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بررسی تاثیر عصارههای دانه رازیانه و کاکوتی کوهی روی رشد کپک آسپرژیلوس فلاووس رب گوجه فرنگی و پیش بینی داده های حاصل شده با استفاده از شبکه های عصبی مصنوعی
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نویسنده
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محبی مریم
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منبع
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علوم و صنايع غذايي ايران - 1401 - دوره : 19 - شماره : 132 - صفحه:327 -340
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چکیده
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ﻓﻌﺎﻟﯿﺖ ﺿﺪ ﻗﺎرﭼﯽ ﻋﺼﺎرهﻫﺎی داﻧﻪ رازﯾﺎﻧﻪ و ﮐﺎﮐﻮﺗﯽ ﮐﻮﻫﯽ ﻋﻠﯿﻪ آﺳﭙﺮژﯾﻠﻮس ﻓﻼووس در رب ﮔﻮﺟﻪﻓﺮﻧﮕﯽ ﺣﺎوی درﺻﺪﻫﺎی ﻣﺨﺘﻠﻒ از ﻋﺼﺎرهﻫﺎ ﻣﻮرد آزﻣﻮن ﻗﺮار ﮔﺮﻓﺖ. ﺑﺮای اﯾﻦ ﻣﻨﻈﻮر ﻋﺼﺎرهﻫﺎی داﻧﻪ رازﯾﺎﻧﻪ و ﮐﺎﮐﻮﺗﯽ ﮐﻮﻫﯽ ﺑﺎ ﻏﻠﻈﺖﻫﺎی ﻣﺘﻔﺎوت 0/5، 1و2 درﺻﺪ ﺗﻬﯿﻪ ﮔﺮدﯾﺪ و در ﻣﺪت زﻣﺎن ﻧﮕﻬﺪاری 35 روز ﺑﺮرﺳﯽ ﺷﺪ. ﺗﺎﺛﯿﺮ ﻋﺼﺎره ﻫﺎی داﻧﻪ رازﯾﺎﻧﻪ و ﮐﺎﮐﻮﺗﯽ ﮐﻮﻫﯽ ﺑﺎ ﻏﻠﻈﺖ ﻫﺎی ﻣﺨﺘﻠﻒ ﺑﻪ ﺗﻨﻬﺎﯾﯽ در ﻣﺤﯿﻂ )in vitro( ﺑﺮرﺳﯽ ﮔﺮدﯾﺪ. ﺑﺎ ﺗﺰرﯾﻖ ﮐﭙﮏ 0/1 ﻣﯿﻠﯽﻟﯿﺘﺮ در ﻣﺤﯿﻂ ﮐﺸﺖ آﺑﮕﻮﺷﺖ ﺳﺎﺑﺮود دﮐﺴﺘﺮوز آﮔﺎر1 و ﺳﭙﺲ ﻗﺮار دادن در دﻣﺎی اﻧﮑﻮﺑﺎﺗﻮر 0/5± 25 درﺟﻪ ﺳﺎﻧﺘﯽ ﮔﺮاد ﺑﻪ ﻣﺪت 5 ﻫﻔﺘﻪ )35 روز( ﻧﮕﻬﺪاری ﺷﺪ ﮐﻪ ﻫﺮ ﻫﻔﺘﻪ ﯾﮏ ﮐﺸﺖ اﻧﺠﺎم ﮔﺮﻓﺖ ﺗﺎ ﻓﻌﺎﻟﯿﺖ ﮐﭙﮏ در ﻏﻠﻈﺖ ﻫﺎی ﻣﺨﺘﻠﻒ ﻋﺼﺎرهﻫﺎ ﺑﺮرﺳﯽ ﮔﺮدد. ﻧﺘﺎﯾﺞ ﻓﻌﺎﻟﯿﺖ ﺿﺪ ﻗﺎرﭼﯽ ﺳﻄﻮح ﻣﺨﺘﻠﻒ ﻋﺼﺎرهﻫﺎ ﻧﺸﺎن داد ﮐﻪ ﺗﯿﻤﺎرﻫﺎی 3 )ﺣﺎوی 2 درﺻﺪ ﻋﺼﺎره داﻧﻪ رازﯾﺎﻧﻪ( و 4 )ﺣﺎوی 3 درﺻﺪ ﻋﺼﺎره داﻧﻪ رازﯾﺎﻧﻪ( ﺗﺎ ﭘﺎﯾﺎن دوره ﻧﮕﻬﺪاری در ﻣﻘﺎﺑﻞ رﺷﺪ ﻣﯿﺴﯿﻠﯿﻮمﻫﺎی ﮐﭙﮏ آﺳﭙﺮژﯾﻠﻮس ﻓﻼووس ﻣﻘﺎوم ﺑﻮدﻧﺪ. ﺑﻪﻃﻮرﮐﻠﯽ ﻣﯽﺗﻮان ﻧﺘﯿﺠﻪ ﮔﺮﻓﺖ ﮐﻪ اﺳﺘﻔﺎده از 2 ﯾﺎ 3 درﺻﺪ ﻋﺼﺎره داﻧﻪ رازﯾﺎﻧﻪ ﺑﻪﻋﻨﻮان ﻧﮕﻪدارﻧﺪه ﻃﺒﯿﻌﯽ در رب ﮔﻮﺟﻪﻓﺮﻧﮕﯽ ﻓﻌﺎﻟﯿﺖ ﺿﺪ ﻗﺎرﭼﯽ ﻣﻄﻠﻮﺑﯽ دارﻧﺪ. ﺑﺮای ﺻﺤﺖ ﺳﻨﺠﯽ و ارزﯾﺎﺑﯽ ﻧﺘﺎﯾﺞ ﺣﺎﺻﻠﻪ از آزﻣﺎﯾﺸﺎت از ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﻣﺼﻨﻮﻋﯽ در ﭘﯿﺶﺑﯿﻨﯽ دادهﻫﺎی رﺷﺪ ﮐﭙﮏ آﺳﭙﺮژﯾﻠﻮس ﻓﻼووس رب ﮔﻮﺟﻪﻓﺮﻧﮕﯽ اﺳﺘﻔﺎده ﮔﺮدﯾﺪ. در اﯾﻦ ﺑﺮرﺳﯽ از ﺗﻌﺪاد دوﻻﯾﻪ ﻣﺨﻔﯽ ﺑﺎ ﺗﻌﺪاد 30 ﻧﺮون اﺳﺘﻔﺎده ﺷﺪ. ﺷﺒﮑﻪ دارای دو ورودی ﻏﻠﻈﺖ ﻋﺼﺎره و ﻣﺪت زﻣﺎن ﻧﮕﻪداری ﺑﻮده و رﺷﺪ ﮐﭙﮏ آﺳﭙﺮژﯾﻠﻮس ﻓﻼووس ﺑﻪﻋﻨﻮان ﺗﺎرﮔﺖ در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﺷﺪ. ﭘﺎراﻣﺘﺮﻫﺎی ارزﯾﺎﺑﯽ از ﻗﺒﯿﻞ ﺿﺮﯾﺐ ﻫﻤﺒﺴﺘﮕﯽ، ﻣﯿﺎﻧﮕﯿﻦ ﻣﺮﺑﻌﺎت ﺧﻄﺎ و ﻣﺎﮐﺰﯾﻤﻢ ﺧﻄﺎ ﻧﺘﺎﯾﺞ ﺑﺴﯿﺎر ﻣﻄﻠﻮﺑﯽ ﺑﺎ ﻣﻘﺎدﯾﺮ 0/9993، 0/10934 و 0/13538 را ﻧﺸﺎن دادﻧﺪ. ﻫﺮ ﭼﻪ ﻣﻘﺪار ﺧﻄﺎ ﮐﻤﺘﺮ ﺑﺎﺷﺪ و ﻣﯿﺰان ﺿﺮﯾﺐ ﻫﻤﺒﺴﺘﮕﯽ ﻧﺰدﯾﮏ ﯾﮏ ﺑﺎﺷﺪ ﻧﺸﺎن از ﯾﮏ ﭘﯿﺶﺑﯿﻨﯽ ﻣﻄﻠﻮب اﺳﺖ.
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کلیدواژه
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کپک آسپرژیلوس فلاووس، ضریب همبستگی، دانه رازیانه، کاکوتی کوهی، شبکه عصبی مصنوعی
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آدرس
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دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران, گروه مهندسی کشاورزی، علوم و صنایع غذایی، میکروبیولوژی مواد غذایی, ایران
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پست الکترونیکی
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m.mohebbi512@gmail.com
