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相关概念视频

Raman Spectroscopy Instrumentation: Overview01:26

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
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Raman Spectroscopy: Overview01:20

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
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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.
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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.
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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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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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深度学习模型在多个拉曼光谱仪之间通过低级适应进行校准转移.

Jiahui Lai1, Miaomiao Li1, Song Chen1

  • 1College of Chemistry and Chemical Engineering, Central South University, Hunan, Changsha 410083, China.

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概括
此摘要是机器生成的。

一种新的基于低级适应的校准转移 (LoRA-CT) 方法使得拉曼光谱仪之间的高效深度学习模型转移成为可能. 这种方法显著提高了采用最小样本的准确性,克服了便携式光谱的设备间变异.

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科学领域:

  • 频谱学是一种光谱学.
  • 化学测量 化学测量 化学测量
  • 机器学习 机器学习

背景情况:

  • 拉曼光谱中的深度学习允许快速分析,但由于设备间的变化,模型可移植性面临挑战.
  • 频谱仪之间的系统差异阻碍了训练模型在不同仪器中直接应用.

研究的目的:

  • 在基于深度学习的拉曼光谱学中引入一个参数有效的校准转移方法.
  • 以最小的数据和计算资源,在各种频谱仪上实现无的模型可移植性.

主要方法:

  • 提出了一种基于低级适应的校准转移 (LoRA-CT) 方法,用于参数高效的微调.
  • 分解重量更新为低级矩阵,以显著减少可训练参数 (600×减小与完整微调).
  • 在三个数据集中验证了LoRA-CT,包括溶剂混合物和混合油.

主要成果:

  • 与传统方法相比,LoRA-CT使用很少的转移样本显示出优异的校准转移性能.
  • 在甲醇混合物试验组中实现了R2 = 0.952和RMSE = 0.072,超过了分片直接标准化和完整参数微调的性能.
  • 实验结果证实了准确性和效率的显著改善.

结论:

  • LoRA-CT为资源效率高的光谱模型部署建立了一个新的范式.
  • 模块化,插即用设计可促进光谱仪之间的动态切换.
  • 这种方法对便携式光谱仪和面临样本稀缺和计算限制的多仪器系统特别有利.