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.
Highlights
This study aimed to (1) compare total curcumin concentration and distribution of different curcuminoids in three varieties of C. longa grown in eastern Australia; and (2) evaluate the potential of Partial least squares regression (PLSR) models developed using visible–near infrared (Vis/NIR) spectra
In particular we explored the potential of hyperspectral imaging to predict curcumin nondestructively in fresh rhizome outer-skin and destructively using a cross-section of cut rhizome flesh
We suggest the decrease in prediction accuracy can be explained by ‘data clusters’ of samples with very low (
Summary
Hyperspectral imaging (HSI) is an emerging technology that has recently been used to non-destructively evaluate a variety of chemical compounds and quality indicators in soils and agricultural products (nuts, fruits, and vegetables) [1,2,3,4]. Traditional laboratory-based methods to detect compounds in plants are destructive and require specialised instrumentation and lengthy sample preparation procedures [5,6]. Soil physico-chemical properties, organic amendments, and crop growing conditions can lead to high variation in the chemical composition of plant materials [7,8].
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