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studying the effect of foeniculum vulgare mill and ziziphora clinopodioides lam. extracts on the growth of aspergillus flavus mold in tomato paste and predicting the data obtained using artificial neural networks
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Authors
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mohebbi maryam
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Abstract
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the antifungal activity of foeniculum vulgare mill and ziziphora clinopodioides lam. extracts against aspergillus flavus in tomato paste containing different percentages of the extracts was tested. to this end, foeniculum vulgare mill and ziziphora clinopodioides lam. extracts with different concentrations of 0.5, 1 and 2% were prepared and studied during different storage times(35 days).the effect of extracts of foeniculum vulgare mill and ziziphora clinopodioides lam with different concentrations was investigated alone in the environment (in vitro). by injecting 0.1 ml of mold in sabouraoud dextrose agar broth culture medium, then placing it in an incubator temperature of 25°c ± 0.5, it was kept for 5 weeks (35 days), and one culture was done every week in order for the activity mold to be investigated in different concentrations of extracts.the results of antifungal activity of different levels of the extracts indicated that treatments 3 (containing 2% foeniculum vulgare mill extract) and 4 (containing 3% foeniculum vulgare mill extract) were resistant to the growth of aspergillus flavus mold mycelium until the end of storage period.generally, it can be concluded that using 2 or 3% foeniculum vulgare mill extract as a natural preservative in tomato paste has a desirable antifungal activity. artificial neural network was used to validate and evaluate the results of the experiments in predicting the data of aspergillus flavus mold growth in tomato paste.in the present study, two hidden layers with 30 neurons were used. the network had two inputs including extract concentration and storage time, and the growth of aspergillus flavus mold was considered as the target.evaluation parameters such as correlation coefficient, mean squared error and maximum error showed very good results with values of 0.9993, 0.10934 and 0.13538. the lower the error and the closer the correlation coefficient to 1, the better the prediction is.
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Keywords
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aspergillus flavus mold ,correlation coefficient ,foeniculum vulgare mill ,ziziphora clinopodioides lam ,artificial neural network
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