Background and objectives: Flour millers are faced with constraints of having to meet proximate specifications, usually defined by supply contracts, while trying to maximize yield. The study investigated the application of response surface methodology (RSM), in a commercial scale flour mill, as a means of maximizing yield while meeting quality constraints. Findings: This study utilized a Box-Behnken design to develop mathematical models using RSM to describe the effect of three independent variables, wheat conditioning level (12%-18%), first break roll gap (350-600 µm), and second break roll gap (200-600 µm) on the responses. The model R 2 for the responses was .98, .96, .98, and .98 for ash, protein and moisture contents, and flour yield, respectively. All models were statistically significant (p < .05) and validated with four independent experiments. RSM models were used to optimize the process to produce flour with a protein content greater than 12.0%, ash content less than 0.54%, moisture content less than 14.5%, and yield greater than 84.2% on a clean wheat, unconditioned basis. This was achieved with conditioning wheat to 18.4%, first break roll gap of 450 µm,and second break roll gap of 250 µm. Conclusion:The optimum mill settings resulted in a flour yield increase of 1.45% and reduction in ash content from 0.58% to 0.53%. Significance and novelty: Using RSM, significant financial gains could be achieved by producing more flour from a given quantity of wheat with lower levels of bran contamination.

Milling and air classification settings were optimised for production of protein concentrates from Australian faba bean, yellow pea and red lentil seed material. Pulses were milled to flour of three progressively finer particle size distributions (D50 of 23–25, 16–18 and 13–14 μm) and air classified at classifier wheel speeds of 7080, 9600 and 10,200 rpm. Maximum protein concentration was reported for pulse flours (D50 of 13–14 μm) at 9600 rpm. Protein concentrations of 61.4, 58.1 and 61.0 g/100 g (db.), reflecting a fold increase in protein content of 1.9, 2.3 and 2.1, were reported for faba bean, yellow pea and red lentil, respectively. Protein, ash, fat and total dietary fibre contents were significantly higher in fine fractions (p < 0.05), compared with coarse fractions, resulting in protein concentrates with enhanced nutritional properties. Amino acid score (AAS) of protein concentrates highlighted deficiencies in sulphur‐containing amino acids, methionine and cysteine (MET + CYS), and tryptophan. Based on the lowest AAS (MET + CYS), protein concentrates were ranked highest for yellow pea (0.75), followed by faba bean (0.58) and red lentil (0.51). Phytochemical analysis demonstrated that bioactive constituents also co‐concentrated with protein (fine fraction), potentially leading to protein concentrates with enhanced health benefits. Shelf‐life assessment for the original flours and protein concentrates indicated the onset of rancidity after 3 months of storage. As fat content co‐concentrated with protein, the rancidity (%) scores were higher for protein concentrates compared with the original flours. This demonstrates the importance of developing effective treatments, suitable for dry processing, which can extend shelf‐life and stability of protein concentrate ingredients for domestic and export markets. The objective of this study was to increase the knowledge available on dry processing of protein concentrates from Australian pulses. The information generated from this study will look to inform future commercial scale processing operations.

Background and objectives Flour millers often produce several flour types from a single wheat grist. Consequently, different specifications characterize each flour. For example, French standards specify six different flour types, each classified by ash content. The proportional blending of different flour streams from a single wheat grist achieves the target flour specifications. This study explores the opportunity to improve flour blending using linear programming and compares it to sequential ash curve blending. Findings Linear programming and ash curve approaches were used to meet specifications for French flour types from a wheat grist milled to produce 10 flour streams, each stream having different flour quality attributes. The first simulation set quantity targets for Types 45, 55, and 65 flour. The balance of the flour went to the lower value Types 80, 110, and 150. The flour type targets were met using Linear Programming. By utilizing the ash curve method, Type 65 flour was under‐delivered. The second simulation aimed to maximize income using the two methods with no constraints on the amount of each flour type. The linear programming approach resulted in a 0.13% increase in revenue compared to the ash curve technique. Conclusion In the first simulation, the linear programming technique reduced the lower‐value high ash flour types, generating an additional $6.16/ton of flour. In the second simulation, linear programming increased income by $0.84/ton of flour. Thus, a milling plant operating for 8,000 hr/year and processing 20 tons of wheat/hr translates to $779,000 and $107,000 per annum, respectively. Significance and novelty This study showed that linear programming could significantly improve flour blending outcomes, resulting in increased profitability and resource utilization in the milling industry.