Mukasa Perez
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Mukasa Perez has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Byoung-Kwan Cho
Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
1 shared publication - Collins Wakholi
Oak Ridge Institute for Science and Education (ORISE) in partnership with USDA‐ARS, Horticultural Crops Production and Genetic Improvement Research Unit worksite Prosser Washington USA
1 shared publication - Hanim Zuhrotul Amanah
Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Bulaksumur, Sleman, Indonesia
1 shared publication - Kyung-Hwan Kim
Supercomputing Center, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of Korea
1 shared publication - Mohammad Akbar Faqeerzada
Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
1 shared publication - Myoung-Gun Choung
Department of Herbal Medicine Resource, Kangwon National University
1 shared publication
Latest Publications
- Open Access
- 25 May 2021
- Published by MDPI AG in Applied Sciences
- Vol. 11 (11) , 4841
Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds
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.
- 25 May 2021
- Published by MDPI AG in Applied Sciences
- Vol. 11 (11) , 4841
Open AccessNear-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds
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.