A Neuro Fuzzy Based Black Tea Classifying Technique Using Electronic Nose and Electronic Tongue Sourav Mondal, Runu Banerjee(Roy), Bipan Tudu, Rajib Bandyopadhyay and Nabarun Bhattacharyya
Abstract This paper presents a neuro-fuzzy classification technique using electronic nose, electronic tongue and the fused response of electronic nose and electronic tongue for the evaluation of black tea quality. In the tea industries an automated, neutral and low cost instrumental system to determine the overall tea quality is in great requirement. A general fuzzy rule based and neural network model can produces accurate predictions. But both models have some weakness. In this pursuit, Pseudo outer-product based fuzzy neural, a kind of fuzzy neural network classifying system has been attempted to classify tea grades. Results show that above model can classify in a better way compared to other models.
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Keywords Electronic nose Electronic tongue c-means Fuzzy neural network POPFNN
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Black tea analysis
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Fuzzy
1 Introduction Tea is one of the most demanding beverages worldwide with an expanding market because of its certain flavor and aroma. Tea leaves processing techniques are one of major factor of determining the final marker price of tea. So before tea leaves are Sourav Mondal (✉) ⋅ Runu Banerjee(Roy) ⋅ Bipan Tudu ⋅ Rajib Bandyopadhyay Jadavpur University, Kolkata, India e-mail:
[email protected] Runu Banerjee(Roy) e-mail:
[email protected] Bipan Tudu e-mail:
[email protected] Rajib Bandyopadhyay e-mail:
[email protected] Nabarun Bhattacharyya Centre for Development of Advanced Computing, Kolkata, India e-mail:
[email protected] © Springer Science+Business Media Singapore 2017 J.K. Mandal et al. (eds.), Proceedings of the First International Conference on Intelligent Computing and Communication, Advances in Intelligent Systems and Computing 458, DOI 10.1007/978-981-10-2035-3_49
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sold in the market, they need severe monitoring and analysis. In spite of favorable economic figures, tea industry follows orthodox method of tea testing by human panel known as ‘Tea tasters’. The different varieties of tea are graded by tea taster based on color, taste and strength of the tea sample. However, this method is highly subjective with various human factors such as individual mental state, reduction of sensitivity due to infection, etc. Electronic nose and electronic tongue have the potential to classify tea samples in a more repetitive and accurate way. Mimicking the sense of smell led to the development of Electronic Nose [1] and electronic tongue [2] has been developed for measures and compares taste. Previously authors have worked with fuzzy based classifiers [3]. In this work, FCM is used to get the significant information from the sensor responses as extracted features for individual sensory systems and those features are used to the next level classifier. These features are in the form of membership function vectors. The membership function vectors are fed into Pseudo Outer-Product based Fuzzy Neural Network (POPFNN) [4, 5] which analyze features and classify black tea according to their gradation.
2 Experimentation Sensor responses of four different classes of tea samples obtained from electronic nose and electronic tongue are used for experimentation purpose, 12 samples for each class. Each sample consists of transient response from 5 sensors. Each transient response consists of 66 data points and 694 data points for electronic nose and electronic tongue, respectively. An array of five MOS sensors [6] has been used to developed electronics nose and a three electrode potentiostat system with the working electrodes made up of five different noble metals are used to set up the electronic tongue system. A Platinum and an Ag/AgCl act as counter and reference electrode, respectively. Details description of sensor used in electronic nose and tongue are given in [7, 8].
3 Data Analysis Data analysis from electronic nose and tongue is difficult because of innumerable compound present in the tea. These data can be analyzed by various pattern recognition models. In this work, POPFNN model is used with certain modifications as per our datasets along with other three classifying techniques. Among these four, POPFNN gives the best result compared to the fuzzy and Neural Network based model.
