Intangible sensor and method for making same

An intangible sensor for measuring intangible properties of a substance and a method for making the sensor is described. The intangible sensor may be embodied in a mapping neural network model. The intangible sensor herein is a device that quantitatively measures complex intangible properties of a sample of a substance. The term intangible implies a subjective connotation such as in the taste, creaminess or softness of a substance or product and therefore can only be subjectively defined. Although an intangible property is known to be a function of certain measurable physical properties of a substance, there are no known definitions of this function. The intangible sensor herein can implement this function simply without having any detailed knowledge of or making any analysis of the function.

The invention herein relates to a sensor for measuring intangible 
properties of a substance and a method for making the sensor. 
An intangible sensor embodying the invention herein and made in accordance 
with the method of the invention may be embodied in a mapping neural 
network model. 
The intangible sensor herein is a device that quantitatively measures 
complex intangible properties of a sample of a substance. It is designed 
to be repeatable as well as non-specific and thus can be tailored to 
identify or detect specific intangible properties. The term intangible 
implies a subjective connotation such as in the taste, creaminess or 
softness of a substance or product and therefore can only be subjectively 
defined. Although an intangible property is known to be a function of 
certain measurable physical properties of a substance, there are no known 
definitions of this function. The intangible sensor herein can implement 
this function simply without having any detailed knowledge of or making 
any analysis of the function. 
Advantages of the intangible sensor herein are: 
i) It is used in conjunction with a set of standard, readily available 
sensors and thus no high-accuracy specific sensors have to be developed. 
The burden and difficulty of developing a specific sensor are thus 
avoided. 
ii) There are no deterministic algorithms to be developed for relating 
every intangible property to a set of measurable properties. The 
intangible sensor learns by example just as humans do. 
iii) Unlike a single physical sensor, the intangible sensor can be 
programmed or trained to different human preferences. For example, if an 
intangible sensor for taste is being developed, different versions can be 
developed by retraining the device to reflect different preferences while 
using the same set of standard input sensors. 
iv) The intangible sensor herein is very robust in terms of its tolerance 
to noise in measurements. This is because a neural network computational 
model is used to capture the relationship between the intangible property 
and measurable physical properties of the substance under test. Neural 
network models are inherently fault-tolerant because they represent data 
in a distributed fashion. 
A main object of the invention is to provide a new intangible sensor and a 
method for making said sensor. 
Other objects and advantages of the invention will become apparent from the 
following specification, appended claims and the attached drawings.

DESCRIPTION OF INTANGIBLE SENSOR 
The intangible sensor herein is based on a sensing scheme which implements 
the functional relation between measurable physical properties that 
determine the intangible property and human assigned scores that 
quantitatively describe the intangible property. FIG. 1 shows a schematic 
representation of the intangibles sensor 10. 
The intangible sensor uses as inputs a number of measurements obtained 
through a set of simple sensors (marked 1 to n in FIG. 1). The set of 
sensors 1-n is chosen as a superset of sensors that measure physical 
properties suspected of influencing the intangible property. The 
intangible sensor as a device maps the physical input values to the human 
assigned scores of the intangible property for different samples of the 
product. Based on a multiplicity of "correct" responses provided by 
humans, the device 10 automatically develops the ability to produce an 
output score that approximates what a human would assign to the specimen 
being sensed or observed. In short, the intangible sensor imitates, and 
therefore can replace, the human monitor to evaluate the intangible 
property without establishing an exact physical definition. 
COMPUTATIONAL MODEL FOR THE INTANGIBLE SENSOR 
The computational model underlying the derivation of the functional 
relation is the mapping neural network 12 paradigm (1). In this model, a 
multilayer neural network is used to learn the relationship between the 
input sensor values of the physical properties of the substance and the 
human encoding (scores) of the intangible property. The multilayer neural 
network model used is usually a neural network employing a supervised 
learning algorithm (1). The preferred learning algorithm that is used is 
the back propagation learning algorithm or its variant (2). Other mapping 
neural network models such as the Kohonen associative model (3) and the 
counterpropagation model (4) can also be used. In case of the multilayer 
model, two to three layers (excluding the input layer where the inputs are 
fed into the network) must be used, that is, one or more hidden layers 
must be used as illustrated in FIG. 2. This is because, in order to 
realize any general function between real inputs and outputs, at least two 
layers must be used (2,5). FIG. 2 shows the typical neural network 
architecture required when employing the back propagation neural network 
to realize the mapping for the intangible sensor. 
The multilayer network 12 approximates the general relationship between the 
measurable sensor values and the human sensory output. The large range of 
possible functions that can be realized by a feed-forward multilayer 
network can be shown by examining outputs 13 of each layer. The output 14 
of any layer in the network can be analyzed by examining the functional 
behavior of an individual neural unit. 
