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This is a Quantitative Study study.

October 8, 2025RSC AdvancesOpen Access

DeepPHSI: attention-driven CNN-LSTM fusion for hyperspectral origin traceability across Pogostemon cablin batches

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DeepPHSI shows potential for effective quality control in P. cablin batches, enhancing traceability.
The method integrates CNN and LSTM architectures, utilizing attention-driven mechanisms for improved analysis.
Hyperspectral analysis enables detailed evaluation of P. cablin, contributing to better batch differentiation.
This transferable method can be adapted for various applications in hyperspectral analysis beyond P. cablin.

Implication

New method demonstrates quality control in Pogostemon cablin using hyperspectral data, highlighting its versatility.

Abstract

DeepPHSI is expected to be applied to the quality control and online detection of P. cablin , and it is a transferable, universal and potential method for hyperspectral analysis.

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Cite This Study

Fan et al. (2025) studied this question.

synapsesocial.com/papers/68e5c1c36950a706b22b5be3 https://doi.org/10.1039/d5ra06579h

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Implication

New method demonstrates quality control in Pogostemon cablin using hyperspectral data, highlighting its versatility.

Abstract

DeepPHSI is expected to be applied to the quality control and online detection of P. cablin , and it is a transferable, universal and potential method for hyperspectral analysis.

DeepPHSI: attention-driven CNN-LSTM fusion for hyperspectral origin traceability across Pogostemon cablin batches

Key Points

Implication

Abstract

Discussion

Cite This Study

DeepPHSI: attention-driven CNN-LSTM fusion for hyperspectral origin traceability across Pogostemon cablin batches

Key Points

Implication

Abstract

Discussion

Cite This Study

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