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POPFNN Model Framework
Hybrid, POPFNN is a combination of neural networks and fuzzy systems. So it has the ability to eliminate the imperfections of the both techniques. POPFNN uses a self-organizing algorithm to learn and initialize the membership function of the input and output variables from a set of training data. The similar details of POPFNN used in this work are described in [9]. It is actually a Multi-Input Multi-Output (MIMO) system with a five-layer neural Network. As defuzzification layer is not required for our experiment so, a four layer POPFNN is shown in Fig. 1. Each layer in POPFNN performs a particular fuzzy operation. The number of neurons in the condition and the rule-base layers are defined in as: l1
l 2 = ∑ Ji i=1 l1
l 3 = ∏ Ji i=1
where, Ji is the numeric labels for the ith input, l1 is the number of inputs, l2 is the number of neurons in the condition layer, and l3 is the number of rules neurons, An overall description of each layer of POPFNN is given as follows: Input Layer: First layer is called input layer where sensor responses from nose and tongue are given to the condition layer. Sensor responses from nose and tongue are fuzzified using FCM at the second layer. Net input: fiI = ni And Net output: OIi = fiI Where: ni = value of the ith input Condition Layer: FCM is used in the first process to obtain the fuzzy information in the form of membership function vectors at the condition layer. The FCM [10] sensor 1
M1
RB1
sensor 2
M2
RB2
M3
RB3
CLASS 1
CLASS 2
sensor 5
Mi
CLASS 3 RBn CLASS 4
Input layer
Condition layer
Fig. 1 Proposed POPFNN model
Rule base
Consequence layer
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employs fuzzy partitioning such that a data point can belong to all groups with different membership values. This iterative algorithm is used to find the cluster centers by minimizing following objective functions in Eq. 1: F S
2
Im = ∑ ∑ μm ij ni − sj , 1 ≤ m < ∞
ð1Þ
i=1 j=1
where, m is the any real number greater than 1 which is weighting coefficient denoting the fuzziness of the cluster, μij is the degree of membership of ni in the cluster j, ni is
the i-th
of d-dimensional measured data, sj is the d-dimension cluster
center, and ni − sj denotes the Euclidian distance between ni and sj. Fuzzy partitioning is accomplished via an iterative optimization process of the objective function shown in above equation, with the update of membership µij and the cluster center sj by the following equations: μij =
S
∑
1 kni − sj k
m 2− 1
ð2Þ
kni − sk k
k=1 F
∑ μm ij .ni
sj =
i=1 F
ð3Þ
∑ μm ij
i=1
where, F number of samples and S is the number of classes. In the Fuzzy neural network as in Fig. 1, M1 to Mi is the membership values of classes of the electronic nose or electronic tongue respectively with respect to their cluster centers. Net output: OIIk = max μ½u × v½w × x ∀½u × v½w × x ; 1 ≥ u, v ≥ 4 and u, v ∈ I
where, μ½u × v½w × x = Membership value from FCM [u × v] = [no. of cluster center × no. of class] [w × x] = [data reading × no. of sample] I = each element of a matrix containing membership values from condition layer. Rule base Layer: The next layer is to create the fuzzy rule base for four classes. By retaining the rules with highest membership value, the rule base is formed. For each class with respect to their cluster center, highest value is determined from its membership values. Net input:
fkIII = μII½u × v½w × x
For individual sensory system rule base is formed by multiplying the maximum value of each element of every row of OIII k . For each class with respect to their
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cluster center we get four rule bases. Rule bases from training are compared with unknown sample at Consequence Layer for classification purpose. Four rule bases are formed using POP (Pseudo Outer Product) learning method during training: RBn = multiplying the maximum values of every rows of OIII k matrix form rule base with respect to 4 cluster center. where n = number of rule base and n varies from 1 to 4. For combined sensory system we multiply two highest membership value of same class of nose and tongue. II Net Combined output: OIII kC = max μN½u × v
and u, v ∈ I where, μIIN½u × v
½w × x
½w × x
× max μIIT½u × v
½y × z
;
= output of the Condition Layer node that forms the antecedent
conditions for the nose response. = output of the Condition Layer node that forms the antecedent μIIT½u × v ½y × z
conditions for the tongue response. 4 combined rule base is formed using same principle which is applied to form rule base for individual sensory system during training. Consequence Layer: classification is done at Consequence Layer. Test sample should test simultaneously through both the sensor systems and for each individual sensor system rule bases are generated from the proposed model of Fig. 1 and fused rule base should compare with training model rule base with the help of Eq. (4). Classification rate = ðDegree of Testing − Degree of TrainingÞ ̸ Degree of TrainingÞ × 100
ð4Þ In our system, POPFNN has two fundamental modes. First is learning mode and second is classification mode. Black tea samples are used to train the POPFNN during learning mode. After training, membership function vectors (feature vectors) are extracted. These feature vectors are then used to initialize and adjust the parameter in POPFNN. Similarly during testing, feature vectors are extracted. Degrees are calculated from both training and testing data set by applying proper rule base technique. Procedure to classify samples is explained in Table 1.
