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Prathamesh Sanjay Pawale
M. Tech Food Technology (II Year)
20412MFT008
Content
• Introduction
• What is E Nose?
• Need of E Nose
• How do we smell?
• Comparison of Human nose with E Nose
• Components of E Nose
• Working Principle of E Nose
• Advantages and Limitations
• Application of E Nose
• Conclusion
• References
Introduction
• Food quality depends on the color, taste, nutrition, safety, and sensory characteristics of foods.
People choose foods according to these aspects.
• It has been reported that the human nose has around 400 scent receptors and can detect at least
one trillion odors. Although the human nose can rate a smell, individuals' judgments may be bias,
and human nose cannot be used to sense toxic gases.
• In addition, human nose has detection limits for difference gases. Those limitations prevent the
human nose from being a universal tool for all smell-related discrimination and classification. (Tan
et al., 2021)
• Some methods/techniques such as total volatile basic nitrogen measurement, spectroscopies,
and chromatography’s are not usually suitable for online quality control of food product.
• New methods like electronic nose have been proposed for in-process and real-time evaluation
and controlling of food products. (kiani et al. 2016)
What is E Nose?
• An electronic nose can be defined as ‘an instrument which is
comprised of an array of electronic chemical sensors with
partial specificity and an appropriate pattern recognition
system, capable of recognizing simple or complex odors.
(Gardner, 1995)
• Electronic noses are engineered to mimic the mammalian
olfactory system.
• Can be regarded as modular system comprising a set of
active material which detect the odor, associated sensors
which transduce the chemical quantity into electrical signals,
followed by appropriate signal conditioning and processing
to classify known odors or identify unknown odors.
Fig. Cyranose 320 E Nose
Source: electronicsforu.com
Need of E
Nose
Human olfactory system may be unstable
with respect to mood or physical condition
Speedy, reliable new sensor technology are
used in e nose
Detection of hazardous and poisonous gas
are not possible with human nose
An e nose also overcome other problems
associated with the human olfactory system
There lies a great chance of difference in the
values got by each individual or each sniffer
How do we smell?
• Whenever we smell something, our nose and brain work
together to make sense of hundreds of very tiny invisible
particles, known as molecules or chemicals, that are floating
in the air.
• Tiny cells inside nostrils that talk to each other by electrical
impulses i.e., neurons, called olfactory neurons, have long
cable-like connections that send electrical messages to a
spot at the front of the brain, known as the olfactory bulb.
• Each olfactory neuron connects with a different neuron in
the olfactory bulb, which then sends this information to
other areas of the brain.
• Odor>olfactory nerve fibers > olfactory bulb > olfactory tract
> primary olfactory cortex > amygdala. Source: Wikipedia
Comparison of Human nose with E Nose
Fig. Structure of human olfactory system and e nose Source: Varnamkhasti et.al.,2016
Comparison of Human nose with E Nose
Source: Yakuba et.al.,2021
Components of
E Nose
1.Sample Delivery
System
2.Detection
System
3.Computing
System
I. A sample delivery system • The sample delivery system
enables the generation of
headspace of sample or
volatile organic compounds
which is a fraction analyzed.
• The system then sends this
head space into the
detection system of the
electronic nose.
• The sample delivery system
is essential to guarantee
constant operating
conditions.
Source: Y Zhong,
2019
II. A detection system • The detection system which
consists of a group of
sensors is the reactive part
of the instrument.
• When in contact with
volatile organic compounds
(VOCs) at that time the
sensors react causing
changes in electrical
characteristics.
• Adsorption of volatile
compounds on the sensor
surface causes a physical
change of the sensor; they
experience a change of
electrical properties.
• A specific response is
recorded by the electronic
interface transforming the
signal into a digital value.
Source: www.ou.edu
III. A computing system • Most of electronic noses use
sensor arrays that react to
volatile compounds.
• This part of the instrument
performs global fingerprint
analysis and provides results
and representation that can
be easily interpreted.
• Whenever the sensors sense
any smell, a specific
response is recorded that
signal is transmitted into the
digital value.
• Recorded data are then
computed based on
statistical models.
Fig. Artificial neural
network
Source: Wikipedia.org
Fig. Principal
component analysis
Source: Yang et al.,2015
E-Nose:
Working
Principle
• An air sample is pulled by a vacuum pump.
• It is led through a tube into a small chamber consisting of electronic
sensor array.
• A transient response is produced as the volatile organic compounds
in the sample interact with the surface of the sensor’s active
material.
• A steady state response is reached within few minutes.
• This response is then sent to a signal processing unit.
Source: Tan et al., 2020
E-Nose:
Working
Principle
• A washing gas such as an alcohol vapor is applied to the
array for a few seconds to a minute.
