Determination of DPPH free radical scavenging activity: Application of artificial neural networks. (1st March 2016)

• Record Type: Journal Article
• Authors: Musa, Khalid Hamid; Abdullah, Aminah; Al-Haiqi, Ahmed
• Is Part Of: Food chemistry. Volume 194(2016)
• Page Start: 705
• Page End: 711
• Publication Date: 2016-03-01
• Subjects: DPPH free radical scavenging activity -- Machine learning -- Artificial neural network -- Image classification; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2015.08.038
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: A computational approach for the determination of DPPH-RSA in food is proposed. Artificial neural network (ANN) was able to determine the DPPH-RSA of samples. This method can be used to obtain semi-quantitative results of DPPH-RSA. 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.