Progressive quality estimation of oyster mushrooms using neural network–based image analysis
Main Article Content
Keywords
artificial intelligence, image feature, smartphone, the accuracy of prediction
Abstract
We have developed an artificial intelligence–based quality prediction model for oyster mushroom samples in this work. The proposed model tends to predict the progressively deteriorating quality of the samples in terms of predicted Hedonic number, which is adjudged as one of the most reliable scales of raw fruit quality assessment parameters. The present scheme attempts to continuously assess the quality of mushrooms by judging the extent of deterioration of the sample images; instead of discrete classification asserting only the edibility or non-edibility of the samples. Thus, the extent of the freshness of any test sample could also be approximated using the predicted Hedonic number from the model. The proposed scheme uses an artificial neural network to develop the estimator. The simplicity of analysis of the scheme and high accuracy of prediction of freshness allow for basic screening of the samples without requiring a panel of experts to judge the same, which is a difficult task, especially under this pandemic circumstance. Besides, implementing the proposed algorithm in designing possible mobile-based application software would widen its applicability in a practical scenario.
References
Bains, A., Chawla, P., Kaur, S., Najda, A., Fogarasi, M. and Fogarasi, S., 2021. Bioactives from mushroom: health attributes and food industry applications. Materials 14(24): 7640. 10.3390/ma14247640
Chaari, M., Elhadef, K., Akermi, S., Ben Akacha, B., Fourati, M., Chakchouk Mtibaa, A., et al., 2022a. Novel active food packaging films based on gelatin–sodium alginate containing beetroot peel extract. Antioxidants 11(11) : 2095. 10.3390/antiox11112095
Chaari, M., Akermi, S., Elhadef, K., Ennouri, K., Hlima, H. B., Fourati, M., et al., 2022b. From modeling and optimizing extraction of peels beetroot (Beta vulgaris L.) betalains to in silico probing of their antibacterial multitarget mechanisms. Biomass Conversion and Biorefinery 1–24. 10.1007/s13399-022-03140-6
Choi, J.W., Kim, S.Y., Lim, S., Choi, H., Yang, H. and Shin, I.S., 2020. Patent prospects and trends in post-harvest management technology of fresh agricultural products. Korean Journal of Food Preservation 27(4): 423–432. 10.11002/kjfp.2020.27.4.423
Ennouri, K., Smaoui, S., Gharbi, Y., Cheffi, M., Ben Braiek, O., Ennouri, M. and Triki, M.A., 2021. Usage of artificial intelligence and remote sensing as efficient devices to increase agricultural system yields. Journal of Food Quality 3: 1–17. 10.1155/2021/6242288
Fourati, M., Smaoui, S., Ben Hlima, H., Ennouri, K., Chakchouk Mtibaa, A., Sellem, I., et al., 2020. Synchronised interrelationship between lipid/protein oxidation analysis and sensory attributes in refrigerated minced beef meat formulated with Punica granatum peel extract. International Journal of Food Science & Technology 55(3): 1080–1087. 10.1111/ijfs.14398
Fu, Z., Zhao, S., Zhang, X., Polovka, M. and Wang, X., 2019. Quality Characteristics analysis and remaining shelf life prediction of fresh Tibetan tricholoma matsutake under modified atmosphere packaging in cold chain. Foods 8(4): 136. 10.3390/foods8040136
Granitto, P.M., Gasperi, F., Biasioli, F., Trainotti, E. and Furlanello, C., 2007. Modern data mining tools in descriptive sensory analysis: a case study with a Random forest approach. Food Quality and Preference 18(4): 681–689. 10.1016/j.foodqual.2006.11.001
Irfan, D., Tang, X., Narayan, V., Mall, P. K., Srivastava, S. and Saravanan, V., 2022. Prediction of quality food sale in mart using the AI-based TOR method. Journal of Food Quality 2022: 1–9. 10.1155/2022/6877520
Jack, F.R. and Steele, G.M., 2002. Modelling the sensory characteristics of Scotch whisky using neural networks—A novel tool for generic protection. Food Quality and Preference 13(3): 163–172. 10.1016/S0950-3293(02)00012-5
Jardim-Botelho, A., de Oliveira, L. C. L., Motta-Franco, J. and Solé, D., 2022. Nutritional management of immediate hypersensitivity to legumes in vegetarians. Allergologia et Immunopathologia 50(SP1): 37–45. 10.15586/aei.v50iSP1.554
Kiran, E., Kaur, K. and Aggarwal, P., 2022. Artificial senses and their fusion as a booming technique in food quality assessment—A review. Quality Assurance and Safety of Crops & Foods 14(3): 9–18. 10.15586/qas.v14i3.1036
Lahiri, D., Nag, M., Sarkar, T., Dutta, B. and Ray, R.R., 2021. Antibiofilm activity of α-amylase from Bacillus subtilis and prediction of the optimised conditions for biofilm removal by response surface methodology (RSM) and artificial neural network (ANN). Applied Biochemistry and Biotechnology 193(6): 1853–1872.
