A A A r r r t t t i i i f f f i i i c c c i i i a a a l l l N N N o o o s s s e e e T T T e e e c c c h h h n n n o o o l l l o o o g g g y y y : : : T T h h e e W W i i – – N N o o s s e e A A P P r r o o f f i i t t a a b b i i l l i i t t y y a a n n d d M M a a r r k k e e t t A A n n a a l l y y s s i i s s f f o o r r t t h h e e D D e e v v e e l l o o p p m m e e n n t t o o f f A A r r t t i i f f i i c c i i a a l l N N o o s s e e T T e e c c h h n n o o l l o o g g y y t t o o M M o o n n i i t t o o r r t t h h e e F F e e r r m m e e n n t t a a t t i i o o n n P P r r o o c c e e s s s s i i n n W W i i n n e e S S h h a a w w n n a a M M . . L L i i n n e e h h a a n n S S a a r r o o s s h h N N . . N N i i z z a a m m i i
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Artificial Nose Technology: The Wi –Nose · Artificial nose technology has proved exceedingly useful in the detection of explosive materials. E-noses have been able to correctly
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This paper is concerned with the development of an artificial nose to monitor the fermentation process in wine. The design objective of the Wi – Nose was to provide the wine industry with a light weight, compact, and accurate sensing device. Other design factors that were considered included easy of installation, level of maintenance, and lifetime of the device. Data was generated from sensor output versus concentration plots and this data was then classified with the use of a neural network model, specifically NeuroSolutions 5, the most powerful and easy to use neural network simulation environment on the market today. Data was classified into three outputs, stage 1, stage 2, and stage 3 of fermentation. The data was trained, cross validated, and ultimately tested. The optimum percentage for these parameters were determined to be 80% training, of which 10% was cross validation, and 20 % testing. This model was used classify the data, giving accuracy results of 100% for all three fermentation stages. A customer satisfaction model was developed by varying design characteristics. This model ultimately resulted in superiority functions that were used to calculate product demands for varying product prices. These demands were then used to develop plots of net present worth’s as functions of product price to determine the optimal design in terms of consumer satisfaction and profitability. The optimal design was determined to be a device with a 100% correct classification rate, with optimum dimensions of 36 cc and a weight of 1 lb. This design resulted in a TCI of approximately $6.5 million, a ROI of approximately 49%, and a NPW or $11.2 million. A risk analysis was performed varying the cost of raw materials, and it was determined that there is a 90% probability that the Wi – Nose will have an ROI between 40.6% and 57.9%. Thus, production and marketing of the Wi – Nose will be a profitable venture.
BACKGROUND
Introduction
Researchers and manufacturers alike have long envisioned creating devices that can “smell”
odors in many different applications. Thanks to recent advances in organic chemistry, sensor
technology, electronics, and artificial intelligence, the measurement and characterization of
aromas by electrical noses (or e-noses) has become a commercial reality. “While electronic noses
were initially developed as laboratory instruments, science is now moving the technology out of
the laboratory and into the workplace, enabling measurements based on smell to be made at the
source,” says Steven Sunshine, president and CEO of CyranoSciences (Pasadena, CA), one of
several companies that have commercialized the technology.
What Is An E-Nose and How Does it Work?
Electronic noses are artificial smelling devices that identify the specific components of an odor
and analyze its chemical makeup to identify it. They can learn to recognize almost any
compound or combination of compounds. They can even be trained to distinguish between Pepsi
and Coke. Like a human nose, the electronic nose is amazingly versatile, yet it's much more
sensitive. "E-Nose can detect an electronic change of 1 part per million," says Dr. Amy Ryan
who heads the E-Nose project at JPL (Jet Propulsion Laboratory).
The e-nose looks nothing like a human olfactory systems but works quite similar to one. The two
main components of an electronic nose are the sensing system, similar to receptors in our nasal
passages, and the automated pattern recognition system, mimicking neurons and our brain. An
odor is composed of molecules, each of which has a specific size and shape. Each of these
molecules has a correspondingly sized and shaped receptor in the human nose. When a specific
receptor receives a molecule, it sends a signal to the brain and the brain identifies the smell
associated with that particular molecule. Electronic noses based on the biological model work in
a similar manner, albeit substituting sensors for the receptors, and transmitting the signal to a
program for processing, rather than to the brain. Electronic noses are one example of a growing
research area called biometrics, or biomimicry, which involves human-made applications
patterned on natural phenomena.
