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Record W3024241208 · doi:10.1002/cche.10296

Frost‐affected lentil (<i>Lens culinaris</i> M.) compositional changes through extrusion: Potential application for the food industry

2020· article· en· W3024241208 on OpenAlexaboutno aff
Drew Portman, Carl Dolgow, Pankaj Maharjan, Stephen Cork, Christopher Blanchard, Mani Naiker, Joe Panozzo

Bibliographic record

VenueCereal Chemistry · 2020
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
FundersAgriculture Victoria
KeywordsFrost (temperature)Food industryExtrusionFood scienceComposition (language)Production (economics)AgronomyChemistryBiologyGeographyEconomicsMaterials science

Abstract

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Abstract Background and objectives Lentil ( Lens culinaris M.) is a high value, highly nutritional grain which originated in Middle‐East. More recently lentil has gained favor in Western countries due to the high value in production and the benefits they provide agronomically; however, growing lentil in countries such as Australia, Canada, and the United States is not without its challenges. One is the high probability of damage due to radiant frost either before flowering or during pod‐filling. The effects of which is most noticeable in the appearance of the seed reduce the value and usability of crops. On the other hand, a generation of well informed, health‐conscious, and environmentally concerned consumers has driven the demand for affordable and healthy food alternatives. Such demand has resulted in a growing industry for protein extraction and novel food production. If used for protein extraction or novel food production, the visual appearance of seeds is no longer an important quality trait. Lentil seeds damaged through frost that still retain a high nutritional composition may be the perfect candidate for a low‐cost substrate in novel food production while improving the outcomes for growers and industry alike. Findings This study used as a model extrusion technology to investigate the use of flour derived from Grade 1 (premium quality) and downgraded frost‐damaged lentil and to monitor compositional changes during extrusion. The study concentrated on how total protein, individual carbohydrates, and phenolic acids changed through high‐temperature, high‐pressure extrusion. Overall flours made from composite lentil–wheat flour had significantly higher concentrations of protein and carbohydrates than the base wheat flour. No significant differences were observed for total protein or carbohydrates between Grade 1 and frost‐damaged flours; however, extrusion significantly reduced total protein concentration as well as maltose and glucose concentration but did not alter the concentration of fructose, sucrose, or the raffinose family oligosaccharides (RFOs). As expected, phenolic acids, procyanidin, kaempferol glycoside, and kaempferol trihexoside were detected in lentil–wheat composites but not in wheat. All phenolic acids significantly increased with increasing concentrations of lentil flour in composite; however, their concentration decreased as a result of the extrusion process. Differences in concentrations of procyanidin and kaempferol glycoside were detected between Grade 1 and frost‐affected lentil in both the composite flour and the extrudate. Conclusions The extrusion process has the effect of altering the composition of the raw material. This was evident by a decrease in protein percentage phenolic compounds and to a lesser effect the water‐soluble carbohydrates. No changes in RFOs was observed. The complete loss of glucose and a significant reduction in maltose provide a healthier carbohydrate profile as the carbohydrates are low fermentable sugars. The reduction in phenolic acids a result of extrusion may help to reduce the antinutritional activity particularly in grains where the concentration of phenolic acids is high. This study found that functionality of downgraded lentil is similar to the premium grade, and more expensive raw material. This knowledge may assist in reducing the increasing issue of food waste where often downgraded products are not used in the production of food. Significants and novelty In order to meet world food security needs, it is predicted that global food production will need to increase by at least 70% by 2050. Therefore, the utilization of all possible protein sources including downgraded pulses, such as lentil, will become increasingly more important. Furthermore, due to climate change increasingly more variable weather conditions will result in the production of below optimum grains. Understanding how to best utilize both premium and downgraded grains is a desirable outcome to minimize food waste.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.253
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2020
Admission routes1
Has abstractyes

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