Rapid Identification of Apple Moldy Core Disease by Near Infrared
Spectroscopy With Information Fusion of Different Illumination
Patterns
XIAO Nan1, LI Han-lin1, WENG Ding-kang1, HU Dong1, SUN Tong1*, XIONG Yong-sen2
1. College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
2. Key Laboratory of Crop Harvesting Equipment Technology of Zhejiang Province, Jinhua 321016, China
Abstract：Apples are crisp, sweet, and inexpensive. They have great economic value in China and are a characteristic pillar industry for rural revitalization in apple-producing areas. Moldy core disease is one of the main internal defects of apples, greatly affecting their quality. Failure to remove it promptly will be detrimental to apple branding and seriously affect the development of the apple industry. In this study, near-infrared spectroscopy was used to obtain the spectra of apples in different poses and illumination modes. Pretreatment methods such as detrending, baseline correction, and second derivative (SD) were used to pre-process the original spectra, and then a support vector machine (SVM) was used to establish a moldy core disease discrimination model for apples. Also, the effect of the illumination modes on mold core disease detection in different poses was analyzed, and the apple mold core disease detection method, by fusing the spectral information of different illumination modes, was investigated. The results indicate that in the single-illumination mode, the 180°illumination mode has a higher correct discrimination rate for regular apples, while the 135°illumination mode has a better correct identification rate for moldy core disease apples; for the single-illumination mode, the optimal SVM model is obtained in the rightward stance of the fruit pedicel and the 135° illumination mode. The model's sensitivity, specificity, and correctness in the calibration set are 1, 0.978 2, and 0.986 3, and the sensitivity, specificity, and correctness of the prediction set are 0.888 8, 0.956 5, and 0.937 5, respectively. For the information fusion of different illumination modes, most of the information fusion of the illumination mode can improve the performance of the apple mildew discrimination model to some extent; the optimal SVM model is obtained in the information fusion of 180° and 135°illumination modes in the rightward stance of the fruit pedicel, the sensitivity, specificity, and correctness of the model in calibration set are all 1. The prediction set's sensitivity, specificity, and correctness are 0.888 8, 1, and 0.968 7, respectively. Compared with the optimal model of single illumination mode, the model's performance is improved. This paper provides a new idea of rapid, nondestructive identification of apple moldy core disease by near-infrared spectra with the information fusion of different illumination modes, and the method can also provide a reference for nondestructive detection of the internal quality of other fruits.
Key words：Near infrared spectroscopy; Information fusion; Illumination patterns; Moldy core disease; Apple
肖 男，李翰林，翁定康，胡 栋，孙 通，熊永森. 不同光照模式信息融合的苹果霉心病近红外快速识别[J]. 光谱学与光谱分析, 2024, 44(08): 2388-2394.
XIAO Nan, LI Han-lin, WENG Ding-kang, HU Dong, SUN Tong, XIONG Yong-sen. Rapid Identification of Apple Moldy Core Disease by Near Infrared
Spectroscopy With Information Fusion of Different Illumination
Patterns. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2388-2394.
[1] RAO Li-bo, CHEN Xiao-yan, PANG Tao(饶利波, 陈晓燕, 庞 涛). Chinese Journal of Luminescence(发光学报), 2019, 40(3): 389.
[2] Xia Y, Huang W, Fan S, et al. Infrared Physics & Technology, 2019, 97: 467.
[3] ZHAO Juan, SHEN Mao-sheng, PU Yu-ge, et al(赵 娟, 沈懋生, 浦育歌, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2023, 54(2): 386.
[4] ZHANG Xin-xin, LI Pao, YU Mei, et al(张欣欣, 李 跑, 余 梅, 等). Food Science(食品科学), 2022, 43(1): 260.
[5] CHU Gang-hui, WANG Kun, YIN Xue-bo(楚刚辉, 王 坤, 尹学博). Chinese Journal of Analytical Chemistry(分析化学), 2020, 48(4): 536.
[6] WU Lin-xia, ZHAI Wen-lei, WEI Di-zhe, et al(武琳霞, 翟文磊, 韦迪哲, 等). Food Science(食品科学), 2020, 41(21): 248.
[7] WU Xiao-lin, SUN Rong, ZHU Peng-li, et al(吴晓琳, 孙 蓉, 朱朋莉, 等). Acta Physico-Chimica Sinica(物理化学学报), 2011, 27(5): 1039.
[8] Zhou Z Y, Lei Y, Su D, et al. International Journal of Agricultural and Biological Engineering, 2016, 9(6): 8.
[9] ZHANG Hai-hui, TIAN Shi-jie, MA Min-juan, et al(张海辉, 田世杰, 马敏娟, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(1): 313.
[10] QIN Kai, CHEN Gang, ZHANG Jian-yi, et al(秦 楷, 陈 刚, 张剑一, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(11): 3405.
[11] Tian S, Zhang J, Zhang Z, et al. Infrared Physics & Technology, 2019, 100: 117.
[12] ZHANG Jin, HU Yun, ZHOU Luo-xiong, et al(张 进, 胡 芸, 周罗雄, 等). Journal of Instrumental Analysis(分析测试学报), 2020, 39(10): 1196.
[13] Agelet L E, Hurburgh Jr C R. Critical Reviews in Analytical Chemistry, 2010, 40(4): 246.
[14] CHEN Yan-yu, CAO Zhen-zhen, ZHANG Bin-jia, et al(陈燕雨, 曹珍珍, 张宾佳, 等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2021, 21(11): 261.
[15] YU Mei, LI Shang-ke, YANG Fei, et al(余 梅, 李尚科, 杨 菲, 等). Journal of Instrumental Analysis(分析测试学报), 2021, 40(1): 65.
[16] ZHAO Feng, LIN He-tong, YANG Jiang-fan, et al(赵 峰, 林河通, 杨江帆, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(2): 269.
[17] JIN Hang-feng, HUANG Ling-xia, WU Di, et al(金航峰, 黄凌霞, 吴 迪, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2010, 29(3): 216.
