Fabrication of electronic nose system and exploration on its applications in mango fruit (M. indica cv. Datainong) quality rapid determination

• Published in: Journal of food measurement & characterization Vol. 11; no. 4; pp. 1969 - 1977
• Main Authors: Lihuan, Shao, Liu, Wei, Xiaohong, Zhang, Guohua, Hui, Zhidong, Zhao
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2017; Springer Nature B.V
• Subjects: Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Chromatography; Data analysis; Eigenvalues; Electronic noses; Engineering; Enzymes; Fabrication; Food; Food quality; Food Science; Fruits; Mangoes; Original Paper; Polyphenol oxidase; Prediction models; Principal components analysis; Qualitative analysis; Room temperature; Sensors; Stochastic resonance; Stochasticity; Weight loss measurement; Yellowing; Rapid determination; Mango fruit quality; Stochastic resonance; Eigen value; Electronic nose
• ISSN: 2193-4126; 2193-4134
• DOI: 10.1007/s11694-017-9579-1

Mango ( M. indica cv. Datainong) fruit quality rapid determination method based on electronic nose (E-nose) was investigated in this research. E-nose responses to mangoes stored at room temperature were examined for 9 days. Meanwhile, physicochemical and microbiological indexes including firmness, weight loss, surface colour, yellowing rate, pH, total soluble solids, polyphenol oxidase activity and total viable counts were measured to provide quality references for E-nose analysis. Principal component analysis (PCA) and stochastic resonance (SR) were utilized for E-nose data analysis. Results indicated that mango fruit quality decreased sharply during storage. PCA just allowed qualitative quality discrimination, while SNR spectrum using eigen values successfully characterized mango quality. Mango quality predictive model was developed by linear fitting SR eigen values. Validation experiment results demonstrated that the forecasting accuracy of the developed model reached 90%.