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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #377611
Research Project: Sensing Technologies for the Detection and Characterization of Microbial, Chemical, and Biological Contaminants in Foods

Location: Environmental Microbial & Food Safety Laboratory

Title: Non-destructive freshness evaluation of intact prones using line-scan spatially offset Raman spectroscopy

Author
item LIU, ZHENFAN - Jiangnan University
item HUANG, MIN - Jiangnan University
item ZHU, QIBING - Jiangnan University
item Qin, Jianwei - Tony Qin
item Kim, Moon

Submitted to: Food Control
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/1/2021
Publication Date: 3/3/2021
Citation: Liu, Z., Huang, M., Zhu, Q., Qin, J., Kim, M.S. 2021. Non-destructive freshness evaluation of intact prones using line-scan spatially offset Raman spectroscopy. Food Control. 126:108054. https://doi.org/10.1016/j.foodcont.2021.108054.
DOI: https://doi.org/10.1016/j.foodcont.2021.108054

Interpretive Summary: The quality of seafood products has been gaining increased attention from researchers worldwide. Prawns are highly popular with consumers but present many technical difficulties for the evaluation of their internal quality when intact (in-shell prawns). This study evaluated a nondestructive method to assess the internal quality of intact prawns (Fenneropenaeus chinensis) by using spatially offset Raman spectroscopy (SORS) technique combined with several methods of data modeling analysis. SORS-enhanced Raman image data for one hundred fresh, intact prawns were collected using a line-scan Raman imaging system over the course of seven days with 24h measurement intervals. Several freshness prediction models using the spectral image data were tested and the best performance achieved from among these models showed that this SORS-based rapid and nondestructive method for quality evaluation may be feasible as a practical means of assessing internal quality of food materials that demonstrate surface interference, such as in-shell prawns.

Technical Abstract: The quality of seafood products has been gaining increased attention from researchers worldwide. Prawns are highly popular with consumers but present many technical difficulties for the evaluation of their internal quality when intact (in-shell prawns). This study proposed a nondestructive method to assess the internal quality of intact prawns (Fenneropenaeus chinensis) by using spatially offset Raman spectroscopy (SORS) technique combined with data modeling analysis. This technique holds promise due to the capacity of SORS to nondestructive obtain chemical information from below the surface of a sample material. Raman image data for one hundred fresh prawns (approximately 15g each) were collected using a line-scan Raman imaging system over the course of seven days with 24h measurement intervals. Measurement anomalies due to physical prawn irregularities were eliminated by using a peak identification method. Twenty feature bands selected by Random Forest (RF) method were input to Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extremely Randomized Tree (ET) models to predict the freshness of prawns over the course of seven days. The prediction model using RF combined with SVR with SORS-enhanced data demonstrated the best performance, with RMSEP, R2, and RPD values of 0.97, 0.87, and 2.39, respectively. This rapid and nondestructive method for quality evaluation, based on SORS data and combining RF feature band selection with SVR in the prediction model, may be feasible as a practical means of assessing internal quality of materials that demonstrate surface interference, such as in-shell prawns.