Instrumentation & Measurement Magazine 25-9 - 50

Accordingly, it is feasible to recognize the adulterations in
pepper powders using AOS with sensor array and ML models.
Although only the pepper samples were tested in this research,
the method proposed in this research can also be applied in
the quality measurement for other food materials. Therefore,
it is a potentially good tool to identify the adulterations in the
food with AOS combined with appropriate machine learning
models.
Conclusion
Fig. 9. The classification accuracy of the three algorithms for several
samples, where labels 1 to 5 indicate pure pepper and the adulterated mixtures
with bran powder, rosin powder, corn flour and rice bran powder, respectively.
Label 6 is the overall recognition accuracy for all samples.
Among different models, SVM with cubic kernel function
had the highest accuracy of 97.2% to identify the adulterations
in the pepper samples. Hence, SVM had the best performance
in pepper powder adulteration identification among these
methods in this research.
An adulteration identification method for condiments powders
based on artificial olfactory system and machine learning
model was discussed in this research. Ten typical kinds of pepper
samples from different origins of varieties were selected as
experimental raw materials. It is difficult to identify the quality
status of different pepper powders through the naked eye observation
when the adulteration substances with similar color
with the pure powders. Therefore, it is necessary to recognize
the adulteration with intelligent measurement method.
Therefore, the senor array of PEN3 device was used to capture
the responses of different samples. However, it is difficult
to distinguish the state of the pepper samples by observing
the response curves directly. Hence, machine learning models
were built to identify the adulterations in pepper samples.
Then, a support vector machine model based on the data
captured with sensor array for pepper samples was built to
Number of algorithms
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
50
Table 12 - Prediction accuracy of different machine learning models for all samples
Algorithms
Tree
Discriminant
Quadratic Discriminant
Linear SVM
Quadratic SVM
Cubic SVM
SVM
Fine Gaussian SVM
Medium Gaussian SVM
Coarse Gaussian SVM
Fine KNN
KNN
Medium KNN
Coarse KNN
Cosine KNN
Cubic KNN
Weighted KNN
Boosted Trees
Bagged Trees
Ensemble
Subspace Discriminant
Subspace KNN
RUS Boosted Trees
IEEE Instrumentation & Measurement Magazine
Complex Tree
Medium Tree
Simple Tree
Linear Discriminant
Accuracy of Testing set
50.0%
27.0%
10.4%
67.0%
failed
84.1%
94.7%
97.2%
91.9%
86.8%
52.3%
96.0%
79.0%
44.5%
79.1%
77.9%
93.5%
36.2%
92.5%
69.6%
95.3%
47.4%
December 2022

Instrumentation & Measurement Magazine 25-9

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