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Comparative study of commercial cold-cuts used NIR S and sensory analysis

Comparative study of commercial cold-cuts used NIR S and sensory analysis. Judit Belovai , R . Romvári, Gy . Bázár, A . Szabó. Introduction. Improvement of the nutritional science and the health-sound behaviour of consumers

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Comparative study of commercial cold-cuts used NIR S and sensory analysis

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  1. Comparative study of commercial cold-cuts used NIRS and sensory analysis Judit Belovai, R. Romvári, Gy. Bázár, A. Szabó

  2. Introduction • Improvement of the nutritional science and the health-sound behaviour of consumers • Sensoryand chemical characterization of heat-treated meat products • Sensory properties haveprimary importance from the aspect of consumer perception • BUT: The ingredients are also highlight of food • Healthy nutrition is a spreading trend • Improvement of the nutritional science and the health-sound behaviour of consumers • Sensoryand chemical characterization of heat-treated meat products • Sensory properties haveprimary importance from the aspect of consumer perception • BUT: The ingredients are also highlight of food

  3. Introduction • Sensory analysis : • Answerimportant questions during processing • Helpto elucidate production faults, to monitor the quality • Helpto compare production lots or differently developed products • Need the chemical composition as well near infrared spectroscopyusedwidely in the food industry: • Minimal sample preparation • Give multitude information from a single spectrum • Quantitativeand qualitative analysis • Estimationmethods

  4. The Purpose This study aimed to classify commercial cold-cut sorts (Lyoner samples of different quality and price), based on sensory tests and NIR spectroscopy.

  5. Materials and methods Sensory analysis • Full profile analysis(MSZ ISO 6564:2001) • 13 university students and teachers • 10 cm long, unstructured scale • SPSS 10.0. for Windows • PanelCheck V.1.3.2. statisticalsoftwares Material NIR spectroscopy • FOSS NIRSystems 6500 spectrometer equipped with: • WinISI II v1.5 spectral analytical software OptiProbe fiber optic module Regular Sample Transport Module (STM)

  6. Results and discussion

  7. Results of thePanel Check Character of taste Spiciness Taste acception Character of odour Oduor acception Texture elasticity Air and gel bubbles Homogenity Texture acception Colour intensity Colour acception General Impression Character of taste Spiciness Taste acception Character of odour Oduor acception Texture elasticity Air and gel bubbles Homogenity Texture acception Colour intensity Colour acception General Impression

  8. PCA analysis of Sensory results (~4 euro) (~3 euro) Homogenity Texture acception General Impression Air and gel bubbles Oduor acception Taste acception (~6 euro) (~3 euro) Colour acception (~2,5 euro) Colour intensity

  9. DFA analysis of Sensory results • Classification: 76.3% • Cross-validation: 49.1%

  10. Near Infrared Spectroscopy STM Optiprobe 0.049 5.374

  11. PCA analysis of Near Infrared Spectroscopy results Optiprobe STM 3 2 (~3 euro) 2 4 1 (~3 euro) 4 3 (~2,5 euro) (~4 euro) 1 5 (~6 euro) 5

  12. Comparative tableof the methods

  13. Summary • Based on the human panel test,thecold-cut sorts of lower quality and price provide compromised homogenity. • In connection with this, panellistsfound air sacs and gel bubbles in these samples. • The preference was markedly higher by samples of higher price-niveau, mostly attributed to the “overall impression” and “preference” characteristics. • As compared to the discriminant factor analysis based on the sensory panel test,the NIR based classification was more successful. • Latter method can be adapted to industrial processes even in an on-line manner, and provides a low-cost analytical possibility athigh sample numbers.

  14. Thank you for your attention

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