The sensing system can be an array of several different sensing elements (e.g., chemical sensors),
where each element measures a different property of the sensed chemical, or it can be a single
sensing device (e.g., spectrometer) that produces an array of measurements for each chemical, or
it can be a combination. Generally, electrical noses use a collection of different sensing elements.
These sensors are specially designed to conduct electricity. When a substance -- such as the stray
molecules from a glass of soda -- is absorbed into these sensors, the sensors expand slightly, and
that changes how much electricity they conduct. Because each sensor is made of a different
substance, each one reacts to each substance, or analyte, in a slightly different way. And, while
the changes in conductivity in a single sensor wouldn't be enough to identify an analyte, the
varied changes in various sensors produce a distinctive, identifiable pattern.
Each chemical vapor presented to the sensor array produces a signature or pattern characteristic
of the vapor, a digital “fingerprint” of the specific odor. By presenting many different chemicals
to the sensor array, a database of “fingerprints”, or more appropriately “smell-prints”, is built up.
This database of labeled signatures is used to train the pattern recognition system. The goal of
this training process is to configure the recognition system to produce unique classifications of
each chemical so that an automated identification can be implemented. Artificial neural networks
(ANNs), which have been used to analyze complex data and to recognize patterns, are showing
promising results in chemical vapor recognition.
An ANN is an information processing system inspired by the way the mammalian brain
processes information. ANNs are collections of mathematical models that attempt to emulate
some of the observed properties of biological nervous systems. An ANN consists of a large
number of highly interconnected processing elements – essentially equations known as “transfer
functions” – that are analogous to neurons and tie together with weighted connections that are
analogous to synapses. A processing unit takes weighted signals from other units, possibly
combines them, and gives a numeric result. The behavior of neural networks – how they map
input data to output data – is influenced primarily by the transfer functions of the processing
elements, how the transfer functions are interconnected, and the weights of those
interconnections. Learning typically occurs by example – through exposure to a set of input-
output data, where the training algorithm adjusts the connection weights (synapses). These
connection weights store the knowledge necessary to solve specific problems.
In general, ANNs are well suited to problems that people are good at solving but computers are
not, including pattern recognition and forecasting. ANNs, like people, learn by example.
However, unlike the human capability in pattern recognition, the ANN’s capability is not
affected by subjective factors such as working conditions and emotional state. Most commercial
electronic noses entering the market today employ some sort of ANN for pattern recognition.
This is because ANNs employ a large number of interconnected processing elements working in
unison to solve specific problems, much like biological nervous systems. ANNs are very general
pattern-recognition systems that one can configure, through a learning process, for specific
applications, such as identifying a chemical vapor. When an ANN is combined with a sensor
array, the number of detectable chemicals is generally greater than the number of sensors. Also,
less selective sensors which are generally less expensive can be used with this approach. Once
the ANN is trained for chemical vapor recognition, operation consists of propagating the sensor
data through the network.
This combination of a sensing system with a pattern-recognition system enables e-noses to
process new odors based on patterns of aromas created by earlier experiences, which is much the
same way the human olfactory system works. According to Sunshine, the human nose uses a
complex system of interconnected receptors and neurons, which conduct signals directly to the
brain’s limbic system. “When an aroma is sensed, the molecules from the vapor interact with
numerous receptors, causing them to send a different signal to the brain. The pattern of signals is
then recognized and interpreted by the brain based on prior training.” The human brain is an
incredibly impressive information processor, even though it "works" quite a bit slower than an
ordinary computer. Think of a sort of "analogy" between the complex webs of interconnected
neurons in a brain and the densely interconnected units making up an artificial neural network
(ANN), where each unit--just like a biological neuron--is capable of taking in a number of inputs
and producing an output.
Figure 1 - Neural Network Model
CURRENT ARTIFICIAL NOSE APPLICATIONS
Research in artificial nose technology began as a NASA projected aimed at detecting ammonia
leaks onboard the space station. Because the human olfactory system is not capable of detecting
numerous toxic chemicals until their concentrations are dangerously high, an alternative
detection method was needed. Although initially began as a detection system for toxic chemical
leaks, artificial nose technology has spread to many other industries. Current artificial nose
applications include those found in environmental monitoring, explosives detection, medical
diagnostics, and the food industry.
Environmental monitoring has exploded to the forefront of political, social, and economic
concerns. Artificial nose technology has been used in various environmental monitoring
applications such as air quality monitoring, identification of oil leaks, analysis of fuel mixtures,
identification of household odors, ground water analysis, as well as monitoring of factory
emissions. The electronic nose, or E-nose, provides a safe, economical way to monitor
environmental changes and prevent air, water, and soil contamination, as well as identify
hazardous situations before they become harmful. E-noses have even been implemented in the
oil and gas industry for the detection of harmful gas buildups in onshore and offshore rigs.
