Quality Management: Important Aspects for the Food Industry

Paiva, Caroline Liboreiro

doi:10.5772/53162

Cited by 11 publications

(12 citation statements)

References 12 publications

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“…Other examples of real appliance of ML solutions in the food industry, that is the one of the most dependent industries of the quality management, where the lack of quality of at least one ingredient can directly impact the quality of the final product [34], is the Kewpie Corporation (a major Japanese food company), that is using Google's TensorFlow machine learning libraries in their visual inspection system, to automatically detect anomalies in their diced potatoes [20].…”

Section: Figure 22: Qc Methodsmentioning

confidence: 99%

Machine Learning Applied to an Intelligent and Adaptive Robotic Inspection Station

Variz

Piardi

Rodrigues

et al. 2019

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

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for all the time dispensed, by the orientations, critical analyses, motivation and confidence they have always transmitted along this path. To the IPB, mainly the professors and members from CeDRI for the support, companionship and availability in the accomplishment of the various tasks, by sharing their knowledge.To all my friends who have always support me, with friendship shown over the years.Good Luck to all.vii Abstract Industry 4.0 promotes the use of emergent technologies, such as Internet of Things (IoT), Big Data, Artificial Intelligence (AI) and cloud computing, sustained by cyber-physical systems to reach smart factories. The idea is to decentralize the production systems and allow to reach monitoring, adaptation and optimization to be made in real time, based on the large amount of data available at shop floor that feed the use of machine learning techniques. This technological revolution will bring significant productivity gains, resources savings and reduced maintenance costs, as machines will have information to operate more efficiently, adaptable and following demand fluctuations. This thesis discusses the application of supervised Machine Learning (ML) techniques allied with artificial vision, to implement an intelligent, collaborative and adaptive robotic inspection station, which carries out the Quality Control (QC) of Human Machine Interface (HMI) consoles, equipped with pressure buttons and Liquid Crystal Display (LCD) displays. Machine learning techniques were applied for the recognition of the operator's face, to classify the type of HMI console to be inspected, to classify the state condition of the pressure buttons and detect anomalies in the LCD displays. The developed solution reaches promising results, with almost 100% accuracy in the correct classification of the consoles and anomalies in the pressure buttons, and also high values in the detection of defects in the LCD displays.

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Section: Figure 22: Qc Methodsmentioning

confidence: 99%

Machine Learning Applied to an Intelligent and Adaptive Robotic Inspection Station

Variz

Piardi

Rodrigues

et al. 2019

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

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“…Considering that the production line hold various processes, there would be technical criteria/specifics to ensure products, as well as processes, are designed with quality. [257] For instance, process standardisation would focus to minimise the variations in product/production quality. Provisions can therefore be made to ensure that analytical and operational procedures, equipment/facilities, machinery, and raw materials get standardised.…”

Section: Process Control/standardisation and Internal (Quality) Auditsmentioning

confidence: 99%

“…[257] Instrumentation patterns demonstrate the goals and procedures aimed to accomplish the work, and classified as follows: a) Standards of Quality -parameters related to the quality of inputs, products, and raw materials; b) Operation Standards -manufacturing processes of products, technical control/ operational parameters; and c) Standards Inspection -criteria/methods to assess the degree of achieved success in delivering activity/work, compared to planned levels of products quality, which can be carried out on either the raw material, finished product or the process itself. [257] Quality outputs can be realised through the wide range of process standardisation, e.g., improved product standardisation/product quality, cost reduction, simplification, and optimisation of production processes. Others include an increase in the technical capacity of process operations, reduction of inventory levels of raw materials/inputs, reducing preparation time of machines, etc.…”

Section: Process Control/standardisation and Internal (Quality) Auditsmentioning

confidence: 99%

“…Others include an increase in the technical capacity of process operations, reduction of inventory levels of raw materials/inputs, reducing preparation time of machines, etc. [257] The internal quality audit program should be a participative type, which ensures that every phase meets up to the prerequisite quality certification standard, especially prior to the arrival of the external standardisation bodies/inspectors as well as their representatives. Essentially, the internal auditors need to undergo a very robust set of preparation/training, which involves quality and quality assurance processes, as well as documentation.…”

Section: Process Control/standardisation and Internal (Quality) Auditsmentioning

confidence: 99%

“…Regardless of the production stage, the traceability is largely based on products' recorded information. [257] In the agro-food industry, the traceability concept remains very relevant to initiate improvement as well as prevention actions, to deter the emergence or repeat of a specific problem. [257] With respect to food quality, traceability provides a history of production, application, or location of any (food) entity, by means of recorded identification as well as (overall) product distribution.…”

Section: Traceability In Food Quality and Safety Contextsmentioning

confidence: 99%

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Understanding the Relevance of Quality Management in Agro-food Product Industry: From Ethical Considerations to Assuring Food Hygiene Quality Safety Standards and Its Associated Processes

Okpala

Korzeniowska

2021

Food Reviews International

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The continuous improvement in good practices and implementation of hazard analysis and critical control points (HACCP) remains very crucial for food hygiene quality safety to steadily thrive in the agro-food product industry sector. To improve the agro-food product quality, the dependency of quality management (QM) on such key facets as quality assurance (QA), control, improvement, and planning appears to be on the rise. Herein, how food hygiene quality safety standards and their associated processes have been assured is described. To understand the relevance of QM in the (abovementioned) processes, we discuss some ethical quality considerations, food quality safety standards, HACCP fundamentals/implementation, QA control systems, other quality standards associated with agro-food industry, together with supplementary essentials associated with quality. Through the combined efforts of HACCP and QA control points (QACP) such as improved food hygiene, both quality, and safety levels can be further enhanced and sustained. Establishing the QM system within a given agrofood product enterprise is not the real deal, what matters most is how to maintain and sustain it. Some challenges encountered during the auditing/ implementation processes of food safety management systems, as well as directions for future studies, involving QM, QA, and food hygiene quality safety, are presented.

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A technological quality control system for rice supply chain

Cai

Cui

2022

Food and Energy Security

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Rice quality affects social stability, consumer health, and corporate brand benefit. The traditional supply chain traceability management system is centralized, monopolized, asymmetric, and opaque. It is the main factor leading to frequent food safety incidents. This paper proposes a technological quality control system for rice supply chain to protect rice products quality and safety. Firstly, it adopts risk assessment and traceability mechanism to prevent the hazard factors in the production and processing. Then, to prevent spoiled food from endangering the consumers’ health and reduce food waste, it explores a shelf life model of rice products to predict the remaining shelf life of products. Finally, this paper constructs a decentralized traceability management system based on blockchain and IoT, which enables consumers to access the real information of rice products and helps supervisors to monitor the quality of products in the supply chain.

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Quality Management: Important Aspects for the Food Industry

Cited by 11 publications

References 12 publications

Machine Learning Applied to an Intelligent and Adaptive Robotic Inspection Station

Machine Learning Applied to an Intelligent and Adaptive Robotic Inspection Station

Understanding the Relevance of Quality Management in Agro-food Product Industry: From Ethical Considerations to Assuring Food Hygiene Quality Safety Standards and Its Associated Processes

A technological quality control system for rice supply chain

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