Multi-Method Convergence Framework with Machine Learning for Upcycled Plant Coagulants: A Case Study of Pineapple Side-Streams in Direct-Acidified Milk Curd Systems

Pineapple processing generates millions of tons of agro-industrial side-streams annually, yet their circular upcycling into functional food ingredients demands rigorous validation—a task frequently constrained by modest experimental sample sizes. While traditional dairy processing relies on standardized chymosin-rennet systems, this study addresses the fundamental biophysical interactions governing alternative, direct-acidified matrices. By shifting the analytical focus from direct industrial substitution to the engineering of novel plant-augmented systems, this work introduces a Multi-Method Convergence Framework (MMCF). This low-compute, data-driven architecture integrates zero-order kinetic modeling, machine learning (ML) ensemble forecasting, and multivariate dimensional reduction to derive robust, predictive conclusions from resource-constrained datasets ( N = 5 biological replicates). The framework was deployed to systematically evaluate six direct-acidified whole-milk curd trajectories over a 28-day refrigerated storage window. The optimized bromelain-enriched pineapple extract (T6) is hypothesized to operate via an enzymatic-acidification convergence (EAC) mechanism, achieving a 17.90% curd yield, 19.46% protein content, and 8.62 N matrix hardness at Day 0. Temporal kinetic profiles demonstrated highly predictable, near-linear maturation pathways ( R 2 ≥ 0.96). Concurrently, a stacked ensemble ML model forecast protein stability at Day 35 within the same treatment set with exceptional precision ( R 2 = 0.98), while principal component analysis captured 95.4% of the total cumulative variance, confirming T6’s distinct, optimized multivariate quality space. This framework successfully mitigates sampling uncertainty without requiring expanded replicate numbers, offering a validated, reproducible template to accelerate the agile screening of functional agricultural by-products within specific, defined food manufacturing systems.