光谱学与光谱分析 |
|
|
|
|
|
3-D Modeling of Origin Discrimination of Fragrant Mushrooms Using Visible/Near Infrared Spectra |
YANG Hai-qing1,2,HE Yong1*,CHEN Yong-ming1,LIN Ping1,WU Di1 |
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China 2. College of Information Engineering, Zhejiang University of Technology, Hangzhou 310032, China |
|
|
Abstract The potential of visible/near infrared absorbance spectroscopy as a way for the nondestructive discrimination of various fragrant mushrooms was evaluated. First, the spectral data ranging from 375 to 1 025 nm were analyzed by principal component analysis (PCA) for data compression and space clustering. The resulting accumulative credibility of 94.37% based on the first three principle components (PCs) was achieved. This signifies that it is possible to establish a model for the sample discrimination in three dimensional space. Then, a new method in which space division planes were established based on the 3-D PC score plot was proposed. Due to the irregular sample distribution, the division planes for sample discrimination were established through genetic algorithm (GA). The fitness function was evaluated based on the number of the samples that have wrong sign by the division plane function. The goal is to achieve the minimum of the fitness function. Various parameters were predetermined, including population size, selection method, crossover rate, mutation rate and iteration number. Three plane functions were conducted as the model for sample discrimination. In order to evaluate the prediction performance of the new model, another model based on PCA and 3-layer BP-ANN was created and brought into comparison. The three PCs were adopted as the input of the BP-ANN. The number of the neurons in the middle layer was optimized based on the calibration error. The output layer was encoded in binary number. In the test, a total of 195 samples were examined, in which 150 samples were selected randomly for model building and the other 45 for model prediction. Both models adopted the same calibration set and prediction set. The result indicated that the two models established by different methods had similar capability of sorting the same samples out of others. Both models featured more than 91% of sample recognition rate. It can be concluded that while BP-ANN tends to solve high-dimension data analysis, the new method proves reliable and practicable in the three dimensional space so that it could serve as an approach to machine recognition of fragrant mushrooms with various origins.
|
Received: 2007-09-08
Accepted: 2007-12-18
|
|
Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
|
|
[1] HE Yong, LI Xiao-li, SHAO Yong-ni. Lecture Notes in Artificial Intelligence, 2005, 3809: 1053. [2] HE Yong, LI Xiao-li(何 勇, 李晓丽). J. Infrared Millim. Waves(红外与毫米波学报), 2006, 25(3): 192. [3] Mohacek-Grosev V, Bozac R, Puppels G. J. Spectrochimica Acta Part A, 2001, 57: 2815. [4] LI Xiao-li, HE Yong, QIU Zheng-jun(李晓丽,何 勇,裘正军). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2007,27(2):279. [5] SHAO Yong-ni, HE Yong, PAN Jia-zhi, et al(邵咏妮,何 勇,潘家志,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2007,27(9):1739. [6] WANG Yan-yan, HE Yong, SHAO Yong-ni, et al(王艳艳,何 勇,邵咏妮,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(4): 702. [7] WU Di, FENG Lei, ZHANG Chuan-qing, et al(吴 迪,冯 雷,张传清,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2007,27(11):2208. [8] FANG Hui, SONG Hai-yan, CAO Fang, et al(方 慧,宋海燕,曹 芳,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2007,27(9):1731. [9] FENG Lei, FANG Hui, ZHOU Wei-jun, et al(冯 雷,方 慧,周伟军,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2006,26(9):1749. [10] HUANG Min, HE Yong, HUANG Ling-xia, et al(黄 敏, 何 勇, 黄凌霞, 等). J. Infrared Millim. Waves(红外与毫米波学报), 2006, 25(5): 342. [11] WANG Hong, CAO Hui, CUI Xing-ming, et al(汪 虹,曹 晖,崔星明,等). Acta Edulis Fungi(食用菌学报), 2005, 12(3): 52. [12] SUN Su-qin, TANG Jun-ming, YUAN Zi-min, et al(孙素琴, 汤俊明, 袁子民, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2003, 23(2): 258. [13] NIE Zhi-dong, HAN Jian-guo, ZHANG Lu-da, et al(聂志东,韩建国,张录达,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(4): 691. [14] ZHAO De-zhang, LIU Gang, SONG Ding-shan, et al(赵德璋, 刘 刚, 宋鼎珊, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(8): 1445. [15] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍, 袁洪福, 徐广通, 等). Modern Near Infrared Spectroscopy Analytical Technology(Second Edition)(现代近红外光谱分析技术, 第2版). Beijing: Chinese Petrochemical Industry Press(北京:中国石化出版社), 2007. |
[1] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[2] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[3] |
GAO Wei-ling, ZHANG Kai-hua*, XU Yan-fen, LIU Yu-fang*. Data Processing Method for Multi-Spectral Radiometric Thermometry Based on the Improved HPSOGA[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3659-3665. |
[4] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[5] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[6] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[7] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[8] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[9] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[10] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[11] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[12] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[13] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
[14] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
[15] |
ZHU Yan-ping1, CUI Chuan-jin1*, CHENG Peng-fei1, 2, PAN Jin-yan1, SU Hao1, 2, ZHANG Yi1. Measurement of Oil Pollutants by Three-Dimensional Fluorescence
Spectroscopy Combined With BP Neural Network and SWATLD[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2467-2475. |
|
|
|
|