Release modeling of nanoencapsulated food ingredients by artificial intelligence algorithms

Djuriš, Jelena; Vidović, Bojana; Ibrić, Svetlana

doi:10.1016/b978-0-12-815665-0.00009-6

Cited by 6 publications

(2 citation statements)

References 76 publications

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“…Input data are further transformed and analyzed via activation functions in the hidden layer. As represented in Figure 2, sigmoid or hyperbolic functions are often used, but many others can be also implemented (3). This versatility of functions that can be applied to model the data is one of the most important reasons why ANNs can outperform conventional regression methods.…”

Section: Overview Of the Available Ai Methodologiesmentioning

confidence: 99%

Review of machine learning algorithms' application in pharmaceutical technology

Đuriš¹,

Kurćubić²,

Ibrić³

2021

Arhiv za farmaciju

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Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.

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Section: Overview Of the Available Ai Methodologiesmentioning

confidence: 99%

Review of machine learning algorithms' application in pharmaceutical technology

Đuriš¹,

Kurćubić²,

Ibrić³

2021

Arhiv za farmaciju

View full text Add to dashboard Cite

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“…Using the MRI technique, the best temperature and the best time for the freezing and thawing process can be determined using imaging of vegetables and fruits (Zhu et al, 2021). For example, in a study of okra, asparagus, soybeans, and broad beans, the results showed that during the thawing process, the curves from the MRI signals in okra and asparagus were linear, but for soybeans and broad beans, they were convex and convex (Djuris et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Application of neural networks in predicting the qualitative characteristics of fruits

et al. 2022

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In this research, the quality properties of persimmon were predicted using artificial intellect techniques. The persimmon samples were transferred to a computer vision lab, room temperature of 24 °C and 22% RH. The samples were divided into three groups for temperature treatment. They were kept at three temperature levels of 5 °C, 15 °C, and 24°C (control group) for 72 hours. The sample was then placed at room temperature and was imaged every second day for a 14 day period. After imaging, each sample underwent destructive tests to determine their quality attributes, including sugar content, firmness, and pH. The results indicate that the neural network's predicted values of acidity, firmness, and sugar of persimmon were not statistically significant differences from their actual values. In predicting the acidity of persimmon, the sugar RMSE is more than the two factors of firmness and acidity. For this reason, the accuracy of firmness and acidity is higher than sugar. MAPE is 10.11, 20.81, and 6.03 for acidity, firmness, and sugar, respectively. The model for sugar indicates a high difference between the actual values and the predicted values.

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