Artificial nose technology has proved exceedingly useful in the detection of explosive materials.
E-noses have been able to correctly identify bombs, landmines, TNT, and other explosive
devices, and when used in combination with bomb sniffing dogs, the E-nose becomes extremely
effective. Many governmental divisions, such as Homeland Security, as well as the military, are
interested in artificial nose technology. It is predicted that E-noses will become a staple in
airport security around the world in the near future.
Artificial nose technology is also making huge strides in the medical diagnostics industry.
Although it is not yet an FDA approved method of disease diagnosis, researches have proven its
effectiveness in various applications such as the detection of bacterial infections as well as the
diagnosis of gastrointestinal disorders, diabetes, liver problems, as well as tuberculosis.
Recently, a group of researchers from the Cleveland Clinic Lerner Research Institute have
demonstrated its capability in detecting lung cancer. Researchers used an E-nose to screen
cancer patients as well as monitor the effectiveness of their cancer treatments. The E-nose
provides physicians and clinics with an easy, economical, non-invasive medical diagnostic tool
to screen incoming patients by smell. It has been predicted that E-noses will play an even great
role in disease diagnostics in the foreseeable future as more and more relevant applications are
discovered.
Perhaps the largest and most diverse business sector to be touched by artificial nose technology
is that of the food industry. E-noses have been implemented in various food processing
applications, including assessment of food production, quality control, as well as control of
cooking processes. Specifically, E-nose technology can be found in the inspection of seafood
products, the grading of whiskey, inspection of cheese composition and flavor, and the
monitoring of such processes as fermentation and distillation. E-noses provide the unique
capability of being able to test a product before distribution, an important characteristic in an
industry notorious for status crippling product recalls resulting in huge capital losses as a result
of a few poorly processed or mishandled batches. Although E-nose technology is a relatively
new field, it has already made huge impact on many products consumers use every day. From
detecting hazardous gas buildups on off shore rigs to monitoring cheese composition, E-noses
technology is becoming more and more integrated into industries whose products shape our
everyday lives.
FERMENTATION
Fermentation in wine is the process whereby yeast converts sugar into Carbon Dioxide and Ethyl
Alcohol (Ethanol).
C6H12O6--->2CO2 + 2C2H5OH
An interesting fact is that the atomic weights of the two products are almost the same, so as you
see the carbon dioxide bubbling off you can have the satisfaction of knowing that the same mass
of alcohol is being produced in the wine. This also gives a chemical means of calculating the
alcohol content of the wine.
There are 3 Stages of fermentation:
• Primary or Aerobic (with air) Fermentation
• Secondary or Anaerobic (without air) Fermentation
• Malolactic Fermentation (possible third stage)
During the primary fermentation of wine, the two grape sugars, glucose and fructose are
converted to alcohol (ethanol) by the action yeast. Carbon dioxide is also produced, and leaves
the solution in the gaseous form, while the alcohol is retained in mix. The by-products of primary
fermentation are aromas, flavors, and heat. This stage generally lasts for about a week and is a
critical stage for yeast reproduction. On average, 70% of fermentation activity will occur during
these first few days. There is vigorous action as a result of the rapid fermentation that often
results in considerable foaming on the surface. Primary fermentation is usually conducted in an
open container, hence the aerobic term, covered with a clean tea towel known as the 'primary
fermentation vessel'.
Secondary fermentation is where the remaining 30% of fermentation activity will occur. It is a
much more gentle process that usually lasts anywhere from 2-3 weeks to a number of months,
depending on the amount of nutrients and sugars still available. Secondary fermentation takes
place in a fermentation jar fitted with an airlock, hence the term anaerobic. The occasional
bubbling of the airlock is often all to show that things are still happening.
Malolactic fermentation is a possible 3rd stage that can occur some time after the original
fermentation process has ended, even a year after bottling. A continuation of the fermentation in
the bottle is to be avoided as it can result in a buildup of carbon dioxide which can cause bottles
to burst. Furthermore, it often results in a semi-carbonated wine that does not taste good.
Therefore, this stage is often induced after secondary fermentation but before bottling, by
inoculating the wine with bacteria. The bacteria, lactobacilli, will convert malic acid into lactic
acid. . A small amount of carbon dioxide will be released which in extreme circumstances can
result in a semi-sparkling wine, but the main result lies in the fact that lactic acid has about half
the acidity of malic acid. This results in a somewhat less acidic wine with a much cleaner,
fresher flavor.
