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.
journal_name
Food Chemjournal_title
Food chemistryauthors
Musa KH,Abdullah A,Al-Haiqi Adoi
10.1016/j.foodchem.2015.08.038subject
Has Abstractpub_date
2016-03-01 00:00:00pages
705-11eissn
0308-8146issn
1873-7072pii
S0308-8146(15)01235-2journal_volume
194pub_type
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