Non-Destructive Prediction of Bread Staling Using Artificial Intelligence Methods

Yıl 2023, Cilt: 12 Sayı: 4, 985 - 993, 28.12.2023

Mustafa Şamil Argun , Fuat Türk , Abdullah Kurt

https://doi.org/10.17798/bitlisfen.1308493

Öz

In foods with limited shelf life and in new product development studies, it is important for producers and consumers to estimate the degree of staling with easy methods. Staling of bread, which has an essential role in human nutrition, is an important physicochemical phenomenon that affects consumer preference. Costly technologies, such as rheological, thermal, and spectroscopic approaches, are used to determine the degree of staling. This research suggests that an artificial intelligence-based method is more practical and less expensive than these methods. Using machine learning and deep learning algorithms, it was attempted to predict how many days old breads are, which provides information on the freshness status and degree of staling, from photos of whole bread and pieces of bread. Among the machine learning algorithms, the highest accuracy rate for slices of bread was calculated as 62.84% with Random Forest, while the prediction accuracy was lower for all bread images. The training accuracy rate for both slice and whole bread was determined to be 99% when using the convolutional neural network (CNN) architecture. While the test results for whole breads were around 56.6%, those for sliced breads were 92.3%. The results of deep learning algorithms were superior to those of machine learning algorithms. The results indicate that crumb images reflect staling more accurately than whole bread images.

