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Updated: Mar 28, 2026

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

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Published on: July 25, 2022

Multicomponent Simultaneous Identification Network (MSINet): An Advanced Deep Learning Model for Boosting Multiplex

Xuehua Zhang¹, Hansheng Li², Hao Si¹

¹School of Chemistry, Chemical Engineering and Life Sciences, Wuhan University of Technology 430070, Wuhan, China.

Analytical Chemistry

|March 27, 2026

Summary

This summary is machine-generated.

A new deep learning model, MSINet, enhances surface-enhanced Raman spectroscopy (SERS) for analyzing untreated samples. This advanced method accurately identifies and quantifies multiple targets in complex mixtures, overcoming previous detection challenges.

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Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets

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Published on: March 17, 2023

Area of Science:

Analytical Chemistry
Spectroscopy
Machine Learning

Background:

Surface-enhanced Raman spectroscopy (SERS) offers high sensitivity and specificity for on-site analysis of diverse sample types.
Challenges in SERS analysis include matrix complexity, competitive adsorption, signal fluctuations, and spectral overlap, hindering multi-target detection in real-world samples.

Purpose of the Study:

To develop a deep learning model for simultaneous identification and quantification of multiple targets in untreated samples using SERS.
To create a robust and accurate method that overcomes spectral interferences and signal variability inherent in complex matrices.

Main Methods:

Designed and implemented a deep learning model, the multicomponent simultaneous identification network (MSINet), for SERS spectral analysis.
MSINet employs hierarchical feature extraction and multiple feature integration to enhance robustness against signal fluctuations and spectral overlaps.
A threshold judgment rule was incorporated for efficient quantification of target contents with minimal training data.

Main Results:

MSINet achieved high accuracy (≥0.90) in detecting and quantifying multiple UV absorbers in seawater, food additives in cocktails, and biomarkers in human serum.
The model demonstrated significant improvement over the standard curve method, which yielded accuracies as low as 0.05 for the same untreated samples.
MSINet proved robust against signal fluctuations and spectral overlaps, enabling reliable multi-component analysis.

Conclusions:

MSINet is a powerful and universal tool for assisting SERS sensors in high-throughput on-site detection of multiple targets in untreated real samples.
The model's ease of use, graphical interface, and zero extra cost position it for broad application in various analytical fields.
This deep learning approach significantly advances the capabilities of SERS for complex sample analysis and on-site monitoring.