• This is done to remove the odorant mixture from the
surface and bulk of the sensor's active material.
• Finally, the reference gas is again applied to the array, to
prepare it for a new measurement cycle.
• A variety of basic sensors can be used according to the nose
strategy chosen.
• Each sensor in the array has different characteristics.
• The pattern of response across all the sensors in the array is
used to identify and/or characterize the odor.
Mechanism of E Nose
Source: Yu et al., 2021
E Nose Sensing System
• Using Array of sensors, each sensor designed to respond to a
specific chemical.
• Number of unique sensors must be at least as the number of
chemicals being monitored.
• Each Chemical vapor presented to the sensor array produces a
signature or pattern characteristics of the vapor.
• Use of Artificial Neural Network (ANN) along with array.
• Used to analyze complex data and to recognize patterns, are
showing promising results in chemical vapor recognition.
• Number of detectable chemicals is greater than that of
sensors.
• Electronic nose incorporating ANNs have been demonstrated
in various applications.
Sensors in E-Nose
• The sensor array is clearly the key element. It forms the primary step
in the detection or identification of an odorant.
Sr.
No.
Type of
Instrument
Model Manufacturer and country
No. of
Sensors
References
1 MOSFET sensor NST3320 Applied Sensor A.G., Sweden 23 Kovacs et al.(2010)
2 MOS sensor PEN2 Airsense Analytics GmbH, Germany 10 Campagnoli et al.(2011)
3 MOS sensor PEN3 Airsense Analytics GmbH, Germany 10 Yang et al.(2015)
4 MOS sensor FOX 4000 Alpha MOS, France 18 Huang et al.(2017)
5 Gas sensor ECGC12S Figaro USA Inc, Japan 6 Tan and Kerr (2019)
6 MOS sensor PEN3 Airsense Analytics GmbH, Germany 10 Long et al.(2019)
7 MOS sensor iNose Ruifen Trading Co., China 14 Chen et al.(2019)
8 MOS sensor SENS Central South University of Forestry &
Technology, China
8 Wen et al.(2019)
Different E-Nose sensors
Advantages
of e nose
• Fast and Precise
• E-Nose has wide range of sensitivity
• Easy to install and use
• Permanent data storage
• Not dependent on reference methods
• High selectivity and specificity
• Portable option available
• Both liquid and solid sample analysis
• It can also detect substances which are not detected
by human nose e.g., Hg
Limitations
of e nose
• The application of an electronic nose is limited due
to sensors and analytical methods.
• The gas sensor array has limitations such as sensor
poisoning, sensor drift, and sensitivity.
• Time delay between successive tests
• High cost of manufacturing and maintenance
• According to application, e-nose use is changed
• Sometimes loss of sensitivity in presence of high
conc. of single component
• The sensor arrays are sensitive to environmental
factors such as humidity and temperature.
• The methods of analyzing the data in the signal
processing system are not easy for food scientists.
APPLICATION
OF E NOSE
Analysis of
Food
Space Program
Application
In QC and R&D
laboratories
Fruit Ripening
Detection of
Explosives
Medical
Diagnosis
(Cancer)
Environmental
Monitoring
Applications of E Nose
Meat and Fish
• Electronic nose systems can be applied to detect
meat freshness (spoilage), and therefore can assess
the shelf life of meat. (Chen et al., 2019)
• It can also be applied to classify beef samples in the
relevant quality class (i.e., fresh, semi fresh, and
spoiled).(Kodogiannis et al., 2017)
• Zhang et al.,2017 explored the discrimination of
marine fish surimi using an electronic nose.
• Guney et al.,2015 used an electronic nose to
discriminate three different fish species.
Edible oils
• The electronic nose technique may be applied in the
classification, geographical origin determination, oil
adulterations, and oxidation assessment of edible oils
(Majchrzak et al., 2018).
• Wei et al..2018 used an electronic nose for rapid
detection of oil adulterations in peony seed oil.
• Xu et al.,2016 developed a qualitative method for
the analysis of edible oil oxidation using an electronic
nose.
Tea and Coffee
• An electronic nose is one of the best solutions to
analyze the quality of tea and coffee.
• The extract of tea leaves has chemical components,
including flavanol, caffeine, phenolic substances, fats,
amino acids, and volatile components. These are
sources of flavors.(Yu et al., 2021)
• Sharma et al.2017 developed a quartz crystal
microbalance sensor-based electronic nose method to
monitor the fermentation process of black tea.
• Coffee provides various flavors. Usually, roasted coffee
contains more than 600 components.
• It is very difficult to discriminate the quality of
adulterated coffee using human sensory panels.
