Determination of tea quality by using a neural network based electronic nose

In this work, the capacity of an electronic nose (E-nose, PEN2) to classify tea quality grades is investigated. Three tea groups with different quality grades were harvested at different times. Principal component analysis (PCA) and artificial neural network (ANN) were applied to identify the different tea samples. PCA provided perfect classification of tea quality grades. In the analysis of age, six groups of XinyangMaojian green tea were distinguished completely by PCA. The results of ANN analysis gave a high percentage of correct discrimination of green tea samples. The correct identification rates of the training and testing data were 98.6% and 83%, respectively, for three grades of green tea samples harvested in 2009. The correct identification rates of the training and testing data were 100% and 87.8%, respectively, for three grades of green tea samples harvested in 2010. In the analysis of age, the correct discrimination percentages for six groups of XinyangMaojian green tea were 99.4% and 88.9% for training and testing data, respectively. These results indicate that the electronic nose could be successfully used for the detection of teas of different quality grades and ages.

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Electronic Nose Application for the Determination of Penicillin G in Saanen Goat Milk with Fisher Discriminate and Multilayer Perceptron Neural Network Analyses

Journal of Food Processing and Preservation ◽

10.1111/jfpp.12305 ◽

2014 ◽

Vol 39 (6) ◽

pp. 927-932 ◽

Cited By ~ 4

Author(s):

Wu Ding ◽

Yao Zhang ◽

Liping Kou ◽

Wayne M. Jurick

Keyword(s):

Neural Network ◽

Electronic Nose ◽

Multilayer Perceptron ◽

Goat Milk ◽

Penicillin G ◽

Saanen Goat ◽

Network Analyses

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Method of determination of the text direction on the image with the use of convolutional neural network

Informatization and communication ◽

10.34219/2078-8320-2020-11-2-96-99 ◽

2020 ◽

pp. 96-99

Author(s):

P.L. Nikolaev

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Binary Classification ◽

Synthetic Data ◽

Real Data ◽

Method Of Determination ◽

Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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Determination of Age, Gender, Dress Color and Type of a Person by Convolutional Neural Network (CNN)

SSRN Electronic Journal ◽

10.2139/ssrn.3601508 ◽

2020 ◽

Author(s):

CSN Koushik ◽

Shruti Bhargava Choubey ◽

Abhishek Choubey ◽

D. Naresh ◽

N. Bhanu Prakash Reddy

Keyword(s):

Neural Network ◽

Convolutional Neural Network

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Operational Determination of the Activated Sludge Process Using Neural Networks

Water Science & Technology ◽

10.2166/wst.1992.0762 ◽

1992 ◽

Vol 26 (9-11) ◽

pp. 2461-2464 ◽

Cited By ~ 2

Author(s):

R. D. Tyagi ◽

Y. G. Du

Keyword(s):

Neural Network ◽

Neural Networks ◽

Steady State ◽

Activated Sludge ◽

Feedforward Neural Network ◽

Training Data ◽

Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

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