This study was done to test the activity of some plant extracts as antioxidant agents. The plants were (Morus rubra, Hibiscus sabdariffa L ., Rhus coriaria L., Anethum graveolens and Petroselinum sativum).
Ethanolic 98% (24 hours/ 25˚c) and distilled water (30 minutes/ 25˚c have been used for extraction.The Total phenols, total flavonoids, total anthocyanin, antioxidant activities were studied.
The extract of Morus rubra was chosen because it has a higher antioxidant activity.
The phenolic extract of Morus rubra was prepare and examined by application it in burger . The antioxidant activity test of Morus rubra was made before and after 3,6 days of cold storage. The sensory evaluation of all treatments were done within 5,10 days.
The results showed:
There was significant different between ethanolic extract and water extract in total phenols and flavonoids compound, the ethanolic extract of Morus rubra shown superior phenolic compound contain (23.3 mg GAE/ gr.), water extract of Morus rubra L., Showed height phenolic compound (20 mg GAE/ gr.).
Ethanolic and water extracts of Morus rubra have a higher Flavonoids (81.1, 69.8 μg /g Rutin Equv.). The Morus rubra shown superior Anthocyanin compound 56.3 μ g Cyanidin 3- glucoside/ gr. The antioxidant activity different according to type of plant and concentration, the ethanolic extract of Morus rubra gave higher antioxidant activity(88.9%) compared with other extract and α- tocopherol (86.5%) and lower from BHT (97 %).
The extract was added to burger at (0.04, 0.08, 0.12gr./ 100 gr.) which were stored at 4C for 5,10 days the peroxide value decreased as the extract concentration was increased.
Voice Activity Detection (VAD) is considered as an important pre-processing step in speech processing systems such as speech enhancement, speech recognition, gender and age identification. VAD helps in reducing the time required to process speech data and to improve final system accuracy by focusing the work on the voiced part of the speech. An automatic technique for VAD using Fuzzy-Neuro technique (FN-AVAD) is presented in this paper. The aim of this work is to alleviate the problem of choosing the best threshold value in traditional VAD methods and achieves automaticity by combining fuzzy clustering and machine learning techniques. Four features are extracted from each speech segment, which are short term energy, zero-crossing rate, auto
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