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 (R²_c = 0.83, R²_p = 0.55, ratio of prediction to deviation (RPD) = 1.51) and was slightly improved by using images of a single variety (orange) only (R²_c = 0.85, R²_p = 0.62, RPD = 1.65). However, prediction of curcumin using outer-skin of rhizomes was poor (R²_c = 0.64, R²_p = 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.

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Vis / NIR高光谱成像预测姜黄素在新鲜姜黄根茎中的浓度的性能评估

高光谱图像（HSI）分析具有估算植物和食品中有机化合物的潜力。姜黄素是用于治疗一系列医学疾病的重要化合物。因此，一种在农场上快速测定姜黄素含量高的根茎的方法对农民来说将具有显着的优势。根茎中姜黄素的含量在变种之内和之间有所不同，但是当前的化学分析方法既昂贵又费时。这项研究比较了三种姜黄（姜黄）中的姜黄素，并检查了基于实验室的HSI使用可见-近红外（400-1000 nm）光谱快速预测姜黄素的潜力。高光谱图像（n= 152）使用三个当地品种（黄色，橙色和红色）捕获了新鲜的根茎外皮和果肉。分析了姜黄素和总姜黄素的分布。开发了偏最小二乘回归（PLSR）模型来预测总姜黄素浓度。所有品种中的总姜黄素和三种姜黄素的比例均存在显着差异。红色姜黄的总姜黄素浓度最高（0.83±0.21％），而橙色（0.37±0.12％）和黄色（0.02±0.02％）最高。PLSR模型使用根茎肉的原始光谱预测姜黄素并汇总所有三个品种的数据（R ² _c = 0.83，R ² _p= 0.55，预测与偏差之比（RPD）= 1.51），仅使用单一品种（橙色）的图像略有改善（R ² _c = 0.85，R ² _p = 0.62，RPD = 1.65）。然而，使用根茎的皮肤对姜黄素的预测很差（R ² _c = 0.64，R ² _p = 0.37，RPD = 1.28）。这些模型可以区分“低”和“高”值，因此可以改编为两级评分系统。HSI有潜力帮助识别高姜黄素浓度的姜黄根茎，并允许更有效地精制姜黄素以用于医学目的。