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Method for predicating fumigated boiled sausage texture sensory evaluation based on BP nerval net

A BP neural network and sensory evaluation technology, which can be used in testing food, measuring devices, instruments, etc., can solve the problems of texture sensory evaluation without patent reports and other problems, so as to eliminate the interference of subjective factors, and achieve high model fitting and simulation. , the effect of comprehensive information

Inactive Publication Date: 2008-08-13
UNIV OF SHANGHAI FOR SCI & TECH
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  • Summary
  • Abstract
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AI Technical Summary

Problems solved by technology

There have been reports abroad based on BP neural network to predict the sensory evaluation of wine flavor (Raptis C G, et al. Classification of aged wine distillates using fuzzy and neural network systems. There is no patent report on network application in texture sensory evaluation of meat products

Method used

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  • Method for predicating fumigated boiled sausage texture sensory evaluation based on BP nerval net
  • Method for predicating fumigated boiled sausage texture sensory evaluation based on BP nerval net
  • Method for predicating fumigated boiled sausage texture sensory evaluation based on BP nerval net

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Embodiment Construction

[0018] Such as figure 1The block diagram of the method for predicting the texture sensory evaluation of smoked sausages based on BP neural network is shown in the diagram. First, different treatments or types of smoked sausages are selected. Sensory evaluation (hardness, elasticity, cohesiveness, chewiness, juiciness, etc.) was carried out; at the same time, the mechanical measurement method was applied, and the mode of shearing or compression was applied respectively, and the texture indicators (shear force value, hardness, elasticity, cohesiveness, chewiness, etc.) for quantitative expression. Then apply the programming method of the neural network toolbox in the MatLab software, take the mechanical measurement value as the input value, and the sensory evaluation value as the output value. Through the simulation, training and iteration of the BP neural network, the preset network error target is achieved, thereby realizing the computer The simulated human brain evaluates an...

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Abstract

The present invention relates to a method for forecasting organoleptic investigation of quality and structure of smoked-and-cooked sausage based on BP NN, which employs computer system making the objective mechanical measurement as input, subjective organoleptic investigation as output to forecast by nicety value detected by apparatus, which not only liberates labour force, eliminates disturb of subjective factors of human, but also determines rapidly and exactly the quality and structure index of smoked-and-cooked sausage in real time to realize the mechanical measurement replacing of organoleptic investigation measurement entirely or partially, and follows the development directions of food industry.

Description

technical field [0001] The invention relates to an evaluation method, in particular to a sensory evaluation method for predicting the texture of smoked and boiled sausages. Background technique [0002] Smoked sausage is made of livestock and poultry meat as the main raw material, which is marinated, minced or chopped and emulsified into minced meat, mixed with various auxiliary materials, then filled into casings to form, and then baked or steamed, smoked and cooled Medium and low temperature Western-style meat products made by other processes. Common types of smoked sausages include Frankfurter, Wiener or Vienna, Bologna, and Knockwurst. Due to the low temperature (68~75°C) heating and sterilization, the shelf life is short, generally 2~3 weeks in refrigeration, and about 7 days at room temperature. Smoked and boiled sausage is deeply loved by consumers for its unique flavor, delicate taste and convenient eating. Texture is one of the important characteristics of meat p...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/02G01N3/00G01N19/00
Inventor 董庆利罗欣李保国李代禧
Owner UNIV OF SHANGHAI FOR SCI & TECH
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