|
|
|
|
|
|
Study on Quality Identification of Macadamia nut Based on Convolutional Neural Networks and Spectral Features |
DU Jian1, 2, HU Bing-liang1*, LIU Yong-zheng1, WEI Cui-yu1, ZHANG Geng1, TANG Xing-jia1 |
1. Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
2. University of Chinese Academy of Sciences, Beijing 100049, China |
|
|
Abstract Macadamia nut is easy to spoil after being stripped off because of the high level of oil in it. Most of the existing traditional methods are destructive which are difficult to satisfy the demand of non-destructive detection. As one of the widely used deep learning models, convolutional neural network (CNN) has stronger capabilities of feature extraction and model formulation than shallow learning methods and great potential for the application of spectral data. We studied suitable CNN architecture to extract spectral features of Macadamia based on Vis-NIRS analysis, and proposed an efficient non-destructive method to identify the quality of Macadamia. At first, we took three kinds of macadamia nut with different qualities (including better nut, worse nut and moldy nut) as the research object and analyzed the spectral information in the wavelength range of 500~2 100 nm. We introduced the concept of whitening in data preprocessing to strengthen the correlation difference. In the process of model training, we divided the sample into training set and prediction set randomly and then discussed the effects of different structure parameters, such as the number of convolution layer, size of convolution kernel, pooling type, number of neuron in full connection layer and activation function. We applied ReLU and Dropout to prevent over-fitting caused by lack of data. At last, through the analysis of the classification accuracy and computational efficiency, a CNN model of 6-layer structure was established: input layer-convolution layer-pooling layer-full connection layer(including 200 neurons)-full connection layer(including 100 neurons)-output layer. The results show that the final classification accuracy of the calibration set and prediction set reached 100%. This improved CNN model can fully learn the spectral features of macadamia and classify effectively. The combination of the deep learning theory and the spectral analysis method can identify the quality of macadamia accurately, and provide a new idea for the efficient, non-destructive, real-time, online detection of macadamia and other nuts.
|
Received: 2017-04-21
Accepted: 2017-10-12
|
|
Corresponding Authors:
HU Bing-liang
E-mail: hbl@opt.ac.cn
|
|
[1] LIU Jian-fu, HUANG Li(刘建福, 黄 莉). Food and Nutrition in China(中国食物与营养), 2005, 2: 25.
[2] Schmutzler M, Huck C W. Vibrational Spectroscopy, 2014, 72: 97.
[3] Ferreira D S, Pallone J A L, Poppi R J. Food Control, 2015, 48: 91.
[4] Miphokasap P, Honda K, Vaiphasa C, et al. Remote Sensing, 2012, 4(6): 1651.
[5] Jakobek L, Barron A R. Journal of Food Composition and Analysis, 2016, 45: 9.
[6] Vanoli M, Rizzolo A, Grassi M, et al. Postharvest Biology and Technology, 2014, 91: 112.
[7] Hinton G E, Salakhutdinov R R. Science, 2006, 313(5786): 504.
[8] Lecun Y, Bottou L, Bengio Y, et al. Proceedings of the IEEE, 1998, 86(11): 2278.
[9] Chen Y, Lin Z, Zhao X, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2094.
[10] Sainath T N, Mohamed A R, Kingsbury B, et al. In Proceedings of the 38th IEEE International Conference on Acoustics (ICASSP ’13), 2013. 8614.
[11] Abdel-Hamid O, Mohamed A R, Jiang H, et al. In Proceedings of the IEEE International Conference on Acoustics (ICASSP ’12), 2012. 4277. |
[1] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[2] |
LAN Yan1,WANG Wu1,XU Wen2,CHAI Qin-qin1*,LI Yu-rong1,ZHANG Xun2. Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 158-163. |
[3] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[4] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[5] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[6] |
WANG Yu-chen1, 2, KONG Ling-qin1, 2, 3*, ZHAO Yue-jin1, 2, 3, DONG Li-quan1, 2, 3*, LIU Ming1, 2, 3, HUI Mei1, 2. Hyperspectral Reconstruction From RGB Images for Tissue Oxygen
Saturation Assessment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3193-3201. |
[7] |
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. |
[8] |
YE Wen-chao1, LUO Shui-yang1, LI Jin-hao1, LI Zhao-rong1, FAN Zhi-wen1, XU Hai-tao1, ZHAO Jing1, LAN Yu-bin1, 2, DENG Hai-dong1*, LONG Yong-bing1, 2, 3*. Research on Classification Method of Hybrid Rice Seeds Based on the Fusion of Near-Infrared Spectra and Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2935-2941. |
[9] |
TANG Ruo-han1, 2, LI Xiu-hua1, 2*, LÜ Xue-gang1, 2, ZHANG Mu-qing2, 3, YAO Wei2, 3. Transmittance Vis-NIR Spectroscopy for Detecting Fibre Content of
Living Sugarcane[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2419-2425. |
[10] |
PU Shan-shan, ZHENG En-rang*, CHEN Bei. Research on A Classification Algorithm of Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2446-2451. |
[11] |
TANG Ting, PAN Xin*, LUO Xiao-ling, GAO Xiao-jing. Fusion of ConvLSTM and Multi-Attention Mechanism Network for
Hyperspectral Image Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2608-2616. |
[12] |
LI Wen-xia1, DU Yu-jun2, WANG Yue1, LIU Zheng-dong3*, ZHENG Jia-hui1, DU Wen-qian1, WANG Hua-ping4. Research on On-Line Efficient Near-Infrared Spectral Recognition and Automatic Sorting Technology of Waste Textiles Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2139-2145. |
[13] |
LIANG Wan-jie1, FENG Hui2, JIANG Dong3, ZHANG Wen-yu1, 4, CAO Jing1, CAO Hong-xin1*. Early Recognition of Sclerotinia Stem Rot on Oilseed Rape by Hyperspectral Imaging Combined With Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2220-2225. |
[14] |
MAO Yi-lin1, LI He1, WANG Yu1, FAN Kai1, SUN Li-tao2, WANG Hui3, SONG Da-peng3, SHEN Jia-zhi2*, DING Zhao-tang1, 2*. Quantitative Judgment of Freezing Injury of Tea Leaves Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2266-2271. |
[15] |
JIANG Xia*, QIU Bo, WANG Lin-qian, GUO Xiao-yu. Automatic Classification Method of Star Spectra Based on
Semi-Supervised Mode[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1875-1880. |
|
|
|
|