Abstract：Fruit freshness is an important quality index reflecting whether the fruit is fresh and full. In order to explore the prediction and discrimination methods of different shelf life of fruits, this paper takes the pear as the research object, and uses hyperspectral imaging technology combined with partial least squares discrimination (PLS). DA and partial least squares support vector machine (LS-SVM) algorithm to distinguish the shelf life of pears. The spectrum of the sample is collected by a high-spectrum imaging device consisting of a light source, an imaging spectrometer, an electronically controlled displacement platform, and a computer. The device light source is designed with a ladder power of 200 W four bromine tungsten bulbs, and the spectral range is 1 000～2 500 nm. 10 nm. The material was selected from 30 high-quality pears, and the shelf life was set to 1 day, 5 days and 10 days. Three spectral images were acquired for 30 samples and the original image was corrected. The experimental results show that the image-based analysis of the shelf life of the pears is carried out by PCA compression of the original images of different shelf life samples, and the weight coefficient data of three different shelf periods are obtained. The wavelength points of PC1 image extraction are 1 280, 1 390 and 1 800 nm. 1 880 and 2 300 nm, with the average gray value of the feature image as the independent variable and the shelf life as the dependent variable to establish a qualitative discriminant model, 68 modeling sets and 22 prediction sets. When the least squares support vector machine uses RBF as the kernel function, the number of misjudgments in the predicted concentrated samples is 1, and the false positive rate is 4.5%. When the lin kernel function is used, the number of misjudgments of the sample is 0, and the false positive rate is 0. The RMSEC for PLS-DA qualitative analysis was 1.24, which was 0.93. The RMSEP is 1, which is 0.96, and the prediction set false positive rate is zero. The characteristic image has better model for the lin kernel function in the LS-SVM of the shelf life of the pear, which is better than the modeling effect of the RBF kernel function and better than the PLS-DA discriminant model. The LS-SVM and PLS-DA discriminant models were established by ENVI software to extract the spectra of the experimental samples. The false positive rates of RB-SVM using RBF and lin kernel functions were 4.5% and 0, respectively. Compared with the RBF kernel function, the model established by the lin kernel function predicts the shelf life of the pears better. The PLS-DA method has a principal component factor of 12, RMSEC and RMSEP of 0.48 and 0.78, respectively, and 0.99 and 0.97, respectively. The false positive rate of the modeling set and the prediction set are both zero. The model established by the lin kernel function in LS-SVM is better than the detection model established by PLS. The spectral information of the pears combined with LS-SVM can realize the detection and discrimination of the shelf life of the pears. Compared with the spectrum, the shelf life prediction model based on the image was used to distinguish the shelf life of the pear, while the feature image method, the selected area was less lost part of the information, the calculation amount was small, and the modeling result was relatively poor. The research on the hyperspectral imaging detection model of the shelf life of the pear provides theoretical guidance for consumers to correctly evaluate the freshness of the fruit, and also provides technical support for the development of the fruit shelf detection instrument in the later stage.
Key words：Hyperspectral imaging technique; Shelf life; Feature image; Least squares discriminant; Partial least squares support vector machine
李 雄，刘燕德，欧阳爱国，孙旭东，姜小刚，胡 军，欧阳玉平. 酥梨货架期的高光谱成像无损检测模型研究[J]. 光谱学与光谱分析, 2019, 39(08): 2578-2583.
LI Xiong, LIU Yan-de, OUYANG Ai-guo, SUN Xu-dong, JIANG Xiao-gang, HU Jun, OUYANG Yu-ping. Study on Non-Destructive Testing Model of Hyperspectral Imaging for Shelf Life of Crisp Pear. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2578-2583.
[1] SONG Xue-jian, WANG Hong-jiang, ZHANG Dong-jie, et al(宋雪健，王洪江，张东杰，等). Nondestructive Testing(无损检测), 2017, 39(10): 71.
[2] SUN Hai-xin, YAN Mei-ling, LU Rong-rong(孙海新，闫美伶，陆容容). Food Technology(食品科技), 2015, (5): 334.
[3] WEN Shu-xian, LI Shao-wen, JIN Xiu, et al(温淑贤，李绍稳，金 秀，等). Journal of Computer Science(计算机科学), 2017, 44(S1): 216.
[4] Manuela Zude, Bernd Herold, Jean-Michel Roger, et al. Journal of Food Engineering, 2005, 77(2)：254.
[5] Sinelli N, Barzaghi S, Giardina C, et al. Journal of Near Infrared Spectroscopy, 2006, 13(5)：293.
[6] SUN Jun, JIN Xia-ming, MAO Han-ping, et al(孙 俊，金夏明，毛罕平，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(10): 167.
[7] HUANG Shuang-ping, QI Long, MA Xu, et al(黄双萍，齐龙，马 旭，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(1): 212.
[8] LIU Ya, Mu Hetar·Migiti, CAO Peng-cheng, et al(刘 亚，木合塔尔·米吉提，曹鹏程，等). Agricultural Technology and Equipment(农业科技与装备), 2016，(5): 50.
[9] LIU Xue, LI Ya-mei, LIU Jiao, et al(刘 雪，李亚妹，刘 娇，等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报)，2015，46(10)：328.
[10] LIU Yan-de, CHENG Meng-jie, HAO Yong, et al(刘燕德，程梦杰，郝 勇). Journal of East China Jiaotong University(华东交通大学学报), 2018, 35(4): 1.
