5 Key Takeaways

• 1

  Researchers analyzed 721 untreated maple syrup samples with 785-nm Raman spectroscopy, collecting 21,630 spectra for machine-learning analysis.

• 2

  Convolutional neural networks classified golden and very dark syrups with 91% and 92% accuracy, respectively, but accuracy was lower for amber and dark grades.

• 3

  Flavor-classification accuracy was 83% for syrups without detectable defects and 78% for syrups with the late-season “buddy” defect.

• 4

  Raman fluorescence background intensity increased as syrup darkened and supported color-grade estimates with 81% to 94% accuracy.

• 5

  A sucrose-standard model yielded an R² of 0.87 and a 2.2% root mean squared error, but syrup estimates were sucrose equivalents, not validated sugar concentrations.