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相关概念视频

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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Instrument Calibration01:12

Instrument Calibration

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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
164
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

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In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
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Glassware Calibration01:11

Glassware Calibration

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Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...
215
Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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相关实验视频

Updated: Jun 18, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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在使用最佳预测性校准子集的NIR仪器之间进行校准传输.

Jan P M Andries1, Yvan Vander Heyden2

  • 1Research Group Analysis Techniques in the Life Sciences, Avans Hogeschool, University of Professional Education, P.O. Box 90116, 4800 RA, Breda, The Netherlands. jp.andries@avans.nl.

Analytical and bioanalytical chemistry
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PubMed
概括
此摘要是机器生成的。

一种新的方法选择了较少的标准化样本用于近红外 (NIR) 模型传输. 最佳预测校准子集 (OPCS) 方法减少了样本大小,同时保持了预测性能,提高了NIR仪器校准的效率.

关键词:
校准转移的转移 校准转移的转移在FCAM-SIG中选择变量.最佳预测性校准子集 (OPCS) 是指最优预测性校准子集.PLS1 PLS1 是一个字母.配对的t-试验零碎直接标准化 (PDS) 方法.

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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相关实验视频

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Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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科学领域:

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

背景情况:

  • 接近红外 (NIR) 仪器之间的校准模型传输对于一致的分析至关重要.
  • 选择信息化的标准化样本是有效和准确的模型转移的关键.
  • 现有的方法可能并不总是产生最优或最小的样本集.

研究的目的:

  • 提出和评估一种用于选择信息化标准化样本的新方法.
  • 为了减少有效的NIR模型转移所需的校准样本数量.
  • 将拟议的方法与现有的技术比较,如肯纳德-斯通.

主要方法:

  • 使用具有PLS回归系数 (FCAM-SIG) 的意义的最终复杂性适应模型 (FCAM) 开发校准模型.
  • 从初始校准集中选择一个最佳预测校准子集 (OPCS).
  • 切片直向标准化 (PDS) 的应用,用于使用响应表面图表进行光谱转移和优化.
  • 评估样本集大小和预测性能 (RMSEP).

主要成果:

  • 拟议的OPCS方法与肯纳德-斯通方法相比,在统计学上显著地确定了较小的标准化集.
  • 使用OPCS选择样本的模型的预测性能与使用传统方法的模型可比.
  • 该研究证明了OPCS在优化NIR模型转移的样本选择方面的有效性.

结论:

  • 在NIR模型转移中,OPCS方法为选择标准化样本提供了一个有效的策略.
  • 这种方法可以减少样本大小,而不会影响预测准确度.
  • OPCS为优化不同NIR仪器之间的校准模型传输提供了一个有价值的工具.