Automated fish bone detection using X-ray imaging

Mery, Domingo; Lillo, Iván; Loebel, Hans; Riffo, Vladimir; Soto, Álvaro; Cipriano, Aldo; Aguilera, José Miguel

doi:10.1016/j.jfoodeng.2011.03.007

Cited by 84 publications

(42 citation statements)

References 24 publications

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“…X rays with photon energies above 5 10 keV (below 0.2 0.1 nm wavelength) are called hard X rays, while those with lower energy are called soft X rays 183,184 . Since Röntgen discovered them in 1895, X rays are widely used to image the inside of objects by using their penetrating ability 185 . FDA (US Food and Drug Administration)…”

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confidence: 99%

“…Some internal disorders with negative effects on quality that should be detectable by X ray techniques include cork spot, bitter pit, water core, and brown core for apple, blossom and decline, membranous stain, black rot, seed germination, and freeze damage for citrus, and hollow heart, bruises, and perhaps black heart for potato 56,191,208,209 , automatically detect fish bones in fish fillets ( Fig. 9 (d)) 185 . Nielsen, Mikkel Schou, et al 189 present a novel approach for detection of organic foreign bodies such as paper and insects in two food products using X ray dark field imaging with a grating interferometer.…”

Section: Page 14 Of 65 Analystmentioning

confidence: 99%

“…9. 185 .Foreign bodies are a major reason for consumer complaints in the food industry. The application of good manufacturing practice and hazard analysis throughout the whole food supply chain is the most effective way to prevent and reduce contamination and thereby protect consumers.…”

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Non-invasive sensing for food reassurance

Zhang

Huang

Povey

2016

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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 ...

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Non-invasive sensing for food reassurance

Zhang

Huang

Povey

2016

Analyst

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“…In this context, x-ray imaging has been a preferred technique which lets one examine the ingredient internally without causing any damage to the ingredient itself. It reveals the internal details which allow the presence of worm damage and other defects to be determined in a safe way [1][2][3]. In nuts selection, the key objective is to reduce the amount of nuts with navel orange worm damage passed to the consumer.…”

Section: Introductionmentioning

confidence: 99%

“…For inspection of the ingredient, image processing is a potential tool for unveiling the hidden damage present inside the ingredient, and to highlight the concealed facts. Work has been carried out in processing the images of food ingredients for identifying the damage present in it [1][2][3][4]. However, the efforts made to demonstrate the extraction of individual nutmeatsRegions of Interest (ROI) -are limited, in particular, where an ample image is captured of a large number of ingredients by the x-ray source.…”

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confidence: 99%

A Machine Vision System for Quality Inspection of Pine Nuts

Khosa¹,

Pasero²

2016

ijacsa

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Abstract-Computers and artificial intelligence have penetrated in the food industry since last decade, for intellectual automatic processing and packaging in general, and in assisting for quality inspection of the food itself in particular. The food quality assessment task becomes more challenging when it is about harmless internal examination of the ingredient, and even more when its size is also minute. In this article, a method for automatic detection, extraction and classification of raw food item is presented using x-ray image data of pine nuts. Image processing techniques are employed in developing an efficient method for automatic detection and then extraction of individual ingredient, from the source x-ray image which comprises bunch of nuts in a single frame. For data representation, statistical texture analysis is carried out and attributes are calculated from each of the sample image on the global level as features. In addition co-occurrence matrices are computed from images with four different offsets, and hence more features are extracted by using them. To find fewer meaningful characteristics, all the calculated features are organized in several combinations and then tested. Seventy percent of image data is used for training and 15% each for crossvalidation and test purposes. Binary classification is performed using two state-of-the-art non-linear classifiers: Artificial Neural Network (ANN) and Support Vector Machines (SVM). Performance is evaluated in terms of classification accuracy, specificity and sensitivity. ANN classifier showed 87.6% accuracy with correct recognition rate of healthy nuts and unhealthy nuts as 94% and 62% respectively. SVM classifier produced the similar accuracy achieving 86.3% specificity and 89.2% sensitivity rate. The results obtained are unique itself in terms of ingredient and promising relatively. It is also found that feature set size can be reduced up to 57% by compromising 3.5% accuracy, in combination with any of the tested classifiers.

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