Antioxidant activity prediction and classification of some teas using artificial neural networks

Cimpoiu, Claudia; Cristea, Mircea Vasile; Hosu, Anamaria; Sandru, Mihaela; Seserman, Liana

doi:10.1016/j.foodchem.2011.01.091

Cited by 73 publications

(38 citation statements)

References 28 publications

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“…There are hundreds of articles published in prestigious scientific magazines stating successful applications of ANNs that have been used for about seventy years, especially in the electrics, electronics, chemistry, production, robotics, material sciences [21][22][23], the economy [24], physical metallurgy, automotive, defense and telecommunication [25] areas.…”

Section: Annsmentioning

confidence: 99%

Approximate and Three-Dimensional Modeling of Brightness Levels in Interior Spaces by Using Artificial Neural Networks

Şahin¹,

Oğuz²,

Büyüktümtürk³

2015

Journal of Electrical Engineering and Technology

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-In this study, artificial neural networks were used to determine the intensity of brightness in interior spaces. The illumination elements to illuminate indoor spaces were considered, not individually, but as a system. So, during the planned maintenance periods of an illumination system, after its design and installation, simple brightness level measurements must be taken. For a threedimensional evaluation of the brightness level in indoor spaces in a speedy and accurate manner, the obtained brightness level measurement results and artificial neural network model were used. Upon estimation of the most suitable brightness level for indoor spaces by using the artificial neutral network model, the energy demands required by the illumination elements decreased. Consequently, in this study, with estimations of brightness levels, the extent to which the artificial neutral networks become successful was observed and more correct results have been obtained in terms of both economy and usage.

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Section: Annsmentioning

confidence: 99%

Approximate and Three-Dimensional Modeling of Brightness Levels in Interior Spaces by Using Artificial Neural Networks

Şahin¹,

Oğuz²,

Büyüktümtürk³

2015

Journal of Electrical Engineering and Technology

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“…Cimpoiu et al [146] used the multi-layer perceptron with the back-propagation algorithm to model the antioxidant activity of some classes of tea such as black, express black and green teas. The authors obtained a correlation of 99.9% between experimental and predicted antioxidant activity.…”

Section: Food Researchmentioning

confidence: 99%

Applications of Artificial Neural Networks in Chemical Problems

Matarollo¹,

Honório²,

Silva³

2013

Artificial Neural Networks - Architectures and Applications

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“…Until now, NIR spectroscopy research on agricultural products has been applied to the internal quality analysis of fruits and vegetables by using multivariate analysis methods, such as PLSR (partial least square regression), PCR (principal component regression), and MLR (multiple linear regression) [13][14][15][16][17][18]. In addition, during the 2000s, artificial neural network (ANN) techniques have been steadily applied to the internal quality analysis of food and agricultural products with visible/NIR spectrum analysis, and various kinds of research have been conducted on production date range distinction of milk products and component analysis and quality prediction for fruit juice, wine, and so forth [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Rancidity Estimation of Perilla Seed Oil by Using Near-Infrared Spectroscopy and Multivariate Analysis Techniques

Hong

Rho

Lee

et al. 2017

Journal of Spectroscopy

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Near-infrared spectroscopy and multivariate analysis techniques were employed to nondestructively evaluate the rancidity of perilla seed oil by developing prediction models for the acid and peroxide values. The acid, peroxide value, and transmittance spectra of perilla seed oil stored in two different environments for 96 and 144 h were obtained and used to develop prediction models for different storage conditions and time periods. Preprocessing methods were applied to the transmittance spectra of perilla seed oil, and multivariate analysis techniques, such as principal component regression (PCR), partial least squares regression (PLSR), and artificial neural network (ANN) modeling, were employed to develop the models. Titration analysis shows that the free fatty acids in an oil oxidation process were more affected by relative humidity than temperature, whereas peroxides in an oil oxidation process were more significantly affected by temperature than relative humidity for the two different environments in this study. Also, the prediction results of ANN models for both acid and peroxide values were the highest among the developed models. These results suggest that the proposed near-infrared spectroscopy technique with multivariate analysis can be used for the nondestructive evaluation of the rancidity of perilla seed oil, especially the acid and peroxide values.

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Antioxidant activity prediction and classification of some teas using artificial neural networks

Cited by 73 publications

References 28 publications

Approximate and Three-Dimensional Modeling of Brightness Levels in Interior Spaces by Using Artificial Neural Networks

Approximate and Three-Dimensional Modeling of Brightness Levels in Interior Spaces by Using Artificial Neural Networks

Applications of Artificial Neural Networks in Chemical Problems

Rancidity Estimation of Perilla Seed Oil by Using Near-Infrared Spectroscopy and Multivariate Analysis Techniques

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