New trends in the development and application of artificial intelligence in food processing

Barthwal, Riya; Kathuria, Deepika; Joshi, Saloni; Kaler, R.S.S.; Singh, Narpinder

doi:10.1016/j.ifset.2024.103600

Cited by 11 publications

(1 citation statement)

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“…The benefits of exploring a novel topic, particularly one related to fundamental human needs like food and artificial intelligence, offer significant research opportunities that surpass any drawbacks (Esmaeily et al, 2024). Barthwal et al (2024) indicated different methods, such as creating hybrid systems that merge ANNs with conventional heuristics, meta-heuristics, evolutionary algorithms, and other techniques. They stated that evolutionary ANNs aim to address the limitations of traditional ANNs.…”

Section: Introductionmentioning

confidence: 99%

Dairy factory milk product processing and sustainable of the shelf-life extension with artificial intelligence: a model study

Oztuna Taner,

Çolak

2024

Front. Sustain. Food Syst.

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This study models milk product processing and sustainable of the shelf-life extension in a dairy factory using artificial intelligence. The Cappadocia dairy factory was used to study chemical processes and computational system modeling and simulation. Levenberg–Marquardt algorithm was used to create an artificial neural network model from real-time data. An AI-based method utilizing a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) model was employed to precisely analyze productivity data in dairy factories. There are 9 product types and production quantities used as input parameters, and 90 datasets of actual dairy products used as output values. The model was trained using the Levenberg–Marquardt algorithm on 62 datasets for training, 14 for validation, and 14 for testing. The accuracy of the model is affected by the optimal data segmentation. The model showed how AI algorithms can improve processes and industrial production by increasing dairy production efficiency from 20 to 40%. Model efficiency values were compared to observed values to determine prediction accuracy. Model mean squared error was 4.02E-06, and coefficient of determination was 0.99984. Model efficiency predictions and observed values differed by −0.04% on average. This study investigated using artificial intelligence to optimize salvage processes and systems to increase energy efficiency and reduce environmental impact. The results show that a neural network model trained with real data can predict dairy plant productivity.

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Section: Introductionmentioning

confidence: 99%