Electronic noses have quantified the concentration
levels of the identified aromas in coffee (Loutfi et al.,
2015).
Milk
• An electronic nose can be used to identify milk
spoilage. Also, an electronic nose was used to
determine the shelf life of milk. (Y Zhong, 2019)
• An electronic nose was also used to determine
differences in milk flavorings(Baldwin et al., 2011).
• Using an electronic nose, Marsili et al. 2000 studied
the off-flavors in milk.
• The e nose proved to be an efficient tool in the rapid
determination of milk, yoghurt and cheese quality,
based on the aroma pattern.
• In recent advances, portable and prototype e-noses
have been used to investigate the quality of dairy
products.(Yakubu et al., 2021)
Fruits and
Vegetables
• An electronic nose can also be applied for
identification, ripeness, and quality grading of fruits
and vegetables (Brezmes et al., 2016).
• Chen et al.2018 confirmed the use of an electronic
nose to evaluate the freshness of fresh-cut green bell
pepper.
• Sanaeifar et al.2016 applied an electronic nose to
predict banana quality properties.
• Ezhilan et al.2018 built an electronic nose-based
method for royal delicious apple quality assessment.
Sr.
No.
Sample Application Data Analysis References
1 Apple/Pear Quality determination PCA Ezhilan et al. (2018)
2 Tomato Shelf life, Maturity PCA, PLS Feng et al. (2018)
3 Peach Fruit decay PLS Huang et al. (2017)
4 Citrus Detection of infection PCA, LDA Wen et al. (2019)
5 Tea Classification of different tea CNN Yu et al. (2021)
6 Saffron Detection of adulteration ANN, PCA Heidarbeigi et al. (2015)
7 Bell pepper Freshness assessment PCA Chen et al. (2018)
8 Coffee bean Taste Properties and volatile profile PCA Dong et al. (2019)
9 Banana Maturity and Quality determination PCA Chen et al. (2018)
10 Rice stem Volatile contents of brown rice plant hopper PCA, SVM Xu et al. (2018)
Conclusion
• An electronic nose is an electronic sensing device intended to detect odors or flavors.
• The electronic nose is an important tool in many different fields such as military, agriculture, processing
methods, and so on.
• The electronic nose technology has gained popularity in sensory science, notwithstanding the
controversies surrounding the nomenclature of the technology.
• We usually use electronic noses to detect the freshness and adulteration of products, as well as set
models to predict the shelf life of different products.
• However, electronic nose technology is still at the developing stage. There are still many problems.
• In the near future, scientists and academics should try their best to optimize the sensor array by
studying sensor material with high specificity and sensitivity according to the physicochemical feature of
the sample.
• We also need to select suitable methods according to the items we want to analyze.
• With the development of sensor technology, the biological chip, and biological information, the function
of the electronic nose will be infinitely close to the human olfactory system so that it will be good
enough to replace it and be widely used in a wider range of applications.
Conclusion
• There is no doubt that the electronic nose will be designed and fabricated into smaller and more
portable devices at low cost.
• One day, every person will be able to use an electronic nose instrument to detect food products to
ensure quality and safety and to make their lives more convenient, efficient, and healthy.
• A universal electronic nose capable of identifying or discriminating any gas sample type with high
efficiency and for all possible applications has not yet been built.
• This fact is largely due to the selectivity and sensitivity limitations of e-nose sensor arrays for specific
analyte gases.
• The application of electronic nose in the food industry can be seen is so widespread, in meat, aquatic
products, fruits and vegetables, dairy products and even spices.
• In a word, with the continuous development of Instrument Science and Computer Science, the
applications of intelligent olfactory system based on electronic nose will be more extensive in the future.
• Compared with sensory panels, e-noses are less biased and give more consistent measurements
between devices. Therefore, e-noses have broad applications.
References
Kiani, S., Minaei, S., & Ghasemi-Varnamkhasti, M. (2016). A portable electronic nose as an expert system for aroma-based classification of
saffron. Chemometrics and Intelligent Laboratory Systems, 156, 148-156.
Tan, J., & Xu, J. (2020). Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties
determination: A review. Artificial Intelligence in Agriculture.
Zhong, Y. (2019). Electronic nose for food sensory evaluation. In Evaluation technologies for food quality (pp. 7-22). Woodhead Publishing.
Ghasemi-Varnamkhasti, M., & Lozano, J. (2016). Electronic nose as an innovative measurement system for the quality assurance and control of
bakery products: A review. Engineering in agriculture, environment and food, 9(4), 365-374.
Yu, D., & Gu, Y. (2021). A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-
Based Electronic Nose. Foods, 10(4), 795.
