Detection and classification of hydrolyzed dairy additives using electronic noses.

ADARO, M.; BOGGIO, NORBERTO G; VOROBIOFF, JUAN; MAGALLANES, J.; BOSELLI, ALFREDO; LAMAGNA, ALBERTO; RINALDI, CARLOS

Ramada Plaza JeJu, Jeju Island, Korea

Congreso; The 16th International Meeting on Chemical Sensors; 2016

Organizing Committee of IMCS 2016

p { margin-bottom: 0.21cm; direction: ltr; color: rgb(0, 0, 0); }p.western { font-family: "Arial",sans-serif; font-size: 11pt; }p.cjk { font-family: "Times New Roman",serif; font-size: 11pt; }p.ctl { font-family: "Arial",sans-serif; font-size: 12pt; }a:link { color: rgb(0, 0, 255);In the present work it is proposed to measure andclassify enzymatically modified dairy additives (EMDI) in a rapidand simple way by means of an Electronic Nose (EN) designed in ourlaboratories. The Patagonia EN is composed by SnO2sensors in a thermally stabilized chamber which improves therepeatability <style type="text/css">p { margin-bottom: 0.21cm; direction: ltr; color: rgb(0, 0, 0); }p.western { font-family: "Arial",sans-serif; font-size: 11pt; }p.cjk { font-family: "Times New Roman",serif; font-size: 11pt; }p.ctl { font-family: "Arial",sans-serif; font-size: 12pt; }a:link { color: rgb(0, 0, 2In the present work it is proposed to measure andclassify enzymatically modified dairy additives (EMDI) in a rapidand simple way by means of an Electronic Nose (EN) designed in ourlaboratories. The Patagonia EN is composed by SnO2sensors in a thermally stabilized chamber which improves therepeatability of the measurements. p { margin-bottom: 0.21cm; direction: ltr; color: rgb(0, 0, 0); }p.western { font-family: "Arial",sans-serif; font-size: 11pt; }p.cjk { font-family: "Times New Roman",serif; font-size: 11pt; }p.ctl { font-family: "Arial",sans-serif; font-size: 12pt; }a:link { color: rgb(0, 0, 255); }In the present work it is proposed to measure andclassify enzymatically modified dairy additives (EMDI) in a rapidand simple way by means of an Electronic Nose (EN) designed in ourlaboratories. The Patagonia EN is composed by SnO2sensors in a thermally stabilized chamber which improves therepeatability of the measurements. Samples of various hydrolyzed cheese weremeasured using air as a reference gas. Then the integrals of thesignals were analyzed using algorithms of linear discriminantanalysis (LDA), principal components analysis (PCA) andbackpropagation neural network. The results obtained indicate thatthe samples through the implementation of a simple and low costmeasurement protocol were correctly classified.