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相关概念视频

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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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Fruits form from a mature flower ovary. As seeds develop from the ovules contained within, the ovary wall undergoes a series of complex changes to form fruit. In some fruits, such as soybeans, the ovary wall dries; in other fruits, such as grapes, it remains fleshy. In some cases, organs other than the ovary contribute to fruit formation; such fruits are called accessory fruits.
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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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Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
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Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
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相关实验视频

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Fruit Volatile Analysis Using an Electronic Nose
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机器学习根据拉曼光谱中的胡卜素含量对果成熟度的分类.

Ji Loun Tan1, Fazida Hanim Hashim1,2, Jahariah Sampe3

  • 1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.

PeerJ
|November 24, 2025
PubMed
概括

拉曼光谱通过分析皮肤中的胡卜素化合物,准确地确定果的成熟度. 这种非侵入性方法为传统评估提供了可靠的替代方案,确保了最佳的水果质量和产量.

关键词:
胡卜素 胡卜素 胡卜素机器学习 机器学习果 (Mango) 果 (Mango) 是一个很好的食物.拉曼光谱法 拉曼光谱法 拉曼光谱法成熟度 成熟度 的成熟度.

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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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科学领域:

  • 农业科学 农业科学
  • 分析化学 分析化学
  • 频谱学是一种光谱学.

背景情况:

  • 果的成熟度对味道,香味和营养至关重要,影响农民的产量.
  • 传统的成熟度评估不一致,不准确,耗时.
  • 果颜色的变化和人类的感知影响了传统方法.

研究的目的:

  • 开发一种非侵入性,高效的方法,使用拉曼光谱检测果成熟度.
  • 从原始拉曼光谱中提取有机化合物数据.
  • 关联胡卜素特性与果成熟程度.
  • 评估果成熟度分类的机器学习模型.

主要方法:

  • 分析了29种果果谱,其中13个样本代表不成熟,成熟和过度成熟的类别.
  • 利用拉曼光谱分析果皮中的有机化合物.
  • 应用统计分析和机器学习模型 (SVM,KNN) 用于分类.

主要成果:

  • 在果皮 (1,4801,550厘米−1) 中鉴定了胡卜素 (利科,β-胡卜素,黄蛋白,新山丁).
  • 证实了胡卜素峰值强度和果成熟度之间的显著相关性 (p < 0.05).
  • 在使用SVM和KNN模型对成熟度进行分类时实现了100%的准确性.

结论:

  • 拉曼光谱是一种可靠,强大,非侵入性的方法,用于评估果的成熟度.
  • 该技术不受外界因素的影响,如光,湿度和噪音.
  • 这种方法有望改善果质量控制和收获管理.