相关实验视频

Updated: Jun 6, 2025

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

提高对人工智能的决策信心,使用蒙特卡洛脱落来对拉曼光谱分类进行分类

Jhonatan Contreras¹, Thomas Bocklitz¹

¹Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743, Jena, Germany; Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz. Centre for Photonics in Infection Research (LPI), Albert Einstein Straße 9, 07745, Jena, Germany.

Analytica chimica acta

|November 23, 2024

概括

本研究引入了一种不确定性引导的预测方法,用于使用机器学习进行细菌识别. 通过专注于高可信度数据子集,该方法在微生物学应用中显著提高了预测准确性和可靠性.

科学领域:

微生物学微生物学
机器学习机器学习
频谱学是一种光谱学.

背景情况:

机器学习 (ML) 模型,包括卷积神经网络 (CNN),是通过拉曼光谱识别细菌菌株的标准.
通常,ML模型经过训练,然后在没有进一步增强的情况下用于推断,假设训练后的峰值性能.
这项研究探讨了深度学习中模型不确定性的关键,经常被忽视的方面.

研究的目的:

开发和验证一种新的方法,在推理阶段量化和利用模型不确定性,以改善细菌识别.
通过专注于具有更高可靠性的预测,提高基于ML的微生物识别的可靠性和准确性.

主要方法:

结合蒙特卡罗脱落 (MCD) 与CNN,使得在推断过程中可以测量不确定性.
利用高斯混合模型 (GMM) 来定义对未见数据进行分类的不确定性值.
专注于数据子集的最终预测,显示较低的模型不确定性.

主要成果:

将不确定性引导方法应用于两个拉曼光谱数据集,显示了显著的准确性改进.
在826个频谱的子集上,数据集1的准确性增加了9% (83.10%至92.10%).
在1700个频谱的子集上,数据集2的准确性增加了12.82% (83.86%至96.68%).

结论:

以不确定性为指导的预测在确保高预测率方面比使用整个数据集更有效.
这种方法在疾病诊断和安全监测等关键应用中提高了分类准确性.
这种方法提升了微生物识别,产生了更可靠的预测.

关键词:

细菌的分类细菌的分类机器学习是机器学习.拉曼光谱法拉曼光谱法拉曼光谱法不确定性不确定性

更多相关视频

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Published on: September 26, 2019

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

相关概念视频

Raman Spectroscopy: Overview

Raman Spectroscopy: Overview

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.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...

Raman Spectroscopy Instrumentation: Overview

Raman Spectroscopy Instrumentation: Overview

A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...