Identification of food spoilage in the smart home based on neural and fuzzy processing of odour sensor responses.
Annu Int Conf IEEE Eng Med Biol Soc
; 2009: 2625-8, 2009.
Article
em En
| MEDLINE
| ID: mdl-19965227
ABSTRACT
Adopting the use of real-time odour monitoring in the smart home has the potential to alert the occupant of unsafe or unsanitary conditions. In this paper, we measured (with a commercial metal-oxide sensor-based electronic nose) the odours of five household foods that had been left out at room temperature for a week to spoil. A multilayer perceptron (MLP) neural network was trained to recognize the age of the samples (a quantity related to the degree of spoilage). For four of these foods, median correlation coefficients (between target values and MLP outputs) of R > 0.97 were observed. Fuzzy C-means clustering (FCM) was applied to the evolving odour patterns of spoiling milk, which had been sampled more frequently (4h intervals for 7 days). The FCM results showed that both the freshest and oldest milk samples had a high degree of membership in "fresh" and "spoiled" clusters, respectively. In the future, as advancements in electronic nose development remove the present barriers to acceptance, signal processing methods like those explored in this paper can be incorporated into odour monitoring systems used in the smart home.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Sinais Assistido por Computador
/
Reconhecimento Automatizado de Padrão
/
Tecnologia de Alimentos
/
Odorantes
Tipo de estudo:
Diagnostic_studies
Limite:
Animals
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Ano de publicação:
2009
Tipo de documento:
Article
País de afiliação:
Canadá