Head Space Sensor Array for the Detection of Aflatoxin M1 in Raw Ewe's Milk

2005 ◽  
Vol 68 (5) ◽  
pp. 1089-1092 ◽  
Author(s):  
S. BENEDETTI ◽  
S. IAMETTI ◽  
F. BONOMI ◽  
S. MANNINO

A novel screening method was developed for simple and rapid detection of aflatoxin M1 contamination in raw ewe's milk samples without the need for sample pretreatment. The method was based on the use of a commercial head space sensor array system constituted by 12 metal oxide semiconductor sensors, 10 metal oxide semiconductor field-effect transistor sensors, and a pattern recognition software. Twenty-four raw milk samples collected from two different groups of ewes fed with a formulated feed that contained increasing amounts of aflatoxin B1 and six noncontaminated ewe's milk samples were analyzed. The results obtained by using the head space sensor array, processed by statistical methods, made it possible to group the samples according to the presence or the absence of aflatoxin M1. Sample classification was in complete agreement with the aflatoxin M1 content measured by an enzyme-linked immunosorbent assay procedure. This is the first report, to our knowledge, of detection of aflatoxin M1 in ewe's milk by a multisensor array.

RSC Advances ◽  
2020 ◽  
Vol 10 (47) ◽  
pp. 28464-28477
Author(s):  
Paula Tarttelin Hernández ◽  
Stephen M. V. Hailes ◽  
Ivan P. Parkin

Metal oxide semiconductor gas sensors based on SnO2 and Cr2O3 were modified with zeolites H-ZSM-5, Na-A and H–Y to create a gas sensor array to detect cocaine by-product, methyl benzoate. SVMs were later used with a 4 sensor array to classify 9 gases of interest.


2017 ◽  
Vol 9 (6) ◽  
pp. 921-928 ◽  
Author(s):  
Hao Wu ◽  
TianLi Yue ◽  
Zhijiao Xu ◽  
Chen Zhang

An electronic nose (PEN3) containing 10 metal oxide semiconductor type chemical sensors was used to discriminate between eight varieties of apple juice.


2010 ◽  
Vol 49 (4) ◽  
pp. 04DL01 ◽  
Author(s):  
Hirokazu Matsumoto ◽  
Junichi Tsukada ◽  
Hiroaki Ozawa ◽  
Shigeyasu Uno ◽  
Kazuo Nakazato ◽  
...  

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 284-290 ◽  
Author(s):  
Lei Zhang ◽  
Fengchun Tian ◽  
Xiongwei Peng ◽  
Xin Yin ◽  
Guorui Li ◽  
...  

Purpose – The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low-cost electronic noses (e-noses) with metal oxide semiconductor sensors in indoor air contaminant monitoring and overcome the potential sensor drift. Design/methodology/approach – In the quantification model, a piecewise linearly weighted artificial neural network ensemble model (PLWE-ANN) with an embedded self-calibration module based on a threshold network is studied. Findings – The nonlinear estimation problem of sensor array-based e-noses can be effectively transformed into a piecewise linear estimation through linear weighted neural networks ensemble activated by a threshold network. Originality/value – In this paper, a number of experimental results have been presented, and it also demonstrates that the proposed model has very good accuracy and robustness in real-time indoor monitoring of formaldehyde.


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