Qualitative and Quantitative Detection of Microplastics in Chicken Feed Using Portable Near-Infrared Spectroscopy

Microplastic pollution has become an emerging concern in feed safety and animal-derived food safety. This study applied near-infrared spectroscopy (NIR) combined with machine learning to perform qualitative and quantitative analysis of seven common microplastics in chicken feed, including polyamide (PA), polycarbonate (PC), polyethylene (PE), polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC). A total of 840 samples with microplastic mass fractions ranging from 0.01% to 1.00% were prepared. Near-infrared spectra were acquired in the 1100–2200 nm range, and multiple spectral preprocessing methods and models were evaluated. The Synthetic Minority Over-sampling Technique (SMOTE) was introduced to assess the effect of data augmentation. For classification, the Extremely Randomized Trees (ET) model achieved the best performance, with an accuracy and F1-score of 0.9603 and 0.9602, respectively. For regression, performance varied among polymers, with PVC showing the best quantitative prediction performance using MA preprocessing combined with SVR (test set R2 = 0.9851), while the raw-spectrum SVR model achieved R2 = 0.9846 and RPD = 8.1642. Spectral preprocessing and data augmentation produced polymer-dependent changes in model performance. These results support portable NIR spectroscopy as a rapid, non-destructive screening approach under the controlled single-polymer conditions studied; mixed-polymer and field robustness require further validation.