Shape similarity measure using turn angle cross-correlation for oyster quality evaluation

Xiong, Guangming; Lee, Dah-Jye; Moon, Kevin R.; Lane, Robert M.

doi:10.1016/j.jfoodeng.2010.03.043

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…With this regard, a MVS capable of sorting oysters by size and detecting irregular shapes was developed to grade oysters into good quality, banana, and irregular grades based on 50 representative oyster images for each shape category, by using a shape similarity measure called turn angle cross-correlation (Xiong et al, 2010). The results obtained in this study showed a very high shape grading accuracy (96.9%, 100%, and 94.3% for good, banana, and irregular categories, respectively) compared to human grading results.…”

Section: Electronic Eyementioning

confidence: 99%

Quality Evaluation of Fish and Other Seafood by Traditional and Nondestructive Instrumental Methods: Advantages and Limitations

Hassoun¹,

Karoui²

2015

Critical Reviews in Food Science and Nutrition

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Although being one of the most vulnerable and perishable products, fish and other seafoods provide a wide range of health-promoting compounds. Recently, the growing interest of consumers in food quality and safety issues has contributed to the increasing demand for sensitive and rapid analytical technologies. Several traditional physicochemical, textural, sensory, and electrical methods have been used to evaluate freshness and authentication of fish and other seafood products. Despite the importance of these standard methods, they are expensive and time-consuming, and often susceptible to large sources of variation. Recently, spectroscopic methods and other emerging techniques have shown great potential due to speed of analysis, minimal sample preparation, high repeatability, low cost, and, most of all, the fact that these techniques are noninvasive and nondestructive and, therefore, could be applied to any online monitoring system. This review describes firstly and briefly the basic principles of multivariate data analysis, followed by the most commonly traditional methods used for the determination of the freshness and authenticity of fish and other seafood products. A special focus is put on the use of rapid and nondestructive techniques (spectroscopic techniques and instrumental sensors) to address several issues related to the quality of these products. Moreover, the advantages and limitations of each technique are reviewed and some perspectives are also given.

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Section: Electronic Eyementioning

confidence: 99%

Quality Evaluation of Fish and Other Seafood by Traditional and Nondestructive Instrumental Methods: Advantages and Limitations

Hassoun¹,

Karoui²

2015

Critical Reviews in Food Science and Nutrition

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“…Although progress has been made in performance of automated grading machines (see Xiong et al . ), single bred, clean, uniformly shaped bivalves would allow better machine‐shucking, saving time and on work that is tedious, demanding and dangerous to ‘shuckers’ (Wheaton & Hall ). In relation to oyster marketing, the shelf life is very important and increased by oyster shelf thickness as it can stand higher impacts during handling and transportation.…”

Section: Discussionmentioning

confidence: 99%

“…Heath & Wilson ; Xiong et al . ) but require calibration for optimal shell shape thresholds which are still not standardized in the shellfish industry.…”

Section: The ‘Good’ Versus the ‘Bad’ Oyster Based On The Shellmentioning

confidence: 99%

Seeking the perfect oyster shell: a brief review of current knowledge

Mizuta

Wikfors

2018

Reviews in Aquaculture

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Current trends in the seafood market indicate increased demand for within-shell oysters, associated with increasing popularity of oyster bars. As this specialized, demanding market increases globally, there is strong incentive to improve quality. In this context, the manipulation of oyster shell traits through aquaculture as a means of improving oyster quality is timely and promising in terms of marketing. Several oyster shell characteristics, most especially shape, measured as length (L), width (W) and depth (D), result from a combination of three factors: genetics, environmental condition to which the oyster is exposed and husbandry practices. Although breeding programmes have targeted several important commercial traits, selection for shell traits has not been widely performed. Additionally, accumulated knowledge of the effects of environmental conditions and farming methods on shell characteristics exists at a local level, but as such, it is not always validated with scientific data. The existing local knowledge and practices, however, are of extreme importance for the improvement and adaptation of the farming sector to market demands. Current knowledge about the genetic, environmental and husbandry effects on shell and related aquaculture practices for manipulation of shell appearance and robustness in commercially important oysters of the Genus Crassostrea is compiled here. As the topic has not been well documented in the academic field, information from scientific articles was complemented by technical reports and commercial sources. A combination of aquaculture practices is needed to produce oysters of acceptable shape, thickness, cleanliness and colour, although suitable shell characteristics may vary with different markets.

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“…Machine vision methods were developed to estimate the volume and weight of raw oyster meat [38,39]. Not many papers in the literature reported research on shape grading specifically for whole oysters [40,41]. Machine learning methods have been successfully used for shape analysis [10,11] but none of the them used machine learning techniques that are able to automatically learn distinct features from the images and adapt for the different grading criteria the grower prefers.…”

Section: Oyster Shape Qualitymentioning

confidence: 99%

Smart Camera for Quality Inspection and Grading of Food Products

et al. 2020

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Due to the increasing consumption of food products and demand for food quality and safety, most food processing facilities in the United States utilize machines to automate their processes, such as cleaning, inspection and grading, packing, storing, and shipping. Machine vision technology has been a proven solution for inspection and grading of food products since the late 1980s. The remaining challenges, especially for small to midsize facilities, include the system and operating costs, demand for high-skilled workers for complicated configuration and operation and, in some cases, unsatisfactory results. This paper focuses on the development of an embedded solution with learning capability to alleviate these challenges. Three simple application cases are included to demonstrate the operation of this unique solution. Two datasets of more challenging cases were created to analyze and demonstrate the performance of our visual inspection algorithm. One dataset includes infrared images of Medjool dates of four levels of skin delamination for surface quality grading. The other one consists of grayscale images of oysters with varying shape for shape quality evaluation. Our algorithm achieved a grading accuracy of 95.0% on the date dataset and 98.6% on the oyster dataset, both easily surpassed manual grading, which constantly faces the challenges of human fatigue or other distractions. Details of the design and functions of our smart camera and our simple visual inspection algorithm are discussed in this paper.Electronics 2020, 9, 505 2 of 18 or sorting machines for food products on the market have been designed to automate these tasks and have proven to be very successful.Many recent developments were aimed at broad grading applications. For example, a machine learning-based system was built to use color and texture features for tomato grading and surface defect detection [1]. Color and texture features were collected for maturity evaluation, defect detection, size and shape analysis for mangoes [2]. Guava fruits were classified into four maturity levels using the K-nearest neighbor (KNN) algorithm to analyze color distribution in HSI (Hue, Saturation, and Intensity) color space [3]. Image processing techniques were used for corn seed grading [4], plum fruit maturity evaluation [5], quality assessment of pomegranate fruits [6], and the ripeness of palm fruit [7]. Two grading systems for date maturity [8] and skin quality [9] evaluation were designed specifically for Medjool dates. All these recent developments were designed for their unique grading applications. Although some may be easier than others to adapt for similar applications, minor adjustments are needed even for a simple generic solution such as color grading or shape analysis.A comprehensive review of fruit and vegetable quality evaluation systems revealed that many systems use slightly different handcrafted features for even the same applications [10]. For example, twenty-six different color features in four different color spaces and twenty-two texture fe...

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Shape similarity measure using turn angle cross-correlation for oyster quality evaluation

Cited by 11 publications

References 9 publications

Quality Evaluation of Fish and Other Seafood by Traditional and Nondestructive Instrumental Methods: Advantages and Limitations

Quality Evaluation of Fish and Other Seafood by Traditional and Nondestructive Instrumental Methods: Advantages and Limitations

Seeking the perfect oyster shell: a brief review of current knowledge

Smart Camera for Quality Inspection and Grading of Food Products

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