Smart Artificial Intelligence Computerized Models for Shelf Life Prediction of Processed Cheese

Goyal, Shweta; Goyal, Gyanendra Kumar

doi:10.14419/ijet.v1i3.201

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Other studies, in which the input variables were the soluble nitrogen, pH; standard counting in plate, fungi and yeasts and spore counting; and output variable, sensorial score, were applied: the ANN-BPCA model [23], as well as LL and RBF artificial intelligence models [24] to estimate the shelf life of the processed cheese stored at other temperature between 7 and 8°C. On the other hand, [25] the Elman model was also developed to predict the processed cheese stored at 30°C.…”

Section: Final Topology Of Annmentioning

confidence: 99%

Application of Weibull analysis and artificial neural networks to predict the useful life of the vacuum packed soft cheese

Oblitas

Sánchez-González

2017

Rev. Fac. Ing. Univ. Antioquia

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Section: Final Topology Of Annmentioning

confidence: 99%

Application of Weibull analysis and artificial neural networks to predict the useful life of the vacuum packed soft cheese

Oblitas

Sánchez-González

2017

Rev. Fac. Ing. Univ. Antioquia

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“…Moreover, beyond production processes, the integration of AI in the analysis of dairy products extends to estimating the shelf life of such products [12,13].…”

Section: Automated Methods In Dairy Industrymentioning

confidence: 99%

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

Loddo,

Ghiani,

Perniciano

et al. 2024

Preprint

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The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company's reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.

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“…Moreover, beyond production processes, the integration of AI in the analysis of dairy products extends to estimating the shelf life of such products [13,14].…”

Section: Automated Methods In Dairy Industrymentioning

confidence: 99%

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

Loddo,

Ghiani,

Perniciano

et al. 2024

Information

View full text Add to dashboard Cite

The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company’s reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly-detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.

show abstract

Smart Artificial Intelligence Computerized Models for Shelf Life Prediction of Processed Cheese

Cited by 6 publications

References 9 publications

Application of Weibull analysis and artificial neural networks to predict the useful life of the vacuum packed soft cheese

Application of Weibull analysis and artificial neural networks to predict the useful life of the vacuum packed soft cheese

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

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