Hyperspectral Imaging for Evaluating Impact Damage to Mango According to Changes in Quality Attributes

Area of Science:

• Agricultural Engineering
• Food Science
• Spectroscopy

Background:

• Impact damage significantly affects mango (Mangifera indica Linn) quality and shelf-life.
• Non-destructive methods are needed to assess post-harvest damage in mangoes.

Purpose of the Study:

• To evaluate hyperspectral imaging (HSI) for detecting impact damage in mangoes.
• To correlate spectral data with key quality attributes affected by impact.

Main Methods:

• Mangoes were dropped from heights of 0.5, 1.0, and 1.5 m.
• Reflectance spectra (900–1700 nm) were acquired.
• Partial Least Squares (PLS) regression models were developed for pulp firmness (PF), total soluble solids (TSS), titratable acidity (TA), and chroma (Δb*).

Main Results:

• Spectral changes strongly correlated with dropping height and impacted quality attributes.
• High predictive performance was achieved for PF (R²=0.84), TSS (R²=0.90), and chroma (R²=0.94).
• Impact damage classification achieved >77.8% accuracy based on the ripening index (RPI).

Conclusions:

• Hyperspectral imaging is a viable tool for non-destructively assessing impact damage in mangoes.
• HSI can detect changes in mango quality attributes resulting from physical impacts.
• This technology offers potential for quality control in the mango supply chain.