Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*
1. Engineering Research Center of Edible and Medicinal Fungi, Ministry of Education, 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 one kind of berrie richer in flavone than blueberry and has thus been approved as a new food resource largely used in the beverage industry. Flavone and polysaccharides have been revealed to be the main bioactive components in its fruit juice and pomace, affecting its quality. Therefore, their contents were predicted by infrared spectroscopy combined with chemometrics, which provided a basis for establishing a simple and rapid method for the quality detection of A. melanocarpa. A total of 750 infrared spectral data of A. melanocarpa from 15 production areas were collected, and their contents in flavone and polysaccharides were measured. The samples were divided into calibration set and validation set by K-S sample division method in the proportion of 4∶1. The spectral information after grouping was pretreated by multiple scattering correction (MSC), standard normalization (SNV), smoothing (SG), first derivative (FD), second derivative (SD) and other spectral preprocessing, and the best spectrum preprocessing method was determined. The competitive adaptive reweighting algorithm (CARS) and continuous projection algorithm (SPA) were used to select the characteristic spectral bands of flavone and polysaccharides in A. melanocarpa. The spectral data selected by the two wave methods were combined with partial least square regression (PLS), limit learning machine (ELM) and support vector machine (SVM) for modeling and comparison, and the algorithm model with the best prediction effect was selected. The results showed that, MSC had the best effect on the original spectrum among the seven spectral pretreatment methods. Under this treatment, the RPD value of the flavone content prediction model was 6.201 7, and 5.447 3 for the polysaccharide content prediction mode, with the error of the prediction model significantly decreased. After extracting the characteristic spectra by CARS and SPA, the modeling results revealed that the RC, RP, and RPD of the flavone content prediction model were respectively 0.997 2, 0.991 2 and 10.631 5, while they were 0.996 5, 0.986 7 and 8.664 7 respectively for the polysaccharide content prediction model. Therefore, infrared spectroscopy combined with chemometrics methods, especially the CARS-ELM model, can accurately predict the contents of flavone and polysaccharides in A. melanocarpa, and the development of this method provides a fast and simple method for its quality evaluation.
杨承恩，李 萌，卢秋宇，王金玲，李雨婷，苏 玲. FTIR结合ELM对黑果腺肋花楸黄酮、多糖含量快速预测[J]. 光谱学与光谱分析, 2024, 44(01): 62-68.
YANG Cheng-en, LI Meng, LU Qiu-yu, WANG Jin-ling, LI Yu-ting, SU Ling. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68.
[1] HU Wen-ze, LI Miao, GUO Dong-xu, et al(胡文泽, 李 淼, 郭东旭, 等). Food and Fermentation Industries(食品与发酵工业), 2020, 46(23): 316.
[2] HUANG Jia-shuang, CAO Qing-chao, JIN Yun-zhe, et al(黄佳双, 曹庆超, 金允哲, 等). Journal of Yangzhou University(Agricultural and Life Science Edition[(扬州大学学报(农业与生命科学版)], 2019, 40(6): 100.
[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] WU Xue-hui, HE Jun-hua, WANG Ze-fu(吴雪辉, 何俊华, 王泽富). China Oils and Fats(中国油脂), 2022, 47(2): 124.
[9] GUAN Ting-yu, HUANG Yong-mei, LIN Min, et al(关婷予, 黄咏梅, 林 敏, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2021, 36(6): 136.
[10] Sylvio Barbon Junior, Saulo Martielo Mastelini，Ana Paula A C Barbon, et al. Information Processing in Agriculture, 2020, 7(2): 342.
[11] LIU Yan, CHENG Lu, SUN Lin(刘 艳, 程 璐, 孙 林). Journal of Henan Normal University(Natural Science Edition)[河南师范大学学报(自然科学版)], 2019, 47(2): 22.
[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] WANG Chun-yan, HOU Yi-min, LI Han-wei, et al(王春艳, 侯益民, 李汉伟, 等). Chinese Traditional Patent Medicine(中成药), 2021, 43(11): 3227.
[14] Subha T D, Subash T D, Claudia Jane K S, et al. Materials Today: Proceedings, 2020, 24(4): 2394.
[15] Zhang Yifan, Liu Yong, Yang Xicheng. International Core Journal of Engineering, 2021, 7(6): 417.
