Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous Abstract"[Health survey of painting and coating workers in an automobile manufacturing enterprise in Guangzhou, China]"    Next AbstractComparison of regional and global land cover products and the implications for biogenic emission modeling »

Food Chem


Title:"Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques"
Author(s):Huang L; Zhao J; Chen Q; Zhang Y;
Address:"School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; College of Biological Science and Engineering, Jiangxi Agricultural University, Nanchang 30045, China. Electronic address: huanglin213@126.com"
Journal Title:Food Chem
Year:2014
Volume:20130625
Issue:
Page Number:228 - 236
DOI: 10.1016/j.foodchem.2013.06.073
ISSN/ISBN:1873-7072 (Electronic) 0308-8146 (Linking)
Abstract:"Total volatile basic nitrogen (TVB-N) content is an important reference index for evaluating pork freshness. This paper attempted to measure TVB-N content in pork meat using integrating near infrared spectroscopy (NIRS), computer vision (CV), and electronic nose (E-nose) techniques. In the experiment, 90 pork samples with different freshness were collected for data acquisition by three different techniques, respectively. Then, the individual characteristic variables were extracted from each sensor. Next, principal component analysis (PCA) was used to achieve data fusion based on these characteristic variables from 3 different sensors data. Back-propagation artificial neural network (BP-ANN) was used to construct the model for TVB-N content prediction, and the top principal components (PCs) were extracted as the input of model. The result of the model was achieved as follows: the root mean square error of prediction (RMSEP) = 2.73 mg/100g and the determination coefficient (R(p)(2)) = 0.9527 in the prediction set. Compared with single technique, integrating three techniques, in this paper, has its own superiority. This work demonstrates that it has the potential in nondestructive detection of TVB-N content in pork meat using integrating NIRS, CV and E-nose, and data fusion from multi-technique could significantly improve TVB-N prediction performance"
Keywords:"Acinetobacter/isolation & purification Animals Bacillus/isolation & purification Biosensing Techniques Brochothrix/isolation & purification *Electronic Nose Food Analysis/methods Food Contamination/analysis Food Microbiology Meat/*analysis Models, Theoret;"
Notes:"MedlineHuang, Lin Zhao, Jiewen Chen, Quansheng Zhang, Yanhua eng Research Support, Non-U.S. Gov't England 2013/10/17 Food Chem. 2014 Feb 15; 145:228-36. doi: 10.1016/j.foodchem.2013.06.073. Epub 2013 Jun 25"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 04-12-2024