FIG. 3 shows the inputs and the output of a single neural unit. The output 
14 of unit s.sub.j is a function of the weighted sum of the inputs 
x.sub.1, x.sub.2, . . . , x.sub.n or 
##EQU1## 
where x.sub.i is between 0 and 1, and w.sub.ij is a real number that 
specifies the strength of connection between input x.sub.i and s.sub.j, 
and t.sub.j is a threshold term for the unit s.sub.j, where i varies from 
1 to n. The weights w.sub.ij are set after the neural network has been 
trained for mapping a specific input/output relationship. The most common 
function for f is the sigmoid function which can be described as follows: 
##EQU2## 
As can be seen from the above function description, the output of f is 
always bounded. The outputs of the network are therefore always normalized 
between 0 and 1. Because the feedback network employs bounded neural 
units, it is guaranteed to be stable. This fact is important since it 
guarantees that the network can always be used to learn any arbitrary 
mapping withough exception. 
The infinite range of mapping functions that can be learned by the network 
can be derived from the architecture of the feedforward network. If n 
input sensors are known to determine the single intangible output 
represented by the unit "o" in the following equation, these input sensor 
values are denoted by s.sub.1, . . . , s.sub.n. If m units x.sub.1, . . . 
, x.sub.m are used in the hidden layer, then the two-layer network 
(excluding the layer of input variables) as shown in FIG. 2 realizes the 
relationships that can be expressed as: 
##EQU3## 
where t.sub.i and t are the threshold terms for units in the hidden and 
output layers, respectively. 
Kolmogorov (5) has shown that by using a layered network of real-valued 
functional units, any mapping can be accomplished by using a network 
containing two layers. Thus, if sigmoid units are used, a two-layered, or 
at worst, a three-layered network, would be sufficient to represent any 
arbitrary mapping. By using the back propagation learning algorithm (2), 
the different weights, w.sub.i and w.sub.ij (in case of a two-layered 
network) can be set to realize any arbitrary mapping required to relate 
the physical parameters to the intangible property. The weights are 
adapted iteratively until each input in the training set of samples yields 
the correct output taste score within acceptable tolerances. 
DESIGNING AND IMPLEMENTATION OF THE INTANGIBLE SENSOR 
The procedure followed in designing and implementing the intangible sensor 
10 has three essential phases: 
Phase I: Data Collection and Selection of sensors 
Phase II: Training the Intangible Sensor 
Phase III: Test and Validation 
In Phase I, all data available for different samples of the product, whose 
intangible property is to be sensed, is collected. This is the critical 
phase because the method of designing the sensor is completely 
data-dependent. In general, it is necessary that the samples collected 
have widely varying values of the intangible property. For example, if a 
sensor for taste for orange juice is being developed, then the sampling 
collected must have tastes varying from very good to very bad. Once the 
samples have been collected, a set of relevant sensors is identified. 
These sensors are those that can monitor measurable and tangible 
properties that are suspected of influencing the intangible property. For 
instance, if the taste of some juice is being measured, the sensors that 
would be considered in the set could include fructose or glucose sensors, 
a pH sensor for acidity, an alcohol sensor, a color sensor and density 
sensors. Each sample is then measured by these sensors (tangible data) as 
well as assessed by human evaluators (intangible data). The sensed and 
human score data for all samples constitute the training set 16 shown in 
FIG. 4 and used in Phase II. 
Phase II uses the training set collected in Phase I and uses it to train 
the neural network 12 that is large enough relative to the size of the 
problem. Thus, if there are n input sensors and m components on which the 
humans evaluate the intangible property, the neural network 12 is defined 
with n inputs and m outputs. The back propagation algorithm (2) or an 
efficient variant is used to train the network the relationship between 
the sensed inputs and the intangible human scores. 
After the training of the neural network 12 is completed (after the 
training converges), the test and validation is carried out in Phase III. 
The testing is done on samples that were not included in the training set 
and the output of the network model 12 is compared to the human-assigned 
score. If the network 12 compares favorably with the human scores for all 
test samples, then the neural network 12 is deemed to have learned the 
required relationship. Otherwise, two options are pursued. First, if the 
number of samples used are not sufficient for training, more samples are 
included (especially, the ones for which the network failed to give good 
results) in the training set. More data collection may then be required 
for testing. Second, the set of sensors used may not be suffient to 
capture the physical properties that influence the intangible property. In 
such a case, other suspected measurable properties of the substance must 
also be sensed. In case of either option, the three phases are repeated 
until the network model provides good prediction of the intangible 
property. 
