MétaCan
Menu
Retour à la cohorte
Enregistrement W7006892006

Wet Fractionation Approaches For Isolating Pulse Proteins And Their Modification Through pH Shifting

2024· dissertation· en· W7006892006 sur OpenAlexfundaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2024
Typedissertation
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueProteins in Food Systems
Établissements canadiensnon disponible
Organismes subventionnairesAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaAlberta Pulse Growers CommissionSaskatchewan Pulse GrowersWorld Health Organization
Mots-clésExtraction (chemistry)FractionationProtein purificationParticle sizePulse (music)Plant proteinWater extraction
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Plant protein extraction involves separating proteins from other plant materials. The extracted proteins are then used in various food and non-food applications depending on their nutritional and functional properties. In the case of food ingredients, markets require consistent and reliable protein ingredients. Pulses are high in proteins, and their extracted proteins exhibit good functionality when used as ingredients, such as foaming or emulsifying agents. However, the processing conditions to which the proteins are subject can impact the protein structure and affect their functionality. The overarching goal of this study was to investigate the impact of extraction conditions, such as flour to water ratio (f:w), particle size of the starting flours and the use of different extraction methods, on the surface and functional properties of four different pulses: yellow peas (YP), green lentils (GL), kabuli chickpeas (CH) and navy beans (NB). Additionally, the effect of a pH shifting method was investigated on a commercial pea protein as a model for a pulse protein, with the aim of enhancing pulse protein solubility, foaming and emulsifying properties.\nThe first study (Chapter 3) examined the effect of reducing water levels used during alkaline extraction, where three different flour:water ratios were tested (1:10, which is the most commonly used ratio, 1:7 and 1:5) on fine pulse flours (sieved through a 0.5 mm mesh size screen, P1) and the resulting extraction, protein yields, and functionality were assessed. The results showed that reducing the water levels used in the extraction process did not result in changes in functionality; however, it led to a significant decrease in the protein yields recovered. Due to these protein losses, a 1:10 f:w ratio was selected to perform extractions with a coarse flour (1.27 mm mesh size screen, P2) to assess extraction yields as well as functionality. Results showed that using a fine flour led to an increase in both extraction (0.4 to 3.2 % increase) and protein yields (7.9 to 10.3 % increase). In terms of the functional properties, P1 and P2 extracted proteins differed in some functional properties, but these differences were not consistent for all the pulses evaluated. Determination of the legumin (L) and vicilin (V) ratios of the pulse types evaluated led to finding differences in functionality between the different pulse types tested: NB exhibited the highest solubility and emulsifying properties, while CH showed the highest foaming capacity (FC). This study highlights the impact of milling and control of the particle size of the starting flours on the extraction of pulse proteins.\nThe second study (Chapter 4) focused on the effect of using different extraction methods, alkaline extraction – isoelectric precipitation - AEIP and salt extraction- SE, on the surface, functional and nutritional properties of protein isolates extracted from the same pulses tested in the first study. The optimum conditions found in the first study were used in this study as well since the 1:10 f:w ratio and P1 flours achieved the highest yields. Results showed that the extraction method influences the type of proteins being extracted, impacting the albumin (A)/ globulin (G) ratios. The SE-extracted proteins extracted a greater proportion of albumins than the AEIP method, and these differences in protein composition had an effect on the nutritional properties as well as on protein functionality, impacting protein solubility, foaming and emulsifying properties. The proteins extracted using an AEIP method showed increased in-vitro protein digestibility (IVPD), while the SE proteins showed overall increased functionality over the AEIP proteins.\nThe last study (Chapter 5) explored the effect of acid (pH 2) and alkaline (pH 10) pH shifting in combination with temperature on a commercial pea protein isolate as a modification method to improve the protein’s solubility, foaming and emulsifying properties. Results from this study indicated that an alkaline pH shifting, as well as in combination with heat, led to increases in protein solubility, while an acidic treatment as well as in combination with heat negatively impacted this functional property. The FC of the samples was increased by an alkaline pH shifting, while FS results were either neutral or negatively impacted by the treated samples. An analysis of the foam structure using a dynamic foam analyzer (DFA) allowed us to explore differences and to monitor the bubble structure of the resulting foams.\nOverall, this research explored the effects of protein extraction methods and conditions on the resulting functionality and nutritional properties of common pulses grown in Canada. Knowledge from these studies can provide further understanding on the impact of changes in protein structure during processing and aid in obtaining consistent ingredients with the expected functionality for food applications.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,604
Score d'incertitude au seuil0,728

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,002
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,035
Tête enseignante GPT0,193
Écart entre enseignants0,159 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueUniversity Library (University of Saskatchewan)Même sujetProteins in Food SystemsTravaux en français237 207