A Review: Artificial Neural Networks as Tool for Control Food Industry Process

Artificial neural networks: fundamentals, computing, design, and application

Basheer, I. A., & Hajmeer, M. N. (2000) · Journal of Microbiological Methods · doi:10.1016/s0167-7012(00)00201-3 · Cited by 3,234 · see citing works

Modeling of tomato drying using artificial neural network

Movagharnejad, K., & Nikzad, M. (2007) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2007.05.003 · Cited by 251 · see citing works

Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data

Uno, Y., Prasher, S. O., Lacroix, R., Goel, P., Karimi, Y., Viau, A. A., & Patel, R. M. (2005) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2004.11.014 · Cited by 215 · see citing works

A classification system for beans using computer vision system and artificial neural networks

Kılıç, K., Boyacı, İ. H., Köksel, H., et al. (2006) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2005.11.030 · Cited by 181 · see citing works

Prediction of process and product parameters in an orange juice spray dryer using artificial neural networks

Chegini, G., Khazaei, J., Ghobadian, B., et al. (2007) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2007.06.007 · Cited by 140 · see citing works

Modeling of wheat soaking using two artificial neural networks (MLP and RBF)

Kashaninejad, M., Dehghani, A. A., & Kashiri, M. (2008) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2008.10.012 · Cited by 128 · see citing works

Artificial neural networks: fundamentals

Mesbahi, E. (2003) · Cited by 116 · see citing works

Application of neural networks to the color grading of apples

Nakano, K. (1997) · Computers and Electronics in Agriculture · doi:10.1016/s0168-1699(97)00023-9 · Cited by 114 · see citing works

Classification of multicomponent analytical data of olive oils using different neural networks

Zupan, J., Novič, M., Li, X., et al. (1994) · Analytica Chimica Acta · doi:10.1016/0003-2670(94)00085-9 · Cited by 110 · see citing works

A neural network approach for thermal/pressure food processing

Torrecilla, J. S., Otero, L., & Sanz, P. D. (2003) · Journal of Food Engineering · doi:10.1016/s0260-8774(03)00174-2 · Cited by 106 · see citing works

Neural network prediction of physical property changes of dried carrot as a function of fractal dimension and moisture content

Kerdpiboon, S., KERR, W. L., & Devahastin, S. (2006) · Food Research International · doi:10.1016/j.foodres.2006.07.019 · Cited by 105 · see citing works

Prediction of the viscosity of clarified fruit juice using artificial neural network: a combined effect of concentration and temperature

Rai, P., Majumdar, G. C., DasGupta, S., et al. (2004) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2004.07.003 · Cited by 103 · see citing works

Use of genetic artificial neural networks and spectral imaging for defect detection on cherries

Guyer, D. E., & Yang, X. (2000) · Computers and Electronics in Agriculture · doi:10.1016/s0168-1699(00)00146-0 · Cited by 97 · see citing works

Prediction of texture characteristics from extrusion food surface images using a computer vision system and artificial neural networks

Fan, F. H., Ma, Q., Ge, J., et al. (2013) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2013.04.015 · Cited by 93 · see citing works

Moisture content and water activity prediction of semi-finished cassava crackers from drying process with artificial neural network

Lertworasirikul, S., & Tipsuwan, Y. (2007) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2007.04.019 · Cited by 92 · see citing works

Neural networks-integrated metal oxide-based artificial olfactory system for meat spoilage identification

Balasubramanian, S., Panigrahi, S., Logue, C. M., Gu, H., & Marchello, M. J. (2008) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2008.08.008 · Cited by 84 · see citing works

Using neural networks to predict thermal conductivity of food as a function of moisture content, temperature and apparent porosity

Sablani, S. S., & Rahman, M. S. (2003) · Food Research International · doi:10.1016/s0963-9969(03)00012-7 · Cited by 84 · see citing works

Determination of anthocyanin concentration in whole grape skins using hyperspectral imaging and adaptive boosting neural networks

Fernandes, A. M., Oliveira, P., Moura, J. P., et al. (2011) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2011.02.018 · Cited by 82 · see citing works

A neural network for predicting moisture content of grain drying process using genetic algorithm

Liu, X., Chen, X., Wu, W., et al. (2006) · Food Control · doi:10.1016/j.foodcont.2006.05.010 · Cited by 82 · see citing works

