Untitled

Gümüş, Bahar; Balaban, Murat Ö.; Ünlüsayın, Mustafa

doi:10.4194/trjfas.2011.0124

Cited by 27 publications

(3 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The human eye can discern thousands of color shades and intensities compared with approximately only 24 shades of gray. On machine vision an image of the sample is digitized into pixels containing levels of the three primary colors (red, green and blue = RGB color system) (Gumus et al, 2011). Skin color of fish is an important quality indicator and affects purchase decisions.…”

Section: Introductionmentioning

confidence: 99%

Color assessment by different techniques of gilthead seabream (Sparus aurata) during cold storage

et al. 2019

Self Cite

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Color assessment by different techniques of gilthead seabream (Sparus aurata) during cold storage

et al. 2019

Self Cite

View full text Add to dashboard Cite

“…[41][42][43] There are extensive reviews about machine vision applications to food. [44][45][46][47] The potential of machine vision in the food industry has long been recognized and the food industry is now ranked among the top 10 industries using this technology. 47,48 Vis/NIR spectroscopy…”

Section: Machine Visionmentioning

confidence: 99%

Non-invasive sensing for food reassurance

Zhang

Huang

Povey

2016

Analyst

View full text Add to dashboard Cite

Consumers and governments are increasingly interested in the safety, authenticity and quality of food commodities. This has driven attention towards non invasive sensing techniques used for rapid analyzing these commodities. This paper provides an overview of the state of the art in, and available alternatives for, food assurance based on non invasive sensing techniques. The main food quality traits of interest using non invasive sensing techniques are sensory characteristics, chemical composition, physicochemical properties, health protecting properties, nutritional characteristics and safety. A wide range of non invasive sensing techniques, from optical, acoustical, electrical, to nuclear magnetic, x ray, biosensor, microwave and terahertz, are organized according to physical principle. Some of these techniques are now in a period of transition between experimental and applied utilization and several sensors and instruments are reviewed. With continued innovation and attention to key challenges, such non invasive sensors and biosensors are expected to open up new exciting avenues in the field of portable and wearable wireless sensing devices and connecting with mobile networks, thus finding considerable use in a wide range of food assurance applications. The need for an appropriate regulatory framework is emphasized which acts to exclude unwanted components in foods and includes needed components, with sensors as part of a reassurance framework supporting regulation and food chain management. The integration of these sensor modalities into a single technological and commercial platform offers an opportunity for a paradigm shift in food reassurance.Non invasive sensing; food, machine vision; Vis/NIR spectroscopy; UV Vis fluorescence; hyperspectral imaging, ultrasound; electronic nose; electronic tongue; conductivity; NMR; X ray; Microwave; Terahertz; quality; reassurance 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 In recent years, consumers and governments are increasingly demanding information and reassurance on the content as well as on the origin of their food 1 3 . Food industry (suppliers, manufactures and supermarkets) are required to provide and confirm the authenticity and point of origin of food products and their components, in order to protect consumer rights and prevent fraudulent or deceptive practices such as food adulteration. Therefore, food reassurance is crucial to food production and security of food supply 4 6 . Page 2 of 65 AnalystThe concept of reassurance relies on the fact that it is desirable to ensure that the food adheres to its implied or commonly understood description. For example, free from inanimate objects such as glass, plastic, metal that can enter through the manufacturing processes; free from unacceptable levels of animate material such as viruses, bacteria, prions and spores that could lead to ...

show abstract

“…CV has been widely applied for quality assurance purposes in different industries. Several researchers, listed in the next section, studied meat products within this perspective, including beef, pork, chicken, and fish, through measuring size, shape, and color parameters using various CV and ML methods [8].…”

Section: Introductionmentioning

confidence: 99%

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

2021

View full text Add to dashboard Cite

Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy.

show abstract

Untitled

Cited by 27 publications

References 80 publications

Color assessment by different techniques of gilthead seabream (Sparus aurata) during cold storage

Color assessment by different techniques of gilthead seabream (Sparus aurata) during cold storage

Non-invasive sensing for food reassurance

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

Contact Info

Product

Resources

About