Prediction of soluble solids content, firmness and pH of pear by signals of electronic nose sensors

Zhang, Hongmei; Wang, Jun; Ye, Sheng

doi:10.1016/j.aca.2007.11.003

Cited by 39 publications

(21 citation statements)

References 19 publications

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“…Figure 1 shows the chromatogram from the electronic nose which is in the form of a graphical display of the derivative of the frequency change versus time. The electronic nose does not resolve the sample's volatiles into its individual components, but responds to a whole set of volatiles in a unique digital pattern [23]. Table 2 shows the set of volatile compounds corresponding to peaks 1-13 and their odor descriptions.…”

Section: Resultsmentioning

confidence: 99%

Analysis of Lard's Aroma by an Electronic Nose for Rapid Halal Authentication

Nurjuliana

Man

Hashim

2010

J Americ Oil Chem Soc

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An electronic nose was successfully used to detect and discriminate lard from other types of animal body fats and samples containing lard. The results are presented in the form of VaporPrint TM , the image of the polar plot of the odor amplitudes from the surface acoustic wave (SAW) detector frequency. In the VaporPrint TM , the radial angles representing the sensor provides individual fingerprints of the aroma of different animal body fats. Principal component analysis (PCA) was used to interpret the data and it provided a good grouping of samples, with 61% of the variation accounted for by PC 1 and 29% accounted for by PC 2. All of the lard-containing samples formed a separate group from the samples that were free from lard. This method can be developed into a rapid method for detecting the presence of lard in food samples for Halal authentication.

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Section: Resultsmentioning

confidence: 99%

Analysis of Lard's Aroma by an Electronic Nose for Rapid Halal Authentication

Nurjuliana

Man

Hashim

2010

J Americ Oil Chem Soc

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“…(2) whether the significant level of the partial correlation coefficient for all regression coefficients is less than 0.05; (3) whether Durbin-Watson (DW) does approach to 2 [15].…”

Section: Quadratic Polynomial Step Regression (Qpsr)mentioning

confidence: 99%

Monitoring storage time and quality attribute of egg based on electronic nose

Wang

Zhang

et al. 2009

Analytica Chimica Acta

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“…Zhang et al have studied a sensor array to detect a quality index model evaluating the peach quality index using various techniques such as linear regressions, quadratic polynomial step regression, and backpropagation network [24]. They have also used the evaluation of the pear aroma of pear along varied picking dates by using multiple linear regressions (MLR) and ANN [25]. Gómez et al have studied scoring the ability of electronic nose to monitor the modification in volatile production of ripeness degrees for tomato by using PCA and LDA [26].…”

Section: Introductionmentioning

confidence: 99%

Quality Control of Olive Oils Using Machine Learning and Electronic Nose

Ordukaya

Karlık

2017

Journal of Food Quality

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The adulteration of olive oils can be detected with chemical test. This is very expensive and takes very long time. Thus, this study is focused on reducing both time and cost. For this purpose, the raw data has been collected from olive oils by using an e-nose from different regions in Balikesir in Turkey. This study presents two methods to analyze quality control of olive oils. In the first method, 32 inputs are applied to the classifiers directly. In the second, 32-input collected data are reduced to 8 inputs by Principal Component Analysis. These reduced data as 8 inputs are applied to the classifiers. Different machine learning classifiers such as Naïve Bayesian, -Nearest Neighbors ( -NN), Linear Discriminate Analysis (LDA), Decision Tree, Artificial Neural Networks (ANN), and Support Vector Machine (SVM) were used. Then performances of these classifiers were compared according to their accuracies.

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Prediction of soluble solids content, firmness and pH of pear by signals of electronic nose sensors

Cited by 39 publications

References 19 publications

Analysis of Lard's Aroma by an Electronic Nose for Rapid Halal Authentication

Analysis of Lard's Aroma by an Electronic Nose for Rapid Halal Authentication

Monitoring storage time and quality attribute of egg based on electronic nose

Quality Control of Olive Oils Using Machine Learning and Electronic Nose

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