SUBMERGED FERMENTATION BIOCONVERSION OF AIR-CLASSIFIED STARCH-RICH PULSE FLOURS TO PROTEIN-RICH PRODUCTS
Bibliographic record
Abstract
Pulse starch is a low-value, often underutilized co-product of the pulse industry. This research focuses on the submerged fermentation of starch-rich pulse fractions by generally recognized as safe (GRAS) microbes that result in production of microbial biomass enriched in crude protein, converting low-value starch to higher-value microbial protein. Accordingly, starch-rich pulse fractions of yellow field pea, yellow lentil and faba bean flours were fermented by Lactobacillus plantarum or Aspergillus oryzae applied as single- and multi-strain cultures. The fermentation process converted starch into microbial protein, increasing protein levels in fermented flour. The protein content of starch-rich yellow pea, yellow lentil and faba bean flours increased from 7.8% to 10.2%, 16.5% to 18.5% and 14.5% to 16.4% respectively. However, the increase in protein content was not sufficient to make the fermented substrates reach the targeted level of >45% protein. This was likely due to the shortage of nitrogen as starch-rich flours have 80% or above carbohydrate. The addition of inexpensive, commonly available nitrogen compounds was tested to increase protein. The starch-rich flours were supplemented with ammonium sulphate, ammonium phosphate or urea at varying concentrations (15 g/L – 35 g/L) over the fermentation time course to aid in de novo microbial protein synthesis. It was found that nitrogen supplementation aided microbial growth during fermentation and resulted in higher protein yield than when no additional nitrogen was added. Supplementation of urea at 35 g/L resulted in highest protein yield in all three pulse flours, resulting in final protein levels above 45%. The protein-rich fermented substrates were then further analyzed for proximate composition including starch, ash, lipid and moisture contents and in vitro protein digestibility (IVPD). It was found that as the protein content increased, the starch and lipid levels in the fermented substrates decreased. The overall protein digestibility of substrates fermented by L. plantarum was also improved and significantly higher (p<0.05) compared to A. oryzae and L. plantarum-A. oryzae co-culture fermented samples. Overall, this research highlights that fermentation by GRAS microbes for single cell protein (SCP) production is a highly efficient method to increase value of under-utilized starch-rich by-products in the pulse industry, as SCP can be used as an alternative for conventional food and feed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".