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Echo-Window Sensitivity of Spectral Features in Ultrasonic Backscatter Characterization of PE and PMMA Microplastics
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This study used sound wave data to detect tiny plastic particles (polyethylene and acrylic), testing how the length of the analyzed sound signal affects results. Some measurements stayed fairly stable regardless of signal length, while others changed substantially, showing that this technical choice matters for getting consistent results.
Ultrasonic backscatter provides a non-destructive approach for characterizing microplastic particles; however, the spectral features extracted from an echo can depend on the signal segment selected for analysis. This study investigates the sensitivity of polyethylene (PE) and poly(methyl methacrylate) (PMMA) spectral-feature differences to echo-analysis window length using a publicly available high-frequency ultrasonic backscatter dataset. Signals sharing a common 50-μm size label were analyzed using peak-centered windows of 64, 128, 256, and 384 samples. After mean removal and Hamming tapering, spectral centroid, spectral spread, and normalized spectral entropy were extracted from the Fourier-domain representation. Spatially neighboring measurements were grouped before statistical comparison to reduce the influence of repeated local observations. The results show clear feature-dependent window sensitivity. The PE–PMMA centroid difference remained comparatively stable, changing by only about 3.2% between the shortest and longest windows, whereas the spectral-spread difference increased by approximately 115%. Spectral entropy also showed substantial but non- monotonic variation with window length. These findings demonstrate that echo-analysis window selection is an important digital signal processing parameter and should be considered when evaluating the robustness and reproducibility of ultrasonic spectral features for microplastic characterization.
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