Prediction of red plum juice permeate flux during membrane processing with ANN optimized using RSM

Nourbakhsh, H., Emam‐Djomeh, Z., Omid, M., et al. (2014) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2013.12.017 · Cited by 81 · see citing works

Optimization of heat treatment for fruit during storage using neural networks and genetic algorithms

Morimoto, T., Purwanto, W., Suzuki, J., & Hashimoto, Y. (1997) · Computers and Electronics in Agriculture · doi:10.1016/s0168-1699(97)00037-9 · Cited by 80 · see citing works

Prediction of rheological properties of Iranian bread dough from chemical composition of wheat flour by using artificial neural networks

Razmi-Rad, E., Ghanbarzadeh, B., Mousavi, M., et al. (2007) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2007.01.009 · Cited by 79 · see citing works

Pattern recognition of fruit shape based on the concept of chaos and neural networks

Morimoto, T., Takeuchi, T., Miyata, H., et al. (2000) · Computers and Electronics in Agriculture · doi:10.1016/s0168-1699(00)00070-3 · Cited by 76 · see citing works

Using historical data for bioprocess optimization: Modeling wine characteristics using artificial neural networks and archived process information

Vlassides, S., Ferrier, J. G., & Block, D. E. (2001) · Biotechnology and Bioengineering · doi:10.1002/1097-0290(20010405)73:1 · Cited by 75 · see citing works

Automatic apple grading model development based on back propagation neural network and machine vision, and its performance evaluation

Bhatt, A. K., & Pant, D. (2013) · AI & Society · doi:10.1007/s00146-013-0516-5 · Cited by 68 · see citing works

Predicting moisture content of agricultural products using artificial neural networks

Topuz, A. (2009) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2009.10.003 · Cited by 68 · see citing works

Artificial neural networks: a promising tool to design and optimize high-pressure food processes

Torrecilla, J. S., Otero, L., & Sanz, P. D. (2004) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2004.08.020 · Cited by 65 · see citing works

Optimum operating conditions for heat and mass transfer in foodstuffs drying by means of neural network inverse

Hernández, J. A. (2008) · Food Control · doi:10.1016/j.foodcont.2008.07.005 · Cited by 61 · see citing works

Use of different artificial neural networks to resolve binary blends of monocultivar Italian olive oils

Marini, F., Magrì, A. L., Bucci, R., et al. (2007) · Analytica Chimica Acta · doi:10.1016/j.aca.2007.08.006 · Cited by 58 · see citing works

Artificial neural network modeling of mass transfer during osmotic dehydration of kaffir lime peel

Lertworasirikul, S., & Sae‐tan, S. (2010) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2009.12.030 · Cited by 57 · see citing works

An intelligent approach for optimal control of fruit-storage process using neural networks and genetic algorithms

Morimoto, T., Baerdemaeker, J. D., & Hashimoto, Y. (1997) · Computers and Electronics in Agriculture · doi:10.1016/s0168-1699(97)00030-6 · Cited by 56 · see citing works

On the usage of acoustic properties combined with an artificial neural network – A new approach of determining presence of dairy fouling

Wallhäußer, E., Hussein, W., Hussein, M. A., et al. (2010) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2010.11.015 · Cited by 51 · see citing works

Classification of lamb carcass using machine vision: Comparison of statistical and neural network analyses

Chandraratne, M. R., Kulasiri, D., & Samarasinghe, S. (2007) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2007.01.003 · Cited by 49 · see citing works

Modeling sterilization process of canned foods using artificial neural networks

Gonçalves, E. C., Minim, L. A., Coimbra, J. S. D. R., et al. (2005) · Chemical Engineering and Processing - Process Intensification · doi:10.1016/j.cep.2005.04.001 · Cited by 48 · see citing works

Optimization of an artificial neural network for thermal/pressure food processing: Evaluation of training algorithms

Torrecilla, J. S., Otero, L., & Sanz, P. D. (2007) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2007.01.005 · Cited by 47 · see citing works

A sensor-software based on artificial neural network for the optimization of olive oil elaboration process

Jiménez, A., Beltrán, G., Aguilera, M. P., et al. (2007) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2007.09.030 · Cited by 47 · see citing works

Application of artificial neural network to the determination of phenolic compounds in olive oil mill wastewater

Torrecilla, J. S., Mena, M. L., Yáñez‐Sedeño, P., et al. (2007) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2006.12.003 · Cited by 45 · see citing works

