|
|
|
|
|
|
| Optimization of Polished Rice Varieties Discrimination Based on
Near Infrared Spectroscopy |
| YANG Sen1, WANG Zhen-min1*, SONG Wen-long1, XING Jian1, DAI Jing-min2 |
1. School of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
2. School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
|
|
|
|
|
Abstract Due to the enormous market value of geographically iconic, adulteration or fraud often occurs. Therefore, to ensure the brand benefits from geographically iconic rice and consumer rights, it is important to identify polished rice varieties accurately. Near-infrared spectroscopy is a common method to distinguish polished rice varieties. The varieties can be classified by extracting the different features of different types in near-infrared spectroscopy. However, there are some problems in the existing studies, such as insufficient characteristic wavelength selection performance and insufficient discrimination accuracy for specific varieties, which limit the improvement of the discrimination accuracy of polished rice varieties based on the near-infrared spectroscopy method. In response to the above problems, this paper studies the optimisation of milled rice variety identification based on near-infrared spectroscopy from the two aspects of characteristic wavelength selection and variety identification strategies for four types of rice, Wuchang, Xiangshui, Koshihikari, and Yinshui in Northeast China. First, permutation entropy (PE) and adaptive sliding window (ASW) were combined to improve the feature wavelength selection performance. An adaptive sliding permutation entropy (ASW-PE) based method for selecting the characteristic wavelength of polished rice spectrum was proposed and compared with the traditional algorithm. Secondly, a discriminant strategy based on the discriminant objective was proposed to improve the discriminant accuracy of different specified varieties. By studying the matching optimisation of the spectral preprocessing algorithm and classification modelling algorithm, a discriminant process of “specified cultivation-selected model-selected algorithm” was established. Experimental results show that using the adaptive sliding permutation entropy algorithm proposed in this article to select characteristic wavelengths can reduce the milled rice variety discrimination error by at least 50% compared with the traditional algorithm; using the milled rice variety judge strategy based on the discrimination target proposed in this article. Compared with the conventional judge strategy based on fixed models, the discrimination accuracy can be improved by at least 2.5%.
|
|
Received: 2022-11-02
Accepted: 2024-01-11
|
|
|
|
Corresponding Authors:
WANG Zhen-min
E-mail: w614335248@163.com
|
|
[1] HU Sheng-ying, REN Hong-bo, ZHANG Jun, et al(胡圣英, 任红波, 张 军, 等). Chinese Agricultural Science Bulletin(中国农学通报), 2020, 36(14): 148.
[2] Zhu L, Sun J, Wu G C, et al. Journal of Cereal Science, 2018, 82: 175.
[3] Hong B Z, Yu Z W, Hao L, et al. Talanta, 2010, 81(1-2): 129.
[4] LIU Yan, LI Xiu-li(刘 妍, 李秀丽). Journal of Heilongjiang Bayi Agricultural University(黑龙江八一农垦大学学报), 2011, 23(4): 108.
[5] XU Qing-wu, YAO Jin-fu(许庆武, 姚劲夫). Agricultural Archaeology(农业考古), 2011, (3): 59.
[6] LIU Xiao-huan, LIU Cui-ling, SUN Xiao-rong, et al(刘晓欢, 刘翠玲, 孙晓荣, 等). Food Science and Technology(食品科技), 2021, 46(4): 244.
[7] SONG Xue-jian, QIAN Li-li, ZHANG Dong-jie, et al(宋雪健, 钱丽丽, 张东杰, 等). Food Science(食品科学), 2017, 38(18): 286.
[8] QIAN Li-li, SONG Xue-jian, ZHANG Dong-jie, et al(钱丽丽, 宋雪健, 张东杰, 等). Food Science(食品科学), 2017, 38(16): 222.
[9] QIAN Li-li, SONG Xue-jian, ZHANG Dong-jie, et al(钱丽丽, 宋雪健, 张东杰, 等). Food Science(食品科学), 2018, 39(8): 231.
[10] QIAN Li-li, SONG Xue-jian, ZHANG Dong-jie, et al(钱丽丽, 宋雪健, 张东杰, 等). Food Science(食品科学), 2018, 39(16): 321.
[11] Min S, Dong D G, Zheng Y Z, et al. Journal of Food Measurement and Characterization, 2019, 13: 1705.
[12] SHI Chun-hong, CAO Mei-ping, HU Gui-xia(石春红, 曹美萍, 胡桂霞). Food Science(食品科学), 2020, 41(16): 300.
