基于近红外光谱特征提取的花椰菜灰霉病早期检测

doi:10.3964/j.issn.1000-0593(2021)08-2543-06

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Abstract Gray mold easily occurs during cauliflower growth, thereby leading to reduced output. Cauliflower infected with gray mold at an early stage is difficult to detect with existing methods. In this study, near-infrared spectroscopy was used to distinguish and detect cauliflower with gray mold, which is highly significant for the disease control of cauliflower. Taking cauliflower with Botrytis cinema spore inoculation as the research object, this study obtained the near-infrared spectra of cauliflower in control and treatment groups and performed de-noising. The spectra of 608 samples in four batches (76 healthy and infected cauliflowers at 0.5, 1, 2 and 3 d old each) were acquired within the waveband range of 500～2 400 nm. After measuring the activity of polyphenol oxidase, peroxidase and malondialdehyde in the cauliflower samples, and one-way ANOVA was used to statistically analyze the quality indices of a single batch of healthy and infected cauliflowers. The Kennard-Stone algorithm was used to divide each day’s samples into a calibration (114 samples) and a prediction (38 samples)set. Competitive adaptive reweighted sampling (CARS) was then used to extract the feature waveband of the spectroscopic data of the four batches of cauliflower samples, and the discrimination models of single and combination batches were established based on of partial least square regression (PLSR). Results indicated that the naked eye could not identify infected cauliflower samples at the early stage of inoculation and could identify them only 3 d after infection when some infected samples showed evident disease characteristics. The measurement of quality indices of the cauliflower in the control and treatment groups showed significant differences in all quality indices between these groups 2 d after infection (p＜0.05); however no significant differences existed in all quality indices at 0.5 d, and a significant difference in MDA value existed only at 1d. These findings suggested that the quality indices of infected cauliflower cannot be discriminated at an early stage. The PLSR discrimination model of a single batch was established, and it showed the following: the discrimination accuracy of the model established for the first batch (0.5 d) reached 94.74%, the root-mean-square error of the prediction set was 0.835, and the discrimination accuracy of the models established for the second to fourth batch (1～3 d) reached 100%. These findings indicated that the PLSR model could detect infected cauliflower samples under a single batch at an early stage. The discrimination accuracy of the PLSR combination discrimination model reached 92.11% and 97.37% at 0.5 and 1 d, respectively, to discriminate a large proportion of infected cauliflower. However, the effect of PLSR combination-batch modeling was inferior to that of PLSR single-batch modeling. Therefore, using near-infrared spectroscopy, extracting the feature waveband through CARS, and establishing a PLSR model can detect cauliflower infected with gray mold at an early stage, thereby providing a reference for the early detection cauliflower with gray mold and has some practical value.

Key words：Cauliflower; Gray mold; Early detection; Near-infrared spectroscopy; Feature band

Received: 2021-03-25 Accepted: 2021-06-18

ZTFLH:

TP391

Corresponding Authors: XUE Jian-xin E-mail: vickyxjx@126.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)08-2543-06 OR https://www.gpxygpfx.com/EN/Y2021/V41/I08/2543

[1] Paula G I, Juan N E, Micaela C. BMC Plant Biol., 2021, 21: 30.
[2] Kapusta-Duch J, Szel

g-Sikora A, Sikora J, et al. Sustainability, 2019, 11(15): 4008.
[3] Abbey J, Percival D, Abbey L, et al. Biocontrol Science and Technology, 2019, 29(3): 207.
[4] ZHANG Zheng-wei, CHEN Xiu, SHI Xiao-yuan, et al(张正炜, 陈秀, 石小媛, 等). China Vegetables(中国蔬菜), 2021, 2: 41.
[5] WU Bin, ZHOU Shu-bin, WU Xiao-hong, et al(武斌, 周树斌, 武小红, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(3): 932.
[6] Siedliska A, Baranowski P, Zubik M, et al. Postharvest Biology and Technology, 2018, 139(5): 115.
[7] Li B, Zhang D P, Shen Y. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 243(5): 118820.
[8] He X M, Zhao T X, Shen F, et al. Biosystems Engineering, 2021, 201(1): 1.
[9] QIN Li-feng, ZHANG Xi, ZHANG Xiao-xi(秦立峰, 张熹, 张晓茜). Transactions of The Chinese Society of Agricultural Machinery(农业机械学报), 2020, 51(11): 212.
[10] Shen F, Wu Q F, Liu P, et al. Food Control, 2018, 93(11): 1.
[11] Sun Y, Gu X Z, Wang Z J, et al. PLOS ONE, 2015, 10(12): e0143400.
[12] XUE Jian-xin, ZHANG Shu-juan, SUN Hai-xia, et al(薛建新, 张淑娟, 孙海霞, 等). Transactions of The Chinese Society of Agricultural Machinery(农业机械学报)，2013, 44(8): 169.
[13] YUAN Zhong-yu, ZHOU Hui-ling, TIAN Rong, et al(袁仲玉, 周会玲, 田蓉, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2014, 30(4): 255.
[14] ZHOU Xiao-wan, TANG Yong-ping, SHI Ya-li, et al(周晓婉, 唐永萍, 石亚莉, 等). Food Science(食品科学), 2016, 37(12): 254.
[15] ZHANG Cheng, LI Ming, LONG You-hua, et al(张承, 李明, 龙友华, 等). Food Science(食品科学), 2016, 37(22): 274.
[16] ZHAO Chuan-yuan, HE Dong-jian, LEE Won Suk(赵川源, 何东健, LEE Won Suk). Transactions of The Chinese Society of Agricultural Machinery(农业机械学报), 2017, 48(5): 356.
[17] YU Lie, ZHU Ya-xing, HONG Yong-sheng, et al(于雷, 朱亚星, 洪永胜, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(22): 138.