Lee, Y.R., Kim, D.S., Byun, M.S. and Kim, E.A., 2021. The knowledge maps using data analytics on the raw material plants, the phytochemical ingredients, and the pharmaceutical efficacy in the tea drinks. Korean Journal of Food Preservation 28(1): 1–12. 10.11002/kjfp.2021.28.1.1
Liu, X., Jiang, Y., Shen, S., Luo, Y. and Gao, L., 2015. Comparison of Arrhenius model and artificial neuronal network for the quality prediction of rainbow trout (Oncorhynchus mykiss) fillets during storage at different temperatures. LWT-Food Science and Technology 60(1): 142–147. 10.1016/j.lwt.2014.09.030
Lu, X., Brennan, M.A., Guan, W., Zhang, J., Yuan, L. and Brennan, C.S., 2021. Enhancing the nutritional properties of bread by incorporating mushroom bioactive compounds: the manipulation of the predictive glycaemic response and the phenolic properties. Foods 10(4): 731. 10.3390/foods10040731
Maiti, S. and Tiwari, R.K., 2010. Neural network modeling and an uncertainty analysis in Bayesian framework: A case study from the KTB borehole site. Journal of Geophysical Research: Solid Earth 115(B10). 10.1029/2010JB000864
Martínez-Simarro, D. and Lázaro-Ramos, J.P., 2022. Applications and business impact of artificial intelligence in the industrial production of food and beverages. In: Carou, D., Sartal, A. and Davim, J.P. (eds.) Machine learning and artificial intelligence with industrial applications. Springer, Cham, pp. 103–126.
Mukherjee, A., Sarkar, T., Chatterjee, K., Lahiri, D., Nag, M., Rebezov, M., et al., 2022. Development of artificial vision system for quality assessment of oyster mushrooms. Food Analytical Methods 15(6): 1663–1676. 10.1007/s12161-022-02241-2
Mukhopadhyay, S., Majumdar, G.C., Goswami, T.K. and Mishra, H.N., 2013. Fuzzy logic (similarity analysis) approach for sensory evaluation of chhana podo. LWT-Food Science and Technology 53(1): 204–210. 10.1016/j.lwt.2013.01.013
Müller, O. and Krawinkel, M., 2005. Malnutrition and health in developing countries. Canadian Medical Association Journal 173(3): 279–286. 10.1503/cmaj.050342
Ojeda, M., Bárcenas, P., Pérez-Elortondo, F.J., Albisu, M. and Guillén, M.D., 2002. Chemical references in sensory analysis of smoke flavourings. Food Chemistry 78(4): 433–442. 10.1016/S0308-8146(02)00154-1
Pleșoianu, A.M. and Nour, V., 2022. Effect of some polysaccharide-based edible coatings on fresh white button mushroom (Agaricus bisporus) quality during cold storage. Agriculture 12(9): 1491.
