Fine milling and air classification (AC) is an attractive method to concentrate protein from pea and faba bean, but many more starch-rich pulses can be fractionated into enriched ingredients. However, switching raw materials may require costly trial-and-error optimization of separation settings. This work aims to predictively model protein separation from starch-rich pulse flours, as function of classifier settings. This was possible by defining populations of microstructures created upon milling, and by using mass balances to deconvolute the particle size distribution (PSD) of the flour into individual PSDs of each microstructure, in a self-consistent approach. The model was successfully developed on data of adzuki bean flour, for which milling and air classification was not reported before, and could also adequately deconvolute flours of mung bean, faba bean, and yellow pea. A particular experimental finding for adzuki bean was efficient protein separation (>90 % enrichment, >60 % protein yield) in a single classifier milling step, omitting the need for an additional air classification step. Next, a cut size model was used to quantify particle size recovery from flours as function of air classifier settings. Coupling this cut size model to the compositionally deconvoluted flours accurately predicted protein separation in the fine fraction over a range of classifier wheel speeds and air flow rates. Conversely, it is also possible to predict e.g. starch separation in the coarse fraction. The model may replace trial-and-error tests regarding optimization of classifier settings, providing both academic and industrial relevance.
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