Authors :
Kalyani Bandi; Manjula Kola
Volume/Issue :
Volume 11 - 2026, Issue 6 - June
Google Scholar :
https://tinyurl.com/7yv7ydmu
Scribd :
https://tinyurl.com/kz8ykade
DOI :
https://doi.org/10.38124/ijisrt/26jun1525
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
In the past few decades, manufacturing has gotten more efficient and modern industry has reached productivity
peaks because to the revolution of artificial intelligence (AI) and automation. The food industry is ready to take advantage
of new opportunities and advancements. More food companies will be utilizing big data and artificial intelligence to enhance
product quality, meet consumer needs, and guide the food service sector toward a more intelligent and sustainable future.
From farm to fork, where research begins with horticulture and ends with food product processing, artificial intelligence is
applied in the food business. Food companies may increase mass demand, reduce waste, improve food safety and quality,
and retain consumers by putting this cutting-edge technology into practice. Along with theses the most important area in
food processing is sensory evaluation which is having a key role in product formulation and marketing. Sensory scientists
are discovering links between individuals' self-reported hedonic reactions to various food and beverage products and their
subconscious biometric responses. The primary objective of the article is to elevate the level of applicability and
approachability of sensory developments in the food processing units.
Keywords :
Artificial Intelligence; Food Industry; Product Formulation; Processing; Sensory Evaluation.
References :
- Ben Ayed, R., & Hanana, M. (2021). Artificial intelligence to improve the food and agriculture sector. Journal of Food Quality, 2021(1), 5584754.
- Chandra, H. (2019). Artificial Intelligence (AI) vs Machine Learning (ML) vs Big Data. Heartbeat. https://heartbeat. fritz. ai/artificial-intelligence-ai-vs-machine-learning-ml-vs-big-data-909906eb6a92.
- Chiu, S. W., & Tang, K. T. (2013). Towards a chemiresistive sensor-integrated electronic nose: a review. Sensors, 13(10), 14214-14247.
- Deshmukh, S., Bandyopadhyay, R., Bhattacharyya, N., Pandey, R. A., & Jana, A. (2015). Application of electronic nose for industrial odors and gaseous emissions measurement and monitoring–an overview. Talanta, 144, 329-340.
- Dhar, P., Kashyap, P., Jindal, N., & Rani, R. (2018). Role of electronic nose technology in food industry. Emerg. Sustain. Technol. Food Process, 1-2.
- Ding, H., Tian, J., Yu, W., Wilson, D. I., Young, B. R., Cui, X., & Li, W. (2023). The application of artificial intelligence and big data in the food industry. Foods, 12(24), 4511.
- Elahi, M., Afolaranmi, S. O., Martinez Lastra, J. L., & Perez Garcia, J. A. (2023). A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment. Discover artificial intelligence, 3(1), 43.
- Goyache, F., Bahamonde, A., Alonso, J., López, S., Del Coz, J. J., Quevedo, J. R. & Díez, J. (2001). The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry. Trends in Food Science & Technology, 12(10), 370-381.
- Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033.
- Kler, R., Elkady, G., Rane, K., Singh, A., Hossain, M. S., Malhotra, D., & Bhatia, K. K. (2022). [Retracted] Machine Learning and Artificial Intelligence in the Food Industry: A Sustainable Approach. Journal of Food Quality, 2022(1), 8521236.
- Kumar, I., Rawat, J., Mohd, N., & Husain, S. (2021). Opportunities of artificial intelligence and machine learning in the food industry. Journal of Food Quality, 2021(1), 4535567.
- Lvova, L. (2016). Electronic tongue principles and applications in the food industry. In Electronic noses and tongues in food science (pp. 151-160). Academic Press.
- Mavani, N. R., Ali, J. M., Othman, S., Hussain, M. A., Hashim, H., & Rahman, N. A. (2022). Application of artificial intelligence in food industry—a guideline. Food Engineering Reviews, 14(1), 134-175.
- Meiselman, H. L. (2013). The future in sensory/consumer research:…evolving to a better science. Food Quality and Preference, 27(2), 208-214.
- Pavani, M., Singha, P., Rajamanickam, D. T., & Singh, S. K. (2023). Application of fuzzy logic techniques for sensory evaluation of plant-based extrudates fortified with bioactive compounds. Exploration of Foods and Foodomics, 1(5), 272-287.
- Podrażka, M., Bączyńska, E., Kundys, M., Jeleń, P. S., & Witkowska Nery, E. (2017). Electronic tongue—A tool for all tastes?. Biosensors, 8(1), 3.
- Schaller, E., Bosset, J. O., & Escher, F. (1998). ‘Electronic noses’ and their application to food. LWT-Food Science and Technology, 31(4), 305-316.
- Sirangelo, T. M. (2019). Sensory descriptive evaluation of food products: A review. Journal of Food Science and Nutrition Research, 2(4), 354-363.
- Thakur, M., Sharma, C., Mehta, A., & Torrico, D. D. (2022). Health claim effects on consumer acceptability, emotional responses, and purchase intent of protein bars. Journal of Agriculture and Food Research, 8, 100291.
- Torrico, D. D., Mehta, A., & Borssato, A. B. (2023). New methods to assess sensory responses: A brief review of innovative techniques in sensory evaluation. Current opinion in food science, 49, 100978.
- Wakihira, T., Morimoto, M., Higuchi, S., & Nagatomi, Y. (2022). Can facial expressions predict beer choices after tasting? A proof of concept study on implicit measurements for a better understanding of choice behavior among beer consumers. Food Quality and Preference, 100, 104580.
- Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The innovation, 2(4).
- Yan, J., Guo, X., Duan, S., Jia, P., Wang, L., Peng, C., & Zhang, S. (2015). Electronic nose feature extraction methods: A review. Sensors, 15(11), 27804-27831.
In the past few decades, manufacturing has gotten more efficient and modern industry has reached productivity
peaks because to the revolution of artificial intelligence (AI) and automation. The food industry is ready to take advantage
of new opportunities and advancements. More food companies will be utilizing big data and artificial intelligence to enhance
product quality, meet consumer needs, and guide the food service sector toward a more intelligent and sustainable future.
From farm to fork, where research begins with horticulture and ends with food product processing, artificial intelligence is
applied in the food business. Food companies may increase mass demand, reduce waste, improve food safety and quality,
and retain consumers by putting this cutting-edge technology into practice. Along with theses the most important area in
food processing is sensory evaluation which is having a key role in product formulation and marketing. Sensory scientists
are discovering links between individuals' self-reported hedonic reactions to various food and beverage products and their
subconscious biometric responses. The primary objective of the article is to elevate the level of applicability and
approachability of sensory developments in the food processing units.
Keywords :
Artificial Intelligence; Food Industry; Product Formulation; Processing; Sensory Evaluation.