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Smart sensor to predict retail fresh fish quality under ice storage

AutorGarcía, Miriam R. ; López Cabo, Marta ; Rodríguez Herrera, Juan José ; Ramilo Fernández, Graciela; Alonso, Antonio A. ; Balsa-Canto, Eva
Palabras claveSmart sensor
Retail fish quality
Quality index method
Predictive microbiology
Core predictions
Fish-to-fish variability
Fecha de publicación2017
CitaciónJournal of Food Engineering 197: 87-97 (2017)
ResumenFish wastage and market prices highly depend on accurate and reliable predictions of product shelf life and quality. The Quality Index Method (QIM) and EU grading criteria for whitefish (Council Regulation(EC) No 2406/96, 1996) are established sensory methods used in the market to monitor fish quality. Each assessment requires the consultation of a panel of trained experts. The indexes refer exclusively to the current state of the fish without any predictions about its evolution in the following days. This work proposes the development of a smart quality sensor which enables to measure quality and to predict its progress through time. The sensor combines information of biochemical and microbial spoilage indexes with dynamic models to predict quality in terms of the QIM and EU grading criteria. Besides, the sensor can account for the variability inside the batch if spoilage indexes are measured in more than one fish sample. The sensor is designed and tested to measure quality in fresh cod (Gadus morhua) under commercial ice storage conditions. Only two spoilage indexes, psychrotrophic counts and total volatile base-nitrogen content, were required to get accurate estimations of the two usual established sensory methods. The sensor is able to account for biological variability as shown with the validation and demonstration data sets. Moreover, new research and technologies are in course to make these measurements faster and non-destructive. This would allow having at hand a smart non-intrusive fish quality sensor
Descripción11 páginas, 4 tablas, 9 figuras
Versión del editorhttp://dx.doi.org/10.1016/j.jfoodeng.2016.11.006
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