[13] WENG Shi-zhuang, TANG Pei-pei, ZHANG Xue-yan, et al(翁士状, 唐佩佩, 张雪艳, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(9): 2826.
|
| [1] |
LI Zhen-yu1, ZHAO Peng1, 2*, WANG Cheng-kun3. Tree Class Recognition in Open Set Based on an Improved Fuzzy
Reasoning Classifier[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1868-1876. |
| [2] |
XIAO Huai-chun1, LIU Yang1, WEI Bing-xue1, GAO Jia-rong1, LIU Yan-de2, XIAO Hui1. Identification of Visible and Short Wave Near Infrared Spectra of
Super-Enriched Plants in Uranium Ore Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1813-1819. |
| [3] |
HUANG Hua1, LIU Ya2, MA Yi-hang1, XIANG Si-han1, HE Jia-ning1, WANG Shi-ting1, GUO Jun-xian3*. Prediction of Soluble Solid Contents in Apples Using Vis-NIRS and
Functional Linear Regression Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1905-1912. |
| [4] |
CUI Hao-fan1, LIU Hong-zhi1, GUO Qin1*, GU Feng-ying1, ZHANG Yu2, WANG Qiang1*. Establishment of High-Throughput Model of Peanut Protein Components and Subunits by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1982-1987. |
| [5] |
NIU Xiao-ying1, 2, 3, MU Xiao-qing1, 2, 3, SUN Jie1, 2, 3, ZHAO Zhi-lei1, 2, 3*, ZHANG Chun-jiang4. Qualitative and Quantitative Analyses of Cooked Donkey Meat
Adulteration Based on NIR Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1993-2001. |
| [6] |
NI Jin1, SUO Li-min1*, LIU Hai-long1, ZHAO Rui2. Identification of Corn Varieties Based on Northern Goshawk Optimization Kernel Based Extreme Learning Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1584-1590. |
| [7] |
YU Shui1, HUAN Ke-wei1*, LIU Xiao-xi2, WANG Lei1. Quantitative Analysis Modeling of Near Infrared Spectroscopy With
Parallel Convolution Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1627-1635. |
| [8] |
WEI Zi-chao1, 2, LU Miao1, 2, LEI Wen-ye1, 2, WANG Hao-yu1, 2, WEI Zi-yuan1, 2, GAO Pan1, 2, WANG Dong1, 2, CHEN Xu1, 2*, HU Jin1, 2*. A Nondestructive Method Combined Chlorophyll Fluorescence With Visible-NIR Spectroscopy for Detecting the Severity of Heat Stress on Tomato Seedlings[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1613-1619. |
| [9] |
GE Qing, LIU Jin*, HAN Tong-shuai, LIU Wen-bo, LIU Rong, XU Ke-xin. Influence of Medium's Optical Properties on Glucose Detection
Sensitivity in Tissue Phantoms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1262-1268. |
| [10] |
LIU Yu-ming1, 2, 3, WANG Qiao-hua1, 2, 3*, CHEN Yuan-zhe1, LIU Cheng-kang1, FAN Wei1, ZHU Zhi-hui1, LIU Shi-wei1. Non-Destructive Near-Infrared Spectroscopy of Physical and Chemical
Indicator of Pork Meat[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1346-1353. |
| [11] |
YANG Zeng-rong1, 2, WANG Huai-bin1, 2, TIAN Mi-mi1, 2, LI Jun-hui1, 2, ZHAO Long-lian1, 2*. Early Apple Bruise Detection Based on Near Infrared Spectroscopy and Near Infrared Camera Multi-Band Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1364-1371. |
| [12] |
HU Jin-quan1, 2, YANG Hui-hua1, ZHAO Guo-liang3, ZHOU Rui-zhi4, LI Ling-qiao5. Prediction Method of Wool Content in Waste Spinning Samples Based on Semi Supervised Regression of Generative Adversarial Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1417-1424. |
| [13] |
LI Zhen, HOU Ming-yu, CUI Shun-li, CHEN Miao, LIU Ying-ru, LI Xiu-kun, CHEN Huan-ying, LIU Li-feng*. Rapid Detection Method of Flavonoid Content in Peanut Seed Based on Near Infrared Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1112-1116. |
| [14] |
MENG Qi1, 3, ZHAO Peng2, HUAN Ke-wei2, LI Ye2, JIANG Zhi-xia1, 3, ZHANG Han-wen2, ZHOU Lin-hua1, 3*. Non-Invasive Blood Glucose Measurement Based on Near-Infrared
Spectroscopy Combined With Label Sensitivity Algorithm and
Support Vector Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 617-624. |
| [15] |
TANG Jie1, LUO Yan-bo2, LI Xiang-yu2, CHEN Yun-can1, WANG Peng1, LU Tian3, JI Xiao-bo4, PANG Yong-qiang2*, ZHU Li-jun1*. Study on One-Dimensional Convolutional Neural Network Model Based on Near-Infrared Spectroscopy Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 731-736. |
|
|
|
|
