Rapid classification of tea using laser-induced breakdown spectroscopy coupled with BP neural network optimized by improved SSA

Q: What type of study is this?

This is a Experimental Study study.

Tianbing Chen; Lin Huang; Jiang Xu; Muhua Liu; Mingyin Yao

doi:10.1039/d5ja00282f

January 1, 2025Journal of Analytical Atomic Spectrometry

Rapid classification of tea using laser-induced breakdown spectroscopy coupled with BP neural network optimized by improved SSA

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Key Points

LIBS exhibited rapid and robust element detection in tea samples, highlighting its efficiency in analysis.
An optimized BP neural network, improved through SSA, significantly enhanced classification accuracy in the experimental setup.
The study utilized advanced LIBS techniques for in-situ detection, addressing challenges in traditional spectra analysis.
The findings may encourage further application of LIBS in various fields, particularly in rapid food product analysis.

Implication

This analysis demonstrates an effective method for element detection in tea using LIBS, suggesting that BP neural networks optimized by SSA enhance accuracy.

Abstract

Laser-induced breakdown spectroscopy (LIBS) is a swift and potent analytical method utilized for element detection, owing to its distinct advantages in online/in-situ detection. However, the analysis of LIBS spectra encounters...

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Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68de68ea83cbc991d0a2144a https://doi.org/10.1039/d5ja00282f

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Rapid classification of tea using laser-induced breakdown spectroscopy coupled with BP neural network optimized by improved SSA

Key Points

Implication

Abstract

Discussion

Cite This Study

Rapid classification of tea using laser-induced breakdown spectroscopy coupled with BP neural network optimized by improved SSA

Key Points

Implication

Abstract

Discussion

Cite This Study

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