Development of a Prototype for the Quality Control of Bovine Meat Determined by its Organoleptic Characteristics, based on an Automatic Inspection System for Artificial Vision

The world beef market is in full growth and with unsatisfied demands and prices up and these depend very much on their quality when arriving at the final consumer, while the national market, our country also has a future potential in the meat industry. In the present investigation is to evaluate the sensory characteristics of color and texture by means of the presence of a bovine meat sample in the system whose final result will be the acceptance or negation of its organoleptic quality. The system is constituted by a transport band in charge of taking the sample to a capsule where both the Lighting System (LS) and the Computer Vision System (CVS) are located, and when leaving there it will be automatically classified by an actuator, guaranteeing less manipulation and estimation of time by human intervention throughout the process while providing greater security to the final consumer about the meat that is entering their home. &nbsp

Computerized and Electronic Controls in Food Packaging

The focus of food packaging is to contain food in a cost-effective way that satisfies industry requirements and consumer desires, maintains food safety and minimizes environmental impact. Currently, with the increasing demand of the consumers the major concern of food packaging industry is on efficiency of the plant process, productivity, quality as well as safety. It becomes necessary for companies to discover ways to improve their productivity in terms of maintaining safety, using sustainable materials in packaging, implementing flexible and standardized technology, and maintaining good quality of foods. Thus, to achieve the required demands, automation and upgradation of the packaging machinery is necessary and this has been accepted because these changes are robust, flexible, reconfigurable, preserve the quality of the food and are efficient. Due to limitation of feasibility study and research in food packaging, most of the studies focus on trends in food packaging materials (smart packaging). Thus, this review focused on advancement in food packaging machines, involvement of softwares in controlling the working of various machines for example open modular architecture control (OMAC), Programmable Logic Controller (PLC), Field bus Technology etc. The automated machines include drive system, sensors, actuators etc. So, the knowledge about these techniques will result in enhancing the efficiency of packaging and productivity of food products

Robust computer vision system for marbling meat segmentation

In this study, we developed a robust automatic computer vision system for marbling meat segmentation. Our approach can segment muscle fat in various marbled meat samples using images acquired with different quality devices in an uncontrolled environment, where there was external ambient light and artificial light; thus, professionals can apply this method without specialized knowledge in terms of sample treatments or equipment, as well as without disruption to normal procedures, thereby obtaining a robust solution. The proposed approach for marbling segmentation is based on data clustering and dynamic thresholding. Experiments were performed using two datasets that comprised 82 images of 41 longissimus dorsi muscles acquired by different sampling devices. The experimental results showed that the computer vision system performed well with over 98% accuracy and a low number of false positives, regardless of the acquisition device employed

Robotics in meat processing

Scientists are currently investigating micro-robotics in the medical field with a potential to provide better medical technology in the near future. When it comes to the food industry, the use of robots has been traditionally limited to picking and palletization. Today, however, robots are used in material handling and secondary or tertiary packing. Recent developments with faster computers and sophisticated sensors have made it possible to use robotics in the meat processing sectors, where their application has reduced processing costs, occupational injuries, improved efficiency and hygiene associated with meat products. Compared to other industries, the working environment in the meat industry is not very conducive to robotics due to the noisy, damp and cold conditions. Slaughtering animals and cutting meat into pieces and disposing waste is an intensive physically demanding task. This chapter reviews the application of robotics in the meat industry and the advancements that have been made until now

Non-destructive evaluation of white striping and microbial spoilage of Broiler Breast Meat using structured-illumination reflectance imaging

Manual inspection is a prevailing practice for quality assessment of poultry meat, but it is labor-intensive, tedious, and subjective. This thesis aims to assess the efficacy of an emerging structured illumination reflectance imaging (SIRI) technique with machine learning approaches for assessing WS and microbial spoilage in broiler breast meat. Broiler breast meat samples were imaged by an in house-assembled SIRI platform under sinusoidal illumination. In first experiment, handcrafted texture features were extracted from direct component (DC, corresponding to conventional uniform illumination) and amplitude component (AC, unique to the use of sinusoidal illumination) images retrieved from raw SIRI pattern images build linear discriminant analysis (LDA) models for classifying normal and defective samples. A further validation experiment was performed using deep learning as a feature extractor followed by LDA. The third experiment was on microbial spoilage assessment of broiler meat, deep learning models were used to extract features from DC and AC images builds on classifiers. Overall, this research has demonstrated consistent improvements of AC over DC images in assessing WS and spoilage of broiler meat and that SIRI is a promising tool for poultry meat quality detection