Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in <i>Brassica juncea</i>
Partial least squares regression (PLSR) prediction models were developed using hyperspectral imaging for noninvasive detection of the five most representative functional components in <i>Brassica juncea</i> leaves: chlorophyll, carotenoid, phenolic, glucosinolate, and anthocyanin content...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/12/10/1515 |
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| author | Jae-Hyeong Choi Soo Hyun Park Dae-Hyun Jung Yun Ji Park Jung-Seok Yang Jai-Eok Park Hyein Lee Sang Min Kim |
| author_facet | Jae-Hyeong Choi Soo Hyun Park Dae-Hyun Jung Yun Ji Park Jung-Seok Yang Jai-Eok Park Hyein Lee Sang Min Kim |
| author_sort | Jae-Hyeong Choi |
| collection | DOAJ |
| description | Partial least squares regression (PLSR) prediction models were developed using hyperspectral imaging for noninvasive detection of the five most representative functional components in <i>Brassica juncea</i> leaves: chlorophyll, carotenoid, phenolic, glucosinolate, and anthocyanin contents. The region of interest for functional component analysis was chosen by polygon selection and the extracted average spectra were used for model development. For pre-processing, 10 combinations of Savitzky–Golay filter (S. G. filter), standard normal variate (SNV), multiplicative scatter correction (MSC), 1st-order derivative (1st-Der), 2nd-order derivative (2nd-Der), and normalization were applied. Root mean square errors of calibration (RMSEP) was used to assess the performance accuracy of the constructed prediction models. The prediction model for total anthocyanins exhibited the highest prediction level (R<sub>V</sub><sup>2</sup> = 0.8273; RMSEP = 2.4277). Pre-processing combination of SNV and 1st-Der with spectral data resulted in high-performance prediction models for total chlorophyll, carotenoid, and glucosinolate contents. Pre-processing combination of S. G. filter and SNV gave the highest prediction rate for total phenolics. SNV inclusion in the pre-processing conditions was essential for developing high-performance accurate prediction models for functional components. By enabling visualization of the distribution of functional components on the hyperspectral images, PLSR prediction models will prove valuable in determining the harvest time. |
| first_indexed | 2024-03-09T20:56:37Z |
| format | Article |
| id | doaj.art-95a68adaf95141dfb211860ebfb0a45c |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T20:56:37Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-95a68adaf95141dfb211860ebfb0a45c2023-11-23T22:19:35ZengMDPI AGAgriculture2077-04722022-09-011210151510.3390/agriculture12101515Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in <i>Brassica juncea</i>Jae-Hyeong Choi0Soo Hyun Park1Dae-Hyun Jung2Yun Ji Park3Jung-Seok Yang4Jai-Eok Park5Hyein Lee6Sang Min Kim7Smart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, KoreaSmart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, KoreaSmart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, KoreaSmart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, KoreaSmart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, KoreaSmart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, KoreaSmart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, KoreaSmart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, KoreaPartial least squares regression (PLSR) prediction models were developed using hyperspectral imaging for noninvasive detection of the five most representative functional components in <i>Brassica juncea</i> leaves: chlorophyll, carotenoid, phenolic, glucosinolate, and anthocyanin contents. The region of interest for functional component analysis was chosen by polygon selection and the extracted average spectra were used for model development. For pre-processing, 10 combinations of Savitzky–Golay filter (S. G. filter), standard normal variate (SNV), multiplicative scatter correction (MSC), 1st-order derivative (1st-Der), 2nd-order derivative (2nd-Der), and normalization were applied. Root mean square errors of calibration (RMSEP) was used to assess the performance accuracy of the constructed prediction models. The prediction model for total anthocyanins exhibited the highest prediction level (R<sub>V</sub><sup>2</sup> = 0.8273; RMSEP = 2.4277). Pre-processing combination of SNV and 1st-Der with spectral data resulted in high-performance prediction models for total chlorophyll, carotenoid, and glucosinolate contents. Pre-processing combination of S. G. filter and SNV gave the highest prediction rate for total phenolics. SNV inclusion in the pre-processing conditions was essential for developing high-performance accurate prediction models for functional components. By enabling visualization of the distribution of functional components on the hyperspectral images, PLSR prediction models will prove valuable in determining the harvest time.https://www.mdpi.com/2077-0472/12/10/1515hyperspectral imagepartial least squares regressionprediction modelsroot mean square error of predictionstandard normal variatetotal anthycyanins |
| spellingShingle | Jae-Hyeong Choi Soo Hyun Park Dae-Hyun Jung Yun Ji Park Jung-Seok Yang Jai-Eok Park Hyein Lee Sang Min Kim Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in <i>Brassica juncea</i> Agriculture hyperspectral image partial least squares regression prediction models root mean square error of prediction standard normal variate total anthycyanins |
| title | Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in <i>Brassica juncea</i> |
| title_full | Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in <i>Brassica juncea</i> |
| title_fullStr | Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in <i>Brassica juncea</i> |
| title_full_unstemmed | Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in <i>Brassica juncea</i> |
| title_short | Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in <i>Brassica juncea</i> |
| title_sort | hyperspectral imaging based multiple predicting models for functional component contents in i brassica juncea i |
| topic | hyperspectral image partial least squares regression prediction models root mean square error of prediction standard normal variate total anthycyanins |
| url | https://www.mdpi.com/2077-0472/12/10/1515 |
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