title: Determination of DPPH free radical scavenging activity: application of artificial neural networks.
abstract: A new computational approach for the determination of 2,2-diphenyl-1-picrylhydrazyl free radical scavenging activity (DPPH-RSA) in food is reported, based on the concept of machine learning. Trolox standard was mix with DPPH at different concentrations to produce different colors from purple to yellow. Artificial neural network (ANN) was trained on a typical set of images of the DPPH radical reacting with different levels of Trolox. This allowed the neural network to classify future images of any sample into the correct class of RSA level. The ANN was then able to determine the DPPH-RSA of cinnamon, clove, mung bean, red bean, red rice, brown rice, black rice and tea extract and the results were compared with data obtained using a spectrophotometer. The application of ANN correlated well to the spectrophotometric classical procedure and thus do not require the use of spectrophotometer, and it could be used to obtain semi-quantitative results of DPPH-RSA.
authors.affiliation: Faculty of Science & Technology, Universiti Kebangsaan Malaysia, UKM, 43600 Bangi Selangor, Malaysia. Electronic address: khalid.hamid@ukm.edu.my.
authors.forename: Khalid Hamid
authors.initials: KH
authors.lastname: Musa
authors.affiliation: Faculty of Science & Technology, Universiti Kebangsaan Malaysia, UKM, 43600 Bangi Selangor, Malaysia.
authors.forename: Aminah
authors.initials: A
authors.lastname: Abdullah
authors.affiliation: Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, 43600 Bangi Selangor, Malaysia.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Al-Haiqi
mesh_terms: D001713:Biphenyl Compounds / Q000737:chemistry*; D016166:Free Radical Scavengers / Q000737:chemistry*; D016571:Neural Networks, Computer; D010851:Picrates / Q000737:chemistry*
chemical_list: D001713:Biphenyl Compounds; D016166:Free Radical Scavengers; D010851:Picrates; C004931:1,1-diphenyl-2-picrylhydrazyl
keywords: Artificial neural network; DPPH free radical scavenging activity; Image classification; Machine learning