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 see...
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MDPI AG
2021-05-01
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| author | Hanim Z. Amanah Collins Wakholi Mukasa Perez Mohammad Akbar Faqeerzada Salma Sultana Tunny Rudiati Evi Masithoh Myoung-Gun Choung Kyung-Hwan Kim Wang-Hee Lee Byoung-Kwan Cho |
| author_facet | Hanim Z. Amanah Collins Wakholi Mukasa Perez Mohammad Akbar Faqeerzada Salma Sultana Tunny Rudiati Evi Masithoh Myoung-Gun Choung Kyung-Hwan Kim Wang-Hee Lee Byoung-Kwan Cho |
| author_sort | Hanim Z. Amanah |
| collection | DOAJ |
| description | 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 <i>R</i><sup>2</sup> 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 (<i>R</i><sup>2</sup>) 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 <i>R</i><sup>2</sup> 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. |
| first_indexed | 2024-03-10T11:04:18Z |
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| last_indexed | 2024-03-10T11:04:18Z |
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| spelling | doaj.art-50de92d427c14d549b8a8b592b9c58802023-11-21T21:16:33ZengMDPI AGApplied Sciences2076-34172021-05-011111484110.3390/app11114841Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice SeedsHanim Z. Amanah0Collins Wakholi1Mukasa Perez2Mohammad Akbar Faqeerzada3Salma Sultana Tunny4Rudiati Evi Masithoh5Myoung-Gun Choung6Kyung-Hwan Kim7Wang-Hee Lee8Byoung-Kwan Cho9Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, KoreaDepartment of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, KoreaDepartment of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, KoreaDepartment of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, KoreaDepartment of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, KoreaDepartment of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Gadjah Mada University, Yogyakarta 55281, IndonesiaDepartment of Herbal Medicine Resource, Dogye Campus, Kangwon National University, Samcheok 25949, KoreaDepartment of Gene Engineering, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, KoreaDepartment of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, KoreaDepartment of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, KoreaAnthocyanins 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 <i>R</i><sup>2</sup> 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 (<i>R</i><sup>2</sup>) 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 <i>R</i><sup>2</sup> 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.https://www.mdpi.com/2076-3417/11/11/4841micro-componentblack riceseed qualitynear-infraredhyperspectral imaging |
| spellingShingle | Hanim Z. Amanah Collins Wakholi Mukasa Perez Mohammad Akbar Faqeerzada Salma Sultana Tunny Rudiati Evi Masithoh Myoung-Gun Choung Kyung-Hwan Kim Wang-Hee Lee Byoung-Kwan Cho Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds Applied Sciences micro-component black rice seed quality near-infrared hyperspectral imaging |
| title | Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds |
| title_full | Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds |
| title_fullStr | Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds |
| title_full_unstemmed | Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds |
| title_short | Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds |
| title_sort | near infrared hyperspectral imaging nir hsi for nondestructive prediction of anthocyanins content in black rice seeds |
| topic | micro-component black rice seed quality near-infrared hyperspectral imaging |
| url | https://www.mdpi.com/2076-3417/11/11/4841 |
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