种Anoectochilus roxburghii物种和两种假冒物种前后叶的超光谱数据.
  • 应用机器学习模型:支持向量机 (SVM),K-最近邻居 (KNN),随机森林 (RF),线性差异分析 (LDA) 和卷积神经网络 (CNN).
  • 开发了一种多视图光谱融合卷积神经网络 (CNN) 模型,整合了叶子两侧的数据.

主要成果:

  • 支持矢量机 (SVM) 在区分黄金线与假货方面实现了100%的分类准确性.
  • 与传统模型相比,SVM在处理高维光谱数据方面表现出优越性.
  • 多视图光谱融合CNN模型也实现了完美的100%分类准确度.
  • 模型有效地捕捉了前叶和后叶之间的光谱差异.

结论:

  • 超光谱成像与机器学习相结合,为植物真实性识别提供了一种高度有效的方法.
  • 开发的SVM和多视图光谱融合CNN模型为检测假冒物种提供了强大的解决方案.
  • 这种方法为草药产品的质量控制提供了新的和有希望的前景.