Kovács, Z., Dalmadi, I., Lukács, L., Sipos, L., Szántai‐Kőhegyi, K., Kókai, Z., & Fekete, A. (2010). Geographical origin identification of pure Sri Lanka
tea infusions with electronic nose, electronic tongue and sensory profile analysis. Journal of Chemometrics, 24(3‐4), 121-130.
Campagnoli, A., Cheli, F., Polidori, C., Zaninelli, M., Zecca, O., Savoini, G., ... & Dell’Orto, V. (2011). Use of the electronic nose as a screening tool
for the recognition of durum wheat naturally contaminated by deoxynivalenol: a preliminary approach. Sensors, 11(5), 4899-4916.
Yang, C. J., Ding, W., Ma, L. J., & Jia, R. (2015). Discrimination and characterization of different intensities of goaty flavor in goat milk by means of an
electronic nose. Journal of dairy science, 98(1), 55-67.
Huang, L., Meng, L., Zhu, N., & Wu, D. (2017). A primary study on forecasting the days before decay of peach fruit using near-infrared spectroscopy
and electronic nose techniques. Postharvest Biology and Technology, 133, 104-112.
Tan, J., & Kerr, W. L. (2019). Characterizing cocoa refining by electronic nose using a Kernel distribution model. Lwt, 104, 1-7.
Long, Q., Li, Z., Han, B., Gholam Hosseini, H., Zhou, H., Wang, S., & Luo, D. (2019). Discrimination of two cultivars of alpinia officinarum hance using an
electronic nose and gas chromatography-mass spectrometry coupled with chemometrics. Sensors, 19(3), 572.
Chen, H. Z., Zhang, M., & Guo, Z. (2019). Discrimination of fresh-cut broccoli freshness by volatiles using electronic nose and gas chromatography-mass
spectrometry. Postharvest Biology and Technology, 148, 168-175.
Chen, H. Z., Zhang, M., Bhandari, B., & Guo, Z. (2018). Evaluation of the freshness of fresh-cut green bell pepper (Capsicum annuum var. grossum)
using electronic nose. LWT, 87, 77-84.
Kodogiannis, V. S. (2017). Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage. Food and
Bioprocess Technology, 10(4), 730-749.
Zhang, X., Wei, W., Hu, W., Wang, X., Yu, P., Gan, J., ... & Xu, C. (2017). Accelerated chemotaxonomic discrimination of marine fish surimi based on Tri-
step FT-IR spectroscopy and electronic sensory. Food Control, 73, 1124-1133.
Güney, S., & Atasoy, A. (2015). Study of fish species discrimination via electronic nose. Computers and Electronics in Agriculture, 119, 83-91.
Majchrzak, T., Wojnowski, W., Dymerski, T., Gębicki, J., & Namieśnik, J. (2018). Electronic noses in classification and quality control of edible oils: A
review. Food chemistry, 246, 192-201.
Wei, X., Shao, X., Wei, Y., Cheong, L., Pan, L., & Tu, K. (2018). Rapid detection of adulterated peony seed oil by electronic nose. Journal of food science
and technology, 55(6), 2152-2159.
Xu, L., Yu, X., Liu, L., & Zhang, R. (2016). A novel method for qualitative analysis of edible oil oxidation using an electronic nose. Food chemistry, 202,
229-235.
Sharma, P., Ghosh, A., Tudu, B., Sabhapondit, S., Baruah, B. D., Tamuly, P., ... & Bandyopadhyay, R. (2015). Monitoring the fermentation process of
black tea using QCM sensor based electronic nose. Sensors and Actuators B: Chemical, 219, 146-157.
Loutfi, A., Coradeschi, S., Mani, G. K., Shankar, P., & Rayappan, J. B. B. (2015). Electronic noses for food quality: A review. Journal of Food
Engineering, 144, 103-111.
Baldwin, E. A., Bai, J., Plotto, A., & Dea, S. (2011). Electronic noses and tongues: Applications for the food and pharmaceutical
industries. Sensors, 11(5), 4744-4766.
Yakubu, H. G., Kovacs, Z., Toth, T., & Bazar, G. (2021). Trends in artificial aroma sensing by means of electronic nose technologies to advance dairy
production–a review. Critical Reviews in Food Science and Nutrition, 1-15.
Brezmes, J., & Llobet, E. (2016). Electronic noses for monitoring the quality of fruit. Electronic Noses and Tongues in Food Science, 49-58.
Ezhilan, M., Nesakumar, N., Babu, K. J., Srinandan, C. S., & Rayappan, J. B. B. (2018). An electronic nose for Royal Delicious Apple Quality
Assessment–a tri-layer approach. Food Research International, 109, 44-51.
