Study On the Detection of Dry Matter in Silage Corn Feed Based on Near Infrared Spectroscopy

This study explored the application of a portable near-infrared (NIR) spectrometer for analyzing silage corn feed quality, specifically focusing on developing a quantitative detection model for dry matter content. Spectral data were collected within the 855-1890 nm range using a portable NIR spectrometer, and the dataset was partitioned into calibration and prediction sets using the SPXY algorithm. An Extreme Learning Machine (ELM) model optimized by Particle Swarm Optimization (PSO) was employed for modeling. Five preprocessing methods were evaluated: Moving Average Filter (MAF), Savitzky-Golay Filter (SGF), Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV) transformation, and First Derivative (FD). To enhance model performance, feature wavelengths were selected using three methods: Bootstrap Soft Shrinkage (BOSS), Competitive Adaptive Reweighted Sampling (CARS), and Iterative Retained Information Variable (IRIV). The optimal model combining SNV preprocessing with BOSS feature selection achieved a prediction correlation coefficient () of 0.8708 and Root Mean Square Error of Prediction (RMSEP) of 0.6802. These results demonstrate the potential of portable NIR spectroscopy for rapid dry matter content determination in silage corn feed.