Optimized Neural Network for Instant Coffee Classification through an Electronic Nose

Flavor is one of the most important features of food, especially of coffee. The evaluation of this sensory feature is complex yet indispensable in quality control of instant coffees. In this work, an artificial neural network (ANN) was developed for instant coffee classification based on an electronic nose (EN) aroma profile. To this purpose, a hybrid algorithm was developed, containing: bootstrap resample methodology; factorial design and sequential simplex optimization to tune network parameters; an ensemble multilayer perceptron (MLP) trained with backpropagation for coffee classification; and causal index procedure for knowledge extraction from the trained ANN. The produced neural network classifier correctly recognizes 100% of coffees studied. Furthermore, the causal index employment allowed inference of some rules on how the coffees were separated according to the sensors available in EN. The results indicate that the applied methodology is a promising tool for instant coffee quality control.

Download Full-text

Classification of instant coffee odors by electronic nose toward quality control of production

2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology ◽

10.1109/ecticon.2013.6559482 ◽

2013 ◽

Cited By ~ 5

Author(s):

Treenet Thepudom ◽

Nuttawoot Sricharoenchai ◽

Teerakiat Kerdcharoen

Keyword(s):

Quality Control ◽

Electronic Nose ◽

Instant Coffee

Download Full-text

Classification of Agarwood Oil Using an Electronic Nose

Sensors ◽

10.3390/s100504675 ◽

2010 ◽

Vol 10 (5) ◽

pp. 4675-4685 ◽

Cited By ~ 25

Author(s):

Wahyu Hidayat ◽

Ali Yeon Md. Shakaff ◽

Mohd Noor Ahmad ◽

Abdul Hamid Adom

Keyword(s):

Neural Network ◽

Electronic Nose ◽

Principal Component ◽

Hierarchical Cluster ◽

Scatter Plot ◽

Different Types ◽

Artificial Neural Network Ann ◽

Distinct Separation ◽

Agarwood Oil

Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.

Download Full-text

Prediction of Tensile Properties of CNF/Epoxy Composites Using Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.898.111 ◽

2014 ◽

Vol 898 ◽

pp. 111-114

Author(s):

Xing Kai Chen

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Tensile Properties ◽

Back Propagation ◽

Epoxy Composites ◽

Ann Model ◽

Mechanical Stirring ◽

Promising Tool ◽

Artificial Neural ◽

Artificial Neural Network Ann

In present study, artificial neural network (ANN) was used to predict the tensile moduli of carbon nanofibers (CNF)/epoxy composites. The tensile properties of CNF/epoxy composites made by different dispersion technique were measured by tensile test. It was found that the tensile properties are seriously affected by the CNF fraction, ultrasonication time and mechanical stirring time. According to the test results, ANN was trained and used to predict the tensile moduli of CNF/epoxy composites. By compared the predicted values with the experimental data, it was demonstrated that the back propagation ANN model is a promising tool for prediction of properties of composites.

Download Full-text

A Computer-Aided Diagnosis System for Lung Cancer Detection with Automatic Region Growing, Multistage Feature Selection and Neural Network Classifier

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a1081.1191s19 ◽

2019 ◽

Vol 9 (1S) ◽

pp. 409-413

Keyword(s):

Neural Network ◽

Cancer Detection ◽

Region Growing ◽

Calcium Deposition ◽

Lung Nodules ◽

Neural Network Classifier ◽

Artificial Neural Network Ann ◽

Sensitivity Specificity ◽

Lung Cancer Detection ◽

Aided Diagnosis

An effective automatic region growing was developed in this work for the segmentation of suspected lung nodules from the Computed Tomography (CT) lung images. After the segmentation of the suspected lung nodules the eccentricity and area features were calculated to eliminate line like structures and tiny clusters below 3mm. The centroid analysis, contrast, autocorrelation and homogeneity features were extracted for the suspected lung nodules. The extracted features were trained and tested with Artificial Neural Network (ANN) to remove the blood vessels and calcifications (calcium deposition in the lungs). This work was carried out on 106 patients images retrospectively collected from Bharat Scans, Chennai, which had 56 cancerous nodules and 745 non-cancerous nodules (size greater than 3 mm). The proposed work yielded sensitivity, specificity and accuracy of 100%, 93% and 94%, respectively.

Download Full-text

Detection of XinyangMaojian Green Tea Quality and Age by Electronic Nose

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.239-242.2096 ◽

2011 ◽

Vol 239-242 ◽

pp. 2096-2100 ◽

Cited By ~ 3

Author(s):

Hong Mei Zhang ◽

Ming Xun Chang ◽

Yong Chang Yu ◽

Hui Tian ◽

Yu Qing He ◽

...

Keyword(s):

Neural Network ◽

Green Tea ◽

Electronic Nose ◽

Principal Component ◽

Correct Identification ◽

Tea Quality ◽

Testing Data ◽

Artificial Neural Network Ann ◽

Correct Discrimination

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.

Download Full-text

Evaluation of Coffee Roasting Degree by Using Electronic Nose and Artificial Neural Network for Off-line Quality Control

Journal of Food Science ◽

10.1111/j.1750-3841.2012.02851.x ◽

2012 ◽

Vol 77 (9) ◽

pp. C960-C965 ◽

Cited By ~ 25

Author(s):

Santina Romani ◽

Chiara Cevoli ◽

Angelo Fabbri ◽

Laura Alessandrini ◽

Marco Dalla Rosa

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Quality Control ◽

Electronic Nose ◽

Artificial Neural ◽

Coffee Roasting

Download Full-text

Determining degree of roasting in cocoa beans by artificial neural network (ANN)-based electronic nose system and gas chromatography/mass spectrometry (GC/MS)

Journal of the Science of Food and Agriculture ◽

10.1002/jsfa.8901 ◽

2018 ◽

Vol 98 (10) ◽

pp. 3851-3859 ◽

Cited By ~ 14

Author(s):

Juzhong Tan ◽

William L Kerr

Keyword(s):

Neural Network ◽

Mass Spectrometry ◽

Artificial Neural Network ◽

Gas Chromatography ◽

Electronic Nose ◽

Gas Chromatography Mass Spectrometry ◽

Cocoa Beans ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Electronic Nose System

Download Full-text

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

Methods of Information in Medicine ◽

10.1055/s-0038-1636858 ◽

1997 ◽

Vol 36 (04/05) ◽

pp. 349-351

Author(s):

H. Mizuta ◽

K. Kawachi ◽

H. Yoshida ◽

K. Iida ◽

Y. Okubo ◽

...

Keyword(s):

Neural Network ◽

Decision Support ◽

Psychiatric Diagnosis ◽

Psychiatric Patients ◽

Bayesian Classifier ◽

Neural Network Classifier ◽

Correct Decision ◽

Neurotic Disorders ◽

The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

Download Full-text