Feng, L., Zhang, M., Bhandari, B., & Guo, Z. (2018). A novel method using MOS electronic nose and ELM for predicting postharvest quality of cherry
tomato fruit treated with high pressure argon. Computers and Electronics in Agriculture, 154, 411-419.
Wen, T., Zheng, L., Dong, S., Gong, Z., Sang, M., Long, X., ... & Peng, H. (2019). Rapid detection and classification of citrus fruits infestation by
Bactrocera dorsalis (Hendel) based on electronic nose. Postharvest Biology and Technology, 147, 156-165.
Heidarbeigi, K., Mohtasebi, S. S., Foroughirad, A., Ghasemi-Varnamkhasti, M., Rafiee, S., & Rezaei, K. (2015). Detection of adulteration in saffron
samples using electronic nose. International Journal of Food Properties, 18(7), 1391-1401.
Dong, W., Hu, R., Long, Y., Li, H., Zhang, Y., Zhu, K., & Chu, Z. (2019). Comparative evaluation of the volatile profiles and taste properties of roasted
coffee beans as affected by drying method and detected by electronic nose, electronic tongue, and HS-SPME-GC-MS. Food chemistry, 272, 723-731.
Karakaya, D., Ulucan, O., & Turkan, M. (2020). Electronic nose and its applications: a survey. International Journal of Automation and
Computing, 17(2), 179-209.
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food products. Trends in Food Sci. Technol. 99, 1–10.
Thank You

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Electronic nose in Food Analysis

  • 1. Prathamesh Sanjay Pawale M. Tech Food Technology (II Year) 20412MFT008
  • 2. Content • Introduction • What is E Nose? • Need of E Nose • How do we smell? • Comparison of Human nose with E Nose • Components of E Nose • Working Principle of E Nose • Advantages and Limitations • Application of E Nose • Conclusion • References
  • 3. Introduction • Food quality depends on the color, taste, nutrition, safety, and sensory characteristics of foods. People choose foods according to these aspects. • It has been reported that the human nose has around 400 scent receptors and can detect at least one trillion odors. Although the human nose can rate a smell, individuals' judgments may be bias, and human nose cannot be used to sense toxic gases. • In addition, human nose has detection limits for difference gases. Those limitations prevent the human nose from being a universal tool for all smell-related discrimination and classification. (Tan et al., 2021) • Some methods/techniques such as total volatile basic nitrogen measurement, spectroscopies, and chromatography’s are not usually suitable for online quality control of food product. • New methods like electronic nose have been proposed for in-process and real-time evaluation and controlling of food products. (kiani et al. 2016)
  • 4. What is E Nose? • An electronic nose can be defined as ‘an instrument which is comprised of an array of electronic chemical sensors with partial specificity and an appropriate pattern recognition system, capable of recognizing simple or complex odors. (Gardner, 1995) • Electronic noses are engineered to mimic the mammalian olfactory system. • Can be regarded as modular system comprising a set of active material which detect the odor, associated sensors which transduce the chemical quantity into electrical signals, followed by appropriate signal conditioning and processing to classify known odors or identify unknown odors. Fig. Cyranose 320 E Nose Source: electronicsforu.com
  • 5. Need of E Nose Human olfactory system may be unstable with respect to mood or physical condition Speedy, reliable new sensor technology are used in e nose Detection of hazardous and poisonous gas are not possible with human nose An e nose also overcome other problems associated with the human olfactory system There lies a great chance of difference in the values got by each individual or each sniffer
  • 6. How do we smell? • Whenever we smell something, our nose and brain work together to make sense of hundreds of very tiny invisible particles, known as molecules or chemicals, that are floating in the air. • Tiny cells inside nostrils that talk to each other by electrical impulses i.e., neurons, called olfactory neurons, have long cable-like connections that send electrical messages to a spot at the front of the brain, known as the olfactory bulb. • Each olfactory neuron connects with a different neuron in the olfactory bulb, which then sends this information to other areas of the brain. • Odor>olfactory nerve fibers > olfactory bulb > olfactory tract > primary olfactory cortex > amygdala. Source: Wikipedia
  • 7. Comparison of Human nose with E Nose Fig. Structure of human olfactory system and e nose Source: Varnamkhasti et.al.,2016
  • 8. Comparison of Human nose with E Nose Source: Yakuba et.al.,2021
  • 9. Components of E Nose 1.Sample Delivery System 2.Detection System 3.Computing System
  • 10. I. A sample delivery system • The sample delivery system enables the generation of headspace of sample or volatile organic compounds which is a fraction analyzed. • The system then sends this head space into the detection system of the electronic nose. • The sample delivery system is essential to guarantee constant operating conditions. Source: Y Zhong, 2019
