Species-Dependent Variations in the Physicochemical Properties of Bio-chips: Implications for Quality Control and Processing Optimization of Cassava

The systematic analysis of edible biochips provides accurate and reproducible data that supports quality assurance, safety assessment, and optimization; for utilization in food and industrial processing applications. Despite their wide deployment, limited studies have comprehensively compared the physicochemical properties of different cassava varieties using standardized analytical methods. This study focuses on the investigation of the species-dependent variations in the physicochemical properties of cassava chips. Game-Changer, Obasanjo-II, Poundable, Hope, and Baba-70 denoted as samples A, B, C, D and E respectively were oven-and-sun dried under controlled conditions. Moisture, cyanide, and physicochemical properties were modeled using Response Surface Methodology (RSM), Principal Component Analysis (PCA), Radar plots and Analysis of Variance (ANOVA) for optimization. The investigation covered percentage composition of fresh and dried samples, vitamins-minerals contents, principal components, correlations matrix, nutrients radar, hydrogen cyanide (HCN) content, moisture content and fundamental chemical attributes affecting the structural integrity of the chips. Sample D exhibited the highest initial moisture content while Sample E recorded the lowest, indicating a species-dependent variation in moisture retention. Minimal cyanide content within acceptable range was achieved, which demonstrates effective detoxification under controlled conditions. Species and temperature exerted a stronger influence than time; and observation identified specie as the most critical factor. PCA revealed complex nutrient trade-offs among minerals and vitamins, while RSM indicated that balanced control of drying time and temperature, rather than extreme settings, ensures optimal moisture reduction and processing efficiency. These findings underscore the hypercritical role of species selection in post-harvest cassava processing and provide baseline data for predicting drying behavior, ensuring product safety, and optimizing processing parameters for food and industrial applications.