Development and characterization of peptide antioxidants from sorghum proteins
Notice bibliographique
Résumé
Antioxidants are widely used in food industries to delay lipid oxidation and prevent oxidative deterioration. In recent years, growing interests in developing safe and efficient antioxidants from natural sources due to the health-related risks associated with synthetic antioxidants. Recently, peptide antioxidants have drawn growing interests as since proteins are a macronutrient with various functionalities and high consumer acceptability. A lot of dietary proteins have been validated for their antioxidant potentials especially those obtained from animal proteins, nuts and pulses. Relatively less information is available on characterizing the antioxidant profile of cereal protein, and even less for sorghum protein. Sorghum is the fifth largest crop worldwide and is the third in United States. U.S. is leading in global sorghum production and distribution, and the state of Kansas is producing nearly half of U.S. sorghum. Currently, about one third of the U.S. sorghum is being used for ethanol production, resulting in more than 450 kilotons of by-products (e.g., DDGS) annually, which were often discarded or underutilized. DDGS is a premium protein source (~ 30% protein) that could be potentially modified into value-added products such as peptide antioxidants. In this study, relevant literatures detailing the extraction of cereal proteins, enzymatic hydrolysis of proteins, purification and characterization of hydrolysates, and evaluation of antioxidant profiles were extensively reviewed in Chapter 1. As preliminary experiments, sorghum kafirin protein was extracted from defatted sorghum white flour and hydrolyzed by 10 different types of enzymes from microbial, plant and animal sources. Hydrolysates prepared with Neutrase, Alcalase, and Papain displayed the most promising antioxidant activities as well as total protein recovery were primarily selected and investigated in depth described in Chapter 2, Chapter 3, and Chapter 4. The reaction conditions including substrate content, enzyme-to-substrate ratio, and hydrolysis time are critical parameters in producing peptides with desired activity and consistency, were therefore examined and optimized for each case of kafirin hydrolysates. The antioxidant capacity of the resulting hydrolysates was measured for antioxidant capacity through in vitro assays (DPPH, ABTS, ORAC, reducing power, and metal chelating) and then demonstrated in model systems (oil-in-water emulsion and ground meat). The fractions of hydrolysates possessing strongest activities were further fractionated by gel filtration and HPLC. Peaks representing the largest areas from HPLC were identified for major sequences by MALDI-TOF-MS. The experiment results indicated that all the three selected fractions of kafirin hydrolysates revealed excellent inhibition effects against oil and fat oxidations, which could be employed as tools to predict their performances in real food products. In addition, the structure studies showed that medium-sized hydrolysates of Neutrase (3 – 10 kDa) and Alcalase (5 – 10 kDa), and small-sized hydrolysates of Papain (1 – 3 kDa) exhibited relatively stronger activities. This study provided a workable processing method and critical reaction parameters for the production of peptide antioxidants from sorghum protein. The experiment results revealed that the sorghum peptide antioxidant could act through multiple mechanisms including free radical scavenging, metal ion chelation, hydrogen donating, and forming physical barriers to minimize the contact of oxidative agents to targets. These antioxidative peptides are a promising ingredient that can be potentially incorporated to food and feed products as alternatives to synthetic antioxidants or synergetic elements to nonpeptic antioxidants for protection of susceptible food ingredients. This study also made a positive impact to sorghum ethanol industry by guiding the conversion of sorghum protein-rich by-products into value-added antioxidant products as an additional revenue stream.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».