  • 11. II. A detection system • The detection system which consists of a group of sensors is the reactive part of the instrument. • When in contact with volatile organic compounds (VOCs) at that time the sensors react causing changes in electrical characteristics. • Adsorption of volatile compounds on the sensor surface causes a physical change of the sensor; they experience a change of electrical properties. • A specific response is recorded by the electronic interface transforming the signal into a digital value. Source: www.ou.edu
  • 12. III. A computing system • Most of electronic noses use sensor arrays that react to volatile compounds. • This part of the instrument performs global fingerprint analysis and provides results and representation that can be easily interpreted. • Whenever the sensors sense any smell, a specific response is recorded that signal is transmitted into the digital value. • Recorded data are then computed based on statistical models. Fig. Artificial neural network Source: Wikipedia.org Fig. Principal component analysis Source: Yang et al.,2015
  • 13. E-Nose: Working Principle • An air sample is pulled by a vacuum pump. • It is led through a tube into a small chamber consisting of electronic sensor array. • A transient response is produced as the volatile organic compounds in the sample interact with the surface of the sensor’s active material. • A steady state response is reached within few minutes. • This response is then sent to a signal processing unit. Source: Tan et al., 2020
  • 14. E-Nose: Working Principle • A washing gas such as an alcohol vapor is applied to the array for a few seconds to a minute. • This is done to remove the odorant mixture from the surface and bulk of the sensor's active material. • Finally, the reference gas is again applied to the array, to prepare it for a new measurement cycle. • A variety of basic sensors can be used according to the nose strategy chosen. • Each sensor in the array has different characteristics. • The pattern of response across all the sensors in the array is used to identify and/or characterize the odor.
  • 15. Mechanism of E Nose Source: Yu et al., 2021
  • 16. E Nose Sensing System • Using Array of sensors, each sensor designed to respond to a specific chemical. • Number of unique sensors must be at least as the number of chemicals being monitored. • Each Chemical vapor presented to the sensor array produces a signature or pattern characteristics of the vapor. • Use of Artificial Neural Network (ANN) along with array. • Used to analyze complex data and to recognize patterns, are showing promising results in chemical vapor recognition. • Number of detectable chemicals is greater than that of sensors. • Electronic nose incorporating ANNs have been demonstrated in various applications.
  • 17. Sensors in E-Nose • The sensor array is clearly the key element. It forms the primary step in the detection or identification of an odorant.
  • 18. Sr. No. Type of Instrument Model Manufacturer and country No. of Sensors References 1 MOSFET sensor NST3320 Applied Sensor A.G., Sweden 23 Kovacs et al.(2010) 2 MOS sensor PEN2 Airsense Analytics GmbH, Germany 10 Campagnoli et al.(2011) 3 MOS sensor PEN3 Airsense Analytics GmbH, Germany 10 Yang et al.(2015) 4 MOS sensor FOX 4000 Alpha MOS, France 18 Huang et al.(2017) 5 Gas sensor ECGC12S Figaro USA Inc, Japan 6 Tan and Kerr (2019) 6 MOS sensor PEN3 Airsense Analytics GmbH, Germany 10 Long et al.(2019) 7 MOS sensor iNose Ruifen Trading Co., China 14 Chen et al.(2019) 8 MOS sensor SENS Central South University of Forestry & Technology, China 8 Wen et al.(2019) Different E-Nose sensors
  • 19. Advantages of e nose • Fast and Precise • E-Nose has wide range of sensitivity • Easy to install and use • Permanent data storage • Not dependent on reference methods • High selectivity and specificity • Portable option available • Both liquid and solid sample analysis • It can also detect substances which are not detected by human nose e.g., Hg
  • 20. Limitations of e nose • The application of an electronic nose is limited due to sensors and analytical methods. • The gas sensor array has limitations such as sensor poisoning, sensor drift, and sensitivity. • Time delay between successive tests • High cost of manufacturing and maintenance • According to application, e-nose use is changed • Sometimes loss of sensitivity in presence of high conc. of single component • The sensor arrays are sensitive to environmental factors such as humidity and temperature. • The methods of analyzing the data in the signal processing system are not easy for food scientists.
  • 21. APPLICATION OF E NOSE Analysis of Food Space Program Application In QC and R&D laboratories Fruit Ripening Detection of Explosives Medical Diagnosis (Cancer) Environmental Monitoring Applications of E Nose
  • 22. Meat and Fish • Electronic nose systems can be applied to detect meat freshness (spoilage), and therefore can assess the shelf life of meat. (Chen et al., 2019) • It can also be applied to classify beef samples in the relevant quality class (i.e., fresh, semi fresh, and spoiled).(Kodogiannis et al., 2017) • Zhang et al.,2017 explored the discrimination of marine fish surimi using an electronic nose. • Guney et al.,2015 used an electronic nose to discriminate three different fish species.
