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Journals / Gazi University Journal of Science Part A: Engineering and Innovation / 2021 / Cilt: 8 - Sayı: 4

Data Mining and Application of Decision Tree Modelling on Electrochemical Data Used for Damaged Starch Detection

Niyazi Alper TAPAN
Nilüfer YILDIRIM
Pages
435–450
Publication Date
2022-10-19
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Therefore machine learning with decision tree modelling could open perspectives for practical and fast prediction of damaged starch ratio which would help food industry to speed up and economize costs for analysis in flour.

📌 3 Key Takeaways

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Objective & Hypothesis:In this study, unsupervised and supervised machine learning techniques, principal component analysis and classification tree modelling which could be improved with additional input variables were applied on iodine oxidation voltammetric data in order to determine routes and extract information about the electrochemical conditions leading to different damaged starch ratios in flour.
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Key Findings & Data:For this purpose a database of 3542 observations which was normalized and filtered from outliers was used.
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Conclusion & Impact:Therefore machine learning with decision tree modelling could open perspectives for practical and fast prediction of damaged starch ratio which would help food industry to speed up and economize costs for analysis in flour.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Decision Tree · Damaged Starch · Electrochemical

Abstract

In this study, unsupervised and supervised machine learning techniques, principal component analysis and classification tree modelling which could be improved with additional input variables were applied on iodine oxidation voltammetric data in order to determine routes and extract information about the electrochemical conditions leading to different damaged starch ratios in flour. For this purpose a database of 3542 observations which was normalized and filtered from outliers was used. It was seen that although it was almost impossible to generalize information or determine correlations from voltammetric data at different conditions, principal component analysis indicate that on platinum electrode UCD values of 16.5 mostly seen at high potentials, optimized decision tree indicate that the impact of variables on UCD values can be ordered as current density > potential > electrode type > KI concentration and give routes to UCD values with high class membership leaf nodes. Therefore machine learning with decision tree modelling could open perspectives for practical and fast prediction of damaged starch ratio which would help food industry to speed up and economize costs for analysis in flour.

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Cite This Article

TAPAN, N. A., YILDIRIM, N. (2022). Data Mining and Application of Decision Tree Modelling on Electrochemical Data Used for Damaged Starch Detection. Gazi University Journal of Science Part A: Engineering and Innovation, Cilt: 8 - Sayı: 4, 435–450. https://ajindex.com/articles/521497

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References (4)

  1. Baysal, M., Günay, M. E., & Yıldırım, R. (2017). Decision tree analysis of past publications on catalytic steam reforming to develop heuristics for performance: A statistical review. International Journal of Hydrogen Energy, 42(1), 243-254. doi:10.1016/j.ijhydene.2016.10.003 Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Chapman and Hall/CRC. doi:10.1201/9781315139470 Boschloo, G., & Hagfeldt, A. (2009). Characteristics of the Iodide/Triiodide Redox Mediator in Dye-Sensitized Solar Cells. Accounts of Chemical Research, 42(11), 1819-1826. doi:10.1021/ar900138m Comon, P. (1994).
  2. Independent component analysis, A new concept?. Signal Processing, 36(3), 287-314. doi:10.1016/0165-1684(94)90029-9
  3. Dhital, S., Shrestha, A. K., Flanagan, B. M., Hasjim, J., & Gidley, M. J. (2011). Cryo-milling of starch granules leads to differential effects on molecular size and conformation. Carbohydrate Polymers, 84(3), 1133-1140. doi:10.1016/j.carbpol.2011.01.002 Günay, M. E., Türker, L., & Tapan, N. A. (2018). Decision tree analysis for efficient CO2 utilization in electrochemical systems. Journal of CO2 Utilization, 28, 83-95. doi:10.1016/j.jcou.2018.09.011 Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). Elsevier. Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374, 20150202. doi:10.1098/rsta.2015.0202 Larose, D. T., & Larose, C. D. (2014). Discovering Knowledge in Data: An Introduction to Data Mining (2nd ed.). John Wiley & Sons. Li, M., Hasjim, J., Xie, F., Halley, P. J., & Gilbert, R. G. (2014). Shear degradation of molecular, crystalline, and granular structures of starch during extrusion. Starch‐Stärke, 66(7-8), 595-605. doi:10.1002/star.201300201 Liu, W-C., Halley, P. J., & Gilbert, R. G. (2010). Mechanism of Degradation of Starch, a Highly Branched Polymer, during Extrusion. Macromolecules, 43(6), 2855-2864. doi:10.1021/ma100067x Liu, X., Xiao, X., Liu, P., Yu, L., Li, M., Zhou, S., & Xie, F. (2017). Shear degradation of corn starches with different amylose contents. Food Hydrocolloids, 66, 199-205. doi:10.1016/j.foodhyd.2016.11.023 Medcalf, D. G., & Gilles, K. A. (1965). Determination of Starch Damaged by Rate of Iodine Absorption. Cereal Chemistry, 42, 546-557.
  4. Myles, A. J., Feudale, R. N, Liu, Y., Woody, N. A., & Brown,