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Fuzzy Model Framework
For solving the problems based on pattern recognition Fuzzy framework described by Wang and Mendel [11] has been favored choice for the researchers. This fuzzy classifier model is a MIMO model framework which adopts the Takagi-Sugeno model where the antecedents are in the form of fuzzy and consequent is in crisp
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Table 1 POPFNN algorithm
Step 1: Input (Input layer) Sensor responses are given as input Step 2: Minimize the objective function for S number of classes and F number of samples (Condition layer) Step 2a: Randomly initialize the membership function Step 2b: Calculate the cluster center Step 2c: Updating the membership function with the help of cluster center Step 2d: Evaluate the objective function Step 3: Rule base is formed using POP learning method (Rule base layer) Step 3a: Weight of the highest membership values is determined for each class with respect to their cluster center Step 3b: Degree is calculated by multiplying the maximum values of every rows of OIII k matrix Step 4: Rule comparison and classification rate declaration (Consequence layer) Step 4a: Degree of training and testing is obtained from sensor responses for both known and unknown tea samples by following the steps 1 to 3 Step 4b: Classification rate calculated by Eq. (4)
form. Wang Mendel method is applied on data from electronic nose and tongue for generating preliminary rules. Details of this work are described in [3].
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Neural Network Framework with BP-MLP Topology
A three-layer BP-MLP model [12] with one input layer, one hidden layer and one output layer has been used in this work. Input layer has five nodes denotes transient response from five sensors. Only one hidden layer has been considered which has eight nodes and the number of output nodes is four as four different class of tea has been consider for correlation study.
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Fuzzy Neural Network Framework (FNN)
This FNN model has four layers. First layer is called input layer which have five nodes. Second layer is used for generating membership function from sensors responses. Next layer represents BP-MLP neural network which is used for realizing fuzzy rules. Output layer is the last layer which gives the gradation of tea. Details of this work are described in [3].
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Table 2 Comparative results of POPFNN classifier for different sensory systems Trials
Electronic nose
Electronic tongue
Combined sensory system
1 2 3 4 5 6 7 8 9 10 Average
83.69 86.34 88.42 74.33 82.38 89.50 88.34 90.12 82.67 81.16 84.69
87.82 85.42 83.24 88.75 80.60 85.71 92.32 89.04 74.78 83.70 85.14
95.44 94.80 95.77 97.05 98.05 93.14 96.41 95.51 92.18 91.94 95.03
4 Results Data analysis on 48 tea samples of four different class of tea of electronic nose and electronic tongue has been performed by fuzzy neural network analyses. Out of the total data set, 60 % of the data have been used for training, and the rest of 40 % were used as testing set. The summary of POPFNN results is given in Table 2. Result shows that, for POPFNN classifier, the electronic tongue can classify better to a small degree than electronic nose. Average results from POPFNN are compared with other models of electronics nose, electronic tongue and combined sensory system which is shown in Table 3. Study shows, our new improved fuzzy neural network model gives better assessment for tea quality analysis. Results also show that combined sensory response is quite improved than individual sensory system. So, this fast, accurate and effective fuzzy neural classifier has the ability to classify tea quality more precisely.
Table 3 Comparative results of classification rate between Fuzzy, neural network, FNN and POPFNN model for different sensory systems Sensory system
Electronic nose Electronic tongue Combination of electronic nose and electronic tongue
Average classification rate (in %) Fuzzy Neural FNN network
POPFNN
74.60 79.41 85.33
84.69 85.14 95.03
75.14 77.34 79.40
76.52 81.61 89.50
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5 Conclusion In this paper one fuzzy based model, one neural network model and two topologies of fuzzy neural networks based models are used to predict the tea quality. Four models are compared. It has been observed that fuzzy neural networks have ability to overcome the weakness of fuzzy and neural network model. But, this new POPFNN system outperformed all other models. Overall, the classifiers which are described here have the ability to assess the aroma, flavor and taste quality of black tea. This technique can be used for other similar applications also.
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