Rapid detection of total phenolics,antioxidant activity and ascorbic acid of dried apples by chemometric algorithms

作     者：NecatiÇetin Cevdet Sağlam 

作者机构：Erciyes UniversityFaculty of AgricultureDepartment of Biosystems EngineeringKayseriTurkey 

出 版 物：《Food Bioscience》 (食品生物科学（英文）)

年 卷 期：2022年第47卷第3期

页      面：662-669页

核心收录：

学科分类：1008[医学-中药学(可授医学、理学学位)] 0832[工学-食品科学与工程（可授工学、农学学位）] 1006[医学-中西医结合] 100602[医学-中西医结合临床] 10[医学] 

基　　金：grateful to Scientific Research Department of Erciyes University-Turkey for their financial support(Project no:FBA-2020-10157) 

主　　题：Apple Drying Biochemical composition VIS/NIR spectra Machine learning 

摘      要：Multivariate approaches like machine learning are commonly used in estimation of biochemical traits from spectral and color characteristics of foodstuffs and agricultural *** present study,windfall apples of Golden Delicious,Oregon Spur and Granny Smith cultivars were dried in open-sun,controlled greenhouse,microwave oven(200W),hybrid system(100W+60℃),convective dryer(70℃)and freeze-dryer(−55℃).Spectral,chromatic and biochemical characteristics of dried apples were determined and assessed through machine learning *** phenolic matter,DPPH(2,2-Diphenyl-1-picrylhydrazyl),FRAP(Ferric Reducing Antioxidant Power)and ascorbic acid content were estimated with the use of five different machine learning algorithms(artificial neural networks,k-nearest neighbor,random forest,gaussian processes and support vector regression).The most successful results were achieved in estimation of total phenolic content(R≥0.85).Additionally,Multilayer Perceptron,Support Vector Regression and Gaussian Processes were identified as the best machine learning algorithms in estimation of biochemical compositions of dried apples.