Optimization of sensor array and detection of stored duration of wheat by electronic nose

Zhang, Hongmei; Wang, Jun; Xiao, Tian; Yu, Hua; Yu, Yong Chang

doi:10.1016/j.jfoodeng.2007.02.005

Cited by 62 publications

(29 citation statements)

References 14 publications

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“…According to Zhang and others (2007) and Hai and Wang (2006), there is a high correlation between the signal of the sensors of the electronic nose. On the basis of the scores of the 1st PC, the Pearson correlation coefficients of the 8 sensors were calculated (Table 3).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Coffee Roasting Degree by Using Electronic Nose and Artificial Neural Network for Off‐line Quality Control

Romani¹,

Cevoli²,

Fabbri³

et al. 2012

Journal of Food Science

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Actually the evaluation of coffee roasting degree is mainly a manned operation, substantially based on the empirical final color observation. For this reason it requires well-trained operators with a long professional skill. The coupling of e-nose and artificial neural networks (ANNs) may represent an effective possibility to roasting process automation and to set up a more reproducible procedure for final coffee bean quality characterization.

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Section: Methodsmentioning

confidence: 99%

Evaluation of Coffee Roasting Degree by Using Electronic Nose and Artificial Neural Network for Off‐line Quality Control

Romani¹,

Cevoli²,

Fabbri³

et al. 2012

Journal of Food Science

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“…[14,15] EN measurement can achieve better stability and reproducibility, compared with human sensory evaluation (HSE). A large number of researches have demonstrated the application of EN coupled with pattern recognition methods in diverse fields, particularly in food quality analysis, including fruits, [16,17] oils, [18,19] grains, [20,21] meat, [22,23] and fish. [6,24] However, there is little information on investigating the usage of EN combined with Vis/NIR spectroscopy for golden pompano freshness prediction.…”

Section: Study Of Golden Pompano (Trachinotusmentioning

confidence: 99%

Study of golden pompano (Trachinotus ovatus) freshness forecasting method by utilising Vis/NIR spectroscopy combined with electronic nose

Zhang

Zhou

Chang

et al. 2018

International Journal of Food Properties

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Golden pompano (Trachinotus ovatus) quality forecasting method utilising Vis/ NIR spectroscopy combined with electronic nose (EN) was investigated in this article. Responses of Vis/NIR spectroscopy and EN to pompanos stored at 4°C were measured for 6 days. Physical/chemical indexes including texture, total volatile basic nitrogen, pH, total viable counts, and human sensory evaluation were synchronously examined as quality references. Chemometric methods including principal component analysis (PCA) and stochastic resonance (SR) were employed for spectroscopic and EN data analysis. Physicochemical examination demonstrated that fish quality decreased rapidly during storage. PCA qualitatively classified freshness degree of pompano samples, while SR signalto-noise ratio (SNR) spectrum using SNR maximum quantitatively characterised quality for all samples. Golden pompano quality predictive models were developed based on spectroscopy, EN, and spectroscopy combined with EN, respectively. Results demonstrated that the model developed based on spectroscopy combined with EN presented a forecasting accuracy of 93.3%. ARTICLE HISTORY

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“…The basic system, described in previous researches (ZHANG et al, 2007), consisted of a sampling apparatus, a detector unit containing the sensor array, and pattern recognition software (Win Muster v.1.6). The sensor array was composed of 10 different metal oxide sensors.…”

Section: The E-nose and Detection Proceduresmentioning

confidence: 99%

Fast Determination of Lycopene Content and Soluble Solid Content of Cherry Tomatoes Using Metal Oxide Sensors Based Electronic Nose

Xiao¹,

Wang

Hong³

et al. 2016

Acta Alimentaria

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Lycopene content (LC) and soluble solid content (SSC) are important quality indicators for cherry tomatoes. This study attempted simultaneous analysis of inner quality of cherry tomato by Electronic nose (E-nose) using multivariate analysis. E-nose was used for data acquisition, the response signals were regressed by multiple linear regression (MLR) and partial least square regression (PLS) to build predictive models. The performances of the predictive models were tested according to root mean square and correlation coeffi cient (R 2 ) in the training set and prediction set. The results showed that MLR models were superior to PLS model, with higher value of R 2 and lower values of for RMSE fi rmness, pH, SSC, and LC. Together with MLR, E-nose could be used to obtain fi rmness, pH, soluble solid and lycopene contents in cherry tomatoes.

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Optimization of sensor array and detection of stored duration of wheat by electronic nose

Cited by 62 publications

References 14 publications

Evaluation of Coffee Roasting Degree by Using Electronic Nose and Artificial Neural Network for Off‐line Quality Control

Evaluation of Coffee Roasting Degree by Using Electronic Nose and Artificial Neural Network for Off‐line Quality Control

Study of golden pompano (Trachinotus ovatus) freshness forecasting method by utilising Vis/NIR spectroscopy combined with electronic nose

Fast Determination of Lycopene Content and Soluble Solid Content of Cherry Tomatoes Using Metal Oxide Sensors Based Electronic Nose

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