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

Authors: LIU, ZHENGANG - Jiangnan University; HUANG, MIN - Jiangnan University; ZHU, QIBING - Jiangnan University; Qin, Jianwei - Tony Qin; Kim, Moon

Submitted to: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/16/2023
Publication Date: 2/18/2023
Citation: Liu, Z., Huang, M., Zhu, Q., Qin, J., Kim, M.S. 2023. Evaluating performance of SORS-based subsurface signal separation methods using statistical replication Monte Carlo simulation. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 293:122520. https://doi.org/10.1016/j.saa.2023.122520.
DOI: https://doi.org/10.1016/j.saa.2023.122520

Interpretive Summary: Nondestructive evaluation of safety and quality for packaged foods is a challenging task due to difficulties in acquiring optical signals from food samples through packaging materials. Spatially offset Raman spectroscopy (SORS) is a promising depth-profiling technique to tackle this problem. However, there is a lack of studies to evaluate the signal separation methods for the SORS technique. This study presented a method based on line-scan SORS and statistical replication Monte Carlo simulation to evaluate effectiveness of retrieving Raman signals from subsurface food samples. The simulation results were verified by three packaged foods (i.e., canned sugar, bagged rice, and boxed butter), in which fast independent component analysis method can effectively separate Raman signals from surface layer of the packaging materials and subsurface layer of the foods. The evaluation method can assist in developing and optimizing SORS-based method for through-package safety and quality inspection of the foods and ingredients. The technique would benefit the regulatory agencies (e.g., FDA and USDA FSIS) and the food industry in enforcing standards of the safety and quality of the packaged food products.

Technical Abstract: Spatially offset Raman spectroscopy (SORS) is a depth-profiling technique with deep information enhancement. However, the interference of the surface layer cannot be eliminated without prior information. The signal separation method is an effective candidate for reconstructing pure subsurface Raman spectra, and there is still a lack of evaluation means for the signal separation method. Therefore, a method based on line-scan SORS combined with improved statistical replication Monte Carlo (SRMC) simulation was proposed to evaluate the effectiveness of food subsurface signal separation method. Firstly, SRMC simulates the photon flux in the sample, generates a corresponding number of Raman photons at each voxel of interest, and collects them by external map scanning. Then, 5625 groups of mixed signals with different optical characteristic parameters were convoluted with spectra of public database and application measurement and introduced into signal separation methods. The effectiveness and application range of the method were evaluated by the similarity between the separated signals and the source Raman spectra. Finally, the simulation results were verified by three packaged foods. FastICA method can effectively separate Raman signals from subsurface layer of food and thus promote deep quality evaluation of food.