Keywords- Odor Classification, Generative Models, Gaussian. Discriminant Analysis, Electronic Nose. I. INTRODUCTION. An odor or fragrance is caused by one ...
Application of Generative Models for Odor Classification Using Electronic Nose Kumar Shashvat, Ritesh Kumar ,Amol P Bhondekar* CSIR-Central Scientific Instruments Organization, Sector 30C, Chandigarh, India The electronic nose system is an alternative method to analyze odor by imitating the human olfactory system various sensors for odors applied as the olfactory receptors are explained .The mechanism of a simple electronic nose that is explains in detail by comparing the function of each part with the human olfactory process. The concern of the work is to able to provide better classification for odor using data extracted from the electronic nose.
Abstract—Amongst the five senses, odor is the most evocative and least understood. Odor testing has been mysterious and odor data mythical to most practitioners. The problem of recognition and classification of odor is important to achieve. The ability to smell and predict whether the product is of further use or it has become undesirable for consumption; the emulation of this problem into a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be better classifier than color based classification and if incorporated in machine will be extremely constructive. For classification of odor for peas, tress and cashews various discriminative approaches have been used .Discriminative methods offer good predictive performance and have been widely used in many applications but are unable to make efficient use of the unlabelled information. In such scenarios generative approaches have better applicability, as they are able to knob problems, such as in scenarios where variability in the range of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an uncertain probability density function. They outperform other techniques in cases where it may be hard or impossible to provide enough labeled training examples. Gaussian discriminant analysis GDA is a generative model for classification where the allocation of each class is modeled as a multivariate Gaussian. GDA makes stronger modeling assumptions, and is more data efficient when the modeling assumptions are correct or at least approximately correct. The Electronic instrument which is being used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the human sense of odor by providing an analysis of individual chemicals or chemical mixtures. We have implemented Gaussian Discriminant Analysis method for odor classification and have achieved better result for the same.
II.
LITERATURE REVIEW
A. Generative Model: In statistics and probability, a generative model is a model for randomly generating observable data values, normally given some hidden parameters[4]. It stipulates a joint probability distribution over observation and label sequence. Generative models are applied in machine learning for either modeling data directly (i.e., modeling observations which are taken up from probability density function), or as a transitional step to forming an uncertain probability density function. A conditional distribution can be produced from a generative model through Bayes' rule. Example of Generative Model: Consider a classification predicament within which we want to study, to distinguish between Apple (y = 1) and Peach (y = 0), based on some features of fruits. Given a training set, an algorithm like logistic regression or the perception algorithm basically tries to discover a straight line—that is, a decision boundary—that segregate the Apple and peach. Then, to classify a new fruit as either an apple or a peach, it checks on which side of the decision boundary it falls, and makes its prediction therefore. Here’s a diverse approach. First, looking at apple, we can build a model of what apple look like. Then, looking at peach, we can build a separate model of what peach look like. Finally, to classify a new fruit, we can match the new fruit against the apple model, and match it against the peach model, to see whether the new fruit looks more like the apple or more like the peach we had seen in the training set. Algorithms that seek to learn directly (such as logistic regression), or algorithms that try to learn mappings directly from the space of participation X to the labels {0, 1}, (such as the perceptron algorithm) are called discriminative learning algorithms.and (y) these algorithms are called generative learning here, we’ll talk about algorithms that instead try to model algorithms[4]. For instance, if y specify whether an example is a peach (0) or an apple (1), then models the
Keywords- Odor Classification, Generative Models, Gaussian Discriminant Analysis, Electronic Nose
I. INTRODUCTION An odor or fragrance is caused by one or more volatilized chemical mix, usually at a very low concentration, that human being or other animals recognize by the sense of olfaction. Odors are also commonly known as scents, which can refer to similarly pleasant and unpleasant odors[3]. The terms fragrance and aroma are used primarily by the food and cosmetic industry to explain a pleasant odor, and are sometimes used to refer to perfumes. In contrast, malodor, stench, reek, and stink are used specifically to describe unpleasant odor . The term smell (in its noun form) is used for both pleasant and unpleasant odors.
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distribution of peach’s features, and models the distribution of apples’ features
Data Collection:
Gaussian Discriminant model
Cashew nut samples were shared in the collection procedure by NANO PIX with CSIO .After which Quantification and identification of volatiles in cashew nuts was performed to differentiate into two categorize and quantize data collected
Hidden markov model
Analysis by E-Nose System:
Naive Baye’s
Alpha MOS FOX 3000 (E-Nose system) was deployed. It consists of 18 sensors which demonstrate specific affinities towards the volatiles. The system consists of sample delivery system, a detection system and the computing system for pattern recognition. Sample delivery enables the generation of the volatile compounds by heating the sample at const temperature. The auto sampler then injects a precise volume of volatile compounds into detection system. The detection system consists of the three gas chambers equipped with six gas sensors each. A change in electrical properties of the gas sensors is recorded when in contact with the volatile compounds and these data constitute input for further analysis. For analysis peak values of sensors were used.
The examples of generative models are:
B. Electronic Nose system Mechanism Odor delivery system The first process of the human olfactory system is to inhale the fragrant molecule into the nose. Thus, the first part of the electronic nose system is the instrument to pass the odorous molecules into the electronic nose system. There are three main methods to deliver the odor to the electronic nose unit, sample flow, static system, and pre-concentration system [5]. The sample flow system is the most popular method to deliver odorous molecule to the electronic nose unit. Odor sensor array The second process of the human olfactory system is to quantify various odors corresponding to various receptors present in the human olfactory system. In order to comprehend many receptors artificially, we adopted two types of sensors []. One is MOG type and the other is QCM type.
