| 光谱学与光谱分析 |
|
|
|
|
|
| Effectively Predicting Soluble Solids Content in Apple Based on Hyperspectral Imaging |
| HUANG Wen-qian, LI Jiang-bo, CHEN Li-ping*, GUO Zhi-ming |
| Beijing Research Center of Intelligent Equipment for Agriculture, National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China |
|
|
|
|
Abstract It is very important to extract effective wavelengths for quantitative analysis of fruit internal quality based on hyperspectral imaging. In the present study, genetic algorithm (GA), successive projections algorithm (SPA) and GA-SPA combining algorithm were used for extracting effective wavelengths from 400～1 000 nm hyperspectral images of Yantai “Fuji” apples, respectively. Based on the effective wavelengths selected by GA, SPA and GA-SPA, different models were built and compared for predicting soluble solids content (SSC) of apple using partial least squares (PLS), least squared support vector machine (LS-SVM) and multiple linear regression (MLR), respectively. A total of 160 samples were prepared for the calibration (n=120) and prediction (n=40) sets. Among all the models, the SPA-MLR achieved the best results, where R2p, RMSEP and RPD were 0.950 1, 0.308 7 and 4.476 6 respectively. Results showed that SPA can be effectively used for selecting the effective wavelengths from hyperspectral data. And, SPA-MLR is an optimal modeling method for prediction of apple SSC. Furthermore, less effective wavelengths and simple and easily-interpreted MLR model show that the SPA-MLR model has a great potential for on-line detection of apple SSC and development of a portable instrument.
|
|
Received: 2013-02-07
Accepted: 2013-04-29
|
|
|
|
Corresponding Authors:
CHEN Li-ping
E-mail: huangwenqian@iea.ac.cn
|
|
[1] HONG Tian-sheng，QIAO Jun，NING Wang Michael , et al(洪添胜，乔 军，Ning Wang Michael，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2007，23(2)：151.
[2] Zhao J W，Vittayapadung S，Chen Q S，et al. Maejo International Journal of Science and Technology，2009，3(1)：130.
[3] Huang M，Lu R. Transactions of the ASABE，2010，53(4)：1175.
[4] MA Ben-xue，XIAO Wen-dong，QI Xiang-xiang, et al(马本学，肖文东，祁想想，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2012，32(11)：3093.
[5] Kawano S，Abe H，Iwamoto M. Journal of Near Infrared Spectroscopy，1995，3(4)：211.
[6] Macho S，Iusa R，Callao M P, et al. Analytica Chimica Acta，2001，445(2)：213.
[7] Sun D. Hyperspectral Imaging for Food Quality Analysis and Control. Massachusetts：Academic Press，2010. 56.
[8] Riccardo L，Amparo L，Gonzalez. Chemometrics and Intelligent Laboratory Systems，1998，41(2)：195.
[9] Mário C U A，Teresa C B S，Roberto K H G，et al. Chemometrics and Intelligent Laboratory Systems，2001，57(2)：65.
[10] Svante W，Michael S，Lennart E. Chemometrics and Intelligent Laboratory Systems，2001，58(2)：109.
[11] Vapnik V N. The nature of statistical learning theory. New York：Springer-Verlag，1995. |
| [1] |
TIAN Xi1, 2, 3, CHEN Li-ping2, 3, WANG Qing-yan2, 3, LI Jiang-bo2, 3, YANG Yi2, 3, FAN Shu-xiang2, 3, HUANG Wen-qian2, 3*. Optimization of Online Determination Model for Sugar in a Whole Apple
Using Full Transmittance Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1907-1914. |
| [2] |
JIANG Xiao-yu1, 2, LI Fu-sheng2*, WANG Qing-ya1, 2, LUO Jie3, HAO Jun1, 2, XU Mu-qiang1, 2. Determination of Lead and Arsenic in Soil Samples by X Fluorescence Spectrum Combined With CARS Variables Screening Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1535-1540. |
| [3] |
ZHANG Tian-liang, ZHANG Dong-xing, CUI Tao, YANG Li*, XIE Chun-ji, DU Zhao-hui, ZHONG Xiang-jun. Identification of Early Lodging Resistance of Maize by Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1229-1234. |
| [4] |
LI Lian-jie1, 2, FAN Shu-xiang2, WANG Xue-wen1, LI Rui1, WEN Xiao1, WANG Lu-yao1, LI Bo1*. Classification Method of Coal and Gangue Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1250-1256. |
| [5] |
SHI Ji-yong, LIU Chuan-peng, LI Zhi-hua, HUANG Xiao-wei, ZHAI Xiao-dong, HU Xue-tao, ZHANG Xin-ai, ZHANG Di, ZOU Xiao-bo*. Detection of Low Chromaticity Difference Foreign Matters in Soy Protein Meat Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1299-1305. |
| [6] |
FAN Nai-yun, LIU Gui-shan*, ZHANG Jing-jing, YUAN Rui-rui, SUN You-rui, LI Yue. Rapid Determination of TBARS Contents in Tan Mutton Using Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 713-718. |
| [7] |
QIAO Lu, WANG Song-lei*, GUO Jian-hong, HE Xiao-guang. Combination of Spectral and Textural Informations of Hyperspectral Imaging for Predictions of Soluble Protein and GSH Contents in Mutton[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 176-183. |
| [8] |
YE Rong-ke1, KONG Qing-chen1, LI Dao-liang1, 2, CHEN Ying-yi1, 2, ZHANG Yu-quan1, LIU Chun-hong1, 2*. Shrimp Freshness Detection Method Based on Broad Learning System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 164-169. |
| [9] |
WU Ye-lan1, CHEN Yi-yu1, LIAN Xiao-qin1, LIAO Yu2, GAO Chao1, GUAN Hui-ning1, YU Chong-chong1. Study on the Identification Method of Citrus Leaves Based on Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3837-3843. |
| [10] |
OUYANG Ai-guo, WAN Qi-ming, LI Xiong, XIONG Zhi-yi, WANG Shun, LIAO Qi-cheng. Research on Rich Borer Detection Methods Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3844-3850. |
| [11] |
DUAN Long1, YAN Tian-ying1, WANG Jiang-li2, 3, YE Wei-xin1, CHEN Wei1, GAO Pan1, 2*, LÜ Xin2, 3*. Combine Hyperspectral Imaging and Machine Learning to Identify the Age of Cotton Seeds[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3857-3863. |
| [12] |
QIN Kai1, CHEN Gang2, ZHANG Jian-yi1,2, FU Xia-ping1*. Optimization of Fruit Pose and Modeling Method for Online Spectral Detection of Apple Moldy Core[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3405-3410. |
| [13] |
CHANG Jin-qiang, ZHANG Ruo-yu*, PANG Yu-jie, ZHANG Meng-yun, ZHA Ya. Classification of Impurities in Machine-Harvested Seed Cotton Using Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3552-3558. |
| [14] |
. Study on Rapid Spectral Reappearing and Hyperspectral Classification of Invisible Writing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3524-3531. |
| [15] |
YANG Bao-hua, GAO Zhi-wei, QI Lin, ZHU Yue, GAO Yuan. Prediction Model of Soluble Solid Content in Peaches Based on Hyperspectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3559-3564. |
|
|
|
|
