Iron biofortification and fortification of lentil (Lens culinaris Medik.)
Notice bibliographique
Résumé
Biofortification and fortification strategies for lentil (Lens culinaris Medik.) were investigated to increase bioavailable iron (Fe) in the human diet. Biofortification studies included, firstly, development of a precise protocol for Fe analysis of seeds of all (seven) Lens species using flame atomic absorption spectrometry (F-AAS). Secondly, genotype (G) × harvest (H) timing interaction of seed Fe accumulation was determined during seed maturation stages in seven lentil species. Thirdly, estimates were made of seed Fe concentration (SFeC), its inheritance, and the effect of genotype (G) × environment (E) interaction for two interspecific recombinant inbred line populations (RILs) of lentil. Finally, molecular markers associated with SFeC across 138 diverse cultivated lentil accessions were identified by phenotyping in four environments in Saskatchewan, Canada. For the fortification strategy, appropriate methods and dosage were determined for Fe fortification of lentil dal with FeSO4·7H2O, NaFeEDTA and FeSO4·H2O. A colorimetric study determined changes in appearance of fortified lentil at various Fe concentrations over three storage periods. Sensory evaluation with panelists in Saskatoon and Bangladesh evaluated cooked and uncooked fortified lentil using a 9-point hedonic scale (1 = dislike extremely to 9 = like extremely). Finally, Fe and phytic acid (PA) concentration and relative Fe bioavailability (RFeB%) were estimated in 30 traditional Bangladeshi dal meals featuring either fortified (fortificant Fe concentration of 2800 µg g-1) or unfortified lentil.\nThe first study determined the minimum lentil seed sample (0.3 g and 0.5 g of wild and cultivated species, respectively) required for an accurate and precise estimation of SFeC. The G × H timing interaction study revealed significant variation for SFeC among genotypes, but a similar seed Fe accumulation trend over the harvest period. Field evaluations revealed significant variability for SFeC among lentil RILs and for G × E interactions with high broad sense heritability for SFeC. Association mapping studies revealed wide variation for SFeC among genotypes. Two SNP markers were tightly linked to SFeC (−log10 P ≥ 4.36) and also seven additional markers were also significant (−log10 P ≥ 3.06) for SFeC. Most (six) markers were found on chromosome 5. Putative candidate genes were identified underlying alleles encoding Fe related functions. The fortification study revealed that NaFeEDTA was the most suitable Fe fortificant for lentil dal, and at 1600 µg g-1 fortificant Fe concentration, it provided 13-14 mg of additional Fe per 100 g of dal. Total Fe and PA concentrations, and RFeB% differed significantly between cooked unfortified and fortified lentil. Significant differences in sensory quality were observed among all uncooked and cooked samples when tested in Canada and Bangladesh. NaFeEDTA had the least effect on consumer perception of colour, taste, texture, odour and overall acceptability of cooked lentil. The meal study revealed that NaFeEDTA fortified lentil increased Fe concentration in lentil from 60 to 439 µg g-1 and RFeB% by 79% as estimated by Caco-2 cell ferritin formation. Phytic acid levels also were reduced from 6.2 to 4.6 mg g-1 when fortified lentil was added, thereby reducing the PA:Fe molar ratio from 8.8 to 0.9. The overall outcomes of this research could help to significantly and cost-effectively increase the amount of bioavailable Fe in lentil, and the consumption of fortified lentil could help to provide a significant part of the consumer’s daily Fe requirement.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».