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Original Article

E-nose coupled with an artificial neural network to detection of fraud in pure and industrial fruit juices

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Pages 592-602 | Received 06 Jan 2021, Accepted 20 Mar 2021, Published online: 05 Apr 2021

Figures & data

Figure 1. Schematic of olfactory system used (a) Air filter (Carbone active), (b) Sample compartment, (c) Solenoid valve, (d) Diaphragm pump, (e) Sensor array, (f) Date acquisition card, (g) PC and (h), Air outlet

Figure 1. Schematic of olfactory system used (a) Air filter (Carbone active), (b) Sample compartment, (c) Solenoid valve, (d) Diaphragm pump, (e) Sensor array, (f) Date acquisition card, (g) PC and (h), Air outlet

Table 1. The used sensors in electronic nose

Figure 2. Schematic of multilayer perceptron neural network

Figure 2. Schematic of multilayer perceptron neural network

Figure 3. Radar graph response of the sensors for the different types of fruit juices obtained by e-Nose

Figure 3. Radar graph response of the sensors for the different types of fruit juices obtained by e-Nose

Figure 4. Loading plot for PCA analysis for fruit juices

Figure 4. Loading plot for PCA analysis for fruit juices

Figure 5. Power of MQ135 and TGS813 sensors response to VOC of different fruit juice samples (PC-1)

Figure 5. Power of MQ135 and TGS813 sensors response to VOC of different fruit juice samples (PC-1)

Table 2. Summary of PCR analysis results

Figure 6. Correlation plots of the calculated versus the experimental a) all sample, b) pure and industrial sample

Figure 6. Correlation plots of the calculated versus the experimental a) all sample, b) pure and industrial sample

Table 3. Performance parameters of ANN models

Figure 7. Confusion matrix obtained for a) all juices, b) pure and industrial juices

Figure 7. Confusion matrix obtained for a) all juices, b) pure and industrial juices