Use of low cost near-infrared spectroscopy, to predict pasting properties of high quality cassava flour.
Breakdown viscosity
Calibration models
Early generations’ phenotyping
Pasting temperature
Peak viscosity
Prediction models
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
25 Jul 2024
25 Jul 2024
Historique:
received:
30
11
2023
accepted:
10
07
2024
medline:
26
7
2024
pubmed:
26
7
2024
entrez:
25
7
2024
Statut:
epublish
Résumé
Determination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740-1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. Based on calibration statistics (R
Identifiants
pubmed: 39054362
doi: 10.1038/s41598-024-67299-w
pii: 10.1038/s41598-024-67299-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
17130Informations de copyright
© 2024. The Author(s).
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