Machine learning-based prediction of total phenolic and flavonoid in horticultural products
The purpose of this study was to predict the total phenolic content (TPC) and total flavonoid content (TFC) in several horticultural commodities using near-infrared spectroscopy (NIRS) combined with machine learning. Although models are typically developed for a single product, expanding the coverag...
Main Authors: | Kusumiyati Kusumiyati, Asikin Yonathan |
---|---|
Format: | Article |
Language: | English |
Published: |
De Gruyter
2023-01-01
|
Series: | Open Agriculture |
Subjects: | |
Online Access: | https://doi.org/10.1515/opag-2022-0163 |
Similar Items
-
Real-Time Detection of the Nutritional Compounds in Green ‘Ratuni UNPAD’ Cayenne Pepper
by: Kusumiyati Kusumiyati, et al.
Published: (2022-06-01) -
Estimation of Total Phenols, Flavanols and Extractability of Phenolic Compounds in Grape Seeds Using Vibrational Spectroscopy and Chemometric Tools
by: Berta Baca-Bocanegra, et al.
Published: (2018-07-01) -
Performance of visible and Near-infrared spectroscopy to predict the energetic properties of wood
by: Franciele Gmach, et al.
Published: (2023-11-01) -
Rapid and Cost-Effective Quantification of Glucosinolates and Total Phenolic Content in Rocket Leaves by Visible/Near-Infrared Spectroscopy
by: Eva María Toledo-Martín, et al.
Published: (2017-05-01) -
Near-Infrared Spectroscopy Integration in the Regular Monitorization of Pasture Nutritional Properties and Gas Production
by: Cristiana Maduro Dias, et al.
Published: (2023-07-01)