Proso Millet (

antiradical activity cinnamic acids glycemic index high amylose resistant starch starch composition

Journal

Molecules (Basel, Switzerland)
ISSN: 1420-3049
Titre abrégé: Molecules
Pays: Switzerland
ID NLM: 100964009

Informations de publication

Date de publication:
30 Aug 2023
Historique:
received: 06 07 2023
revised: 20 08 2023
accepted: 28 08 2023
medline: 11 9 2023
pubmed: 9 9 2023
entrez: 9 9 2023
Statut: epublish

Résumé

Proso millet has been proposed as an effective anti-diabetic food thanks to the combined action of polyphenols and starch. This study aimed to characterize the chemical composition of twenty-five accessions, in order to enhance this cereal as an alternative to available starch for food applications or to propose new food ingredients with health benefits. Proso millet contained a high percentage of starch, reaching values of 58.51%. The amylose content showed high variability, with values ranging from 1.36 to 42.70%, and significantly higher values were recorded for the white accessions than for the colored ones. High-resistant starch content (13.41-26.07%) was also found. The HPLC-MS analysis showed the same phenolic pattern in all the samples. Cinnamic acids are the most abundant compounds and significant differences in their total content were found (0.69 to 1.35 mg/g DW), while flavonoids were only detected in trace amounts. Statistical results showed significantly higher antiradical activity in the colored accessions than in the white ones.

Identifiants

pubmed: 37687168
pii: molecules28176339
doi: 10.3390/molecules28176339
pmc: PMC10489065
pii:
doi:

Substances chimiques

Starch 9005-25-8
Phenols 0
Polyphenols 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

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Auteurs

Diletta Balli (D)

Department of NEUROFARBA, University of Florence, Via Ugo Schiff 6, 50019 Florence, Italy.

Maria Bellumori (M)

Department of NEUROFARBA, University of Florence, Via Ugo Schiff 6, 50019 Florence, Italy.

Alberto Masoni (A)

Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, 50144 Florence, Italy.

Michele Moretta (M)

Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, 50144 Florence, Italy.

Enrico Palchetti (E)

Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, 50144 Florence, Italy.

Bruno Bertaccini (B)

Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Viale Morgagni 59, 50134 Florence, Italy.

Nadia Mulinacci (N)

Department of NEUROFARBA, University of Florence, Via Ugo Schiff 6, 50019 Florence, Italy.

Marzia Innocenti (M)

Department of NEUROFARBA, University of Florence, Via Ugo Schiff 6, 50019 Florence, Italy.

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Classifications MeSH