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EVALUATION OF INFORMATION LABEL FOR SOME LOCAL PICKLED PRODUCTS AND ESTIMATION OF SODIUM BENZOATE THEREIN: EVALUATION OF INFORMATION LABEL FOR SOME LOCAL PICKLED PRODUCTS AND ESTIMATION OF SODIUM BENZOATE THEREIN
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ABSTRACT

The study aimed to evaluate the information label of some local pickle products and estimate sodium benzoate therein. 85 samples of locally made pickles were collected from Baghdad city markets and randomly from five different areas in Baghdad it included (Al-Shula, Al-Bayaa, Al-Nahrawan, Al-Taji, and Abu Ghraib), which were divided into groups P1, P2, P3, P4 and P5, respectively, according to those areas,  samples information label was scanned and compared with the Iraqi standard specification for the information card of packaged and canned food IQS 230, the results showed that 25.9% of the samples were devoid of the indication card information, 32.9% matched and 41.20% did not match, and 71.8% of the pickled samples gave a positive test result when the qualitative detection of sodium benzoate, this indicates that it is used to preserve the product , as for 28.2%, the result was negative, the average concentration of this substance for the samples for groups P1, P2, P3, P4 and P5 was 1704,23, 973,67, 995,81, 1216,27 and 1142,19 mg/kg respectively, 40.98% of the samples analyzed in which the concentration of sodium benzoate exceeds the permissible limit of 1000 mg/kg that was determined by the Iraqi standard specification for pickles IQS 1128.

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Publication Date
Mon Apr 11 2011
Journal Name
Icgst
Employing Neural Network and Naive Bayesian Classifier in Mining Data for Car Evaluation
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In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

Publication Date
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Journal Name
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