Prediction of Cake Texture during Conventional Baking Based on AdaBoost Algorithm

Document Type : Original Research

Authors
S. Soleimanifard 1
N. Jahanbakhshian 2
S. Niknia 1
1 Department of Food Science and Technology, College of Agriculture, University of Zabol, Zabol, Islamic Republic of Iran.
2 Department of Food Science and Technology, Islamic Azad University, Shahrekord Branch, Shahrekord, Islamic Republic of Iran.
10.48311/jast.2025.16814
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

Subjects

1. Abbasi, H., Ardabili, S. M. S., Emam-Djomeh, Z., Mohammadifar, M. A., Zekri, M., and Aghagholizadeh, R. 2012. Prediction of extensograph properties of wheat-flour dough: artificial neural networks and a genetic algorithm approach. J. Texture Stud., 43(4): 326–337.
2. Ahmad, I., Jeenanunta, C., Chanvarasuth, P., and Komolavanij, S. 2014. Prediction of physical quality parameters of frozen shrimp (Litopenaeus vannamei): An artificial neural networks and genetic algorithm approach. Food Bioprocess Tech., 7(5): 1433–1444.
3. Al-Muhtaseb, A. H., McMinn, W., Megahey, E., Neill, G., Magee, R., and Rashid, U. 2013. Textural characteristics of microwave-baked and convective-baked Madeira cake. J. Food Process. Technol., 4(2): 209.
4. Bambil, D., Pistori, H., Bao, F., Weber, V., Alves, F. M., Gonçalves, E. G., de Alencar Figueiredo, L. F., Abreu, U. G. P., Arruda, R., and Bortolotto, I. M. 2020. Plant species identification using color learning resources, shape, texture, through machine learning and artificial neural networks. Environ. Syst. Decis., 40(4): 480–484.
5. Barzegar, H., Alizadeh Behbahani, B., Mirzaei, A., and Ghodsi Sheikhjan, M. 2024. Prediction of physicochemical and sensory parameters of coated lamb meat based on a novel edible coating. J. Food Meas. Charact., 18(3): 1664–1678. 7
6. Batista, L. F., Marques, C. S., dos Santos Pires, A. C., Minim, L. A., Soares, N. de F. F., and Vidigal, M. C. T. R. 2021b. Artificial neural networks modeling of non-fat yogurt texture properties: effect of process conditions and food composition. Food Bioprod. Process., 126: 164–174.
7. Bouysset, C., Belloir, C., Antonczak, S., Briand, L., and Fiorucci, S. 2020. Novel scaffold of natural compound eliciting sweet taste revealed by machine learning. Food Chem., 324: 126864.
8. Cauvain, S. P., aj. J. nd Young, L. S. 2009. Bakery food manufacture and quality: water control and effects. John Wiley and Sons.
9. Chen, Q., Hui, Z., Zhao, J., and Ouyang, Q. 2014. Evaluation of chicken freshness using a low-cost colorimetric sensor array with AdaBoost–OLDA classification algorithm. LWT - Food Sci. Technol., 57(2): 502–507.
10. Chuan, Y., Zhao, C., He, Z., and Wu, L. 2021. The Success of AdaBoost and Its Application in Portfolio Management. ArXiv Preprint ArXiv:2103.12345.
11. Clarke, C. I., and Farrell, G. M. 2000. The effects of recipe formulation on the textural characteristics of microwave‐reheated pizza bases. J. Sci. Food Agric., 80(8): 1237–1244.
12. Crispín-Isidro, G., Lobato-Calleros, C., Espinosa-Andrews, H., Alvarez-Ramirez, J., and Vernon-Carter, E. J. 2015. Effect of inulin and agave fructans addition on the rheological, microstructural and sensory properties of reduced-fat stirred yogurt. LWT - Food Sci. Technol., 62(1): 438–444.
13. Darnay, L., Králik, F., Oros, G., Koncz, Á., and Firtha, F. 2017. Monitoring the effect of transglutaminase in semi-hard cheese during ripening by hyperspectral imaging. J. Food Eng., 196: 123–129.
14. Das, L., Raychaudhuri, U., and Chakraborty, R. 2012. Effect of baking conditions on the physical properties of herbal bread using RSM. Int. j. Food agric. Vet. Sci., 2(2): 106–114.
15. Freund, Y., and Schapire, R. E. 1997. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. J. Comput. Syst. Sci., 55(1): 119–139.
16. Gaber, T., Tharwat, A., Hassanien, A. E., and Snasel, V. 2016. Biometric cattle identification approach based on weber’s local descriptor and adaboost classifier. Comput. Electron. Agric., 122: 55–66.
17. Gond, P., Lohani, U.C., Shahi, N.C. and Aman, J., 2023. Process standardization of infrared assisted pulsed microwave baked biscuits and its comparison with conventionally baked biscuits. J. Food Process Eng., 46(11): e14432.
18. Içöz, D., Sumnu, G., and Sahin, S. 2004. Color and texture development during microwave and conventional baking of breads. Int. J. Food Prop., 7(2): 201–213.
19. Jiang, X., Bu, Y., Han, L., Tian, J., Hu, X., Zhang, X., Huang, D., and Luo, H. 2023. Rapid nondestructive detecting of wheat varieties and mixing ratio by combining hyperspectral imaging and ensemble learning. Food Control, 150: 109740.
20. Khan, M. I., Acharya, B., and Chaurasiya, R. K. 2022. Automatic Prediction of Glycemic Index Category from Food Images Using Machine Learning Approaches. Arab. J. Sci. Eng., 47: 10823–10846.
