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A Machine-Learning-Based Prediction Model for Total Glycoalkaloid Accumulation in Yukon Gold Potatoes.

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Summary

Hyperspectral imaging can detect total glycoalkaloids (TGA) in potatoes, a quality concern linked to greening. This non-destructive method shows promise for ensuring potato quality throughout the supply chain.

Keywords:
TGAhyperspectral imagingpotato quality

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Area of Science:

  • Agricultural Science
  • Food Quality Control
  • Spectroscopy

Background:

  • Potatoes are a major Canadian crop, with quality crucial for processed foods.
  • Greening in potatoes leads to the accumulation of toxic total glycoalkaloids (TGA).
  • Monitoring TGA is vital for food safety and potato product quality.

Purpose of the Study:

  • To develop a non-destructive method for predicting TGA levels in potatoes.
  • To assess the utility of short-wave infrared (SWIR) hyperspectral imaging for TGA detection.
  • To optimize spectral data analysis for practical quality control applications.

Main Methods:

  • Yukon Gold potatoes were artificially greened under controlled light conditions.
  • Short-wave infrared (SWIR) hyperspectral imaging (900-2500 nm) was used for spectral data acquisition.
  • Partial least squares regression (PLSR) models were built using spectral data and High-Performance Liquid Chromatography (HPLC) for TGA quantification.

Main Results:

  • Prediction models were developed using hyperspectral imaging data.
  • Wavelength selection techniques (CARS, BE) were employed to enhance model efficiency.
  • The best model achieved a cross-validation coefficient of determination (R²cv) of 0.72 and RMSEcv of 51.50 ppm.

Conclusions:

  • SWIR hyperspectral imaging is a viable tool for non-destructively estimating TGA in potatoes.
  • This technology can aid in maintaining potato quality and safety by detecting greening-induced TGA.
  • Further refinement of spectral analysis can lead to practical applications in the potato industry.