Non-invasive identification of potato varieties and prediction of the origin of tuber cultivation using spatially offset Raman spectroscopy.
Identification
Nutrient content
Phenotyping
Potato varieties
Raman spectroscopy
SORS
Journal
Analytical and bioanalytical chemistry
ISSN: 1618-2650
Titre abrégé: Anal Bioanal Chem
Pays: Germany
ID NLM: 101134327
Informations de publication
Date de publication:
Jul 2020
Jul 2020
Historique:
received:
30
03
2020
accepted:
13
05
2020
revised:
05
05
2020
pubmed:
27
5
2020
medline:
9
2
2021
entrez:
27
5
2020
Statut:
ppublish
Résumé
High starch content, simplicity of cultivation, and high productivity make potatoes (Solanum tuberosum) a staple in the diet of people around the world. On average, potatoes are composed of 83% water and 12% carbohydrates, and the remaining 4% includes proteins, vitamins, and other trace elements. These proportions vary depending on the type of potato and location where they were cultivated. At the same time, the chemical composition determines the nutritional value of potato tubers and can be proved using various wet chemistry and spectroscopic methods. For instance, gravity measurements, as well as several different colorimetric assays, can be used to investigate the starch content. However, these approaches are indirect, often destructive, and time- and labor-consuming. This study reports on the use of Raman spectroscopy (RS) for completely non-invasive and non-destructive assessment of nutrient content of potato tubers. We also show that RS can be used to identify nine different potato varieties, as well as determine the origin of their cultivation. The portable nature of Raman-based identification of potato offers the possibility to perform such analysis directly upon potato harvesting to enable quick quality evaluation. Graphical abstract.
Identifiants
pubmed: 32451641
doi: 10.1007/s00216-020-02706-5
pii: 10.1007/s00216-020-02706-5
doi:
Substances chimiques
Carbohydrates
0
Plant Proteins
0
Starch
9005-25-8
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4585-4594Subventions
Organisme : Governor's University Research Initiative (GURI) grant program of Texas A&M University
ID : 12-2016, M1700437
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