Robust Near Infrared Spectroscopy for Rapid and Simultaneous Determination of Fermented Cocoa Pod Husk Feed Quality Attributes

Abstract

In this study, we attempted to develop a robust and accurate near infrared spectroscopic model for nutrient content analysis in fermented Cocoa Pod Husk (CPH), a viable but underexploited byproduct in cocoa production with great potential for use in animal feed. Recognizing the necessity for sustainable feed options, precise nutrient profiling of CPH is critical for balanced diets and effective feed formulation. To achieve this, specific spectral pre-processing techniques, namely multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SGs), and the first derivative (1st D) were purposefully chosen for their individual and combined abilities to correct for scattering effects, smooth out noise, and enhance spectral resolution, respectively. These methods significantly contribute to the model's superior performance by improving the quality of the spectral data input. Furthermore, Partial Least Squares Regression (PLSR) was selected over other multivariate algorithms due to its robustness in handling collinear and noisy data, making it well-suited for complex biological matrices such as fermented CPH. Employing the Unscrambler X 10.4 software, the PLSR model was rigorously assessed using a range of statistical tools to ensure validity, with notable precision in predicting key nutritional components. The findings not only confirm the model's excellence but also hold promising implications for the agriculture industry, particularly in the development of cost-effective, nutrient-rich animal feed solutions. By capitalizing on the compositional richness of CPH and refining NIRS modeling for its analysis, this study contributes to the enhanced utilization of agricultural byproducts and the sustainability of animal nutrition practices.