Rossini, K., Verdun, S., Cariou, V., Qannari, E.M., Fogliatto, F.S., 2012. PLS discriminant analysis applied to conventional sensory profiling data. Food Quality and Preference 40(1): 33–51. 10.1016/j.foodqual.2011.01.005
Sarkar, T., Bhattacharjee, R., Salauddin, M., Giri, A., and Chakraborty, R., 2020. Application of fuzzy logic analysis on pineapple rasgulla. Procedia Computer Science 167: 779–787. 10.1016/j.procs.2020.03.410
Sarkar, T., Salauddin, M., Hazra, S. K., Choudhury, T. and Chakraborty, R., 2021. Comparative approach of artificial neural network and thin layer modelling for drying kinetics and optimization of rehydration ratio for bael (Aegle marmelos (L) Correa) powder production. Economic Computation & Economic Cybernetics Studies & Research 55(1): 167–184. 10.24818/18423264/55.1.21.11
Singh, A., Vaidya, G., Jagota, V., Darko, D.A., Agarwal, R.K., Debnath, S., et al., 2022. Recent advancement in postharvest loss mitigation and quality management of fruits and vegetables using machine learning frameworks. Journal of Food Quality 2022: 1–9. 10.1155/2022/6447282
Stangierski, J., Weiss, D. and Kaczmarek, A., 2019. Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese. European Food Research and Technology 245(11): 2539–2547.
Wang, P., Zhang, K.S. and Ren, Y.X., 2012. Kinetic model of quality change and prediction of the shelf-life of stored mushroom. Science and Technology of Food Industry 33; 313–316.
Xu, H.R., Zhang, Y.Q., Wang, S., Wang, W.D., Yu, N.N., Gong, H., et al., 2022. Optimization of functional compounds extraction from Ginkgo biloba seeds using response surface methodology. Quality Assurance and Safety of Crops & Foods 14(1): 102–112. 10.15586/qas.v14i1.1033
You, S.W., Hoskin, R.T., Komarnytsky, S. and Moncada, M. 2022. Mushrooms as functional and nutritious food ingredients for multiple applications. ACS Food Science & Technology 2(8): 1184–1195. 10.1021/acsfoodscitech.2c00107
Yu, P., Low, M.Y. and Zhou, W., 2018. Design of experiments and regression modelling in food flavour and sensory analysis: a review. Trends in Food Science & Technology 71: 202–215. 10.1016/j.tifs.2017.11.013
Chaari, M., Elhadef, K., Akermi, S., Ben Akacha, B., Fourati, M., Chakchouk Mtibaa, A., et al., 2022a. Novel active food packaging films based on gelatin–sodium alginate containing beetroot peel extract. Antioxidants 11(11) : 2095. 10.3390/antiox11112095
Chaari, M., Akermi, S., Elhadef, K., Ennouri, K., Hlima, H. B., Fourati, M., et al., 2022b. From modeling and optimizing extraction of peels beetroot (Beta vulgaris L.) betalains to in silico probing of their antibacterial multitarget mechanisms. Biomass Conversion and Biorefinery 1–24. 10.1007/s13399-022-03140-6
Choi, J.W., Kim, S.Y., Lim, S., Choi, H., Yang, H. and Shin, I.S., 2020. Patent prospects and trends in post-harvest management technology of fresh agricultural products. Korean Journal of Food Preservation 27(4): 423–432. 10.11002/kjfp.2020.27.4.423
Ennouri, K., Smaoui, S., Gharbi, Y., Cheffi, M., Ben Braiek, O., Ennouri, M. and Triki, M.A., 2021. Usage of artificial intelligence and remote sensing as efficient devices to increase agricultural system yields. Journal of Food Quality 3: 1–17. 10.1155/2021/6242288
