Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Light Acquisition

Light Acquisition

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.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Integrating Pharmacogenomics and Network Topology for Machine Learning Prediction of HLA-Associated Severe Cutaneous Adverse Drug Reactions.

International journal of molecular sciences·2026

Same author

IGM: Integrated Gene-expression Modeling for multi-condition flux-preserving genome-scale metabolic models.

PloS one·2026

Same author

Integrated effects of pre-harvest high blue light and postharvest sodium nitroprusside on volatile oil composition and quality of cold-stored holy basil.

Food chemistry: X·2026

Same author

Unraveling the role of Cullin3 of E3 ubiquitin ligase in salt stress tolerance via proteomics.

BMC plant biology·2025

Same author

Transcriptomic analysis of Thai rice varieties at the reproductive stage uncovers potential genetic determinants of salt tolerance.

BMC plant biology·2025

Same author

F<sub>2</sub> bulk segregant analysis reveals salt-tolerant QTLs related to the ubiquitination process.

Scientific reports·2025

Search research articles

Related Experiment Video

Updated: Jun 11, 2025

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

Phenolic content discrimination in Thai holy basil using hyperspectral data analysis and machine learning techniques.

Apichat Suratanee^1,2, Panita Chutimanukul³, Tanapon Saelao⁴

¹Department of Mathematics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.

|October 2, 2024

Summary

Hyperspectral imaging combined with machine learning accurately predicts phenolic content in holy basil. This non-destructive method offers a rapid alternative to traditional antioxidant analysis for plant research.

More Related Videos

HPLC Coupled with Chemical Fingerprinting for Multi-Pattern Recognition for Identifying the Authenticity of Clematidis Armandii Caulis

HPLC Coupled with Chemical Fingerprinting for Multi-Pattern Recognition for Identifying the Authenticity of Clematidis Armandii Caulis

Published on: November 11, 2022

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

Published on: October 2, 2016

Related Experiment Videos

Last Updated: Jun 11, 2025

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

HPLC Coupled with Chemical Fingerprinting for Multi-Pattern Recognition for Identifying the Authenticity of Clematidis Armandii Caulis

HPLC Coupled with Chemical Fingerprinting for Multi-Pattern Recognition for Identifying the Authenticity of Clematidis Armandii Caulis

Published on: November 11, 2022

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

Published on: October 2, 2016

Area of Science:

Agricultural science
Plant biochemistry
Spectroscopy

Background:

Traditional antioxidant analysis of holy basil is time-consuming.
Hyperspectral imaging offers a rapid, non-destructive method for plant property assessment.
Phytochemical quantification, including phenolic content, is crucial for understanding plant properties.

Purpose of the Study:

To develop a rapid, non-destructive method for determining total phenolic content in Thai holy basil using hyperspectral imaging and machine learning.
To classify phenolic content levels into 'low' and 'normal-to-high' categories.
To evaluate the performance of a neural network model against other machine learning techniques.

Main Methods:

Acquired hyperspectral data from 26 holy basil cultivars across different growth stages.
Extracted 22 statistical features in time and frequency domains from spectral data.
Developed and validated a neural network model for phenolic content classification.

Main Results:

The neural network model achieved an area under the receiver operating characteristic curve of 0.8113.
The model demonstrated superior performance compared to other machine learning techniques.
The model showed increased confidence in predicting phenolic content for older holy basil samples.

Conclusions:

Hyperspectral imaging integrated with feature extraction and machine learning provides an effective tool for rapid, non-destructive phenolic content assessment in holy basil.
This approach has the potential to streamline the screening of antioxidant properties in plants.
The study facilitates efficient decision-making for researchers through comprehensive spectral data analysis.