Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds

Abstract

Anthocyanins are an important micro-component that contributes to the quality factors and health benefits of black rice. Anthocyanins concentration and compositions differ among rice seeds depending on the varieties, growth conditions, and maturity level at harvesting. Chemical composition-based seeds inspection on a real-time, non-destructive, and accurate basis is essential to establish industries to optimize the cost and quality of the product. Therefore, this research aimed to evaluate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) to predict the content of anthocyanins in black rice seeds, which will open up the possibility to develop a sorting machine based on rice micro-components. Images of thirty-two samples of black rice seeds, harvested in 2019 and 2020, were captured using the NIR-HSI system with a wavelength of 895–2504 nm. The spectral data extracted from the image were then synchronized with the rice anthocyanins reference value analyzed using high-performance liquid chromatography (HPLC). For comparison, the seed samples were ground into powder, which was also captured using the same NIR-HSI system to obtain the data and was then analyzed using the same method. The model performance of partial least square regression (PLSR) of the seed sample developed based on harvesting time, and mixed data revealed the model consistency with R2 over 0.85 for calibration datasets. The best prediction models for 2019, 2020, and mixed data were obtained by applying standard normal variate (SNV) pre-processing, indicated by the highest coefficient of determination (R2) of 0.85, 0.95, 0.90, and the lowest standard error of prediction (SEP) of 0.11, 0.17, and 0.16 mg/g, respectively. The obtained R2 and SEP values of the seed model were comparable to the result of powder of 0.92–0.95 and 0.09–0.15 mg/g, respectively. Additionally, the obtained beta coefficients from the developed model were used to generate seed chemical images for predicting anthocyanins in rice seed. The root mean square error (RMSE) value for seed prediction evaluation showed an acceptable result of 0.21 mg/g. This result exhibits the potential of NIR-HSI to be applied in a seed sorting machine based on the anthocyanins content.

Estimation of amino acid contents in maize leaves based on hyperspectral imaging

“…The R 2 of the prediction model reached 0.80. Amanah et al (2021) used near-infrared hyperspectral imaging to realize the non-destructive detection of anthocyanin content in black rice seeds, and the R 2 of the best prediction model was 0.95. These studies showed that hyperspectral technology had high feasibility in the physiological indexes of crops.…”

Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions

“…The relationship between the total phenolic content and the spectral data was developed using the least squares method, as given in Equation 4. This technique was recommended for this study since it was successfully used to predict phytochemical compounds in previous studies (Amanah et al, 2021).…”

“…Aflatoxin has been detected from peanuts using deep learning and HSI (Han and Gao, 2019). The machine learning method has been applied on black rice HSI data to predict the anthocyanin content (Amanah et al, 2021).…”

Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview

“…These fundamental quantitative applications in NIRS have also been effective in NIR-HSI techniques. For example, calibration models were established to quantitate moisture [134], oil [135], anthocyanins [136] and fungal contamination in fruit using quantitative visualization methods [137].…”