کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7142639 | 1462053 | 2017 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Intelligent evaluation of total volatile basic nitrogen (TVB-N) content in chicken meat by an improved multiple level data fusion model
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کلمات کلیدی
ACOHyperspectral imaging (HSI)LLATVB-NBP-ANNMLFRPDHSIROIRMSEPRMSECVVOCsiLA - ILAHLA - آنتیژن گلبول سفید انسانیPrincipal components - اجزای اصلیAnt colony optimization (ACO) - بهینه سازی مورچه ها (ACO)Ant Colony Optimization - بهینهسازی گروه مورچهها Volatile organic compounds - ترکیبات آلی فرارHyperspectral imaging - تصویربرداری فراطیفیPCs - رایانه های شخصیRoot mean square error of cross validation - ریشه میانگین خطای مربع اعتبارسنج متقابل استregion of interest - منطقه مورد نظرtotal volatile basic nitrogen - کل نیتروژن پایدار فرارPoultry meat - گوشت مرغ
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Intelligent evaluation of total volatile basic nitrogen (TVB-N) content in chicken meat by an improved multiple level data fusion model Intelligent evaluation of total volatile basic nitrogen (TVB-N) content in chicken meat by an improved multiple level data fusion model](/preview/png/7142639.png)
چکیده انگلیسی
The objective of this paper is to present a fusion model of an odor sensor and highly advanced optical sensor to evaluate total volatile basic nitrogen (TVB-N) content in chicken meat. Here, the aroma or the odor data variables obtained from the odor sensor i.e. colorimetric sensor and the spectral as well as textural data variables obtained from the optical sensor i.e. HSI, were fused together for further data processing. 36 odor variables obtained via the low-level data abstraction (LLA) were simply concatenated with the 30 texture feature variables obtained by middle/intermediate level data abstraction (ILA) totaling to a 66 variables' dataset. This approach of multiple level data fusion (MLF) produced the better PCA-BPANN prediction results than either of the individual system did, with the higher Rp of 0.8659, lower RMSEP of 4.587Â mg/100Â g along with the increased calibration model efficacy. Furthermore, the prediction level escalated with Rp of 0.8819 and RMSEP of 4.3137Â mg/100Â g when the data fusion technique was improved by applying Pearson's correlation analysis and uncorrelated data variables were removed from each of the dataset at the statistical level of significance. This step reduced the data variables but not the original information. Therefore, the results highly encourage multiple sensor fusion and the improved MLF technique for better model performance to evaluate chicken meat's freshness.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Sensors and Actuators B: Chemical - Volume 238, January 2017, Pages 337-345
Journal: Sensors and Actuators B: Chemical - Volume 238, January 2017, Pages 337-345
نویسندگان
Urmila Khulal, Jiewen Zhao, Weiwei Hu, Quansheng Chen,