A fast and non-destructive LF-NMR and MRI method to discriminate adulterated shrimp


Abstract:

Gel injection for shrimp is a commonly adulterated way to obtain illegal profits by unscrupulous traders. In this study, a method based on low field nuclear magnetic resonance (LF-NMR) and magnetic resonance imaging was developed to investigate the gel-injected adulteration in a fast and non-destructive manner. Shrimp samples injected with carrageenan in the range of 0.2–1.2 mL at 0.2 mL increments were measured and analyzed. The Carr–Purcell–Meiboom–Gill (CPMG) decay curves of LF-NMR indicated that the adulterated shrimp decayed slower as the injected gel increased. Differences of relaxation times were observed from spin–spin relaxation curve (T2) for different amount of gel adulteration. Besides, the areas of expelled bulk water (A24) and the areas of all populations (ATotal) vary with the quantity of injected gel regularly. Partial least square model was applied using CPMG decay signals, A24, and ATotal for the prediction of gel injection in shrimps. Different dimension processed methods were used for modeling, in which multi-exponential fitting method exhibited the best model stability and a better accuracy (R2 of prediction sets = 0.9156). The characteristics of MR images from the adulterated shrimps were extracted using image processing techniques. The MR image processing results revealed that adulterated shrimp samples can be identified based on the gray level and area of injection sites from the MR images.

Año de publicación:

2018

Keywords:

  • Partial least square
  • magnetic resonance imaging
  • Low field nuclear magnetic resonance
  • SHRIMP
  • Gel injection

Fuente:

scopusscopus

Tipo de documento:

Article

Estado:

Acceso restringido

Áreas de conocimiento:

    Áreas temáticas de Dewey:

    • Física aplicada
    • Química analítica
    • Biología
    Procesado con IAProcesado con IA

    Objetivos de Desarrollo Sostenible:

    • ODS 12: Producción y consumo responsables
    • ODS 2: Hambre cero
    • ODS 3: Salud y bienestar
    Procesado con IAProcesado con IA