Abstract
This study encapsulates the efficient prediction of moisture content in cocoa beans through Near Infrared Spectroscopy (NIRS) and Partial Least Squares (PLS) regression, showcasing a strong model fit with a high R square value of 0.92 and low Root Mean Square Error (RMSE) of 0.36% in calibration; these values underscore the model's accurate estimation of moisture levels. In the realm of electro-optics properties, this success highlights NIRS's capability in assessing key attributes like moisture content in cocoa beans based on their unique spectral signatures, emphasizing the technology's role in quality control for chocolate production. Furthermore, the precise predictions align with the broader objective of leveraging NIRS to evaluate and optimize the electro- optics properties of cocoa beans, fostering informed decision-making for enhanced processing and quality assurance in the cocoa industry.
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