IMPORTANCE OF MONITORING FERMENTATION
Malolactic Fermentation Control
There are various situations where monitoring the fermentation process of many products can be
extremely valuable. For example, malolactic fermentation is not a desirable process in bottle
because it often produces a semi carbonated wine of poor quality and taste; however, the process
is often initiated pre-bottling to convert lactic acid to malic acid, to produce a much softer,
fresher tasting product. It is imperative for wineries who employ this flavor controlling process
to be able to initiate malolactic fermentation early enough to avoid its continuation after bottling,
and consequently avoid the production an inferior product that would ultimately result in a loss
of profit.
Champagne and Sparkling Wine Production
Champagnes and sparkling wines are produced by adding rock sugar and supplementary yeast to
partially fermented wine after the primary stage of fermentation has occurred, but prior to the
onset of the secondary stage. This combination of ingredients results in the characteristic
carbonation found in champagnes and sparkling wines. In order to minimize the production time
and thus maximize profit, it is imperative that champagne and sparkling wine producers know
precisely when their batches have completed primary fermentation so that they can add the
appropriate ingredients to achieve their final product sooner and then proceed with the
production of a new batch with as little down time as possible.
Champagne and Sparkling Wine Bottling
In order for champagnes and sparkling wines to be bottled, the fermentation process must be
stopped or severely reduced to avoid carbonation buildup in-bottle that often results in an unsafe
product. Bottles have been known to explode due to carbonation buildups and many times a
chain reaction will be initiated where one bottle after another explodes causing the entire stock to
be destroyed. Because the wine and sparkling wine industries are centered around a single
year’s production, from the yearly grape harvest to the final product that must mature for a
certain amount of time, often years, before being ready for distribution, a loss like this can be
economically devastating. If a year’s stock is ruined, the winery must wait a full year before
beginning the next production process, resulting in an entire year’s loss of revenue in the future.
Knowing precisely when the fermentation process has ceased would be extremely beneficial to
wine and sparkling wine producers. This knowledge would insure that unsafe levels of
carbonation did not build up in-bottle and would ultimately give more economic security to a
relatively risky industry.
Additives to Effect Wine Characteristics
Two major characteristics of wine are sweetness and alcohol content. Control of these
characteristics can be achieved by adding additional nutrients and/or adding yeast retarding
products to the mix, respectively. Sweetness can be increased by adding additional sugars to the
partially fermented stock, where as adding yeast retarding agents impairs and ultimately destroys
yeast cells causing fermentation to cease, resulting in a product with a specific alcohol content.
In order to achieve a specific, desired product, it is crucial to be able to use these additives at the
appropriate time. Waiting too long or adding them too soon will result in either a bitter product
with too high of an alcohol content or an overly sweet product with a low alcohol content,
neither of which are desirable by the majority of wine consumers. Being able to precisely know
when to use additives would be beneficial because it would allow for the production of a
specifically defined product, one that was designed based on key characteristics desired by the
majority of the market. By appealing to a larger consumer base, product sales would begin to
increase and profit would be maximized.
THE WI – NOSE DESIGN
General Design
The design objective of the Wi – Nose was to provide the wine industry with a light weight,
compact, and accurate sensing device. Other design factors that were considered included easy
of installation, level of maintenance, and lifetime of the device. The Wi-Nose consists of a rigid,
plastic hemisphere that contains the workings of the artificial nose. The main components of the
Wi-Nose include a sensor array, a microprocessor with RAM, a wireless transmitter, and a
pneumatic pump. The sample of the test gas enters the device through a small intake. The
pneumatic pump provides the pressure differential necessary for the sample to enter the device.
The sample is then directed towards the sensor array, where it accumulates in the device
headspace. Once in the headspace, the sample gas interacts with the sensor array and is then
expelled through the sample exhaust. The sensor array contains three sensors, two metal oxide
sensors for the detection of ethanol and one electrolyte sensor for the detection of carbon
dioxide.
Figure 2 - Cross sectional view of the Wi-Nose
Upon interaction with the sample gas, the sensors send their respective output signals to the
microprocessor where the information stored in the device’s RAM and then transformed and sent
to the wireless transmitter. The wireless transmitter sends the data to the hub computer where
the information is classified with the aid of an artificial neural network. The proposed system
would allow for the addition of multiple devices for the monitoring of multiple products. By
having all devices transmit their signals to the same computer, the consumer will be only be
responsible for purchasing a single artificial neural network, a program that accounts for a large
percent of the device’s cost.
