Related Experiment Video
Updated: Jun 11, 2025
Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
Published on: December 16, 2016
Feasibility study on non-destructive detection of microplastic content in flour based on portable Raman spectroscopy
Jiaming Kan1, Jihong Deng1, Zhidong Ding2
1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
Abstract:
Microplastics, as emerging environmental pollutants, have garnered considerable attention due to their contamination of both the environment and food. Microplastics can infiltrate the human food chain through multiple pathways, potentially posing health risks to humans. Currently, non-destructive testing of microplastics in food is considered challenging. This study aims to investigate the feasibility of employing a portable Raman spectroscopy system for non-destructive detection of microplastic content (polystyrene, PS; polyethylene, PE) in flour. In this study, a portable spectrometer was used to collect flour spectra of different abundances of microplastics. To enhance the predictive performance of the partial least squares (PLS) model, a mixed variable selection strategy that combined the wavelength interval selection method (Synergy interval partial least squares, siPLS) and the wavelength point selection method (Least absolute shrinkage and selection operator, LASSO; Multiple feature-spaces ensemble by least absolute shrinkage and selection operator, MFE-LASSO) was proposed. Four regression models (PLS, siPLS, siPLS-LASSO, siPLS-MFE-LASSO) were developed and compared for detecting PS and PE content in flour. The siPLS-MFE-LASSO model exhibited the best generalization performance in the prediction set, and was considered to have the best generalization performance (PS: RP2 = 0.9889, RMSEP=0.0344 %; PE: RP2 = 0.9878, RMSEP=0.0361 %). In conclusion, this study has demonstrated the potential of using a portable Raman spectrometer in conjunction with a mixed variable selection algorithm for non-destructive detection of PS and PE content in flour, providing more possibilities for non-destructive detection of microplastic content in food.
Insights
Portable Raman spectroscopy offers a non-destructive method for detecting microplastics like polystyrene (PS) and polyethylene (PE) in flour. A novel mixed variable selection strategy significantly improved detection accuracy, showing promise for food safety analysis.
Area of Science:
- Environmental Science
- Analytical Chemistry
- Food Science
Background:
- Microplastics are pervasive environmental pollutants contaminating food and potentially posing human health risks.
- Non-destructive detection of microplastics in food matrices remains a significant analytical challenge.
- Understanding microplastic presence in the food chain is crucial for assessing safety.
Purpose of the Study:
- To evaluate the feasibility of using portable Raman spectroscopy for non-destructive microplastic detection in flour.
- To develop and compare chemometric models for quantifying polystyrene (PS) and polyethylene (PE) in flour.
- To introduce and validate a novel mixed variable selection strategy for enhanced model performance.
Main Methods:
- Acquisition of Raman spectra from flour samples spiked with varying concentrations of PS and PE.
- Development of partial least squares (PLS) regression models.
- Implementation of a mixed variable selection strategy combining Synergy interval partial least squares (siPLS) with Least absolute shrinkage and selection operator (LASSO) and Multiple feature-spaces ensemble by least absolute shrinkage and selection operator (MFE-LASSO).
Main Results:
- The siPLS-MFE-LASSO model demonstrated superior predictive performance for both PS and PE.
- High coefficients of determination (RP2 > 0.98) and low root-mean-square errors of prediction (RMSEP) were achieved.
- The developed method enabled accurate non-destructive quantification of microplastic content in flour.
Conclusions:
- Portable Raman spectroscopy, coupled with advanced chemometric methods, is a viable tool for non-destructive microplastic analysis in food.
- The proposed mixed variable selection strategy (siPLS-MFE-LASSO) significantly enhances the accuracy and reliability of microplastic quantification.
- This approach offers a promising solution for routine monitoring of microplastic contamination in food products.
