  The pulp and paper manufacturing industry requires precise quality control to
ensure pure, contaminant-free end products suitable for various applications.
Fungal spore concentration is a crucial metric that affects paper usability,
and current testing methods are labor-intensive with delayed results, hindering
real-time control strategies. To address this, a machine learning algorithm
utilizing time-series data and domain knowledge was proposed. The optimal model
employed Ridge Regression achieving an MSE of 2.90 on training and validation
data. This approach could lead to significant improvements in efficiency and
sustainability by providing real-time predictions for fungal spore
concentrations. This paper showcases a promising method for real-time fungal
spore concentration prediction, enabling stringent quality control measures in
the pulp-and-paper industry.
