Quantifying Extraneous Matter in Shredded Sugarcane Using Near-Infrared Spectroscopy

Sugar cane is one
of the most important agricultural commodities
globally, serving as a vital source of sugar, bioethanol, and employment
for millions of people across more than 100 countries. Rising rainfall
due to climate change, evolving environmental regulations, and cost-driven
harvesting practices have increased extraneous matter (EM) in sugar
cane, reducing factory efficiency and sugar recovery. Despite its
significant impact, EM cannot be regularly quantified in sugar factories
due to the lack of practical measurement methods and is therefore
still excluded from cane payment systems. We introduce near-infrared
(NIR) spectroscopy as a rapid, nondestructive solution to this challenge.
NIR calibration models for leaf content in shredded cane were developed
using mixtures of clean cane, soil, and leaves with known EM concentrations,
and soil content calibrations were built using incinerated ash as
the reference method. Partial least-squares regression models with
k-fold cross-validation were developed to correlate NIR spectra with
reference values. Soil content based on ash analysis yielded strong
calibration results (R2 = 0.88), markedly outperforming
sediment analysis (R2 = 0.12). For the first time, NIR
successfully predicted brown leaves (R2 = 0.72), green
leaves (R2 = 0.73), and total leaves (R2 = 0.88).
These findings prove the potential of NIR spectroscopy to revolutionize
EM analysis, providing a practical pathway for its integration into
cane payment systems and improving sugar cane quality assessment.