基于近红外光谱分析贡梨可溶性固形物全局模型的鲁棒性

doi:10.3964/j.issn.1000-0593(2022)09-2781-07

Abstract
References
Related Citation (15)

Download: PDF (2556 KB)
Export: BibTeX | EndNote (RIS)

Abstract Gongpear is a popular fruit. In order to study the influence of different detection directions on the online detection of soluble solid SSC in Gongpear by NIR, a global model was proposed, and its robustness was analyzed. The spectra were collected from Gongpears in six directions: stem-calyx axis vertical, stem-upward (A1) and stem-downward (A5), between the stem-calyx axis and horizontal 45°, stem-upward sloping (A2) and stem-downward sloping (A4), stem-calyx axis horizontal, stem-right light oriented (A3), stem-calyx axis horizontal, stem-band moving direction (A6). The 150 samples with SSC ranging from 9.53 to 14. 70 were divided into 115 calibration sets with a standard deviation of 1.05 and 35 prediction sets with a standard deviation of 0.93. Six local models and one global model were established by partial least-squares regression (PLSR). The local models were established by partial least-squares regression (PLSR) after 115 calibration sets of data in each direction were preprocessed by Savitzky-Golay convolution smoothing, Multiple Scattering Correction（MSC）and Gaussian Filtering Smoothing (GFS). The local model established by the local correction set was used to verify the data of 35 prediction sets in the local direction. Compared with the PLSR model established by the three pretreatment methods, the results showed that the model established by GFS processing had the best validation effect. Therefore, the PLSR model established by GFS processing was used for all the six local and global models. The global model is a Gongpears SSC model established by PLSR after GFS pretreatment from 690 calibration sets of spectral data in A1, A2, A3, A4, A5 and A6. The prediction sets in each direction verified the seven models. The verification results showed that although the prediction effect of the local model was stronger than that of the global model in the local direction, it could not be verified in other directions and the robustness was poor. Therefore, different detection directions had a great influence on the prediction effect. The global model can accurately predict the SSC of Gongpears pear in each detection direction. The global model’s correlation coefficient （R_c）is 0.828, and the root mean square error RMSEC is 0.424. The correlation coefficients （R_p） of A1, A2, A3, A4, A5 and A6 prediction sets were 0.818, 0.765, 0.799, 0.821, 0.794 and 0.824, and the root mean square errors RMSEP were 0.446, 0.525, 0.478, 0.538, 0.486 and 0.619, respectively. The R_p and R_c in six directions are close to each other and are around 0.800, the RMSEC and RMSEP are around 0.500. The results show that the global model has excellent robustness in detecting gongpear SSC in different directions.

Key words：Near-infrared; Gongpear; Soluble solid SSC; Global model; Robustness

Received: 2021-07-17 Accepted: 2021-10-10

ZTFLH:

O657.3

Cite this article:

LIU Yan-de,LIAO Jun,LI Bin, et al. Robustness of Global Model of Soluble Solids in Gongli Pear Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2781-2787.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)09-2781-07 OR https://www.gpxygpfx.com/EN/Y2022/V42/I09/2781

[1] Gabrieli Alves de Oliveira, Sylvie Bureau, Catherine Marie-Geneviève Claire Renard, et al. Food Chemistry, 2014, 143：223.
[2] QIN Shan-zhi, CHEN Bin, LU Dao-li，et al（秦善知, 陈斌, 陆道礼，等）. Jiangsu Agricultural Sciences（江苏农业科学）, 2014, 42(8): 284.
[3] JIANG Shui-quan, SUN Tong（江水泉, 孙通）. Food and Machinery（食品与机械）, 2020, 36(2): 89.
[4] Lourdes Salguero-Chaparro, Vincent Baeten, Ouissam Abbas, et al. Journal of Food Engineering, 2012, 112(3)：152.
[5] MENG Qing-long, SHANG Jing, HUANG Ren-shuai, et al（孟庆龙, 尚静, 黄人帅, 等）. Food and Fermentation Industry（食品与发酵工业）, 2020, 46(19): 205.
[6] Guo Zhiming, Huang Wenqian, Peng Yankun, et al. Postharvest Biology and Technology, 2016, 115：81.
[7] MO Bao-qing, ZHU Hui-juan, XIAO Li（莫宝庆, 朱慧娟, 肖黎）. Chinese Journal of Health Inspection（中国卫生检验杂志）, 2012, 22(1): 125.
[8] Fan Shuxiang, Zhang Baohua, Li Jiangbo, et al. Biosystems Engineering, 2016, 143：9.
[9] Xia Yu, Huang Wenqian, Fan Shuxiang, et al. Journal of Food Process Engineering, 2018, 41(8): e12915.
[10] Xia Yu, Huang Wenqian, Fan Shuxiang, et al. Infrared Physics and Technology, 2019, 97：467.
[11] HAN Dong-hai, ZHOU En-yang, QI Shu-ye(韩东海, 周恩洋, 戚淑叶). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2015, 46(11): 260.
[12] SUN Xu-dong, LIU Yan-de, LI Yi-fan, et al（孙旭东, 刘燕德, 李轶凡, 等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报）, 2016, 47(1): 227.
[13] LI Xiong, LIU Yan-de, SUN Xu-dong, et al（李雄, 刘燕德, 孙旭东, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2020, 40(10): 3265.
[14] Liu Yande, Zhang Yu, Jiang Xiaogang, et al. Vibrational Spectroscopy, 2020, 111: 103152.
[15] HUANG Jian-jun, LI Xue-mei, TENG Hong-quan（黄建军, 李雪梅, 滕宏泉）. Disaster Science（灾害学）, 2021, 36(2): 60.