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A Performance Evaluation of Vis-NIR Hyperspectral Imaging to Predict Curcumin Concentration in Fresh Turmeric Rhizomes6.51 MB
Published VersionCC BY V4.0, Open Access
Journal article
A Performance Evaluation of Vis/NIR Hyperspectral Imaging to Predict Curcumin Concentration in Fresh Turmeric Rhizomes
Remote Sensing, Vol.13(9), pp.1-20
2021
Abstract
Hyperspectral image (HSI) analysis has the potential to estimate organic compounds in plants and foods. Curcumin is an important compound used to treat a range of medical conditions. Therefore, a method to rapidly determine rhizomes with high curcumin content on-farm would be of significant advantage for farmers. Curcumin content of rhizomes varies within, and between varieties but current chemical analysis methods are expensive and time consuming. This study compared curcumin in three turmeric (Curcuma longa) varieties and examined the potential for laboratory-based HSI to rapidly predict curcumin using the visible–near infrared (400–1000 nm) spectrum. Hyperspectral images (n = 152) of the fresh rhizome outer-skin and flesh were captured, using three local varieties (yellow, orange, and red). Distribution of curcuminoids and total curcumin was analysed. Partial least squares regression (PLSR) models were developed to predict total curcumin concentrations. Total curcumin and the proportion of three curcuminoids differed significantly among all varieties. Red turmeric had the highest total curcumin concentration (0.83 ± 0.21%) compared with orange (0.37 ± 0.12%) and yellow (0.02 ± 0.02%). PLSR models predicted curcumin using raw spectra of rhizome flesh and pooled data for all three varieties (R2c = 0.83, R2p = 0.55, ratio of prediction to deviation (RPD) = 1.51) and was slightly improved by using images of a single variety (orange) only (R2c = 0.85, R2p = 0.62, RPD = 1.65). However, prediction of curcumin using outer-skin of rhizomes was poor (R2c = 0.64, R2p = 0.37, RPD = 1.28). These models can discriminate between ‘low’ and ‘high’ values and so may be adapted into a two-level grading system. HSI has the potential to help identify turmeric rhizomes with high curcumin concentrations and allow for more efficient refinement into curcumin for medicinal purposes.
Details
- Title
- A Performance Evaluation of Vis/NIR Hyperspectral Imaging to Predict Curcumin Concentration in Fresh Turmeric Rhizomes
- Authors
- Michael B Farrar (Author) - University of the Sunshine Coast, Queensland, GeneCology Research Centre - LegacyHelen M Wallace (Author) - University of the Sunshine Coast, Queensland, GeneCology Research Centre - LegacyPeter Brooks (Author) - University of the Sunshine Coast, Queensland, GeneCology Research Centre - LegacyCatherine M Yule (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringIman Tahmasbian (Author) - Queensland GovernmentPeter K Dunn (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringShahla Hosseini Bai (Author) - University of the Sunshine Coast, Queensland, GeneCology Research Centre - Legacy
- Publication details
- Remote Sensing, Vol.13(9), pp.1-20
- Publisher
- MDPI AG
- Date published
- 2021
- DOI
- 10.3390/rs13091807
- ISSN
- 2072-4292
- Copyright note
- Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)
- Organisation Unit
- School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 99534608702621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- Web Of Science research areas
- Environmental Sciences
- Geosciences, Multidisciplinary
- Imaging Science & Photographic Technology
- Remote Sensing
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