Abstract:In order to provide theoretical guidance for the grading of winter jujube maturity after harvesting, this study applied hyperspectral technique to obtain the characteristic wavelengths and calculate the spectral indices to achieve its maturity visualsort. A total of 336 samples of jujube with three types of maturity (immature fruit, white ripeness and primary red fruit, half red and red fruit) were collected and their hyperspectral information wereacquired. The samples were divided into training set (226) and testing set (110) using Kennard-Stone (K-S) method after the original spectral noise was reduced by Savitzky-Golay(S-G) smoothing algorithm. The characteristic wavelengths (CWs) were selected withsuccessive projections algorithm (SPA) and Competitive adaptive reweighted sampling (CARS). At the same time, 7 Spectral indices (SIs) were imported from the perspective of fruit varied physiological components. Three partial least squares discriminant analysis (PLS-DA) models were established based on the CWs selected by SPA and CARS and the introduced SIs, and the classification results of three models were compared. The results show that the discrimination accuracy of PLS-DA models based on two kind of CWs(selected by SPA and CARS, respectively)and SIs wereseparately 97.27%, 95.45%, and 98.18%. For the purpose of showing the discriminant results intuitively, a regression equation of the discriminant vector Y was fitted with SIs joint its PLS-DA regression coefficients, and the discriminant results were visually displayed by different colors in accordance with the rule that the corresponding category of the maximum value in Y is the sample belonging category. This study will contribute some proposals to visual grading of winter jujube maturity, and the imported SIs parameters will provide technical support for the manufacture of device that suitable for multiple fruits maturity sorting.
曹晓峰,任惠如,李幸芝,余克强,苏宝峰. 高光谱技术结合特征波长/光谱指数对冬枣成熟度可视化判别[J]. 光谱学与光谱分析, 2018, 38(07): 2175-2182.
CAO Xiao-feng, REN Hui-ru, LI Xing-zhi, YU Ke-qiang, SU Bao-feng. Discrimination of Winter Jujube’s Maturity Using Hyperspectral Technique Combined with Characteristic Wavelength and Spectral Indices. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2175-2182.
[1] Hui G, Jin J, Deng S, et al. Food Chemistry, 2015, 170: 484.
[2] Wang H, Chen F, Yang H, et al. Carbohydrate Polymers, 2012, 89(4): 1180.
[3] SUN Lei, WANG Tai-ming, LIU Yuan-qian, et al(孙 蕾, 王太明, 刘元铅, 等). Nonwood Forest Research(经济林研究), 2004, 22(2): 33.
[4] Wang H, Peng J, Xie C, et al. Sensors, 2015, 15(5): 11889.
[5] Wang N N, Sun D W, Yang Y C, et al. Food Analytical Methods, 2015, 9(1): 1.
[6] Munera S, Besada C, Aleixos N, et al. LWT-Food Science and Technology, 2017, 77: 241.
[7] Zhang C, Guo C, Liu F, et al. Journal of Food Engineering, 2016, 179: 11.
[8] SUN Jing-tao, MA Ben-xue, JIANG Wei, et al(孙静涛, 马本学, 蒋 伟, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(7): 2184.
[9] Pan L, Sun Y, Xiao H, et al. Postharvest Biology & Technology, 2017, 126: 40.
[10] Pu H, Liu D, Wang L, et al. Food Analytical Methods, 2016, 9(1): 235.
[11] CAO Li-chun, XU Xiang-yu, MA Hui-qin, et al(曹丽春, 徐翔宇, 马会勤, 等). Journal of China Agricultural University(中国农业大学学报), 2017, 2: 54.
[12] SONG Cheng-xiu, ZHANG Xiu-de, ZHANG Cai-xia, et al (宋成秀, 张修德, 张彩霞, 等). South China Fruits(中国南方果树), 2016, 4: 106.
[13] Overbeck V, Schmitz M, Blanke M. Sensors, 2017, 17(2): 277.
[14] DENG Shu-bin, CHEN Qiu-jin, DU Hui-jian, et al(邓书斌, 陈秋锦, 杜会建, 等). ENVI Remote Sensing Image Processing Method(ENVI遥感图像处理方法). Beijing: Higher Education Press(北京:高等教育出版社), 2014. 381.
[15] Ballabio D, Consonni V. Analytical Methods, 2013, 5(16): 3790.
[16] Brereton R G, Lloyd G R. Journal of Chemometrics, 2014, 28(4): 213.
[17] Schweiggert R M, Vargas E, Conrad J, et al. Food Chemistry, 2016, 200: 274.
[18] Sun Y, Wang Y, Xiao H, et al. Food Chemistry, 2017, 235: 194.