Anahtar Kelimeler

Bread image processing, Bread staling, Deep learning, Machine learning

Kaynakça

• [1] G. M. Bosmans, B. Lagrain, E. Fierens, and J. A. Delcour, "The impact of baking time and bread storage temperature on bread crumb properties," Food chemistry, vol. 141, no. 4, pp. 3301-3308, 2013.
• [2] I. Demirkesen, O. H. Campanella, G. Sumnu, S. Sahin, and B. R. Hamaker, "A Study on Staling Characteristics of Gluten-Free Breads Prepared with Chestnut and Rice Flours," Food and Bioprocess Technology, vol. 7, no. 3, pp. 806-820, 2014/03/01 2014, doi: 10.1007/s11947-013-1099-3.
• [3] J. Gray and J. Bemiller, "Bread staling: molecular basis and control," Comprehensive reviews in food science and food safety, vol. 2, no. 1, pp. 1-21, 2003.
• [4] L. Wang et al., "Effect of buckwheat hull particle-size on bread staling quality," Food Chemistry, vol. 405, p. 134851, 2023/03/30/ 2023, doi: https://doi.org/10.1016/j.foodchem.2022.134851.
• [5] H. An et al., "Quantitative analysis of Chinese steamed bread staling using NIR, MIR, and Raman spectral data fusion," Food Chemistry, vol. 405, p. 134821, 2023/03/30/ 2023, doi: https://doi.org/10.1016/j.foodchem.2022.134821.
• [6] K. Wang, D.-W. Sun, and H. Pu, "Emerging non-destructive terahertz spectroscopic imaging technique: Principle and applications in the agri-food industry," Trends in Food Science & Technology, vol. 67, pp. 93-105, 2017.
• [7] T. Ringsted, H. W. Siesler, and S. B. Engelsen, "Monitoring the staling of wheat bread using 2D MIR-NIR correlation spectroscopy," Journal of Cereal Science, vol. 75, pp. 92-99, 2017.
• [8] J. M. Amigo, A. d. Olmo, M. M. Engelsen, H. Lundkvist, and S. B. Engelsen, "Staling of white wheat bread crumb and effect of maltogenic α-amylases. Part 3: Spatial evolution of bread staling with time by near infrared hyperspectral imaging," Food Chemistry, vol. 353, p. 129478, 2021/08/15/ 2021, doi: https://doi.org/10.1016/j.foodchem.2021.129478.
• [9] S. J. Olakanmi, D. S. Jayas, and J. Paliwal, "Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review," Comprehensive Reviews in Food Science and Food Safety, vol. 22, no. 3, pp. 1817-1838, 2023, doi: https://doi.org/10.1111/1541-4337.13131.
• [10] Y. Liu, H. Pu, and D.-W. Sun, "Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices," Trends in Food Science & Technology, vol. 113, pp. 193-204, 2021/07/01/ 2021, doi: https://doi.org/10.1016/j.tifs.2021.04.042.
• [11] W. da Silva Cotrim, V. P. R. Minim, L. B. Felix, and L. A. Minim, "Short convolutional neural networks applied to the recognition of the browning stages of bread crust," Journal of Food Engineering, vol. 277, p. 109916, 2020.
• [12] C.-J. Du and D.-W. Sun, 4 - Object Classification Methods" in Computer Vision Technology for Food Quality Evaluation, D.-W. Sun Ed. Amsterdam: Academic Press, 2008, pp. 81-107.
• [13] A. Taheri-Garavand, S. Fatahi, M. Omid, and Y. Makino, "Meat quality evaluation based on computer vision technique: A review," Meat science, vol. 156, pp. 183-195, 2019.
• [14] J. Joshi and M. Phadke, "Feature extraction and texture classification in MRI," Energy, vol. 1, no. 0, 2010.
• [15] D. Kumar, "Feature extraction and selection of kidney ultrasound images using GLCM and PCA," Procedia Computer Science, vol. 167, pp. 1722-1731, 2020.
• [16] S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, "Supervised machine learning: A review of classification techniques," Emerging artificial intelligence applications in computer engineering, vol. 160, no. 1, pp. 3-24, 2007.
• [17] T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction. Springer, 2009.
• [18] C. Wang, Y. Zhang, J. Song, Q. Liu, and H. Dong, "A novel optimized SVM algorithm based on PSO with saturation and mixed time-delays for classification of oil pipeline leak detection," Systems Science & Control Engineering, vol. 7, no. 1, pp. 75-88, 2019.
• [19] M. Fan, L. Wei, Z. He, W. Wei, and X. Lu, "Defect inspection of solder bumps using the scanning acoustic microscopy and fuzzy SVM algorithm," MiRe, vol. 65, pp. 192-197, 2016.
• [20] S. Long, X. Huang, Z. Chen, S. Pardhan, and D. Zheng, "Automatic detection of hard exudates in color retinal images using dynamic threshold and SVM classification: algorithm development and evaluation," BioMed research international, vol. 2019, 2019.
• [21] I. S. Abd Elkarim and J. Agbinya, "A Review of Parallel Support Vector Machines (PSVMs) for Big Data classification," Australian Journal of Basic and Applied Sciences, vol. 13, no. 12, pp. 61-71, 2019.
• [22] Q. Li, X. Du, H. Zhang, M. Li, and W. Ba, "Liquid pipeline leakage detection based on moving windows LS-SVM algorithm," in 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2018: IEEE, pp. 701-705.
• [23] C. Dai, J. Yang, Y. Qin, and J. Liu, "Physical layer authentication algorithm based on SVM," in 2016 2nd IEEE International Conference on Computer and Communications (ICCC), 2016: IEEE, pp. 1597-1601.
• [24] D. M. Abdullah and A. M. Abdulazeez, "Machine Learning Applications based on SVM Classification A Review," Qubahan Academic Journal, vol. 1, no. 2, pp. 81-90, 2021.
• [25] V. Bijalwan, V. Kumar, P. Kumari, and J. Pascual, "KNN based machine learning approach for text and document mining," International Journal of Database Theory and Application, vol. 7, no. 1, pp. 61-70, 2014.
• [26] L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. 1st Editio, ed: Routledge, 1984.
• [27] V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, "An assessment of the effectiveness of a random forest classifier for land-cover classification," ISPRS journal of photogrammetry and remote sensing, vol. 67, pp. 93-104, 2012.
• [28] V. Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica-Olmo, and M. Chica-Rivas, "Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines," Ore Geology Reviews, vol. 71, pp. 804-818, 2015.
• [29] A. Yılmaz, "Diagnosing COVID-19 from X-Ray images with using multi-channel CNN architecture," Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 36, no. 4, pp. 1761-1774, 2021.
• [30] T. Fuat and Y. Kökver, "Application with deep learning models for COVID-19 diagnosis," Sakarya University Journal of Computer and Information Sciences, vol. 5, no. 2, pp. 169-180, 2022.
• [31] S. Nashat and M. Z. Abdullah, "Multi-class colour inspection of baked foods featuring support vector machine and Wilk’s λ analysis," Journal of Food Engineering, vol. 101, no. 4, pp. 370-380, 2010/12/01/ 2010, doi: https://doi.org/10.1016/j.jfoodeng.2010.07.022.
• [32] S. Nashat, A. Abdullah, S. Aramvith, and M. Z. Abdullah, "Support vector machine approach to real-time inspection of biscuits on moving conveyor belt," Computers and Electronics in Agriculture, vol. 75, no. 1, pp. 147-158, 2011/01/01/ 2011, doi: https://doi.org/10.1016/j.compag.2010.10.010.
• [33] R. B. Archandani, F. Mohanna, and M. J. Ahsani, "Introducing an automatic bread quality assessment algorithm using image processing techniques," European Journal of Electrical Engineering and Computer Science, vol. 6, no. 6, pp. 31-38, 2022.
• [34] P. Tantiphanwadi and K. Malithong, "Bread Browning Stage Classification Model using VGG-16 Transfer Learning and Fine-tuning with Small Training Dataset," Engineering Journal, vol. 26, no. 11, pp. 1-12, 2022.
• [35] O. Paquet-Durand, D. Solle, M. Schirmer, T. Becker, and B. Hitzmann, "Monitoring baking processes of bread rolls by digital image analysis," Journal of Food Engineering, vol. 111, no. 2, pp. 425-431, 2012/07/01/ 2012, doi: https://doi.org/10.1016/j.jfoodeng.2012.01.024.
• [36] W. d. S. Cotrim, V. P. R. Minim, L. B. Felix, and L. A. Minim, "Short convolutional neural networks applied to the recognition of the browning stages of bread crust," Journal of Food Engineering, vol. 277, p. 109916, 2020/07/01/ 2020, doi: https://doi.org/10.1016/j.jfoodeng.2020.109916.
• [37] C. Zhang, J. Wang, G. Lu, S. Fei, T. Zheng, and B. Huang, "Automated tea quality identification based on deep convolutional neural networks and transfer learning," Journal of Food Process Engineering, vol. 46, no. 4, p. e14303, 2023/04/01 2023, doi: https://doi.org/10.1111/jfpe.14303.
• [38] C. Wang et al., "Convolutional neural network-based portable computer vision system for freshness assessment of crayfish (Prokaryophyllus clarkii)," Journal of Food Science, vol. 87, no. 12, pp. 5330-5339, 2022, doi: https://doi.org/10.1111/1750-3841.16377.