  • 23. Edible oils • The electronic nose technique may be applied in the classification, geographical origin determination, oil adulterations, and oxidation assessment of edible oils (Majchrzak et al., 2018). • Wei et al..2018 used an electronic nose for rapid detection of oil adulterations in peony seed oil. • Xu et al.,2016 developed a qualitative method for the analysis of edible oil oxidation using an electronic nose.
  • 24. Tea and Coffee • An electronic nose is one of the best solutions to analyze the quality of tea and coffee. • The extract of tea leaves has chemical components, including flavanol, caffeine, phenolic substances, fats, amino acids, and volatile components. These are sources of flavors.(Yu et al., 2021) • Sharma et al.2017 developed a quartz crystal microbalance sensor-based electronic nose method to monitor the fermentation process of black tea. • Coffee provides various flavors. Usually, roasted coffee contains more than 600 components. • It is very difficult to discriminate the quality of adulterated coffee using human sensory panels. Electronic noses have quantified the concentration levels of the identified aromas in coffee (Loutfi et al., 2015).
  • 25. Milk • An electronic nose can be used to identify milk spoilage. Also, an electronic nose was used to determine the shelf life of milk. (Y Zhong, 2019) • An electronic nose was also used to determine differences in milk flavorings(Baldwin et al., 2011). • Using an electronic nose, Marsili et al. 2000 studied the off-flavors in milk. • The e nose proved to be an efficient tool in the rapid determination of milk, yoghurt and cheese quality, based on the aroma pattern. • In recent advances, portable and prototype e-noses have been used to investigate the quality of dairy products.(Yakubu et al., 2021)
  • 26. Fruits and Vegetables • An electronic nose can also be applied for identification, ripeness, and quality grading of fruits and vegetables (Brezmes et al., 2016). • Chen et al.2018 confirmed the use of an electronic nose to evaluate the freshness of fresh-cut green bell pepper. • Sanaeifar et al.2016 applied an electronic nose to predict banana quality properties. • Ezhilan et al.2018 built an electronic nose-based method for royal delicious apple quality assessment.
  • 27. Sr. No. Sample Application Data Analysis References 1 Apple/Pear Quality determination PCA Ezhilan et al. (2018) 2 Tomato Shelf life, Maturity PCA, PLS Feng et al. (2018) 3 Peach Fruit decay PLS Huang et al. (2017) 4 Citrus Detection of infection PCA, LDA Wen et al. (2019) 5 Tea Classification of different tea CNN Yu et al. (2021) 6 Saffron Detection of adulteration ANN, PCA Heidarbeigi et al. (2015) 7 Bell pepper Freshness assessment PCA Chen et al. (2018) 8 Coffee bean Taste Properties and volatile profile PCA Dong et al. (2019) 9 Banana Maturity and Quality determination PCA Chen et al. (2018) 10 Rice stem Volatile contents of brown rice plant hopper PCA, SVM Xu et al. (2018)
  • 28. Conclusion • An electronic nose is an electronic sensing device intended to detect odors or flavors. • The electronic nose is an important tool in many different fields such as military, agriculture, processing methods, and so on. • The electronic nose technology has gained popularity in sensory science, notwithstanding the controversies surrounding the nomenclature of the technology. • We usually use electronic noses to detect the freshness and adulteration of products, as well as set models to predict the shelf life of different products. • However, electronic nose technology is still at the developing stage. There are still many problems. • In the near future, scientists and academics should try their best to optimize the sensor array by studying sensor material with high specificity and sensitivity according to the physicochemical feature of the sample. • We also need to select suitable methods according to the items we want to analyze. • With the development of sensor technology, the biological chip, and biological information, the function of the electronic nose will be infinitely close to the human olfactory system so that it will be good enough to replace it and be widely used in a wider range of applications.
  • 29. Conclusion • There is no doubt that the electronic nose will be designed and fabricated into smaller and more portable devices at low cost. • One day, every person will be able to use an electronic nose instrument to detect food products to ensure quality and safety and to make their lives more convenient, efficient, and healthy. • A universal electronic nose capable of identifying or discriminating any gas sample type with high efficiency and for all possible applications has not yet been built. • This fact is largely due to the selectivity and sensitivity limitations of e-nose sensor arrays for specific analyte gases. • The application of electronic nose in the food industry can be seen is so widespread, in meat, aquatic products, fruits and vegetables, dairy products and even spices. • In a word, with the continuous development of Instrument Science and Computer Science, the applications of intelligent olfactory system based on electronic nose will be more extensive in the future. • Compared with sensory panels, e-noses are less biased and give more consistent measurements between devices. Therefore, e-noses have broad applications.
