Enhanced powder characteristics of succinic acid through crystallization techniques for food industry application

Abstract

During crystallization, succinic acid, an important food additive, exhibit severe agglomeration behaviour, uneven crystal size distribution (CSD), and irregular crystal morphology, resulting in poor powder flow properties. This paper presents an innovative approach to optimize succinic acid crystallization through investigation of novel blade milling method, seeding, and inclusion of green additive, cetyltrimethylammonium bromide (CTAB). CTAB for crystal habit regulation and formation of desired cubic-like morphology. We employ Mask R-CNN, a deep learning-based image analysis tool to quantitatively assess crystal and powder flow properties. Results demonstrate that the inclusion of 0.5 wt% CTAB with two-stage cooling method yields uniform CSD ranging from 30 μm to 160 μm and improves powder properties, reducing the agglomeration degree by 46% and caking ratio to only 6.78%, indicating a decreased tendency for clumping. The integration of crystallization with the proposed deep learning framework not only improves succinic acid food functionality but also showcases highly efficient method for optimizing crystal properties, setting new standard for food crystallization processes. It has been adequately presented with real-time high-precision analysis of CSD, agglomeration, etc, facilitating rapid screening for optimized food quality improvements.