What is it about?

This study focused on using near-infrared hyperspectral imaging to assess the size of polymer particles in model fractions. The accuracy of particle size estimation based on hyperspectral imaging was compared with basic single-spot near-infrared measurements. Hyperspectral imaging provides additional information about the spatial distribution of sample components, which helps in gaining a better understanding of scattering phenomena and particle size distribution. It was also found that partial least-squares models constructed using hyperspectral images outperformed models built for mean spectra, regardless of the evaluated type of powdered polymer. This research was supported by the National Science Centre, Poland (research grant no. 2018/29/N/ST4/01547).

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Why is it important?

Our findings demonstrate the potential of using hyperspectral imaging to estimate the size of polymer particles in model fractions. This is significant because characterizing the size of polymer particles is critical for various industries. Traditional methods for particle size analysis can be time-consuming and require expensive equipment, making them less accessible for many applications. The use of hyperspectral imaging provides a more efficient and cost-effective approach that can improve the accuracy of particle size estimation. Additionally, the study highlights the importance of considering the spatial distribution of sample components in particle size analysis, which can provide a more comprehensive understanding of particle size distribution and scattering phenomena.


First, the study highlights the potential of hyperspectral imaging for accurately estimating particle size in polymer samples, which can be applied to various industries, such as pharmaceuticals, food processing, and materials science. This can lead to the development of more efficient and cost-effective methods for particle size analysis, which could improve the quality control and safety of products. Second, the use of hyperspectral imaging provides additional information about the spatial distribution of sample components, which can help to understand the physical properties of materials better. This can lead to the development of advanced methods for characterizing materials, which could have significant applications in materials science, biomedical engineering, and other fields.

Professor Michal Daszykowski
University of Silesia in Katowice, Poland

Hyperspectral imaging facilitates fast prediction of particle size in powdered samples without extensive sample preparation, additional reagents usage, or sample destruction. It is also ideal for online measurements over production lines for polymer feedstock quality control. The proposed application and hyperspectral data processing approach contribute to the development of modern sustainable analytical techniques that can provide a greater understanding of sample heterogeneity while using limited amounts of chemicals and reducing the cost of sample analysis.

Łukasz Pieszczek
University of Silesia in Katowice

Read the Original

This page is a summary of: Near-infrared hyperspectral imaging for polymer particle size estimation, Measurement, December 2021, Elsevier,
DOI: 10.1016/j.measurement.2021.110201.
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