  • 30. References Kiani, S., Minaei, S., & Ghasemi-Varnamkhasti, M. (2016). A portable electronic nose as an expert system for aroma-based classification of saffron. Chemometrics and Intelligent Laboratory Systems, 156, 148-156. Tan, J., & Xu, J. (2020). Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review. Artificial Intelligence in Agriculture. Zhong, Y. (2019). Electronic nose for food sensory evaluation. In Evaluation technologies for food quality (pp. 7-22). Woodhead Publishing. Ghasemi-Varnamkhasti, M., & Lozano, J. (2016). Electronic nose as an innovative measurement system for the quality assurance and control of bakery products: A review. Engineering in agriculture, environment and food, 9(4), 365-374. Yu, D., & Gu, Y. (2021). A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS- Based Electronic Nose. Foods, 10(4), 795. Kovács, Z., Dalmadi, I., Lukács, L., Sipos, L., Szántai‐Kőhegyi, K., Kókai, Z., & Fekete, A. (2010). Geographical origin identification of pure Sri Lanka tea infusions with electronic nose, electronic tongue and sensory profile analysis. Journal of Chemometrics, 24(3‐4), 121-130. Campagnoli, A., Cheli, F., Polidori, C., Zaninelli, M., Zecca, O., Savoini, G., ... & Dell’Orto, V. (2011). Use of the electronic nose as a screening tool for the recognition of durum wheat naturally contaminated by deoxynivalenol: a preliminary approach. Sensors, 11(5), 4899-4916.
  • 31. Yang, C. J., Ding, W., Ma, L. J., & Jia, R. (2015). Discrimination and characterization of different intensities of goaty flavor in goat milk by means of an electronic nose. Journal of dairy science, 98(1), 55-67. Huang, L., Meng, L., Zhu, N., & Wu, D. (2017). A primary study on forecasting the days before decay of peach fruit using near-infrared spectroscopy and electronic nose techniques. Postharvest Biology and Technology, 133, 104-112. Tan, J., & Kerr, W. L. (2019). Characterizing cocoa refining by electronic nose using a Kernel distribution model. Lwt, 104, 1-7. Long, Q., Li, Z., Han, B., Gholam Hosseini, H., Zhou, H., Wang, S., & Luo, D. (2019). Discrimination of two cultivars of alpinia officinarum hance using an electronic nose and gas chromatography-mass spectrometry coupled with chemometrics. Sensors, 19(3), 572. Chen, H. Z., Zhang, M., & Guo, Z. (2019). Discrimination of fresh-cut broccoli freshness by volatiles using electronic nose and gas chromatography-mass spectrometry. Postharvest Biology and Technology, 148, 168-175. Chen, H. Z., Zhang, M., Bhandari, B., & Guo, Z. (2018). Evaluation of the freshness of fresh-cut green bell pepper (Capsicum annuum var. grossum) using electronic nose. LWT, 87, 77-84. Kodogiannis, V. S. (2017). Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage. Food and Bioprocess Technology, 10(4), 730-749. Zhang, X., Wei, W., Hu, W., Wang, X., Yu, P., Gan, J., ... & Xu, C. (2017). Accelerated chemotaxonomic discrimination of marine fish surimi based on Tri- step FT-IR spectroscopy and electronic sensory. Food Control, 73, 1124-1133. Güney, S., & Atasoy, A. (2015). Study of fish species discrimination via electronic nose. Computers and Electronics in Agriculture, 119, 83-91. Majchrzak, T., Wojnowski, W., Dymerski, T., Gębicki, J., & Namieśnik, J. (2018). Electronic noses in classification and quality control of edible oils: A review. Food chemistry, 246, 192-201. Wei, X., Shao, X., Wei, Y., Cheong, L., Pan, L., & Tu, K. (2018). Rapid detection of adulterated peony seed oil by electronic nose. Journal of food science and technology, 55(6), 2152-2159. Xu, L., Yu, X., Liu, L., & Zhang, R. (2016). A novel method for qualitative analysis of edible oil oxidation using an electronic nose. Food chemistry, 202, 229-235. Sharma, P., Ghosh, A., Tudu, B., Sabhapondit, S., Baruah, B. D., Tamuly, P., ... & Bandyopadhyay, R. (2015). Monitoring the fermentation process of black tea using QCM sensor based electronic nose. Sensors and Actuators B: Chemical, 219, 146-157.
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