Logo of Humboldt-Universität zu BerlinLogo of Humboldt-Universität zu Berlin
edoc-Server
Open-Access-Publikationsserver der Humboldt-Universität
de|en
Header image: facade of Humboldt-Universität zu Berlin
View Item 
  • edoc-Server Home
  • Artikel und Monographien
  • Zweitveröffentlichungen
  • View Item
  • edoc-Server Home
  • Artikel und Monographien
  • Zweitveröffentlichungen
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
View Item 
  • edoc-Server Home
  • Artikel und Monographien
  • Zweitveröffentlichungen
  • View Item
  • edoc-Server Home
  • Artikel und Monographien
  • Zweitveröffentlichungen
  • View Item
2021-10-11Zeitschriftenartikel DOI: 10.3390/pr9101804
Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott)
Ndisya, John cc
Gitau, Ayub
Mbuge, Duncan
Arefi, Arman
Bădulescu, Liliana
Pawelzik, Elke cc
Hensel, Oliver
Sturm, Barbara cc
Lebenswissenschaftliche Fakultät
In this study, hyperspectral imaging (HSI) and chemometrics were implemented to develop prediction models for moisture, colour, chemical and structural attributes of purple-speckled cocoyam slices subjected to hot-air drying. Since HSI systems are costly and computationally demanding, the selection of a narrow band of wavelengths can enable the utilisation of simpler multispectral systems. In this study, 19 optimal wavelengths in the spectral range 400–1700 nm were selected using PLS-BETA and PLS-VIP feature selection methods. Prediction models for the studied quality attributes were developed from the 19 wavelengths. Excellent prediction performance (RMSEP < 2.0, r2P > 0.90, RPDP > 3.5) was obtained for MC, RR, VS and aw. Good prediction performance (RMSEP < 8.0, r2P = 0.70–0.90, RPDP > 2.0) was obtained for PC, BI, CIELAB b*, chroma, TFC, TAA and hue angle. Additionally, PPA and WI were also predicted successfully. An assessment of the agreement between predictions from the non-invasive hyperspectral imaging technique and experimental results from the routine laboratory methods established the potential of the HSI technique to replace or be used interchangeably with laboratory measurements. Additionally, a comparison of full-spectrum model results and the reduced models demonstrated the potential replacement of HSI with simpler imaging systems.
Files in this item
Thumbnail
processes-09-01804-v2.pdf — Adobe PDF — 12.93 Mb
MD5: eb9df01e0e09767f497e9283a9ec613c
Notes
Cite
BibTeX
EndNote
RIS
(CC BY 4.0) Attribution 4.0 International(CC BY 4.0) Attribution 4.0 International
Details
DINI-Zertifikat 2019OpenAIRE validatedORCID Consortium
Imprint Policy Contact Data Privacy Statement
A service of University Library and Computer and Media Service
© Humboldt-Universität zu Berlin
 
DOI
10.3390/pr9101804
Permanent URL
https://doi.org/10.3390/pr9101804
HTML
<a href="https://doi.org/10.3390/pr9101804">https://doi.org/10.3390/pr9101804</a>