Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work,  will analyze the response of a Metal Oxide Gas Sensor (MOGS) array to a flow controlled stimulus generated in a pressure controlled canister produced by a homemade olfactometer to build an E-nose. The built E-nose is capable of chocolate identification beten the 26 analyzed chocolate bar samples and four features recognition (chocolate type, extra ingredient, setener and expiration date status). The data analysis tools used re Principal Components Analysis (PCA) and Artificial Neural Networks (ANNs). The chocolate identification E-nose average classification rate was of 81.3% with 0.99 accuracy (Acc), 0.86 precision (Prc), 0.84 sensitivity (Sen) and 0.99 specificity (Spe) for test. The chocolate feature recognition E-nose gives a classification rate of 85.36% with 0.96 Acc, 0.86 Prc, 0.85 Sen and 0.96 Spe. In addition, a preliminary sample aging analysis was made. The results prove the pressure controlled generated stimulus is reliable for this type of studies.
['Luis F. Valdez', 'Juan Manuel Gutiérrez']
I am writing a research paper on a idea Chocolate Classification by an Electronic Nose with Pressure Controlled Generated Stimulation
[{"from": "human", "value": "I am writing a research paper on a idea Chocolate Classification by an Electronic Nose with Pressure Controlled Generated Stimulation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work,  will analyze the response of a Metal Oxide Gas Sensor (MOGS) array to a flow controlled stimulus generated in a pressure controlled canister produced by a homemade olfactometer to build an E-nose. The built E-nose is capable of chocolate identification beten the 26 analyzed chocolate bar samples and four features recognition (chocolate type, extra ingredient, setener and expiration date status). The data analysis tools used re Principal Components Analysis (PCA) and Artificial Neural Networks (ANNs). The chocolate identification E-nose average classification rate was of 81.3% with 0.99 accuracy (Acc), 0.86 precision (Prc), 0.84 sensitivity (Sen) and 0.99 specificity (Spe) for test. The chocolate feature recognition E-nose gives a classification rate of 85.36% with 0.96 Acc, 0.86 Prc, 0.85 Sen and 0.96 Spe. In addition, a preliminary sample aging analysis was made. The results prove the pressure controlled generated stimulus is reliable for this type of studies."}]