Abstract：In order to use three-dimensional (3D) fluorescence technology to realize early warning of banana spoilage based on storage room gas, the storage room gas corresponding to two batches of banana with different storage dates were tested to collect 3D fluorescence data. Firstly, the 3D fluorescence data was pre-processed: to eliminate the overall drift of the scanning data of the 3D fluorescence instrument, the drift of the obtained 3D fluorescence data was processed; the removal and interpolation of Rayleigh and Raman scattering were handled by using the eemscat toolbox in matlab platform, which effectively eliminated the adverse effects of Rayleigh scattering and Raman scattering; and the Savitzky-Golar (SG) method was employed for data smoothing to reduce the influence of noise on the fluorescence signal. Meanwhile, the 3D fluorescence data were preliminarily screened, the emission wavelengths with fluorescence intensity close to 0 were removed, and the more discrete excitation wavelengths were removed by using a third-order Gaussian mixture distribution to fit the emission spectra at different excitation wavelengths. Then, aiming at the feature selection of 3D fluorescence data, a feature wavelength selection strategy based on Wilks Λ statistic combined with interval partial least squares (iPLS) was proposed. The specific steps are: step 1, using Wilks Λ statistics to select feature excitation wavelengths, and five feature excitation wavelengths were preliminarily selected; step 2, based on the initially selected feature excitation wavelengths, the iPLS method was used to select the feature emission bands in combination with pH and relative conductivity, and feature emission band including 14 wavelengths was selected at each feature excitation wavelength; step 3, in order to further reduce the number of analysis variables, according to the selected feature emission band, Wilks Λ statistics was used againto select the feature excitation wavelengths inversely, and 3 feature excitation wavelengths were finally obtained. Combined with 14 emission wavelengths at each feature excitation wavelength, a total of 42 feature emission wavelengths were selected. Finally, considering the time-varying characteristic of banana quality during storage, with the help of the 42 feature emission wavelengths, systematic cluster analysis (SCA) was employed to define the benchmark for banana spoilage, and the cluster results showed that both batches of bananas had abrupt changes in quality on the 5th day of storage. Therefore, the fluorescence information of the storage room gas on the 5th day was used to characterize the banana spoilage. In addition, these feature wavelength variables were computed by principal component analysis (PCA), and the first principal component was preliminary explore to realize early warning of banana spoilage. The research results show that the selection strategy of feature wavelengths of the 3D fluorescence data proposed in this paper can effectively reduce the complexity of the spectral data so as to facilitate subsequent analysis and the early warning method of banana spoilage is also feasible.
李孟丽，殷 勇，袁云霞，李 欣，刘雪茹. 香蕉贮藏气体3D荧光表征特征选择及早期腐败预警初探[J]. 光谱学与光谱分析, 2021, 41(02): 558-564.
LI Meng-li, YIN Yong, YUAN Yun-xia, LI Xin, LIU Xue-ru. Feature Selection of 3D Fluorescence Data Based on Storage Room Gas and Preliminary Early Warning of Banana Spoilage. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 558-564.
[1] Escalante-Minakata P, Ibarra-Junquera V, de Jesús Ornelas-Paz J, et al. 3 Biotech, 2017, 8(1): 30.
[2] JIA Cai-hong, JIN Zhi-qiang, LIU Ju-hua, et al(贾彩红, 金志强, 刘菊华, 等). Chinese Journal of Tropical Crops(热带作物学报), 2012, 33(1): 189.
[3] ZHANG Jiang-zhong, WANG Bin, LIU Lin, et al(张江周, 王 斌, 刘 林, 等). Chinese Journal of Tropical Crops(热带作物学报), 2018, 39(11): 2272.
[4] Ma B, Han Y M, Cui S Y, et al. Food Control, 2020, 108: 106824.
[5] HAI Jin-ping, TONG Han-qing, LUO Xin-ling(海金萍, 童汉清, 罗新玲). Food Science and Technology(食品科技), 2017, 42(8): 54.
[6] LI Yuan-hong, WANG Hong-jun, DENG Jian-meng, et al(黎源鸿, 王红军, 邓建猛, 等). Modern Food Science and Technology(现代食品科技), 2017, 33(10): 268.
[7] WU Hai-long, LI Yong, KANG Chao, et al(吴海龙, 李 勇, 康 超, 等). Chinese Journal of Analytical Chemistry(分析化学), 2015, 43(11): 1629.
[8] Xu Jing, Liu Xiao-fei, Wang Yu-tian. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2016, 165: 1.
[9] SUN Jun, ZHOU Xin, MAO Han-ping, et al(孙 俊, 周 鑫, 毛罕平, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(19): 302.
[10] Yin Y, Chu B, Yu H C, et al. Journal of Food Measurement and Characterization, 2014, 8(1): 29.
[11] ZHANG Xiao-yun, WANG Fei, LI Guo-sheng, et al(张晓云, 王 飞, 李国省, 等). Chinese Journal of Analytical Chemistry(分析化学), 2019, 47(1): 99.
[12] Jafarzadegan M, Safi-Esfahani F, Beheshti Z. Expert Systems With Applications, 2019, 137: 1.
