عبير عبدالله الشجاري

 


      
 
الاسم الاول: 
عبير
اسم العائلة: 
الشجاري
الدرجة العلمية: 
دكتوراة
مجال الدراسة: 
العلوم والتقنية
المؤسسة التعليمية: 
University of Westminster

مجال التميز

تميز دراسي و بحثي

 

 

البحوث المنشورة

 

البحث (1):

 

عنوان البحث:

An adaptive neuro-fuzzy identification model for the detection of meat spoilage

رابط إلى البحث:

Click here

تاريخ النشر:

13/06/2014

 

 

 

 

 

 

موجز عن البحث:

 

In food industry, safety and quality are considered important issues worldwide that are directly related to health and social progress. To address the rapid and non-destructive detection of meat spoilage microorganisms during aerobic storage at chill and abuse temperatures, Fourier transform infrared (FTIR) spectroscopy with the aid of a neuro-fuzzy identification model has been considered in this research. FTIR spectra were obtained from the surface of beef samples, while microbiological analysis determined the total viable count for each sample. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from FTIR spectra. The proposed model utilises a prototype defuzzification scheme, whereas the number of input membership functions is directly associated to the number of rules, reducing thus, the curse of dimensionality problem. Results confirmed the advantage of the proposed scheme against the adaptive neuro-fuzzy inference system (ANFIS), in terms of prediction accuracy and structure simplicity. Subsequent comparison against multilayer perceptron (MLP) and partial least squares technique indicated that FTIR spectral information in combination with the proposed modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage.

 

 

المؤتمرات العلمية:

 

 

 

المؤتمر (1):

 

عنوان المؤتمر:

50th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

تاريخ الإنعقاد:

2-5/08/2015

مكان الإنعقاد:

Istanbul, Turkey

طبيعة المشاركة:

Paper presentation

عنوان المشاركة:

An adaptive neuro-fuzzy model for the detection of meat spoilage using multispectral images

ملخص المشاركة:

 

The use of vision technology for quality testing of food production has the obvious advantage of being able to continuously monitor a production using non-destructive methods thus increasing the quality and minimizing cost. The performance of a multispectral imaging system has been evaluated in monitoring the spoilage of minced beef stored either aerobically or under modified atmosphere packaging (MAP), at different storage temperatures (0, 5, 10, and 15 °C). The detection system explores both qualitative and quantitative information extracted from spectral data with the aid of an advanced neuro-fuzzy identification model. The proposed model constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. Results indicated that multispectral information could be considered as an alternative methodology for the accurate evaluation of meat spoilage.

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