Fortification of lentil chips with multiple vitamins and minerals using hot extrusion technology: effects of moisture, extrusion temperature, conditioning, and storage techniques
Bibliographic record
Abstract
This study sought to explore the development and fortification of lentil chips using hot extrusion technology (HET) to find out the effect of moisture, extrusion temperature, conditioning time, and storage life. The chips were fortified using four food fortifying agents, viz. FAO recommended vitamin and mineral premixes (T 2 ), carrot powder (T 3 ), pumpkin powder (T 4 ), and a combination of carrot and pumpkin powder (T 5 ) with control (T 1 ). High barrel temperature, increasing viscosity of the dough in the shaft of the gearbox, feed moisture, material ratio, feeding rate, and feeding & conditioning time were the main barriers of single screw extruders for producing high-yield quality chips. Results revealed that incorporation of whole lentils with other ingredients, 1.0 % feed moisture, conditioning 2-3 hrs, feed rate and time 4.12 kg/hr, and extrusion temperature 115 ± 5 °C led to an increase in the yield of chips (4 kg/hr or 8000 pouches/hr) with higher swelling capacity. The study indicates that fortification process increased crude protein, β-carotene, vitamin C, vitamin D and bioactive compounds as compared to control with a range of 23.07 to 26.31 %, 39.43 to 43.81 mg/100 g, 3.21 to 3.53 mg/100 g, 53.48 to 69.34 µg/100 g and 7.14 to 9.95 mg GAE/100 g respectively. The chips fortified with native sources of raw materials (T 3 -T 5 ) contained higher levels of nutritional and bioactive compounds compared to those with multiple vitamins and minerals (T 2 ). Similarly, the loss of vitamin C and β-carotene during processing and fortification had been documented from 19.19 to 20.49 % and 2.08 to 40.34 % respectively. Moreover, native sources of fortification agents, especially combined pumpkin and carrot powder (T 5 ), were found to be promising as an alternative to FAO-recommended multi-vitamin and mineral premixes. The sensory evaluation confirms that the fortified chips accomplished the highest overall acceptability score from 7.13 to 7.25, with better crispiness (7.50-7.57). The marketable life of the chips could be extended up to 4 months at ambient conditions. The benefit-cost ratio (BCR) of the chips for retail and wholesale prices was calculated as 2:1 and 1.6:1. The findings suggest that the processors could simply process the lentil chips as a low-cost technique by fixing the feed moisture, extrusion temperature, and conditioning time with a single screw extruder.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".