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相关概念视频

UV–Vis Spectrometers01:14

UV–Vis Spectrometers

1.2K
The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell.
1.2K
Spectrophotometry: Introduction01:16

Spectrophotometry: Introduction

2.8K
Spectrophotometry is the quantitative measurement of the absorption, reflection, diffraction, or transmission of electromagnetic radiation through a material as a function of the intensity and wavelength of the radiation. A spectrophotometer is a device used to measure the change in the radiation intensity caused by its interaction with the material.
The essential components of a spectrophotometer include a source of electromagnetic radiation, a slot for placing a material to be analyzed, and a...
2.8K
Classifying Matter by Composition03:35

Classifying Matter by Composition

69.6K
Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
69.6K
Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

2.4K
Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material,  molecules absorb light depending on the energy required for...
2.4K
IR Spectrometers01:25

IR Spectrometers

1.0K
There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
1.0K
UV–Vis Spectroscopy: Woodward–Fieser Rules01:29

UV–Vis Spectroscopy: Woodward–Fieser Rules

23.2K
UV–Visible absorption spectra of conjugated dienes arise from the lowest energy π → π* transitions. The light-absorbing part of the molecule is called the chromophore, and the substituents directly attached to the chromophore are called auxochromes. A strong correlation exists between the absorption maxima, λmax, and the structure of a conjugated π system. The Woodward–Fieser rules predict the value of λmax for a given...
23.2K

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相关实验视频

Updated: May 12, 2025

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

Published on: June 28, 2016

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可解释的机器学习模型通过光谱学对矿物质进行分类.

R Smith1, Tyler L Spano2, Marshall McDonnell1

  • 1Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN, United States.

Scientific reports
|May 6, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的机器学习方法,用于使用拉曼光谱识别矿. 这种方法绕过了传统的图书馆匹配,使未知样本根据其化学和物理性质能够快速分类.

关键词:
机器学习是机器学习.材料识别 材料识别拉曼光谱法 拉曼光谱法矿物质是矿物的矿物.

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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
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Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

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

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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
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Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

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科学领域:

  • 矿物学是什么?矿物学是什么?
  • 地质化学 地质化学
  • 机器学习应用 机器学习应用

背景情况:

  • 准确和快速的矿物质识别在各种科学学科中至关重要.
  • 传统的拉曼光谱分析依赖于模式匹配,这对于水晶性较差或混合相环境样本来说具有挑战性.
  • 现有的方法在复杂的样本矩阵和图书馆中缺乏精确的光谱匹配方面存在困难.

研究的目的:

  • 开发可解释的机器学习 (ML) 模型,仅根据拉曼光谱数据对矿物质进行分类.
  • 为了快速识别未知的矿物质,而不需要精确的光谱图书馆匹配.
  • 创建一种方法,为未知样本提供物理化学性质的矿物质概况.

主要方法:

  • 在拉曼光谱数据上训练可解释的机器学习模型的开发.
  • 矿物质的分类基于二次氧化离子的化学和来自光谱的其他物理化学性质.
  • 通过与已发表的光谱赋值和新型矿物样本分类的相关性来验证ML模型.

主要成果:

  • 成功开发了能够根据拉曼光谱对矿物质进行分类的ML模型.
  • 这些模型生成矿物质概况,详细说明物理和化学特性,而无需直接匹配光谱库.
  • 模型性能通过与已建立的光谱数据的强烈相关性和未经训练的矿物质的准确分类来验证.

结论:

  • 开发的ML方法为使用拉曼光谱学进行矿物识别提供了快速而可靠的方法.
  • 从物理上有意义的分类模型可以从未知的矿物中提取关键的结构和化学信息.
  • 总的来说,该方法在不同矿物阶段的分类方面具有广泛的适用性.