Instrumentation & Measurement Magazine 25-9 - 42

The Quality Detection and
Recognition for Food Seasoning
Based on an Artificial
Olfactory System
Denglong Ma, Chang Liu, Fangjun Wu, Zekang Li, Xiuben Wu,
Jianmin Gao, Hong Zhao, and Zaoxiao Zhang
T
he quality of food seasoning affects the flavor and
the safety of the food. Traditional detection methods,
such as test-paper, cyclotron or chromatography,
have some limits in accuracy, detection range, time consumption,
portability and cost. The artificial olfactory system (AOS)
can detect and identify the food quality by simulating animal
olfactory sensors, which has the advantages of short detection
cycle, high sensitivity, and no complicated pre-processing. A
sample data source for AOS to detect pepper powder was built
by collecting the odor data of the condiment powders under
different origins and adulteration conditions using the sensor
array of the PEN3 electronic nose system (AIRSENSE, Germany).
Then, machine learning algorithms including support
vector machine, decision tree and random forest were introduced
to form the intelligent models to evaluate the quality of
the pepper powder from different origins and with different
adulterated substances. The results indicated that the recognition
accuracy reached to over 98% for all types of pepper
samples with the radial basis function-support vector machine
(RBF-SVM) model. The pure pepper could be distinguished
from the adulterated samples successfully with the accuracy of
near 100% using machine leaning models. Finally, other more
machine learning algorithms were compared to recognize the
states of different pepper powders. The results showed that the
SVM method has the highest accuracy in classifying the adulteration
of pepper powder among different machine learning
models discussed in the research. It is feasible to detect and
recognize the quality of the food seasoning with AOS based on
machine learning algorithms and sensor array.
Introduction
An artificial olfactory system (AOS) simulates the mechanism
of the animal olfactory perception to detect and identify
the smell. It could be a potentially good method to detect food
quality. As a bionic measurement method, AOS mainly accomplishes
the signal recognition and processing by simulating
the process of the human olfaction system. The sensor array
was designed to mimic human olfactory receptors while the
electrochemical feedback signals between the sensors and
42
gases are viewed as the enzyme cascade reactions in the human
olfactory system. AOS has been widely used in various
fields such as environmental monitoring, food industry, chemical
industry and medical production [1], [2].
The sensor array of the AOS demonstrates different patterns
for varied odors, which can be used to identify the status of the
odors with pattern recognition algorithms [3]. There have been
many methods utilized in food safety detection, such as principal
components analysis (PCA), linear discriminant analysis
(LDA), K-Nearest Neighbor (KNN), back propagating artificial
neutral net (BP-ANN), learning vector quantization (LVQ), selforganizing
map (SOM), probabilistic neural network (PNN)
and others. AOS has been applied in food sensory evaluation.
A machine learning model (ML) is the core for AOS. ML
has been applied widely on food quality evaluation in recent
years. A non-invasive ML driven technique to monitor variations
of moisture content in fruits has been proposed by Ren
et al. [4]. Matteo et al. has used a novel E-Nose with PCA and
multilayer perceptrons to classify different groups of coffee [5].
Palash et al. developed a system with a parallel combination of
several neural network classifiers to identify the category of
water samples [6]. Antony et al. reviewed the development of
ML techniques in the food safety domain, including detecting
the quality of fruits, vegetables, seafood, meat and dairy products
[7]. All of these published results showed that it is a useful
tool to utilize the ML method in food quality analysis.
In this research, ten typical peppers spices in China were
sampled and different species including corn, rice bran, wheat
bran and rosin flour were added to the pure samples to form artificially
adulterated samples. Then, the responses of different
samples were detected with the sensor array of a PEN3 electronic
nose devise. Further, different ML models were built and
compared to evaluate the quality status of the pepper spices.
Methods
For food safety detection, a fast and accurate method is important.
But it is difficult to recognize adulterated materials for
condiment powders with human eyes. AOS based on a sensor
array and machine learning algorithm is a potential useful
IEEE Instrumentation & Measurement Magazine
1094-6969/22/$25.00©2022IEEE
December 2022

Instrumentation & Measurement Magazine 25-9

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