The steps used in the design and implementation of the intangible sensor 
are depicted in the flowchart of FIG. 5. 
EXAMPLES OF THE INTANGIBLE SENSOR 
Two different domains of intangible properties of materials are chosen to 
demonstrate how the concept behind the invention can be used to design 
specific intangible sensors. The first intangible sensor is applicable to 
the domain of taste, specifically to taste fruit juices such as orange 
juice. The second sensor is applicable to characterizing solid food in the 
way humans do. It can be used to determine the chewiness of food, for 
instance cookies. 
I. Intangible Sensor for Tasting Juice 
The intangible sensor 18 for tasting juice is shown in FIG. 6. Orange juice 
is chosen for illustrating this sensor. Here, two major physical 
(currently available) sensors are used. These are a twelve-channel 
Honeywell color sensor 19 and an electrochemical sensor 20 that provides 
the chemical signature of the juice sample 21. This minimal set of 
measurable physical inputs are expected to sufficiently characterize the 
taste of orange juice. For designing this sensor, the three phases 
previously outlined are followed. 
Sample Data Collection: Two sorts of data are collected. Different orange 
juice samples that have a wide range of taste are collected. A taste panel 
tastes the different juice samples and scores them on either one (rating 
from good to bad) or many other (freshness, aroma, sweetness, etc.) 
attributes. The data possibly used in the training set 18 is shown in FIG. 
7. Each sample is also measured physically for color and electrochemical 
signatures. 
Training the sensor: A two-layered feedforward neural network 18 is set up 
to learn the mapping between the color and electrochemical inputs and the 
taste output/outputs. An iterative learning procedure, using back 
propagation, is used to establish the network weights and, therefore, the 
desired function to be realized by the neural network 18. 
Testing the sensor: The trained network 18 is tested on samples not 
included in the training set. If the sensor scores are close, that is, 
within preset tolerance limits to the human scores, then the network has 
been trained well. After testing and validation is completed, the network 
18 may be deployed in an operational mode. In the operational mode, the 
neural network model 18 is hosted on some computing platform, such as a 
personal computer 23 as shown in FIG. 6, and set up so as to predict taste 
scores when receiving data from the color and electrochemical sensors. 
If the trained network did not score well, then more sample data is 
necessary to train the network. Training is complete only when the network 
has learned a sufficiently generalized mapping between the physical 
sensors and the human scores. 
II. Intangible Sensor for Chewiness 
FIG. 8 shows an intangible sensor 24 for chewiness. The physical data that 
are measured in this sensor via sensor's 25, 26 and 27 are color, weight 
and dimensional measurements such as diameter and thickness. It is assumed 
that in the manufacturing of cookies in a batch process, a fixed weight of 
cookie dough is used. The network 24 is trained to map the physical data 
(average color (light brown to dark brown), thickness (how much rise in 
baked dough), weight after baking and the diameter of the cookie) to the 
intangible data of chewiness as determined by humans. The same three steps 
required in the previously described orange juice sensor are used to 
specify the neural network and the final intangible sensor. 
DEFINITION 
It is contemplated that the term "Physical Property" as used herein have a 
broad meaning in a general sense so as to mean any property of a 
substance, including any electrical, mechanical, chemical property, for 
which a physical measurement can be made by a single sensor of a known 
type. Thus, the term pH, for example, or if the degree of a reaction may 
be the parameter sensed, such are meant to be included. 
REFERENCES 
1. R. P. Lippmann, `An Introduction to Computing with Neural Nets`, IEEE 
ASSP Magazine, April 1987, pp. 4-22. 
2. D. E. Rumelbart, G. E. Hinton, and R. J. Williams, `Learning Internal 
Representations by Error Propagation`, Parallel Distributed 
Processing--Explorations in the Microstructure of Cognition, Volume 1: 
Foundations. Editors: David E. Rumelhart, James L. McClelland, and the PDP 
Research Group. MIT Press, 1987. 
3. T. Kohonen, `Self-organization and Associative Memory`, (second 
edition), Springer-Verlag: New York, 1988. 
4. R. Hecht-Nielsen, `Applications of Counterpropagation Networks`, Journal 
of the International Neural Networks Society, Vol. 1, No. 2, 1988. 
5. A. N. Kolmogorov, `On the Representation of Continuous Functions of 
Several Variables by Superposition of Continuous Functions of One Variable 
and Addition`, Dokl. Akad. Nauk SSR, Vol 114, 1957, pp. 369-373: as cited 
in D. A. Sprecher, "On the Structure of Continuous Functions of Several 
Variables", Transactions of American Mathematical Sciences 115, 340-355, 
March, 1965.