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Evaluating performance of SORS-based subsurface signal separation methods using statistical replication Monte Carlo

Zhenfang Liu1, Min Huang1, Qibing Zhu1

  • 1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|February 22, 2023
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Summary

A new method using line-scan Spatially Offset Raman Spectroscopy (SORS) and statistical replication Monte Carlo (SRMC) simulation effectively separates subsurface food signals. This advance enables deeper food quality evaluation by isolating signals from the surface layer interference.

Keywords:
Monte CarloPackaged foodsSORSSignal separationSubsurface non-invasive evaluation

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Food Science

Background:

  • Spatially offset Raman spectroscopy (SORS) offers depth-profiling capabilities for enhanced information retrieval.
  • Interference from surface layers in SORS hinders accurate subsurface analysis without prior knowledge.
  • A robust evaluation method for signal separation techniques in SORS is currently lacking.

Purpose of the Study:

  • To develop and evaluate a novel method for assessing the effectiveness of subsurface signal separation in food analysis using SORS.
  • To establish a reliable means for quantifying the performance of signal separation algorithms in complex food matrices.

Main Methods:

  • Line-scan SORS combined with an improved statistical replication Monte Carlo (SRMC) simulation was employed.
  • SRMC simulated photon flux and generated Raman photons within the sample for external map scanning.
  • 5625 mixed signal groups, convolved with database and measured spectra, were used to test signal separation methods.

Main Results:

  • The developed method successfully evaluated the effectiveness and application range of signal separation techniques.
  • The FastICA algorithm demonstrated a strong capability in separating subsurface Raman signals from food samples.
  • Simulation results were validated using three distinct packaged food products.

Conclusions:

  • The proposed line-scan SORS and SRMC simulation method provides a viable means to evaluate food subsurface signal separation.
  • FastICA is confirmed as an effective algorithm for isolating subsurface Raman signals in food.
  • This approach facilitates advanced, in-depth quality assessment of food products.