Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology

Area of Science:

• Agricultural Science
• Food Science
• Spectroscopy

Background:

• Ensuring corn freshness is crucial for storage, processing, and utilization.
• Rapid, non-destructive detection methods are needed to assess corn quality.

Purpose of the Study:

• To develop a rapid, non-destructive method for detecting corn freshness and staleness.
• To investigate the relationship between hyperspectral imaging data and corn fatty acid values during aging.

Main Methods:

• Hyperspectral imaging was used to collect spectral data from corn samples subjected to accelerated aging.
• Spectral data preprocessing included SG2 smoothing, and characteristic wavelengths were extracted using SPA.
• Prediction models were built using Random Forest (RF) for fatty acid value quantification.

Main Results:

• Fatty acid values showed an increasing trend with prolonged aging time across three corn varieties.
• The SG2-SPA-RF model achieved a high prediction accuracy (R_P² = 0.9655) for fatty acid values.
• The model identified key wavelengths, significantly reducing data dimensionality.

Conclusions:

• Fatty acid value is a reliable indicator of corn aging.
• Hyperspectral imaging combined with chemometrics provides an effective method for non-destructive corn freshness testing.
• This technology can support quality control in the corn industry.