## 1 Introduction
Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) can also be applied to predict anthocyanins content in black soybean based on the seed's spectra [21]. Many researchers have favored the hyperspectral imaging (HSI) system, which integrates the spectroscopic and imaging techniques due to the capability in acquiring spectral and spatial information simultaneously. In recent years, HSI has been adopted to assess chlorogenic acid content in _Flos Ionicereae_[22], to determine deoxynivalenol in bulk wheat kernel [23], and to monitor the quality of strawberries during storage [24]. The potential capability of HSI to predict macro-components in grain, such as starch in corn and protein in wheat, based on single kernel data, has also been proven [25; 26]. For micro-component, particularly in anthocyanins detection, visible hyperspectral imaging (Vis-HSI) has been applied to slices of purple potato [27; 28] and Lychee pericarp [29]. PLSR models based on the full wavelength in the lychee pericarp sample resulted in performance over 0.9 for both calibration and prediction models, while the pre-processing method (SNV, MSC, and SG) slightly improves the performance model to predict anthocyanins in purple-fleshed sweet potato slices from 0.876 to 0.889. Furthermore, this hyperspectral method also successfully visualizes the anthocyanins distribution throughout the sample. HSI in the NIR region also has been used to predict micro-component, including total anthocyanins, flavonoids, and phenolics of dry black goji berries (_Lycium ruthenium_ Murr.) [30]. The PLS regression model using a full range of spectra (975-1646 nm) resulted in the performance of the calibration model of total anthocyanins and phenolics over 0.90 and 0.85 for total flavonoids. This technique also resulted in high performance of the prediction model of 0.89, 0.84, and 0.85 for anthocyanins, flavonoids, and phenolics, respectively. In addition, the result of the PLS regression method was comparable to the other techniques, including LS-SVM and CNN, that were applied in the study. However, in this sample, anthocyanins were uniformly distributed throughout the sample, and the content was higher than those in black rice. Anthocyanins are a micro-component in rice, mainly available in its aleurone, and are not distributed uniformly throughout the rice seed. Anthocyanins produce a dark color (purple to black) in the seed surface that may absorb the light emitted from the instrument. However, most of these chemicals are present on the rice seed's surface, which might indicate one of the advantages of measurement using a non-destructive system, which works based on the interaction between light and matter. At present, no research has been done on the application of near-infrared hyperspectral imaging (NIR-HSI) for evaluating anthocyanins content in a single seed of rice. Perhaps, the lack of such studies is due to the low concentration of anthocyanins in pigmented rice, and the market demand for this rice is not high. However, with the gradual increase in functional food demand in the last decade, the research interest related to anthocyanins in rice has risen. Thus, this study aims to evaluate the potential of near-infrared hyperspectral imaging (NIR-HSI) for predicting anthocyanins in individual rice seeds. For comparison, this study also developed a model for the prediction of anthocyanins based on the powder sample of the same rice variety.