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Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

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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 electronic transitions. As a result...

Spectrophotometry: Introduction

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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. Samples for...

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Related Experiment Video

Updated: Jun 15, 2026

Evaluation of Photosynthetic Behaviors by Simultaneous Measurements of Leaf Reflectance and Chlorophyll Fluorescence Analyses

Evaluation of Photosynthetic Behaviors by Simultaneous Measurements of Leaf Reflectance and Chlorophyll Fluorescence Analyses

Published on: August 9, 2019

[Chlorophyll content nondestructive measurement method based on Vis/NIR spectroscopy].

Qing-Bo Li¹, Yan-Wen Huang, Guang-Jun Zhang

¹Optoelectronics Engineering, Beihang University, Beijing 100191, China. qbleebuaa@buaa.edu.cn

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu

|March 10, 2010

Summary

This summary is machine-generated.

This study demonstrates a feasible method for non-destructively measuring plant chlorophyll content using visible/near-infrared (Vis/NIR) spectral analysis. The developed model achieved high prediction accuracy, showing potential for rapid chlorophyll assessment.

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Evaluation of Photosynthetic Behaviors by Simultaneous Measurements of Leaf Reflectance and Chlorophyll Fluorescence Analyses

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Published on: August 9, 2019

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

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

Published on: June 28, 2016

Area of Science:

Plant Physiology
Spectroscopy
Agricultural Science

Context:

Accurate and non-destructive measurement of plant chlorophyll is crucial for assessing plant health and photosynthetic capacity.
Traditional methods for chlorophyll measurement are often destructive and time-consuming.
Visible/Near-Infrared (Vis/NIR) spectroscopy offers a promising non-invasive alternative.

Purpose:

To develop and validate a quantitative model for non-destructive plant chlorophyll content measurement using Vis/NIR spectral analysis.
To investigate the effectiveness of different spectral data preprocessing techniques (moving average, Savitzky-Golay derivative, wavelet transform) in improving model accuracy.
To apply the Partial Least Squares (PLS) regression method for chlorophyll content prediction.

Summary:

Vis/NIR spectra (500-900 nm) were acquired from 35 plant leaf samples using transmittance and reflectance methods.
Spectral data underwent preprocessing, followed by PLS model development using 23 samples and prediction on 12 unknown samples.
The model demonstrated strong predictive performance with a correlation coefficient of 0.93 and a Root Mean Squared Error of Prediction (RMSEP) of 1.1 SPAD units.

Impact:

The study confirms the feasibility of using Vis/NIR spectroscopy for rapid and non-destructive determination of plant chlorophyll content.
This approach has significant implications for precision agriculture, enabling real-time monitoring of crop health and nutrient status.
The validated methodology provides a foundation for developing advanced tools for plant phenotyping and stress detection.