Determination of DPPH free radical scavenging activity: Application of artificial neural networks

Khalid Hamid Musa, Aminah Abdullah, Ahmed Mubarak Ahmed Al-Haiqi

Research output: Contribution to journalArticle

37 Citations (Scopus)

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.

Original languageEnglish
Article number17985
Pages (from-to)705-711
Number of pages7
JournalFood Chemistry
Volume194
DOIs
Publication statusPublished - 01 Mar 2016

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Scavenging
neural networks
Free Radicals
Neural networks
Spectrophotometers
spectrophotometers
Cinnamomum zeylanicum
biphenyl
Syzygium
Tea
black rice
red rice
red beans
cinnamon
cloves
black tea
brown rice
artificial intelligence
Color
mung beans

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Food Science

Cite this

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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.",
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Determination of DPPH free radical scavenging activity : Application of artificial neural networks. / Musa, Khalid Hamid; Abdullah, Aminah; Ahmed Al-Haiqi, Ahmed Mubarak.

In: Food Chemistry, Vol. 194, 17985, 01.03.2016, p. 705-711.

Research output: Contribution to journalArticle

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