Identification of Aronia Melanocarpa Fruits From Different Areas by Mid-Infrared Spectroscopy
YANG Cheng-en1, 2, LI Meng3, WANG Tian-ci1, 2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*
1. Engineering Research Center of Ministry of Education for Edible and Medicinal Fungi, Jilin Agricultural University, Changchun 130118, China
2. College of Life Science, Jilin Agricultural University, Changchun 130118, China
3. Department of Modern Agriculture, Changchun Vocational Institute of Technology, Changchun 130504, China
4. Department of Quality Research, Sinopharm A-Think Pharmaceutical Co., Ltd., Changchun 130600, China
Abstract:Aronia melanocarpa is a small berry listed in the list of new food raw materials, rich in anthocyanins and other ingredients, which has been widely used in alcohol, beverages, functional food, cosmetics and other fields, with high economic value. Due to the influence of environmental factors such as climate and planting conditions in different areas, the fruit quality of A. melanocarpa is significantly different. Therefore, to standardize the market management of A. melanocarpa fruit, the fruit of A. melanocarpa from different places of origin was identified by mid-infrared spectroscopy combined with chemometrics. 750 infrared spectral data of A. melanocarpa fruit from 15 production areas were collected. After spectral pretreatments, such as multiple scattering corrections (MSC), standard normalization (SNV), moving smoothing (SG), first derivative (FD), second derivative (SD), and so on, the optimal spectral pretreatment method was determined by comparing the recognition effect of support vector machine (SVM) modeling with the original spectrum. The K-S sample division method divides the samples into training sets and test sets at a ratio of 4∶1, and then the samples are normalized. The competitive adaptive reweighting algorithm (CARS) and continuous projection algorithm (SPA) are used to extract the spectral feature information, and the best model is determined by modeling and comparing with random forest (RF), extreme learning machine (ELM) and support vector machine (SVM). The results show that MSC is the best spectral preprocessing method; the recognition rate of the MSC-SVM training set is 93.33%, and the recognition rate of the test set is 92.67%, which can effectively reduce the random error generated during spectral acquisition. After extracting the MSC characteristic spectral wavenumber by CARS and SPA, the modeling results of the three algorithms are compared, and the SPA-SVM model is determined to be the best recognition model. The recognition rate of its training set and test version is 100%, and only 16 wavelength points are needed to complete the accurate recognition. Therefore, the combination of mid-infrared spectroscopy and chemometrics, especially SPA-SVM model, can accurately identify the origin of A. melanocarpa fruit, provide a fast and simple method support for the origin traceability and quality evaluation of A. melanocarpa fruit, and provide a technical basis for building a unique brand with regional characteristics.
Key words:Aronia melanocarpa; Infrared spectroscopy; Origin identification; Support vector machine
杨承恩,李 萌,王天赐,王金玲,李雨婷,苏 玲. 红外光谱的不同产地黑果腺肋花楸果实鉴别[J]. 光谱学与光谱分析, 2024, 44(04): 991-996.
YANG Cheng-en, LI Meng, WANG Tian-ci, WANG Jin-ling, LI Yu-ting, SU Ling. Identification of Aronia Melanocarpa Fruits From Different Areas by Mid-Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 991-996.
[1] CHEN Tong-yao, ZHOU Li-si, LI Bing, et al(陈彤垚, 周丽思, 李 兵,等). Chinese Pharmaceutical Journal(中国药学杂志), 2021, 56(17): 1361.
[2] YANG Shu-qiao, WANG Di, GAO Yan-xiang(杨舒乔, 王 迪, 高彦祥). Food Research and Development(食品研究与开发), 2021, 42(13): 206.
[3] SUN Yi, XING Li-ying, LI Yan-lin, et al(孙 怡, 邢丽颖, 李艳林, 等). Food Research and Development(食品研究与开发), 2022, 43(5): 217.
[4] CONG Long-jiao, SHI Rui, WU Peng, et al(丛龙娇, 史 锐, 吴 鹏, 等). Journal of Liaoning University of Traditional Chinese Medicine(辽宁中医药大学学报), 2021, 23(1): 31.
[5] WANG Shen-meng, GUAN Qing-jie, ZHANG Ting-xiu, et al(王申萌, 管清杰, 张廷秀, 等). Food Science and Technology(食品科技), 2021, 46(5): 64.
[6] SUN Yi, LI Jian-ying, JIANG Dong-yang, et al(孙 怡, 李建颖, 蒋冬阳, 等). Journal of Food Science and Biotechnology(食品与生物技术学报), 2022, 41(4): 45.
[7] LIU Xiao-huan, LIU Cui-ling, SUN Xiao-rong, et al(刘晓欢, 刘翠玲, 孙晓荣, 等). Food Science and Technology(食品科技), 2021, 46(4): 244.
[8] CHEN Wen-jing, LI Liang-xing, LI Ming, et al(陈文静, 李亮星, 李 明, 等). Journal of Food Safety & Quality(食品安全质量检测学报), 2021, 12(18): 7119.
[9] LI Jia-yi, YU Mei, ZHENG Yu, et al(李嘉仪, 余 梅, 郑 郁, 等). Chinese Journal of Analysis Laboratory(分析试验室), 2021, 40(12): 1381.
[10] AN Shu-jing, WANG Ting, NIU Dou, et al(安淑静, 王 婷, 牛 豆, 等). Journal of Chinese Medicine(中医药学报), 2021, 49(8): 49.
[11] WU Qing-ying, ZHU Zhen-yu, WU Jian-ming, et al(武晴滢, 祝震予, 吴剑鸣, 等). Chemical Journal of Chinese Universities(高等学校化学学报), 2022, 43(10): 150.
[12] JIANG Xiao-yu, LI Fu-sheng, WANG Qing-ya, et al(江晓宇, 李福生, 王清亚, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2022, 42(5): 1535.
[13] Zhang Yifan, Liu Yong, Yang Xicheng. International Core Journal of Engineering, 2021, 7(6): 417.
[14] Wu Yuqiang, Cao Yifei, Zhai Zhaoyu. Sustainability, 2022, 14(20): 13168.
[15] Min Mengcan, Chen Xiaofang, Xie Yongfang. Journal of Systems Engineering and Electronics, 2021, 32(1): 209.