United detection method for determinming freshness of prepared aquatic product at low temperature shelf life

A joint detection and aquatic product technology, which is applied in the direction of testing food, material inspection products, and using nuclear magnetic resonance to analyze, etc., can solve the problems that the freshness model system error cannot be ignored, achieve fast and accurate prediction of simulation effects, and meet fast and accurate requirements Detection requirements, the effect of high prediction accuracy

Active Publication Date: 2017-01-11
JIANGNAN UNIV +1
View PDF15 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the freshness model in this invention is a typical empirical model. Because the nutritional components and storage environments of the products are slightly different, the systematic error of the freshness model obtained by applying the empirical model cannot be ignored.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • United detection method for determinming freshness of prepared aquatic product at low temperature shelf life

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0035] Example 1: A joint freshness detection method for determining the low-temperature shelf life of conditioned surimi products.

[0036] First, add 0.5% salt to thawed 10% frozen surimi and grind for 15 minutes, then add 6% soybean oil, 6% egg white, 7% soybean protein, 5% potato starch and ice water to continue mixing for 5 minutes, and finally put Put it into a steamer (steam at 100°C for 15 minutes) to get the conditioned surimi product, and put it in an environment of 0-4°C for storage. The detection steps are as follows: firstly detect the TVB-N value change of the conditioned surimi products after different storage time, and carry out low-field nuclear magnetic technology and electronic nose flavor measurement, and calculate T 2 The weighted value of the peak of the curve and the electronic nose S1 value. Finally, with TVB-N value, T 2 The peak weighted value of the curve and the S1 value of the electronic nose are the input layer, and the RBF neural network shelf ...

Embodiment 2

[0037] Example 2: A combined freshness detection method for determining the low-temperature shelf life of conditioned grass carp pieces.

[0038] First, wash and cut fresh grass carp into pieces, then cut into standard pieces and drain the raw fish pieces into the seasoning liquid for dipping. The cooked fish pieces are packaged in plastic bags and placed in a refrigerated environment (0-4°C). The change of TVB-N value after different storage time of the conditioned grass carp pieces was carried out by low-field nuclear magnetic technology and electronic nose flavor determination, and the calculation of T 2 The weighted value of the peak of the curve and the electronic nose S1 value. Finally, with TVB-N value, T 2 The peak weighted value of the curve and the electronic nose S1 value are the input layer, and the RBF neural network shelf-life prediction model is output after repeated calculations through the Matlab programming software. Finally, the TVB-N value, low-field nuc...

Embodiment 3

[0039] Example 3: A combined freshness detection method for determining the low-temperature shelf life of conditioned prawns.

[0040] First, wash the Penaeus vannamei to remove the sediment impurities, and then boil it in water. The number of shrimps is limited to submerge the Penaeus vannamei in the boiling liquid; after boiling, boil for 2 to 4 minutes, then pick up the vannamei shrimp, cool and drain the water , and finally weighed and bagged and then vacuum-packed to obtain the conditioned prawn product, which was then refrigerated at 0-4°C. The TVB-N value changes of the conditioned prawns after different storage time were measured by low-field nuclear magnetic technology and electronic nose flavor, and the T 2 The weighted value of the peak of the curve and the electronic nose S1 value. Finally, with TVB-N value, T 2 The peak weighted value of the curve and the S1 value of the electronic nose are the input layer, and the RBF neural network shelf life prediction model ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

A united detection method for determinming freshness of prepared aquatic product at low temperature shelf life belongs to the technical field of food preservation. This invention is based on low field nuclear magnetic resonance (NMR) combined with the electronic nose as the main measurement tools, on the basis of regulating RBF neural network model which is eatablished among the relaxation time data, flavor changing data and volatile base nitrogen (TVB- N) data of aquatic products and based on regulating nuclear magnetic resonance (NMR) and electronic nose signal in the process of low temperature storage of aquatic products as the main target of observation, through the analysis of relaxation time data and the flavor changing data of aquatic products in the process of low temperature storage, and makes the judgement to regulate shelf life limits of aquatic products. This invention has the advantages of simple analysis process, small sample usage quantity, and high accuracy, short time-consuming, low cost and easy popularization. By adopting the similar method of the invention, the corresponding database and the prediction model can be established for other meat products, and the accurate prediction for the limit value of the shelf life during the storage process can be realized.

Description

technical field [0001] The invention relates to a combined freshness detection method for determining the low-temperature shelf life of conditioned aquatic products, which is used for judging the end of the shelf life during low-temperature storage of conditioned aquatic products, and belongs to the technical field of food preservation. Background technique [0002] In recent years, people have higher and higher requirements on the accuracy of judging the shelf life of conditioned aquatic products, hoping to intuitively and quickly understand the freshness of aquatic products at different storage times. Under such demands, the shelf-life safety and accuracy of conditioned aquatic products have become the focus of increasing attention. [0003] The evaluation methods for the freshness of prepared aquatic products include sensory evaluation, spoilage decomposition products and the degree of bacterial contamination. Among them, the method of sensory test to evaluate the freshn...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G01N24/08G01N33/02
CPCG01N24/08G01N33/02
Inventor 张慜王琳陈镇司新超
Owner JIANGNAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap