Use of colorimetric data and artificial neural networks for the determination of freshness in fish

Rezende-de-Souza, Jonatã Henrique; Moraes-Neto, Venancio Ferreira de; Cassol, Geodriane Zatta; Camelo, Marteson Cristiano dos Santos; Silva, Luciana Kimie Savay da

doi:10.1016/j.focha.2022.100129

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…The simplest and most common methods to characterize the quality of fish include sensory evaluation, microbiological examination, pH, total volatile basic nitrogen content determination, etc. 3,4 These methods usually do not provide a good balance of accuracy and nondestructiveness and rely on large industrial equipment and manual proficiency to some extent. At the same time, storage time and temperature are important factors that affect the final quality of fish products.…”

Section: Introductionmentioning

confidence: 99%

“…Freshness is one of the key quality indicators during fish processing, storage, transportation, and marketing, especially for filleted products (post-mortem). , After death, fish are highly susceptible to endogenous microbial contamination and gradually lose nutrients, accompanied by water loss during the process. The simplest and most common methods to characterize the quality of fish include sensory evaluation, microbiological examination, pH, total volatile basic nitrogen content determination, etc. , These methods usually do not provide a good balance of accuracy and nondestructiveness and rely on large industrial equipment and manual proficiency to some extent. At the same time, storage time and temperature are important factors that affect the final quality of fish products .…”

Section: Introductionmentioning

confidence: 99%

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Conformal Temperature/Impedance Sensing Patch Based on Graphene Materials for Nondestructive Detection of Fish Freshness

Xia,

Huang,

Majer-Baranyi

et al. 2023

ACS Appl. Mater. Interfaces

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Rapid nondestructive detection of fish freshness is essential to ensure food safety and nutrition. In this study, we demonstrate a conformal temperature/impedance sensing patch for temperature monitoring, as well as freshness classification during fish storage. The optimization of the flexible laser-induced graphene electrodes is studied based on both simulation and experimental validation, and dimensional accuracy of 5‰ and high impedance reproducibility are obtained. A laser-assisted thermal reduction technology is innovatively introduced to directly form a reduced graphene oxide-based temperature-sensitive layer on the surface of a flexible substrate. The comprehensive performance is superior to that of most reported temperature-sensitive devices based on graphene materials. As an application demonstration, the fabricated flexible dual-parameter sensing patch is conformed to the surface of a refrigerated fish. The patch demonstrates the ability to accurately sense low temperatures in a continuous 120 min monitoring, accompanied by no interference from high humidity. Meanwhile, the collected impedance data are imported into the support vector machine model to obtain a freshness classification accuracy of 93.07%. The conformal patch integrated with crosstalk-free dual functions costs less than $1 and supports free customization, providing a feasible methodology for rapid nondestructive detection or monitoring of food quality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Conformal Temperature/Impedance Sensing Patch Based on Graphene Materials for Nondestructive Detection of Fish Freshness

Xia,

Huang,

Majer-Baranyi

et al. 2023

ACS Appl. Mater. Interfaces

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A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing

Hoffmann,

Reich

2023

Electronics

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Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes. In this study, we conducted a systematic literature review (SLR) to explore AI and XAI approaches for visual QA (VQA) in manufacturing. Our objective was to assess the current state of the art and identify research gaps in this context. Our findings revealed that AI-based systems predominantly focused on visual quality control (VQC) for defect detection. Research addressing VQA practices, like process optimization, predictive maintenance, or root cause analysis, are more rare. Least often cited are papers that utilize XAI methods. In conclusion, this survey emphasizes the importance and potential of AI and XAI in VQA across various industries. By integrating XAI, organizations can enhance model transparency, interpretability, and trust in AI systems. Overall, leveraging AI and XAI improves VQA practices and decision-making in industries.

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Use of colorimetric data and artificial neural networks for the determination of freshness in fish

Cited by 2 publications

References 28 publications

Conformal Temperature/Impedance Sensing Patch Based on Graphene Materials for Nondestructive Detection of Fish Freshness

Conformal Temperature/Impedance Sensing Patch Based on Graphene Materials for Nondestructive Detection of Fish Freshness

A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing

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