A modified nanocomposite biosensor for quantitative l-glutamate detection in beef.

A new biosensor for detecting l-glutamate (l-Glu) in beef was developed. Firstly, a bare Au electrode was surface-modified by gold nanoparticles (Au NPs), graphene oxide (GO), and chitosan (CS) as immobilized materials, and then its surface was connected with l-glutamate oxidase (GluOx). The modified Au NPs/GO/CS electrode was characterized by scanning electron microscopy, and the formation mechanism was elaborated. The response current of the l-Glu biosensor maximized to 0.08 mA at pH 7.5 and 0.09 mA at 30 °C, with a detection range of 0.2-1.4 mM and a detection limit of 0.023 mM. The l-Glu biosensor had high accuracy, and its results linearly fitted with those of the amino acid analyzer with a coefficient of 0.996. The l-Glu biosensor had high selectivity, repeatability, and stability and detected higher l-Glu content in the cooked beef than in the raw beef.

A PVC/polypyrrole sensor designed for beef taste detection using electrochemical methods and sensory evaluation.

An electrochemical sensor for detection of beef taste was designed in this study. This sensor was based on the structure of polyvinyl chloride/polypyrrole (PVC/PPy), which was polymerized onto the surface of a platinum (Pt) electrode to form a Pt-PPy-PVC film. Detecting by electrochemical methods, the sensor was well characterized by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The sensor was applied to detect 10 rib-eye beef samples and the accuracy of the new sensor was validated by sensory evaluation and ion sensor detection. Several cluster analysis methods were used in the study to distinguish the beef samples. According to the obtained results, the designed sensor showed a high degree of association of electrochemical detection and sensory evaluation, which proved a fast and precise sensor for beef taste detection.

Sensor-array-based evaluation and grading of beef taste quality.

A sensor array composed of ion electrodes including 2 glass electrodes, 3 liquid-membrane electrodes and 7 insoluble salt electrodes was built. Before detection, the working electrodes were activated as required in activate fluids, and the stability states of sensors were analyzed in deionized water. Beef samples were evaluated after all working electrodes stabilized. The response signals from the samples were recorded by an electrochemical workstation and used as the evaluation results. A beef taste sensory evaluation criterion was built and used into sensory evaluation of beef samples. The samples were scored with quality grades according to this criterion, and the results were compared with the results of the sensor array in evaluation of beef broth samples. The evaluation results were processed by principal component analysis and used to build a beef taste quality evaluation model based on artificial neural networks. Tests show this model has an accuracy of 90% in classification of beef taste quality grades.