21. Khawas, P., Dash, K. K., Das, A. J., and Deka, S. C. 2016. Modeling and optimization of the process parameters in vacuum drying of culinary banana (Musa ABB) slices by application of artificial neural network and genetic algorithm. Dry. Technol., 34(4): 491–503.
22. Kuang, J., Luo, N., Hao, Z., Xu, J., He, X., and Shi, J. 2022. NI-Raman spectroscopy combined with BP-Adaboost neural network for adulteration detection of soybean oil in camellia oil. J. Food Meas. Charact., 1–8.
23. Kumari, A., Eljeeva Emerald, F. M., Simha, V., and Pushpadass, H. A. 2015. Effects of baking conditions on colour, texture and crumb grain characteristics of C hhana P odo. Int. J. Dairy Technol., 68(2): 270–280.
24. Lee, D., Jeong, S., Yun, S., and Lee, S. 2024. Artificial intelligence-based prediction of the rheological properties of hydrocolloids for plant-based meat analogues. J. Sci. Food Agric., 104(9): 5114–5123.
25. Li, Y., and Li, Y. 2020. Study of merchant adoption in mobile payment system based on ensemble learning. Am. J. Ind. Bus. Manag., 10(5): 861.
26. Lin, G., and Zou, X. 2018. Citrus Segmentation for Automatic Harvester Combined with AdaBoost Classifier and Leung-Malik Filter Bank. IFAC-PapersOnLine, 51(17): 379–383.
27. Lin, H., Song, X., Dai, F., Zhang, F., Xie, Q., and Chen, H. 2024. Research on machine learning models for maize hardness prediction based on indentation test. J. Agric.14(2): 224.
28. Lostie, M., Peczalski, R., Andrieu, J., and Laurent, M. 2002. Study of sponge cake batter baking process. Part I: Experimental data. J. Food Eng., 51(2): 131–137.
29. Matos, M. E., and Rosell, C. M. 2012. Relationship between instrumental parameters and sensory characteristics in gluten-free breads. Eur. Food Res. Technol., 235(1): 107–117.
30. Meng, X., Zhang, M., and Adhikari, B. 2012. Prediction of storage quality of fresh-cut green peppers using artificial neural network. Int. J. Food Sci. Technol., 47(8): 1586–1592.
31. Niu, Y., Yun, J., Bi, Y., Wang, T., Zhang, Y., Liu, H., and Zhao, F. 2020. Predicting the shelf life of postharvest Flammulina velutipes at various temperatures based on mushroom quality and specific spoilage organisms. Postharvest Biol. Technol., 167: 111235
32. Osman, R.M., Yang, T.A., Ahmed, A.H.R., Ahmed, K.E., Ali, M.H. and Khair, S.M., 2017. Optimization of bread baking conditions in superheated steam oven using response surface methodology. Int. J. Agri & Env. Res., 3(3): 290-301.
33. Pan, H., Yang, J., Shi, Y., and Li, T. 2015. BP neural network application model of predicting the apple hardness. J. Comput. Theor. Nanosci., 12(9): 2802–2807.
34. Polak, A., Coutts, F. K., Murray, P., and Marshall, S. 2019. Use of hyperspectral imaging for cake moisture and hardness prediction. IET Image Process., 13(7): 1152–1160.
35. Qiao, J., Wang, N., Ngadi, M. O., and Kazemi, S. 2007. Predicting mechanical properties of fried chicken nuggets using image processing and neural network techniques. J. Food Eng., 79(3): 1065–1070.
36. Shahapuzi, N. S., Taip, F. S., Ab Aziz, N., and Ahmedov, A. 2015. Effect of oven temperature profile and different baking conditions on final cake quality. Int. J. Food Sci., 50(3): 723–729.
37. Soleimanifard, S., Emam-Djomeh, Z., Askari, G., and Shahedi, M. 2024. The effect of a microwave susceptor on the textural properties of cupcakes during baking – A comparison with microwave and conventional baking methods . Acta Sci. Pol. Technol. Aliment., 23: 123–132.
38. Sun, R., Zhou, J. yu, and Yu, D. 2021. Nondestructive prediction model of internal hardness attribute of fig fruit using NIR spectroscopy and RF. Multimed. Tools Appl., 80(14): 21579–21594.
39. Tharwat, A., Gaber, T., Hassanien, A. E., and Elhoseny, M. 2018. Automated toxicity test model based on a bio-inspired technique and AdaBoost classifier. Comput. Electr. Eng., 71: 346–358
40. Vásquez, N., Magán, C., Oblitas, J., Chuquizuta, T., Avila-George, H., and Castro, W. 2018. Comparison between artificial neural network and partial least squares regression models for hardness modeling during the ripening process of Swiss-type cheese using spectral profiles. J. Food Eng., 219: 8–15.
41. Zareifard, M. R., Boissonneault, V., and Marcotte, M. 2009. Bakery product characteristics as influenced by convection heat flux. Food Res. Int., 42(7): 856–864.
42. Zhou, Y., Ma, Y., Sun, X., Peng, A., Zhang, B., Gu, X., Wang, Y., He, X., and Guo, Z. 2024. Quality prediction of kiwifruit based on transfer learning. J. Intell. Fuzzy Syst., 46(3): 7389–7400.
43. Zhu, H., Chu, B., Fan, Y., Tao, X., Yin, W., and He, Y. 2017. Hyperspectral imaging for predicting the internal quality of kiwifruits based on variable selection algorithms and chemometric models. Sci. Rep.,7(1): 1–13.
Journal of Agricultural Science and Technology
Volume 27, Issue 5
July and August 2025
Pages 1031-1042