Classification of apple surface features using machine vision and neural networks

Yang, Q. (1993) · Computers and Electronics in Agriculture · doi:10.1016/0168-1699(93)90025-v · Cited by 45 · see citing works

Stepwise geographical traceability of virgin olive oils by chemical profiles using artificial neural network models

García‐González, D. L., Luna, G., Morales, M. T., et al. (2009) · European Journal of Lipid Science and Technology · doi:10.1002/ejlt.200900015 · Cited by 45 · see citing works

Multilayer perceptron neural networks and radial-basis function networks as tools to forecast accumulation of deoxynivalenol in barley seeds contaminated with Fusarium culmorum

Mateo, F., Gironés, R. G., Mateo, E. M., et al. (2010) · Food Control · doi:10.1016/j.foodcont.2010.05.013 · Cited by 45 · see citing works

Artificial neural network model for prediction of cold spot temperature in retort sterilization of starch-based foods

Llave, Y., Hagiwara, T., & Sakiyama, T. (2011) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2011.10.024 · Cited by 44 · see citing works

Predicting Total Acceptance of Ice Cream Using Artificial Neural Network

BahramParvar, M., Salehi, F., & Razavi, S. M. A. (2013) · Journal of Food Processing and Preservation · doi:10.1111/jfpp.12066 · Cited by 43 · see citing works

Prediction of fatty acid composition of vegetable oils based on rheological measurements using nonlinear models

Yalçın, H., Toker, Ö. S., Öztürk, İ., et al. (2012) · European Journal of Lipid Science and Technology · doi:10.1002/ejlt.201200040 · Cited by 41 · see citing works

ARTIFICIAL NEURAL NETWORK MODELING OF APPLE DRYING PROCESS

Khoshhal, A., Alizadehdakhel, A., Etemadi, A., et al. (2009) · Journal of Food Process Engineering · doi:10.1111/j.1745-4530.2009.00435.x · Cited by 40 · see citing works

Predicting mechanical properties of fried chicken nuggets using image processing and neural network techniques

Qiao, J., Wang, N., Ngadi, M. O., et al. (2006) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2006.03.026 · Cited by 40 · see citing works

A NEW APPROACH TO MODELING AND CONTROL OF A FOOD EXTRUSION PROCESS USING ARTIFICIAL NEURAL NETWORK AND AN EXPERT SYSTEM

Popescu, O., Popescu, D. C., WILDER, J., et al. (2001) · Journal of Food Process Engineering · doi:10.1111/j.1745-4530.2001.tb00529.x · Cited by 37 · see citing works

Duroc and Iberian pork neural network classification by visible and near infrared reflectance spectroscopy

del Moral, F. G., Guillén, A., del Moral, L. G., et al. (2008) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2008.07.027 · Cited by 36 · see citing works

Artificial neural network software for real-time estimation of olive oil qualitative parameters during continuous extraction

Furferi, R., Carfagni, M., & Daou, M. (2007) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2006.12.006 · Cited by 35 · see citing works

Olive oil content prediction models based on image processing

Ram, T. K. J., Wiesman, Z., Parmet, I., et al. (2009) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2009.10.011 · Cited by 35 · see citing works

A rapid method to discriminate season of production and feeding regimen of butters based on infrared spectroscopy and artificial neural networks

Gori, A., Cevoli, C., Fabbri, A., et al. (2011) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2011.10.029 · Cited by 33 · see citing works

Artificial Neural Networks and their ApplicationsPreprint

Malik, N. (2005) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0505019 · Cited by 30 · see citing works

Artificial neural networks: a new tool for prediction of pressure drop of non-Newtonian fluid foods through tubes

Adhikari, B., & Jindal, V. K. (2000) · Journal of Food Engineering · doi:10.1016/s0260-8774(00)00072-8 · Cited by 29 · see citing works

A new method for dynamic modelling of bread dough kneading based on artificial neural network

Lamrini, B., Valle, G. D., Trelea, I.-C., et al. (2012) · Food Control · doi:10.1016/j.foodcont.2012.01.011 · Cited by 28 · see citing works

Chemometric classification of several olive cultivars from Trás-os-Montes region (northeast of Portugal) using artificial neural networks

Peres, A. M., Baptista, P. C., Malheiro, R., et al. (2010) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2010.11.001 · Cited by 27 · see citing works

