Soleimanifard, Sediqeh, Jahanbakhshian, Nafiseh, Niknia, Somayeh (2025). Prediction of Cake Texture during Conventional Baking Based on AdaBoost Algorithm. , 27(5), 1031-1042. doi: 10.48311/jast.2025.16814
Sediqeh Soleimanifard; Nafiseh Jahanbakhshian; Somayeh Niknia. "Prediction of Cake Texture during Conventional Baking Based on AdaBoost Algorithm". , 27, 5, 2025, 1031-1042. doi: 10.48311/jast.2025.16814
Soleimanifard, Sediqeh, Jahanbakhshian, Nafiseh, Niknia, Somayeh (2025). 'Prediction of Cake Texture during Conventional Baking Based on AdaBoost Algorithm', , 27(5), pp. 1031-1042. doi: 10.48311/jast.2025.16814
Soleimanifard, Sediqeh, Jahanbakhshian, Nafiseh, Niknia, Somayeh Prediction of Cake Texture during Conventional Baking Based on AdaBoost Algorithm. , 2025; 27(5): 1031-1042. doi: 10.48311/jast.2025.16814

Prediction of Cake Texture during Conventional Baking Based on AdaBoost Algorithm

Journal of Agricultural Science and Technology
Article 5, Volume 27, Issue 5, July and August 2025, Pages 1031-1042    PDF (1.19 M)
Document Type: Original Research
DOI: 10.48311/jast.2025.16814
Authors
Sediqeh Soleimanifard* 1; Nafiseh Jahanbakhshian2; Somayeh Niknia1
1Department of Food Science and Technology, College of Agriculture, University of Zabol, Zabol, Islamic Republic of Iran.
2Department of Food Science and Technology, Shk. C., Islamic Azad University, Shahrekord, Islamic Republic of Iran.
Abstract
The present study investigates the effect of baking temperatures (140, 160, 180, 200, and 220℃) on texture kinetics. It also explores a statistical classification meta-algorithm, called Adaptive Boosting (AdaBoost), to predict texture changes during conventional cake baking. The experimental results indicated that texture properties were significantly affected by baking temperature and time. As time and temperature increased, there was an increase in hardness, cohesiveness, gumminess, and chewiness and a decrease in springiness. However, the impact of time and temperature on resilience was inconsistent, as it was maximum in the last quarter of the process. The predicted results revealed that the AdaBoost algorithm accurately predicted the texture properties with a high coefficient of determination (R2 > 0.989) and minimal root mean square error (RMSE < 0.0019) across all textural properties. Therefore, it can serve as an efficient tool for predicting the texture properties of cakes during baking. Furthermore, the proposed methodology can be extended to predict the texture properties of other baked goods.
Keywords
Adaptive Boosting; Conventional baking; Machine learning; Texture profile analysis
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