Acta Optica Sinica, Volume. 39, Issue 10, 1030004(2019)
Nondestructive Detection of Sugar Content and Firmness of Red Globe Grape by Hyperspectral Imaging
Figures & Tables(15)
Fig. 1. Hyperspectral images in three placement orientations. (a) Horizontal; (b) fruit stalk-side down; (c) fruit stalk-side up
Fig. 2. Reflectivity of background and red globe grape area in hyperspectral images
Fig. 3. Hyperspectral image processing of red globe grapes. (a) Hyperspectral image at 726.6 nm; (b) mask template image; (c) masked image of red globe grape area
Fig. 5. GA characteristic wavelength extraction of sugar content of red globe grape. (a) GA-screened image; (b) change of RMSECV
Fig. 6. SPA characteristic wavelength extraction of sugar content of red globe grape. (a) Change of RMSE; (b) selected variables of SPA
Fig. 7. CARS characteristic wavelength extraction of sugar content of red globe grape. (a) Number of sampled variables; (b) RMSECV; (c) paths of regression coefficients
Fig. 8. UVE characteristic wavelength extraction of sugar content of red globe grape
Table 1. Full-band PLSR prediction model using different preprocessing methods
View tableTable 1. Full-band PLSR prediction model using different preprocessing methods
Index Pretreatment LVs Calibration set Prediction set R cRMSEC R pRMSEP Sugar content Raw 19 0.827 0.564 0.726 0.474 SNV 14 0.808 0.597 0.712 0.493 S_G 2 0.602 0.769 0.483 0.619 MSC 8 0.811 0.595 0.665 0.515 MA 8 0.479 0.782 0.373 0.881 MC 18 0.825 0.617 0.701 0.503 Firmness RAW 8 0.696 4.743 0.675 4.575 SNV 6 0.605 5.015 0.684 4.365 MSC 9 0.685 4.613 0.569 4.789 MA 16 0.730 4.226 0.808 3.821 MC 10 0.646 4.652 0.691 4.830
Table 2. Datastatistics of partitioning sample sets by SPXY algorithm
View tableTable 2. Datastatistics of partitioning sample sets by SPXY algorithm
Number of samples Index Minimum Maximum Mean Standard deviation Calibration set (126 samples) Sugar content /(° Brix) 13.875 18.625 16.109 0.971 Firmness /N 1.200 27.000 13.711 6.213 Prediction set (42 samples) Sugar content /(° Brix) 15.000 17.5000 15.858 0.653 Firmness /N 2.700 23.500 12.774 6.264
Table 3. Full-band PLSR prediction model with different placementorientations
View tableTable 3. Full-band PLSR prediction model with different placementorientations
Placement position Index LVs Calibration set Prediction set R cRMSEC R pRMSEP Fruit stalk-side down Sugar content 17 0.807 0.628 0.712 0.488 Firmness 3 0.546 5.290 0.534 5.017 Fruit stalk-side up Sugar content 13 0.792 0.659 0.705 0.492 Firmness 6 0.646 4.854 0.606 4.780 Horizontal Sugar content 19 0.805 0.631 0.690 0.497 Firmness 6 0.558 5.245 0.602 4.785 Whole fruit Sugar content 19 0.827 0.564 0.726 0.474 Firmness 19 0.730 4.226 0.808 3.821
Table 4. Results of prediction model for sugar content and firmness based on characteristic wavelengths of red globe grape
View tableTable 4. Results of prediction model for sugar content and firmness based on characteristic wavelengths of red globe grape
Index Modeling method Extraction method No. of wavelength Calibration set Prediction set R cRMSEC R pRMSEP Sugar content PLSR Raw 438 0.827 0.564 0.726 0.474 GA 26 0.875 0.469 0.728 0.443 SPA 17 0.862 0.492 0.745 0.429 CARS 24 0.879 0.461 0.753 0.422 UVE 47 0.863 0.490 0.729 0.444 LSSVM Raw 438 0.825 0.568 0.486 0.675 GA 26 0.870 0.479 0.759 0.415 SPA 17 0.864 0.489 0.752 0.426 CARS 24 0.866 0.486 0.810 0.376 UVE 47 0.875 0.470 0.749 0.426 RF Raw 438 0.954 0.260 0.873 0.402 GA 26 0.969 0.266 0.928 0.254 SPA 17 0.962 0.268 0.895 0.411 CARS 24 0.946 0.296 0.890 0.406 UVE 47 0.961 0.267 0.917 0.297 Firmness PLSR MA-Raw 438 0.730 4.226 0.808 3.821 MA-GA 60 0.802 3.696 0.898 3.273 MA-SPA 24 0.802 3.699 0.903 2.888 MA-CARS 22 0.731 4.224 0.886 3.215 MA-UVE 139 0.804 3.687 0.887 3.114 LSSVM MA-Raw 438 0.738 4.224 0.754 4.021 MA-GA 60 0.795 3.788 0.901 3.023 MA-SPA 24 0.741 4.183 0.870 3.578 MA-CARS 22 0.746 4.163 0.893 3.288 MA-UVE 139 0.833 3.444 0.921 2.674 RF MA-Raw 438 0.960 2.195 0.905 3.049 MA-GA 60 0.950 2.132 0.918 2.031 MA-SPA 24 0.961 2.119 0.932 1.634 MA-CARS 22 0.948 2.199 0.911 2.053 MA-UVE 139 0.959 2.120 0.921 1.893
Table 5. Characteristic wave points of optimal model for sugar content and firmness
View tableTable 5. Characteristic wave points of optimal model for sugar content and firmness
Index Modeling method Selected variables (wavelength) /nm Sugar content (26 points) GA-RF 452.76, 456.53, 461.55, 600.98, 626.10, 627.36, 628.62, 631.13, 633.64, 639.92, 644.95, 646.20, 647.46, 648.71,651.23, 655.00, 859.75, 894.92, 918.78, 922.55, 927.58, 936.37, 941.40, 943.91, 945.16, 969.03 Firmness (24 points) MA-SPA-RF 450.24, 451.50, 454.01, 464.06, 476.62, 489.19, 505.51, 557.02, 677.61, 688.91, 706.50, 825.83, 938.88, 947.68, 952.70, 958.98, 961.49, 962.75, 965.26, 969.03, 977.82, 990.38, 996.67, 997.92
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Sheng Gao, Qiaohua Wang, Dandan Fu, Qingxu Li. Nondestructive Detection of Sugar Content and Firmness of Red Globe Grape by Hyperspectral Imaging[J]. Acta Optica Sinica, 2019, 39(10): 1030004
Paper Information
Category: Spectroscopy
Received: Mar. 26, 2019
Accepted: Jul. 8, 2019
Published Online: Oct. 9, 2019
The Author Email: Wang Qiaohua (wqh@mail.hzau.edu.cn)
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