Neural network as tool for virgin olive oil elaboration process optimization

Márquez, A. J., Herrera, M. P. A., Ojeda, M. U., et al. (2009) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2009.04.021 · Cited by 25 · see citing works

An analysis of the performance of Artificial Neural Network technique for apple classification

Bhatt, A. K., Pant, D., & Singh, R. (2012) · AI & Society · doi:10.1007/s00146-012-0425-z · Cited by 25 · see citing works

Predicting shrinkage of ellipsoid beef joints as affected by water immersion cooking using image analysis and neural network

Zheng, C., Sun, D., & Zheng, L. (2006) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2006.04.010 · Cited by 25 · see citing works

MODELING AND OPTIMIZATION OF CONSTANT RETORT TEMPERATURE (CRT) THERMAL PROCESSING USING COUPLED NEURAL NETWORKS AND GENETIC ALGORITHMS

CHEN, C. R., & Ramaswamy, H. S. (2002) · Journal of Food Process Engineering · doi:10.1111/j.1745-4530.2002.tb00571.x · Cited by 25 · see citing works

Field determination of phenolic compounds in olive oil mill wastewater by artificial neural network

Torrecilla, J. S., Mena, M. L., Yáñez‐Sedeño, P., et al. (2007) · Biochemical Engineering Journal · doi:10.1016/j.bej.2007.06.013 · Cited by 25 · see citing works

Artificial neural network model with a culture database for prediction of acidification step in cheese production

Horiuchi, J., Shimada, T., Funahashi, H., et al. (2003) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2003.09.005 · Cited by 24 · see citing works

Neural network models to classify olive oils within the protected denomination of origin framework

Torrecilla, J. S., Cancilla, J. C., Matute, G., et al. (2013) · International Journal of Food Science & Technology · doi:10.1111/ijfs.12245 · Cited by 24 · see citing works

Predictive controller with estimation of measurable disturbances. Application to an olive oil mill

Bordons, C., & Cueli, J. R. (2003) · Journal of Process Control · doi:10.1016/s0959-1524(03)00043-x · Cited by 24 · see citing works

Classification of Boiled Shrimp's Shape Using Image Analysis and Artificial Neural Network Model

Poonnoy, P., Yodkeaw, P., Sriwai, A., et al. (2014) · Journal of Food Process Engineering · doi:10.1111/jfpe.12081 · Cited by 23 · see citing works

Prediction of storage quality of fresh‐cut green peppers using artificial neural network

Meng, X., Zhang, M., & Adhikari, B. (2012) · International Journal of Food Science & Technology · doi:10.1111/j.1365-2621.2012.03007.x · Cited by 22 · see citing works

PREDICTION OF EXTENSOGRAPH PROPERTIES OF WHEAT‐FLOUR DOUGH: ARTIFICIAL NEURAL NETWORKS AND A GENETIC ALGORITHM APPROACH

Abbasi, H., Ardabili, S. M. S., Emam‐Djomeh, Z., et al. (2012) · Journal of Texture Studies · doi:10.1111/j.1745-4603.2011.00342.x · Cited by 21 · see citing works

Prediction of problematic wine fermentations using artificial neural networks

Román, R. C., Hernández, O. G., & Urtubia, A. (2011) · Bioprocess and Biosystems Engineering · doi:10.1007/s00449-011-0557-4 · Cited by 21 · see citing works

APPLYING OF NEURAL NETWORK ON THE WINE SENSORIAL ANALYSIS FROM BARBADOS CHERRY

Santana, J. C. C., Dias, C. G., de Souza, R. R., et al. (2009) · Journal of Food Process Engineering · doi:10.1111/j.1745-4530.2009.00521.x · Cited by 20 · see citing works

Forecasting maturity of green peas: An application of neural networks

Higgins, A., Prestwidge, D., Stirling, D., et al. (2009) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2009.09.018 · Cited by 19 · see citing works

Classification, modeling and prediction of the mechanical behavior of starch-based films

Dieulot, J., & Skurtys, O. (2013) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2013.05.028 · Cited by 14 · see citing works

MODELING DEAD‐END ULTRAFILTRATION OF APPLE JUICE USING ARTIFICIAL NEURAL NETWORK

Gökmen, V., Açar, Ö. Ç., Serpen, A., et al. (2008) · Journal of Food Process Engineering · doi:10.1111/j.1745-4530.2007.00214.x · Cited by 14 · see citing works