Figure 3 - Top View of Wi – Nose Device
The Wi - Nose is designed to be easy to install. Four screws and hex nuts are included with the
device, as well as multiple rubber O-rings to prevent moisture from contacting the metal
components. Because most of these units will be installed in metal fermentation vats or tanks, it
is important to reduce the risk of rusting. Moisture control issues are bound to be encountered,
and the Wi – Nose is designed specifically with these potential problems in mind. Moisture
buildup is directed away from the metal interface and sensitive device components due to the
apparatus’s unique shape. The hemisphere design allows for condensation to run down outside
of the plastic cover and drip off the device without damaging the water sensitive components
housed inside.
This design also promotes device longevity. The Wi – Nose has a minimum lifetime of 5 years.
The device should be tested on a semi-regular basis throughout the first five years to guarantee
that it is providing reliable results, however the sensors should be replaced and the device should
be serviced every six to seven years. Usually, the only parts that should have to be replaced at
this time are the sensors; however, they are of relatively low cost and replacing them on this
timetable should not amount to much cost to the consumer.
Tin Oxide Sensor
Sensor Array Board Microprocessor/RAM
Installation Screw
Sensor Choice
Three different sensors are used in the Wi – Nose design. The first is Figaro’s TGS 822 for the
detection of ethanol. The TGS 822 is a tin oxide sensor that features the following
characteristics:
� High sensitivity to organic solvent vapors such as ethanol
� Unresponsive to carbon dioxide
� High stability and reliability over a long period (lifetime ≥ 5 years, up to 200 ºC)
� Long life and low cost
It uses a simple electrical current to produce a resistance output in response to a detectable gas’s
concentration (ppm). Because the TGS 822 is unresponsive to carbon dioxide, this allowed for
its flawless integration into the Wi – Nose. This characteristic eliminates carbon dioxide’s effect
as an interference gas, and ultimately removes carbon dioxide hindrance from the design
parameters.
Figure 4 - a) Figaro’s TGS 822 b) respective resistance versus concentration plot
c) sensor structure
The Wi – Nose also features another metal oxide sensor for the detection of ethanol, the Figaro
TGS 2060. This sensor is based on an alumina substrate and features the following
characteristics:
� Low power consumption
� High sensitivity to alcohol and organic solvent vapors
� Unresponsive to carbon dioxide
� Long life and low cost
The TGS 2060 also utilizes a simple electrical circuit; however, this sensor produces a
conductivity output signal in response to a detectable gas’s concentration. Again, this sensor was
chosen because of its insensitivity to carbon dioxide, thus allowing for the output signal to be
purely based on the concentration of ethanol and not on the concentrations of both ethanol and
carbon dioxide.
Figure 5 - a) TGS 2060 b) Respective output signal versus concentration
The final sensor used in the Wi – Nose is Figaro’s TGS 4160. Figure 6 -TGS 2060
This sensor is utilized in the Wi – Nose for the detection of Structure
carbon dioxide. The TGS 4160 has the following features:
� High selectivity for carbon dioxide
� Unresponsive to ethanol
� Compact size
� Long life
The TGS 4160 differs from both the TGS 822 and the TGS
2060 because it is an electrolyte type sensor. It utilizes
electromotive force to create a signal output that corresponds to
a detectible gas’s concentration. Unlike the previous two
sensors, the TGS 4160 is unresponsive to ethanol, making it an
excellent choice for the Wi – Nose design for the same reason
that the TGS 822 and the TGS 2060 were good choices based
on their insensitivity to carbon dioxide.
Figure 7 - a) TGS 4160 b) Respective output signal versus concentration
SENSOR DATA
Each sensor’s output versus concentration plot was reproduced in Microsoft Excel by fitting
sample data points with the following model:
( ) 1−= nionConcentratmOutput Equation 1
where m and n were parameters that were allowed to vary while the sum of the square of the
difference of output and calculated output was minimized in the Excel Solver add in.
TGS 822 Conc. (ppm) Rs/Ro Rs/Ro calc Squ.Diffe. Sum of sq.
Frauenfelder Mark. “Company Profile: Cyrano Sciences, Inc. – Electronic Nose Sniffs Out New Markets for California Firm.” <http://www.smalltimes.com/Articles/Article_Display.cfm?ARTICLE_ID=267768&p=109>
Freund Michael S., Lewis Nathan S. A chemically diverse conducting polymer-based “electronic
nose”. Proc. Natl. Acad. Sci. Chemistry, Vol. 92, March 1995: 2652-2656.