doi:10.1016/j.bios.2006.11.006
Copyright © 2006 Elsevier B.V. All rights reserved.
Evaluation of a novel chemical sensor system to detect clinical mastitis in bovine milk
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Toby Mottrama, 1, Alisa Rudnitskayab,
,
, Andrey Leginb, Julie L. Fitzpatrickc and P. David Eckersallc
aSilsoe Research Institute, Wrest Park, Silsoe, Bedford, MK45 4HS, UK
bLaboratory of Chemical Sensors, Department of Chemistry, University of St. Petersburg, St. Petersburg 199034, Russia
cDepartment of Veterinary Clinical Studies, University of Glasgow Veterinary School, Bearsden Rd, Glasgow, UK
Received 21 June 2006;
revised 21 September 2006;
accepted 9 November 2006.
Available online 15 December 2006.
Abstract
Automatic detection of clinical mastitis is an essential part of high performance and robotic milking. Currently available technology (conductivity monitoring) is unable to achieve acceptable specificity or sensitivity of detection of clinical mastitis or other clinical diseases. Arrays of sensors with high cross-sensitivity have been successfully applied for recognition and quantitative analysis of other multicomponent liquids. An experiment was conducted to determine whether a multisensor system (“electronic tongue”) based on an array of chemical sensors and suitable data processing could be used to discriminate between milk secretions from infected and healthy glands. Measurements were made with a multisensor system of milk samples from two different farms in two experiments. A total of 67 samples of milk from both mastitic and healthy glands were in two sets. It was demonstrated that the multisensor system could distinguish between control and clinically mastitic milk samples (p = 0.05). The sensitivity and specificity of the sensor system (93 and 96% correspondingly) showed an improvement over conductivity (56 and 82% correspondingly). The multisensor system offers a novel method of improving mastitis detection.
Keywords: Mastitis; Milk; Electronic tongue; Conductivity; Potentiometric chemical sensors
Fig. 1. A schematic diagram of the sensor array operating in static mode.
Fig. 2. PCA score plot of the first set of mastitic (●) and healthy (○) milk samples.
Fig. 3. Results of class membership prediction for the first set of milk samples. Classification was done using SIMCA method. Sample to model distance and Leverage are two parameters defining class boundaries for the class of healthy milk. Samples of mastitic milk (●) and healthy milk (○).
Fig. 4. PCA score plot of the second set of mastitic (●) and healthy (○) milk samples.
Fig. 5. Results of class membership prediction for the second set of milk samples. Classification was done using SIMCA method. Sample to model distance and Leverage are two parameters defining class boundaries for the class of healthy milk. Samples of mastitic milk (●) and healthy milk (○).
Table 1.
Summary of classification of healthy and mastitic milk using conductivity measurements, measurements with three discrete ion-selective electrodes and the electronic tongue
a Sensitivity = True Positives/(True Positives + False Negatives).
b Specificity = True Negative/(True Negatives + False Positives).
c Correct classification—percentage of cases assigned to correct classes.

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