Figure 1: Electronic Nose System
Data recording The data recording is corresponding to temporal memory for the human olfactory system. In the second scenario, later than learning odors we could recognize an odor suddenly; we store sensing data of odors in a computer.
Classification: Classification is a general process to categorization. It is a process in which ideas and objects are recognized, differentiated, and understood[1]. After the data was collected, it was classified using generative learning techniques: Naïve Bayes and Gaussian Discriminant analysis.
Data processing Using the data base of odor, we must apply an smart signal processing technique to recognize odors correctly. We preprocess the odor data intended for sound reduction, normalization, feature extraction, and so on. Then we use layered neural networks and competitive networks for odor classification since learning ability and robustness are important in odor classification.
Gaussian Discriminant Analysis model: When we have a classification problem in which the input features x are continuous-valued random variables, we can then apply the Gaussian Discriminant
III METHODOLOGY
Analysis (GDA) model, which .models using a multi variate normal distribution The model is: (1) (2) (3) Writing out the distributions, this is:
(4)
2
(5)
(6) Here, the parameters of our model are φ, Σ, µ0 and µ1. (While there’re two different mean vectors µ0 and µ1, this model is commonly applied using only one covariance matrix Σ). Naive Bayes Classifier: For the Bayes classifier, we need to “discover out” two utility, the likelihood and the prior[2].
Figure 2: Electronic Nose System
Likelihood: A likelihood function is the probability or probability density for the amount of a sample configuration[1], given that the probability density with parameter is known.
The more commonly used sensors for electronic noses includes 1.Metal–oxide–semiconductor (MOSFET) devices - a transistor used for magnifying or switching electronic signals. This works on the principle that molecules entering the sensor area will be charged either positively or negatively, which should have a direct effect on the electric field inside the MOSFET.
Prior: The prior probability of an event is the probability of the event computed before the collection of new data.
2.Conducting Polymers - organic polymers that demeanor electricity.
Bayesian classifiers use Bayes theorem, which says
3. Polymer Composites - similar in use to conducting polymers but formulate of non-conducting polymers with the computation of conducting material such as carbon black.
(7)
•
= probability of generating illustration d given
4. Quartz crystal microbalance - a way of evaluating mass per unit area by measuring the change in rate of recurrence of a quartz crystal resonator. This can be accumulate in a database and used for future reference.
• = probability of occurrence of class cj, This is just how frequent the class cj , is in our database
5. Surface acoustic wave (SAW) - a set of micro electro mechanical systems (MEMS) which rely on the inflection of surface acoustic waves to sense a physical phenomenon
= probability of occurrence d being in class cj, this is what we are trying to compute • class cj,
• = probability of instance d occurring this can truly be ignored, since it is the same for all classes
Cashews: We have used six cashew Kernel from different cashew nut processors were obtained. Bennim [BEN], Goa [GOA], BSK and Sons(BSK), Kalabavi [KLB], Kaladhar[KLD], Local Market [MKT] and. These samples consist of various grades. Cashews were divided into two categories good and bad. Following is distribution:
Results: After the classification using generative models such as Naïve Bayes Classifier and Gaussian Discriminant Analysis, different graphs representing and differentiating cashews on basis of their odor, as in good cashew and Bad were achieved.
Good Cashews 154
Bad Cashews 9
IV . EXPERIMENTAL SETUP Electronic Nose: The Electronic instrument which we have used to execute our work is Electronic Nose[5]. An electronic nose is a device which comprises of an array of electronic chemical sensors with partial specificity and an appropriate pattern recognition system, capable of recognizing simple or complex odors.
V. RESULTS After retrieving the result from Electronic Nose we then applied the various generative algorithms like Naive Bayes Classifier and Gaussian Discriminant analysis for the classification of good and bad cashews.
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Following are the results obtained when Naive Bayes classifier was used aaccuracy and error rate as given:
CONCLUSION AND FUTURE SCOPE Previously, most of the classification work has been implemented with Discriminative Methods. We have implemented the Navie Bayes Classifier and Gaussian Discriminant Analysis which are both Generative Methods. We have achieved better results by the Naive Bayes Classifier. More Generative Models can be applied on Electronic Nose Data for better classification results.
ACCURACY AND ERR Err
0.0833
Accuracy
91.6667%
Elapsed Time
1.211201Seconds
ACKNOWLEDGEMENT The authors are extremely thankful to Director, CSIO for his kind permission to carry out this work.
Confusion Matrix: confusion matrix is a table that shows the performance of classifier model. There are no sources in the current document.
REFERENCES CONFUSION MATRIX Predicted Good
Predicted Bad
Bad
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1
Good
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Sample Classification
[1] McCallum, A., & Nigam, K. (1998). A Comparison of Event Models for Naive Bayes Text Classification. AAAI/ICML-98 Workshop on Learning for Text Categorization, 41–48. [2] Rish, I. (2001). An empirical study of the naive Bayes classifier. IJCAI-2001 Workshop on Empirical Methods in AI (Also, IBM Technical Report RC22230), Pp. 41--46, (April). Retrieved from
When Comparison of Naive Bayes and Gaussian Discriminant Analysis. From the graphical depictions it was evidently seen that Naïve Bayes performed much better in the scenario of cashew classification.
[3] Phaisangittisagul, E., & Nagle, H. T. (2011). Predicting odor mixture’s responses on machine olfaction sensors. Sensors and Actuators, B: Chemical, 155(2), 473–482. doi:10.1016/j.snb.2010.12.049
[4] Ng, A. (2008). CS229 Lecture notes 2 - Generative Learning algorithms, (0), 1–14 [5] Science, C., & Studies, M. (2013). Electronic Nose : Feature Extraction and their Applications, 1(6), 96–102.
Figure 3: Classification on Basis Of Naive Bayes
Figure 3(a): Classification Basis on Gaussian Discriminant Analysis
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