应用分子光谱信息融合判别不同产地大米

doi:10.3964/j.issn.1000-0593(2023)09-2818-07

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Abstract Due to the lack of confirmation technology for rapid identification of rice origin, 186 rice samples from Wuchang, Northeast and South of China, were identified by near-infrared mid-infrared, and Raman combined with chemometric analysis in this study. Firstly, the recognition effects of three algorithms of k-nearest neighbor method (KNN), linear discriminant analysis (LDA) and least squares-support vector machine (LS-SVM), combined with five preprocessing methods on three single spectrum rice origin identification models are compared. The results show that the Raman spectrum model of the LS-SVM algorithm combined with the SNV+2nd preprocessing method is the best, and the accuracy of the calibration set and validation set are 100% and 93.48% respectively. In order to further improve the accuracy of the identification model, the rice-origin identification models of data layer fusion, feature layer fusion and decision-layer fusion based on near-infrared spectroscopy, mid-infrared spectroscopy and Raman spectroscopy are established innovatively. The results show that the recognition accuracy of the three spectral information fusion model levels is greatly improved compared with the single spectral model. In the data layer fusion rice origin identification model, the LS-SVM algorithm combined with SNV+2nd preprocessing method is the best model. The accuracy of the calibration set and validation set are 100% and 95.65% respectively, which is 2.17% higher than that of a single spectral optimal model. In the decision-level fusion identification model, the LS-SVM algorithm combined with the SNV+1st preprocessing method is the best model. The accuracy of the calibration set and validation set are 100% and 97.83% respectively, which is 4.35% higher than that of a single spectral optimal model. In the feature layerfusion origin identification model, the LS-SVM algorithm combined with SNV+2nd preprocessing method is the best identification model. The recognition accuracy of the calibration set and validation set are both 100%, which is 6.52% higher than that of the single spectral optimal model. The results show that it is feasible to use near-infrared spectroscopy, mid-infrared spectroscopy and Raman spectroscopy combined with chemometrics to identify rice origin, and the rice origin identification model based on Raman spectroscopy combined with LS-SVM algorithm is the best. The recognition accuracy of the three spectral information fusion model levels is greatly improved compared with the single spectral model. Among them, the feature level fusion method is more suitable for the data type of this fusion and can quickly and accurately identify the origin of Wuchang rice, Southern rice and Northeast rice. This study provides a new method for rapidly and accurately identifying rice-producing areas.

Key words：Spectral information fusion; Rice origin; Identification analysis

Received: 2022-05-03 Accepted: 2022-08-31

ZTFLH:

O657.33

Corresponding Authors: HU Li-ming E-mail: huliming@bjut.edu.cn

Cite this article:

LUAN Xin-xin,ZHAI Chen,AN Huan-jiong, et al. Applications of Molecular Spectral Information Fusion to Distinguish the Rice From Different Growing Regions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2818-2824.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)09-2818-07 OR https://www.gpxygpfx.com/EN/Y2023/V43/I09/2818

[1] Qian L L, Zuo F, Liu H Y, et al. Journal of Food Quality, 2019, 2019(2): 8396865.
[2] WU Jing-zhu, LI Xiao-qi, LIN Long, et al(吴静珠, 李晓琪, 林珑, 等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2022, 22(1): 282.
[3] Tong P J, Lim K J L, Wei T T, et al. Journal of Cereal Science, 2021, 102: 103322.
[4] CHEN Ming-ming, FU Li-xue, LI Dian-wei, et al(陈明明, 符丽雪, 李殿威, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2022, 23(4): 1.
[5] QIAN Li-li, SONG Xue-jian, ZHANG Dong-jie, et al(钱丽丽, 宋雪健, 张东杰). Food Science(食品科学), 2018, 39(8): 231.
[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] SHA Min, LI Liang-cui, HUANG Jia-le, et al(沙敏, 李良翠, 黄家乐, 等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2021, 21(5): 369.
[8] DU Meng-jia, MAO Bo, SHEN Fei, et al(杜梦佳, 毛波, 沈飞, 等). Food Science(食品科学), 2018, 39(8): 243.
[9] WANG Ya-xuan, TAN Feng, XIN Yuan-ming, et al(王亚轩, 谭峰, 辛元明, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(2): 565.
[10] Xia Zhenzhen, Yang Jie, Wang Jing, et al. Applied Spectroscopy, 2020, 74(4): 417.
[11] Zhu Siyu, He Chongnan, Song Mingjuan, et al. Journal of Intelligent and Fuzzy Systems, 2021, 41(1): 1837.
[12] Hu Xueping, Wang Jingya, Wang Liuliu, et al. Axioms, 2022, 11(3): 102.
[13] Kim Nam Hoon, Choi Chae man, Lee Young Ju, et al. Analytical Science and Technology, 2021, 34(3): 134.
[14] GU Li-jun, LIN Zhen-hua, WU Shi-yu, et al(古丽君, 林振华, 吴世玉, 等). Food and Fermentation Industries(食品与发酵工业), 2019, 45(18): 243.
[15] Chia See Leng, Chia Min Yan, Koo Chai Hoon, et al. Water Supply, 2022, 22(2): 1951.