Instrumentation & Measurement Magazine 25-9 - 44

Number
Table 2 - The numbers of adulterated components
1
2
Adulterated ingredients
Wheat bran flour
Rosin powder
3
Cornstarch
4
Rice bran powder
between pure and adulterated samples with human eyes even
when the concentration of cornstarch was as high as 28%.
The sensor array of PEN3 (AIRSENSE, Germany) was utilized
to collect the data from different samples. The principle
of an electronic nose collecting odor information is that volatile
gas reacts with a gas sensor, and the sensor converts chemical
signal into electrical signal. PEN3 is a commercial electronic
nose which has ten different metal oxide sensors with varied
response properties. When either the concentrations or species
of VOCs for detection are changed, the response map of the
sensor array will be altered. Here, the parameter of sensitivity
characteristic for each sensor, (G/ G0
), is used to present the reFig.
1. Some samples of No. 10 pepper.
sponse of the sensor array, where G is the resistance value of
the gas to be detected, and G0
is that value of the clean air. The
sample bottle was placed in the refrigerator for 24 h to make
the odor volatilization stable completely, and then the sample
bottle was placed in a water bath at 40 °C for 4 min to make the
components volatilize fully. Fig. 2a shows a general graphic of
the workflow carried out in the research work, and Fig. 2b illustrates
the PEN3 system.
The various samples were detected with PEN3 for 120 s.
The data of the sensor array were captured with the software
of Win Muster. Fig. 3 shows the response curves of the sensor
array to No. 1 pepper with different adulterated powders with
the concentration of 20%. It is noted that almost each sensor
had a response to the odors from different samples, but it was
hard to distinguish the differences among them just with human
eyes. Therefore, it was difficult to identify whether there
was adulteration in the pepper powder or not with the response
curves of the sensor array.
All data were collected with PEN3. For each pepper sample,
we obtained 400 data sets, where 280 sets were used to
train the model and 70 sets to test. All computation was processed
with MATLAB R2017b software.
Results of SVM Algorithm
In this research, the LibSVM tool was utilized to train and test
the model. The generalizability of the model was improved
by randomly generating different training and test sets. First,
all data from the sensor array were normalized. Since the RBF
function is suitable for problems with high-dimensional data
sets and multiple classifications, the RBF was selected as the
kernel function in this research. For the SVM with the RBF
model, the variance parameter δ and the regularization parameter
c in the decision function were optimized using the
cross-validation method.
Test Results
Fig. 2. (a) Experimental workflow chart. (b) The image of PEN3.
44
Since 400 sets of data were randomly split into the training and
test with the ratio of 3:1 for each type of pepper, the results of
IEEE Instrumentation & Measurement Magazine
December 2022

Instrumentation & Measurement Magazine 25-9

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