Fourati, M., Smaoui, S., Ben Hlima, H., Ennouri, K., Chakchouk Mtibaa, A., Sellem, I., et al., 2020. Synchronised interrelationship between lipid/protein oxidation analysis and sensory attributes in refrigerated minced beef meat formulated with Punica granatum peel extract. International Journal of Food Science & Technology 55(3): 1080–1087. 10.1111/ijfs.14398
Fu, Z., Zhao, S., Zhang, X., Polovka, M. and Wang, X., 2019. Quality Characteristics analysis and remaining shelf life prediction of fresh Tibetan tricholoma matsutake under modified atmosphere packaging in cold chain. Foods 8(4): 136. 10.3390/foods8040136
Granitto, P.M., Gasperi, F., Biasioli, F., Trainotti, E. and Furlanello, C., 2007. Modern data mining tools in descriptive sensory analysis: a case study with a Random forest approach. Food Quality and Preference 18(4): 681–689. 10.1016/j.foodqual.2006.11.001
Irfan, D., Tang, X., Narayan, V., Mall, P. K., Srivastava, S. and Saravanan, V., 2022. Prediction of quality food sale in mart using the AI-based TOR method. Journal of Food Quality 2022: 1–9. 10.1155/2022/6877520
Jack, F.R. and Steele, G.M., 2002. Modelling the sensory characteristics of Scotch whisky using neural networks—A novel tool for generic protection. Food Quality and Preference 13(3): 163–172. 10.1016/S0950-3293(02)00012-5
Jardim-Botelho, A., de Oliveira, L. C. L., Motta-Franco, J. and Solé, D., 2022. Nutritional management of immediate hypersensitivity to legumes in vegetarians. Allergologia et Immunopathologia 50(SP1): 37–45. 10.15586/aei.v50iSP1.554
Kiran, E., Kaur, K. and Aggarwal, P., 2022. Artificial senses and their fusion as a booming technique in food quality assessment—A review. Quality Assurance and Safety of Crops & Foods 14(3): 9–18. 10.15586/qas.v14i3.1036
Lahiri, D., Nag, M., Sarkar, T., Dutta, B. and Ray, R.R., 2021. Antibiofilm activity of α-amylase from Bacillus subtilis and prediction of the optimised conditions for biofilm removal by response surface methodology (RSM) and artificial neural network (ANN). Applied Biochemistry and Biotechnology 193(6): 1853–1872.
Lee, Y.R., Kim, D.S., Byun, M.S. and Kim, E.A., 2021. The knowledge maps using data analytics on the raw material plants, the phytochemical ingredients, and the pharmaceutical efficacy in the tea drinks. Korean Journal of Food Preservation 28(1): 1–12. 10.11002/kjfp.2021.28.1.1
Liu, X., Jiang, Y., Shen, S., Luo, Y. and Gao, L., 2015. Comparison of Arrhenius model and artificial neuronal network for the quality prediction of rainbow trout (Oncorhynchus mykiss) fillets during storage at different temperatures. LWT-Food Science and Technology 60(1): 142–147. 10.1016/j.lwt.2014.09.030
Lu, X., Brennan, M.A., Guan, W., Zhang, J., Yuan, L. and Brennan, C.S., 2021. Enhancing the nutritional properties of bread by incorporating mushroom bioactive compounds: the manipulation of the predictive glycaemic response and the phenolic properties. Foods 10(4): 731. 10.3390/foods10040731
Maiti, S. and Tiwari, R.K., 2010. Neural network modeling and an uncertainty analysis in Bayesian framework: A case study from the KTB borehole site. Journal of Geophysical Research: Solid Earth 115(B10). 10.1029/2010JB000864
Martínez-Simarro, D. and Lázaro-Ramos, J.P., 2022. Applications and business impact of artificial intelligence in the industrial production of food and beverages. In: Carou, D., Sartal, A. and Davim, J.P. (eds.) Machine learning and artificial intelligence with industrial applications. Springer, Cham, pp. 103–126.
