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Article

A Performance Evaluation of Vis/NIR Hyperspectral Imaging to Predict Curcumin Concentration in Fresh Turmeric Rhizomes

1
Genecology Research Centre, School of Science, Technology and Engineering, University of the Sunshine Coast, ML 40, Locked Bag 4, Maroochydore DC, QLD 4558, Australia
2
School of Science, Technology and Engineering, University of the Sunshine Coast, ML 40, Locked Bag 4, Maroochydore DC, QLD 4558, Australia
3
Centre for Planetary Health and Food Security, School of Environment and Sciences, Griffith University, Nathan, Brisbane, QLD 4111, Australia
4
Department of Agriculture and Fisheries, Queensland Government, Toowoomba, QLD 4350, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Chein-I Chang
Remote Sens. 2021, 13(9), 1807; https://doi.org/10.3390/rs13091807
Received: 10 March 2021 / Revised: 30 April 2021 / Accepted: 2 May 2021 / Published: 6 May 2021
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. View Full-Text
Keywords: curcumin; curcuminoids; hyperspectral imaging; jack-knifing; partial least squares regression (PLSR); turmeric (Curcuma longa); visible–near infrared (Vis/NIR) curcumin; curcuminoids; hyperspectral imaging; jack-knifing; partial least squares regression (PLSR); turmeric (Curcuma longa); visible–near infrared (Vis/NIR)
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MDPI and ACS Style

Farrar, M.B.; Wallace, H.M.; Brooks, P.; Yule, C.M.; Tahmasbian, I.; Dunn, P.K.; Hosseini Bai, S. A Performance Evaluation of Vis/NIR Hyperspectral Imaging to Predict Curcumin Concentration in Fresh Turmeric Rhizomes. Remote Sens. 2021, 13, 1807. https://doi.org/10.3390/rs13091807

AMA Style

Farrar MB, Wallace HM, Brooks P, Yule CM, Tahmasbian I, Dunn PK, Hosseini Bai S. A Performance Evaluation of Vis/NIR Hyperspectral Imaging to Predict Curcumin Concentration in Fresh Turmeric Rhizomes. Remote Sensing. 2021; 13(9):1807. https://doi.org/10.3390/rs13091807

Chicago/Turabian Style

Farrar, Michael B., Helen M. Wallace, Peter Brooks, Catherine M. Yule, Iman Tahmasbian, Peter K. Dunn, and Shahla Hosseini Bai. 2021. "A Performance Evaluation of Vis/NIR Hyperspectral Imaging to Predict Curcumin Concentration in Fresh Turmeric Rhizomes" Remote Sensing 13, no. 9: 1807. https://doi.org/10.3390/rs13091807

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