中红外光谱对黑果腺肋花楸中多酚含量的定量检测  

Quantitative Determination of Polyphenols in Aronia Melanocarpa(Michx.)Elliott.by Mid-Infrared Spectroscopy

作　　者：杨承恩 郭瑞雪 辛明昊 李萌 李雨婷[2] 苏玲[1,3] YANG Cheng-en;GUO Rui-xue;XIN Ming-hao;LI Meng;LI Yu-ting;SU Ling(Engineering Research Center of Ministry of Education for Edible and Medicinal Fungi,Jilin Agricultural University,Changchun130118,China;College of Life Science,Jilin Agricultural University,Changchun 130118,China;College of Plant Protection,Jilin Agricultural University,Changchun 130118,China;College of Modern Agriculture,Changchun Vocational Institute of Technology,Changchun 130504,China)

机构地区：[1]吉林农业大学食药用菌教育部工程研究中心,吉林长春130118 [2]吉林农业大学生命科学学院,吉林长春130118 [3]吉林农业大学植物保护学院,吉林长春130118 [4]长春职业技术学院现代农学院,吉林长春130504

出　　处：《光谱学与光谱分析》2024年第11期3075-3081,共7页Spectroscopy and Spectral Analysis

基　　金：吉林省教育厅科学技术研究项目(JJKH20220324KJ);国家重点研发项目(2018YFD1001001)资助。

摘　　要：黑果腺肋花楸是富含多酚类物质的蔷薇科浆果。多酚是黑果腺肋花楸的主要化学成分,包括花青苷、黄酮苷、单宁等,具有抗氧化、抑菌、抗肿瘤、抗炎、减肥及调节血糖、血脂等药理活性。黑果腺肋花楸现已进入新食品原料名单。黑果腺肋花楸多酚含量与其功效价值关系密切,因此黑果腺肋花楸多酚含量检测方法的完善对规范黑果腺肋花楸原料及产品市场至关重要。现行检测方法操作繁琐、用时长,难以满足黑果腺肋花楸进入新食品原料名单后的产业发展需求,亟待开发快速测定多酚含量的方法。使用中红外光谱技术,建立了一种黑果腺肋花楸多酚含量快速定量检测方法。采集15个地区共750份黑果腺肋花楸红外光谱数据,进行光谱解析并测量每份样品的多酚含量;采用K-S样本划分法按4∶1的比例将样本划分为校正集和验证集;对分组后的光谱信息进行多元散射校正(MSC)、标准正态化(SNV)、平滑(SG)、一阶导数(FD)、二阶导数(SD)等光谱预处理,与原始光谱进行随机森林回归(RFR)建模预测效果对比,确定最佳光谱预处理方法为MSC。采用竞争性自适应重加权算法(CARS)和连续投影算法(SPA)选取黑果腺肋花楸多酚最优特征光谱波长,将两种方法选取的光谱数据结合随机森林回归(RFR)、偏最小二乘回归法(PLSR)、极限学习机(ELM)、支持向量机回归(SVR)进行建模对比,确定最佳算法模型。结果表明,CARS算法可有效减少红外光谱数据冗余,提高模型预测的精确性与稳定性;CARS-RFR模型具有最佳预测性能,其校正集Rc为0.9865,RMSEC为0.0732,验证集Rp为0.9743,RMSEP为0.1006,RPD为6.2356。结果表明,中红外光谱技术与化学计量学方法的结合,特别是CARS-RFR模型能够高效、快速、准确地实现黑果腺肋花楸多酚含量的检测,研究结果可为快速测定黑果腺肋花楸多酚含量提供技术支持。Aronia melanocarpa(Michx.)Elliott.It is a berry from the Rosaceae Family rich in polyphenols,known as its main chemical components,including anthocyanins,flavonoid glycosides,tannins,etc.,of A.melanocarpa.It has shown antioxidant,bacteriostatic,anti-tumor,anti-inflammatory,weight loss,glucose regulation,lipids,and other pharmacological activities.It has now been added to the list of new raw food materials.The polyphenol content of A.melanocarpa is closely related to its efficacy value.Therefore,improving their detection method is crucial to standardizing the raw material and product market from A.melanocarpa.However,the current detection method is cumbersome and time-consuming,and it is difficult to meet the industrial development needs of A.melanocarpa after it enters the list of new food raw materials.Thus,It is urgent to develop a method for rapidly determining polyphenol content.Mid-infrared spectroscopy established a rapid and quantitative determination method of polyphenol content in A.melanocarpa.The infrared spectral data of 750 samples from A.melanocarpa in 15 regions were collected for the spectral analysis,and the content of polyphenols in each sample was measured.The K-S sample division method was used to divide the sample into a correction set and verification set in the proportion of 4∶1.The grouped spectral information was pretreated by multiple scattering correction(MSC),standard normalization(SNV),smoothing(SG),first derivative(FD),second derivative(SD)and other spectral preprocessing methods.Compared with the original spectrum by random forest regression(RFR)modeling and prediction,the best spectral preprocessing method was determined as MSC.The competitive adaptive reweighting algorithm(CARS)and continuous projection algorithm(SPA)were used to select the optimal characteristic spectral wavelength of the polyphenols of A.melanocarpa.The spectral data selected by the two methods were combined with random forest regression(RFR),partial least squares regression(PLSR),limit learning machine(ELM),a

关 键 词：中红外光谱 黑果腺肋花楸 多酚 随机森林回归 

分 类 号：O433.4[机械工程—光学工程]