Abstract：Soluble solids and bruise are the two main factors affecting tomato quality. The purpose of the study was to explore the feasibility of simultaneous on-line detection of bruises and soluble solids in tomatoes by near-infrared diffuse transmission spectroscopy. The near-infrared diffuse transmission spectra of tomato were collected under the condition of a single-channel fruit delivery speed of 5/s. The near-infrared diffuse transmission spectrum characteristics of the bruised and normal tomato samples were compared and analyzed. The results showed that there was a significant difference in the light intensity between the bruises and the normal tomato samples. The light intensity of the bruises was stronger than that of the normal fruits. The reason may be that the meat becomes soft and the light transmission becomes stronger after the bruising. The two absorption peaks are more than the normal fruit at 650 and 675 nm. The reason may be that the color of the tomato skin changes before and after theinjury. The first three main scores with the highest contribution rate were selected. After qualitative analysis of principal components of near-infrared diffuse transmission spectra of normal fruits and bruises, normal fruits and bruises could not be effectively clustered. Therefore, high-dimensional near-infrared diffuse transmission spectral qualitative discriminant model was selected. By establishing the near-infrared diffuse transmission spectrum partial least squares qualitative discriminant model of the injured tomato sample, the false positive rate of the partial least squares qualitative discriminant model is 0%, which can correctly discriminate the fruit, so the near-infrared diffuse transmission spectroscopy partial least squares qualitative discriminant model of the touched tomato sample was selected as the online knockout sorting model for tomato touch injury. Validation of samples that have not been involved in modeling can correctly identify bruises. After the injurious fruit was removed by the near-infrared diffuse transmission spectroscopic partial least-squares qualitative discriminant model, the classification was based on the soluble solids index. The model is preprocessed using all the bands and the 606～850 nm band, and the second-order derivative preprocessing is performed on all the bands and the 606～850 nm band spectrum, and the front-back smoothing is set to 9, and the continuous projection algorithm and genetic algorithm are used to optimize the soluble solids. The spectral modeling variables, through comparison found that the use of non-algorithm screening 606～850 nm band spectral variables modeling, the best effect, established a soluble solids online detection model, the prediction set root mean square error of 0.43 Brix°. Simultaneous on-line detection of bruising and soluble solids using samples not involved in modeling demonstrated that the accuracy of sorting of bumped specimens was 96%, and the accuracy of sorting of soluble solids samples was 91%. The experimental results show that the simultaneous on-line detection of tomato bruising and soluble solids near-infrared diffuse transmission spectroscopy is feasible.
刘燕德，饶 宇，孙旭东，肖怀春，姜小刚，祝 柯，徐 海. 番茄碰伤和可溶性固形物近红外光谱同时在线检测[J]. 光谱学与光谱分析, 2019, 39(12): 3910-3915.
LIU Yan-de, RAO Yu, SUN Xu-dong, XIAO Huai-chun, JIANG Xiao-gang, ZHU Ke, XU Hai. The Online Detection Model Research of Tomatoes’ Bruise and SSD. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3910-3915.
[1] Zhiguo Li, Colin Thomas. Trends in Food Science & Technology, 2014, 35: 138.
[2] Tian Jinjin, Zhang Zhe, Yan Lei, et al. Science and Technology of Food Industry, 2017，7: 340.
[3] Shimeles Tilahun, Do Su Park, Mu Hong Seo, et al. Postharvest Biology and Technology, 2018, 136: 50.
[4] Wu Guifang, Wang Chunguang. Postharvest Biology and Technology, 2014, 98: 41.
[5] Ding Xiaoxiao, Guo Ying, et al. Vibrational Spectroscopy, 2016, 82: 1.
[6] Liu Yande, Shi Yu, Cai Lijun. Chinese Journal of Agricultural Machinery, 2013, 9: 138.
[7] Xin Huang, Li Xia. Chemometrics and Intelligent Laboratory Systems, 2017, 6: 22.
[8] Habib Motieghader, Ali Najafi, Balal Sadeghi, et al. Informatics in Medicine Unlocked, 2017, 9: 246.
[9] Zhao Yize, Long Qi. Wiley Interdisciplinary Reviews: Computational Statistics, 2017, 9(5): doi: 10.1002/wics.1402.
[10] Xu Shu, Lu Bo, Baldea Michael, et al. Journal of Process Control, 2018, 67: 83.