Mukherjee, A., Sarkar, T., Chatterjee, K., Lahiri, D., Nag, M., Rebezov, M., et al., 2022. Development of artificial vision system for quality assessment of oyster mushrooms. Food Analytical Methods 15(6): 1663–1676. 10.1007/s12161-022-02241-2
Mukhopadhyay, S., Majumdar, G.C., Goswami, T.K. and Mishra, H.N., 2013. Fuzzy logic (similarity analysis) approach for sensory evaluation of chhana podo. LWT-Food Science and Technology 53(1): 204–210. 10.1016/j.lwt.2013.01.013
Müller, O. and Krawinkel, M., 2005. Malnutrition and health in developing countries. Canadian Medical Association Journal 173(3): 279–286. 10.1503/cmaj.050342
Ojeda, M., Bárcenas, P., Pérez-Elortondo, F.J., Albisu, M. and Guillén, M.D., 2002. Chemical references in sensory analysis of smoke flavourings. Food Chemistry 78(4): 433–442. 10.1016/S0308-8146(02)00154-1
Pleșoianu, A.M. and Nour, V., 2022. Effect of some polysaccharide-based edible coatings on fresh white button mushroom (Agaricus bisporus) quality during cold storage. Agriculture 12(9): 1491.
Rossini, K., Verdun, S., Cariou, V., Qannari, E.M., Fogliatto, F.S., 2012. PLS discriminant analysis applied to conventional sensory profiling data. Food Quality and Preference 40(1): 33–51. 10.1016/j.foodqual.2011.01.005
Sarkar, T., Bhattacharjee, R., Salauddin, M., Giri, A., and Chakraborty, R., 2020. Application of fuzzy logic analysis on pineapple rasgulla. Procedia Computer Science 167: 779–787. 10.1016/j.procs.2020.03.410
Sarkar, T., Salauddin, M., Hazra, S. K., Choudhury, T. and Chakraborty, R., 2021. Comparative approach of artificial neural network and thin layer modelling for drying kinetics and optimization of rehydration ratio for bael (Aegle marmelos (L) Correa) powder production. Economic Computation & Economic Cybernetics Studies & Research 55(1): 167–184. 10.24818/18423264/55.1.21.11
Singh, A., Vaidya, G., Jagota, V., Darko, D.A., Agarwal, R.K., Debnath, S., et al., 2022. Recent advancement in postharvest loss mitigation and quality management of fruits and vegetables using machine learning frameworks. Journal of Food Quality 2022: 1–9. 10.1155/2022/6447282
Stangierski, J., Weiss, D. and Kaczmarek, A., 2019. Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese. European Food Research and Technology 245(11): 2539–2547.
Wang, P., Zhang, K.S. and Ren, Y.X., 2012. Kinetic model of quality change and prediction of the shelf-life of stored mushroom. Science and Technology of Food Industry 33; 313–316.
Xu, H.R., Zhang, Y.Q., Wang, S., Wang, W.D., Yu, N.N., Gong, H., et al., 2022. Optimization of functional compounds extraction from Ginkgo biloba seeds using response surface methodology. Quality Assurance and Safety of Crops & Foods 14(1): 102–112. 10.15586/qas.v14i1.1033
You, S.W., Hoskin, R.T., Komarnytsky, S. and Moncada, M. 2022. Mushrooms as functional and nutritious food ingredients for multiple applications. ACS Food Science & Technology 2(8): 1184–1195. 10.1021/acsfoodscitech.2c00107
Yu, P., Low, M.Y. and Zhou, W., 2018. Design of experiments and regression modelling in food flavour and sensory analysis: a review. Trends in Food Science & Technology 71: 202–215. 10.1016/j.tifs.2017.11.013
Article Sidebar
Published
Jun 26, 2023
Article Details
Section
Artificial Intelligence in Food
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.