کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
223098 464333 2014 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Partial scanning using computed tomography for fat weight prediction in green hams: Scanning protocols and modelling
ترجمه فارسی عنوان
اسکن جزئی با استفاده از توموگرافی کامپیوتری برای پیش بینی وزن چربی در پروتئین های سبز: پروتکل های اسکن و مدل سازی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
چکیده انگلیسی


• Fat weight prediction models for ham have been developed.
• The widest part of the ham and the knuckle area are used for models.
• Model using only 3 tomograms gives a good accuracy.
• For ham classification purposes, only one tomogram would be sufficient.

The objective of this work was to study the feasibility of computed tomography (CT) for predicting fat weight using a complete or partial scanning of green hams. Sixty-eight hams covering a wide range of fat weight were divided into calibration (total weight 11.46 ± 0.97 kg) and validation (total weight 11.35 ± 1.13 kg) sets, fully scanned by CT and dissected. Virtual slices were constructed to standardise the number of slices for hams of different length and their fat weight was estimated. Different predictive models were established with partial least square regression (PLS) and ordinary linear regression (OLR) using all the tomograms and with OLR and multi-linear regression (MLR) using a reduced number of virtual slices. The MLR model with 3 virtual slices gave a better accuracy (RMSEV = 145 g) than the PLS model which used all the tomograms (RMSE = 156 g). MLR model using two virtual slice could be accurate enough (RMSEV = 205 g) for industrial monitoring applications.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Food Engineering - Volume 142, December 2014, Pages 146–152
نویسندگان
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