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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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NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
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Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Calibration Curves: Correlation Coefficient01:10

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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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Distance Corrections01:15

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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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相关实验视频

Updated: Jun 23, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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一种LIBS频谱基线校正方法,基于非参数的先前处罚最小方程算法.

Shengjie Ma1,2,3, Shilong Xu1,2,3, Youlong Chen1,2,3

  • 1State Key Laboratory of Pulsed Power Laser Technology, National University of Defense Technology, Hefei 230037, People's Republic of China. skl_hyh@163.com.

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概括

一个新的非参数先前惩罚最小方程 (NPPPLS) 算法通过有效纠正光谱背景噪声来改进激光诱导分解光谱 (LIBS) 分析. 这种方法提高了定量分析的准确性,并显示出其他光谱技术的前景.

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科学领域:

  • 频谱学是一种光谱学.
  • 分析化学 分析化学
  • 数据科学数据科学数据科学

背景情况:

  • 激光诱导分解光谱 (LIBS) 提供实时,非破坏性的多元元素分析.
  • 在LIBS的一个重大挑战是频谱中存在连续的背景噪声,这阻碍了准确的分析.
  • 现有的基线校正方法通常需要先前的参数知识,并且可能缺乏稳定性.

研究的目的:

  • 为LIBS频谱开发一个先进的基线校正方法.
  • 通过解决光谱背景干扰来提高LIBS定量分析的准确性.
  • 创建一个强大的,适应性的算法,不需要先前的参数设置.

主要方法:

  • 为LIBS光谱基线校正提出了一种新的非参数先前惩罚最小方程 (NPPPLS) 算法.
  • 引入了一种新的加权方法,以实现更快的收,并将Adam算法结合起来,以进行自适应参数更新.
  • 使用模拟数据和实验LIBS光谱验证了该方法,随后进行了单变量和多变量分析.

主要成果:

  • 在模拟数据上,NPPPLS算法表现出极好的基线校正性能,即使没有参数先验.
  • 该方法显示稳定性和稳定性得到改善,不受初始平衡参数值的影响.
  • 基线校正显著提高了定量分析的准确性,多变量分析实现了元素检测的R2为0.99.

结论:

  • 拟议的NPPPLS算法有效地纠正了LIBS中的光谱基线,从而提高了定量分析的准确性.
  • 由于NPPPLS的适应性和稳定性,使其成为传统方法的优越替代方案.
  • 这种方法有可能在其他光谱技术 (如拉曼光谱和近红外光谱) 中进行基线校正.