What question did this study set out to answer?

To examine how hammer mill particle size affects NIR prediction models for corn constituents.

March 30, 2026Journal of Food Composition and Analysis0 citationsOpen Access

Effect of Hammer Mill Particle Size on the Performance of NIR Prediction Models for Whole-Plant Corn Constituents

TLTingyu LiNortheast Agricultural University LCLiuyang ChengQingdao Center of Resource Chemistry and New Materials SWShuo WangNortheast Agricultural University

Key Points

To examine how hammer mill particle size affects NIR prediction models for corn constituents.
Utilized a portable NIRS analyzer for 180 corn samples from China.
Prepared samples at different hammer mill sizes (1.0 mm, 2.0 mm, 3.0 mm, and 4.0 mm).
Conducted NIRS scanning followed by wet chemistry analysis for dry matter, starch, and ash.
Built PLS models for each particle size and constituent.
Hammer mill particle size of 2.0 mm significantly improved starch predictions.
Spectral absorbance was lower for 2.0 mm samples compared to larger sizes (3.0 mm and 4.0 mm).
Prediction models for dry matter and starch at 2.0 mm and 3.0 mm exhibited high accuracy (R2 > 0.9).
Ash prediction model was less effective, with lower R2 values, indicating a need for further optimization.

Abstract

This study used a portable NIRS analyzer to explore the feasibility of modeling NIRS dry milling using hammer mill-prepared samples and determine the optimal particle size. 180 whole-plant corn samples from across China underwent four treatments: milling to cyclone 1.0 mm (control), hammer 2.0 mm, 3.0 mm and 4.0 mm. Samples underwent NIRS scanning followed by wet chemistry for DM, starch, and ash. PLS models were built for each constituent and particle size. Results showed the hammer 2.0 mm sample had significantly lower mean spectral absorbance than hammer 3.0 mm and 4.0 mm samples ( P 0.9, R 2 cv > 0.9, R 2 p > 0.9, RPD > 3), demonstrating practical a p plication potential. The Ash prediction model performed poorly overall (R 2 c < 0.8, R 2 cv < 0.6, R 2 p < 0.8, RPD < 3) and requires optimization. Considering spectral quality and model performance, hammer 2.0 mm was determined optimal for NIRS sample preparation. • Portable infrared device tested for corn milling analysis. • Found best corn particle size: 2.0 mm using hammer mill. • Accurately predicted corn starch and dry matter content. • Ash content prediction needs improvement for practical use. • Smaller corn particles gave clearer infrared signals.

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