MODELING OF SQUID PROTEIN HYDROLYSIS: ARTIFICIAL NEURAL NETWORK APPROACH

Abakarov, A., Teixeira, A. A., Simpson, R., et al. (2010) · Journal of Food Process Engineering · doi:10.1111/j.1745-4530.2009.00567.x · Cited by 11 · see citing works

Use of Artificial Neural Network for Industrial Prediction of Final Temperature of Chicken Carcasses

da Silveira, C. L., Belledeli, B. D., Soares, M. B. A., et al. (2013) · Journal of Food Process Engineering · doi:10.1111/jfpe.12053 · Cited by 10 · see citing works

Predicción por redes neuronales artificiales de la calidad fisicoquímica de vinagre de melaza de caña por efecto de tiempo- temperatura de alimentación a evaporador-destilador flash

Vásquez-Villalobos, V., & Lescano, C. (2010) · Scientia Agropecuaria · doi:10.17268/sci.agropecu.2010.01.06 · Cited by 7 · see citing works

Real-time estimation of peroxides and acidity level of extra-virgin olive oil: an integrated approach

Furferi, R., Carfagni, M., & Daou, M. (2008) · Cited by 7 · see citing works

Real-time estimation of olive oil quality parameters: a combined approach based on ANNs and machine vision

Carfagni, M., Daou, M., & Furferi, R. (2008) · Annual Conference on Computers · Cited by 6 · see citing works

Prediction by Artificial Neural Networks (ANN) of the diffusivity, mass, moisture, volume and solids on osmotically dehydrated yacon (Smallantus sonchifolius)

Naccha, J. R., & Villalobos, V. V. (2012) · Scientia Agropecuaria · doi:10.17268/sci.agropecu.2012.03.02 · Cited by 6 · see citing works

NEURAL NETWORK MODELING OF END‐OVER‐END THERMAL PROCESSING OF PARTICULATES IN VISCOUS FLUIDS

Yang, M., & Ramaswamy, H. S. (2009) · Journal of Food Process Engineering · doi:10.1111/j.1745-4530.2008.00272.x · Cited by 6 · see citing works

APLICACIÓN DE REDES NEURONALES ARTIFICIALES EN LA MODELIZACIÓN DEL TRATAMIENTO TÉRMICO DE ALIMENTOS APPLICATION OF NEURAL NETWORKS IN THE MODELLING OF THE THERMAL TREATMENT OF FOOD APLICACIÓN DE REDES NEURONAIS ARTIFICIAIS NA MODELIZACIÓN DO TRATAMENTO TÉRMICO DOS ALIMENTOS

Peña, W. E. L., & Minim, L. A. (2001) · Ciencia y Tecnologia Alimentaria · doi:10.1080/11358120109487650 · Cited by 2 · see citing works

Diseño y optimización de una red neuronal artificial para el modelado del proceso de obtención de jarabe a partir del germen de maíz desgrasado Design and optimization of an artificial neural network for modeling syrup production process from defatted cor

Millãn, F., & Gil, L. (2011) · Cited by 1 · see citing works

Harnessing Artificial Intelligence to Safeguard Food Quality and Safety

Singh, D. (2025) · Journal of Food Protection · doi:10.1016/j.jfp.2025.100621 · Cited by 37 · see citing works

Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives

Podevin, M., Fotidis, I. A., & Angelidaki, I. (2018) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2017.1398132 · Cited by 34 · see citing works

DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION

Mustafa, N. H., Kelesbayev, D., & Alibekova, Z. D. (2018) · The Journal of Economic Research & Business Administration · doi:10.26577/be-2018-2-2141

A Parallel BP Neural Network Based on PVM

Yuan, H. (2005) · Jisuanji gongcheng · Cited by 1 · see citing works

A Tool for Selecting the Right Target Machine for Parallel Scientific Applications

Panadero, J., Wong, A., Rexachs, D. I., et al. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.05.351 · Cited by 11 · see citing works

Case study investigating the application of neural networks for process modelling and condition monitoring

Lennox, B., Rutherford, P., Montague, G. A., et al. (1998) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(98)00234-8 · Cited by 20 · see citing works

Quality Inspection of the Riveting Process by Neural Networks

Chen, H. C., Chen, Y., Wang, H. L., et al. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.284-287.2276