# Assessing Melt Flow Rate in Post-Consumer Polypropylene via Near-Infrared Hyperspectral Imaging

## TL;DR

This study shows that near-infrared imaging can predict melt flow rate in recycled polypropylene, helping improve recycling quality.

## Contribution

The novel use of near-infrared hyperspectral imaging for predicting melt flow rate in post-consumer polypropylene is demonstrated.

## Key findings

- Tree-based models achieved R2 = 0.85 for white samples and R2 = 0.61 for clear samples in predicting MFR.
- Median spectral representations outperformed pixel-wise aggregation in model performance.
- Balanced accuracies of 0.82–0.92 were achieved for binary classification of MFR thresholds.

## Abstract

Mechanical recycling of polypropylene (PP) is constrained by the heterogeneous properties of post-consumer feedstocks. Melt flow rate (MFR) is a key property relevant to processing, and it varies widely across packaging grades, which limits the quality and substitutability of recyclates. This study evaluates near-infrared hyperspectral imaging (NIR-HSI) for predicting MFR in post-consumer PP packaging. Eighty-two rigid PP samples (46 white, 36 clear) with MFR values between 2 and 108 g 10 min−1 were collected from an Austrian material recovery facility. Thirteen different linear and non-linear regression models were examined using median and pixel-wise aggregated spectral representations across the samples. Tree-based models consistently achieved best performances with R2 = 0.85, RMSE = 12.4 g 10 min−1 on white samples and R2 = 0.61, RMSE = 14.0 g 10 min−1 on clear samples. On the combined sample set, R2 = 0.66 and RMSE = 17.3 g 10 min−1 were reached. Informative spectral regions correspond to typical bands of PP. Binary classification at different thresholds (6, 12, 30, 60 g 10 min−1) were also examined and achieved balanced accuracies of 0.82–0.92. Median spectral representations consistently outperformed pixel-wise aggregation. Results demonstrate that NIR-HSI can support grade-directed sorting of post-consumer PP, particularly for opaque white samples, though heteroscedasticity at high MFR values and irreducible outliers represent inherent limitations.