Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Human telomerase reverse transcriptase (hTERT) promotes gastric cancer invasion through cooperating with c-Myc to upregulate heparanase expression.

Oncotarget·2015
Same author

Serum miR-21, miR-26a and miR-101 as potential biomarkers of hepatocellular carcinoma.

Clinics and research in hepatology and gastroenterology·2015
Same author

Brain tumor-targeted delivery and therapy by focused ultrasound introduced doxorubicin-loaded cationic liposomes.

Cancer chemotherapy and pharmacology·2015
Same author

[Establishment and characterization of a minipig model of microvascular coronary artery spasm].

Zhonghua xin xue guan bing za zhi·2015
Same author

Substrate Selectivity of Lysophospholipid Transporter LplT Involved in Membrane Phospholipid Remodeling in Escherichia coli.

The Journal of biological chemistry·2015
Same author

Three-dimensional verification of ¹²⁵I seed stability after permanent implantation in the parotid gland and periparotid region.

Radiation oncology (London, England)·2015

相关实验视频

Updated: Jul 7, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.7K

选近红外定量模型的方法,具有高稳定性.

Zhuopin Xu1, Xiaohong Li2, Zhiyi Zhang2

  • 1Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.

Analytica chimica acta
|July 3, 2025
PubMed
概括

一种称为外部校准辅助选 (ECA) 的新方法提高了近红外 (NIR) 定量模型的稳定性. 这种方法有助于选择在不同测量条件下可靠执行的模型,减少频繁重新校准的需要.

关键词:
竞争性适应性重量调整采样外部校准进行校准.模型的坚固性 模型的坚固性近红外光谱学近红外光谱学变量选择 变量选择

更多相关视频

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

6.7K
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
11:02

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

658

相关实验视频

Last Updated: Jul 7, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.7K
O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

6.7K
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
11:02

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

658

科学领域:

  • 化学测量 化学测量 化学测量
  • 频谱学是一种光谱学.
  • 分析化学 分析化学

背景情况:

  • 近红外 (NIR) 校准模型通常对不断变化的测量条件表现出敏感性.
  • 现有的算法优先考虑准确性,但稳定性对于持续的模型性能和最小化重新校准至关重要.
  • 一个更强大的模型,即使稍微不太准确,也可以在现实应用中更实用.

研究的目的:

  • 开发一种简单的方法,即外部校准辅助选 (ECA),用于识别最强大的定量模型.
  • 引入一个新的指标,PrRMSE,用于通过交叉验证和外部校准来评估模型的稳定性.
  • 将ECA与竞争性自适应重量化抽样 (CARS) 整合起来,以提高模型优化.

主要方法:

  • 外部校准辅助选 (ECA) 方法涉及外部校准先前开发的模型,使用来自新条件的样本.
  • 引入了一个新的指标,PrRMSE,以根据交叉验证和外部校准结果量化模型的稳定性.
  • 该ECA方法与竞争性自适应重量化采样 (CARS) 集成,创建了ECCARS方法,并对大米面粉和玉米数据集进行了测试.

主要成果:

  • 与标准CARS方法相比,ECCARS方法在不同数据集中显著提高了模型的稳定性.
  • 在不同条件下,ECCARS选择的模型在校准 (12.15%-725%) 和验证 (27.63%-482.00%) 两种情况下都显著降低了根平均平方误差 (RMSE).
  • 结果表明,在使用拟议的ECCARS方法时,NIR定量模型的可靠性明显提高.

结论:

  • 开发的ECA方法提供了一种简单的方法来评估和选择可靠的NIR定量模型.
  • 在各种分析场景中,ECCARS方法为提高NIR模型的稳定性和可靠性提供了一个实际的解决方案.
  • 这种方法有可能降低近红外光谱学 (NIRS) 采用的障碍,并提高其整体有效性.