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Artificial Neural Network and Regression Models to Evaluate Rheological Properties of Selected Brazilian Honeys Cover

Artificial Neural Network and Regression Models to Evaluate Rheological Properties of Selected Brazilian Honeys

Open Access
|Nov 2020

Figures & Tables

Fig. 1

RMSE of training (solid line) and test (dotted line) sets versus number of iterations for optimum MLP ANN: a) model 1; b) model 2; c) model 3; d) model 4.
RMSE of training (solid line) and test (dotted line) sets versus number of iterations for optimum MLP ANN: a) model 1; b) model 2; c) model 3; d) model 4.

Statistical Indexes of Input and Output data in the training and test process of Multilayer Perceptron Feedforward Neural Network

ModelTraining dataTest dataTotal data
MeanSTDMinMaxMeanSTDMinMax
1(*)InputsWC (%)15.781.0214.2318.8615.781.0314.2318.86320
T (°C)30.9015.8810.0060.0032.3117.8210.0060.00
Outputsη (Pa.s)24.9236.910.37225.2727.1738.730.23177.99
2(*)InputsWC (%)15.821.0314.2318.8615.750.9914.2318.86800
T (°C)39.2821.423.6774.5739.2221.833.6174.48
OutputsG′ (Pa)1.273.560.0036.671.182.760.0020.82
G″ (Pa)299.97744.950.926174.37308.74676.121.434571.52
η* (Pa.s)47.74118.560.15982.7049.14107.610.23727.59
3(*)InputsWC (%)15.801.0314.2318.8615.730.9914.2318.86800
T (°C)35.9621.350.5571.4335.9521.710.5771.42
G′ (Pa)28.2970.770.00772.1322.8548.720.00354.15
OutputsG″(Pa)493.781169.190.838455.12530.611282.021.028398.52
η* (Pa.s)78.93186.420.131346.1484.66204.160.161336.82
4(**)WC (%)15.781.0214.2318.8615.791.0214.2318.864160
InputsT (°C)31.0816.2410.0060.0031.7616.6710.0060.00
F (Hz)2.422.950.1010.002.342.890.1010.00
G′ (Pa)2.428.290.00233.522.297.140.00151.11
OutputsG″ (Pa)407.861081.780.1816404.51405.201052.590.239751.40
η* (Pa.s)27.7440.980.28268.3226.9439.960.28266.10

RMSE and correlation coefficient (r) of model 4 variables from the development and test process of a multiple-second order polynomial regression

VariableModel Coefficient1Training dataTest data
β0β1β2β3β12β13β23β123β11β22β33RMSErRMSEr
G′ (Pa)-0.01−0.040.10−0.02−0.17−0.170.26-0.050.060.02950.61890.01700.6932
G″ (Pa)0.03−0.03−0.200.38-−0.40−0.510.560.040.20-0.03620.85030.02930.8539
η* (Pa.s)0.49−0.53−1.29-0.52---0.160.81-0.06800.89640.06400.9018

RMSE and correlation coefficient (r) of models 1, 2 and 3 variables from the development and test process of a nonlinear exponential and of models 1, 2, 3 and 4 from the best ANNs models

ModelEstimated variableEmpirical constants1Exponential Model (Training data)Exponential Model (Test data)ANN (Training)ANN (Test)
ABCRMSErRMSErRMSErRMSEr
1η (Pa.s)0.7992.0366.4150.01010.99810.04330.97000.03590.97600.04300.9681
G′ (Pa)0.8053.30214.6960.03430.93560.02900.92930.03380.93980.02610.9390
2G″ (Pa)0.8812.54910.9530.02820.97250.02720.96880.02960.97040.02520.9731
η* (Pa.s)0.8812.54910.9530.02820.97250.02720.96880.02950.97050.02520.9731
G′ (Pa)0.4892.6288.5420.06590.69480.05210.70550.06750.69690.04860.6629
3G″ (Pa)0.9111.68412.5490.03130.97420.03200.97790.03080.97580.03260.9794
η* (Pa.s)0.9131.68712.5260.03140.97420.03200.97790.03090.97590.03260.9794
G′ (Pa)-------0.02600.72440.01580.7301
4G″ (Pa)-------0.01950.96040.01760.9581
η* (Pa.s)-------0.04200.96360.04060.9647
DOI: https://doi.org/10.2478/jas-2020-0017 | Journal eISSN: 2299-4831 | Journal ISSN: 1643-4439
Language: English
Page range: 219 - 228
Submitted on: Jul 12, 2019
Accepted on: May 8, 2020
Published on: Nov 7, 2020
Published by: Research Institute of Horticulture
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2020 Vanelle M. D. Silva, Wilian S. Lacerda, Jaime V. de Resende, published by Research Institute of Horticulture
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.