食品科学 ›› 2020, Vol. 41 ›› Issue (1): 55-60.doi: 10.7506/spkx1002-6630-20190619-219

• 基础研究 • 上一篇    下一篇

基于自组织映射模型对香肠产品喜好度的预测

刘宇佳,朱杰,张书艳,李琳   

  1. (东莞理工学院化学工程与能源技术学院,食品营养健康工程与智能化加工研究中心,广东 东莞 523808)
  • 出版日期:2020-01-15 发布日期:2020-01-19
  • 基金资助:
    “十三五”国家重点研发计划重点专项(2016YFD0400206);广东省普通高校青年创新人才类项目(2018KQNCX259); 东莞理工学院高层次人才(创新团队)科研启动项目(KCYCXPT2017007)

Prediction of Preference for Sausage Based on Self-Organizing Maps Model

LIU Yujia, ZHU Jie, ZHANG Shuyan, LI Lin   

  1. (Engineering Research Center of Health Food Design & Nutrition Regulation, School of Chemical Engineering and Energy Technology, Dongguan University of Technology, Dongguan 523808, China)
  • Online:2020-01-15 Published:2020-01-19

摘要: 本研究基于感官评定结合自组织映射(self-organizing maps,SOM)模型预测香肠产品的喜好度。采集9 种原料制备的99 个香肠样品的质构参数与颜色信息作为研究对象,对上述信息与感官评定结果进行线性回归分析与相关性评价,进一步结合主成分分析方法消除冗余数据,最终建立竞争层为6、输出层为36的SOM模型。结果表明,通过对香肠样本特征值的提取与分类预测,基于SOM的模型预测准确率为100%,此时预测集均方根误差为0.118 4,模型具有良好的泛化能力。本研究旨在建立一种准确高效的食品喜好度预测方法,为食品新产品研发及其市场喜好度预期提供数据参考。

关键词: 自组织映射, 香肠, 喜好度, 预测

Abstract: Sausage preference was predicted using self-organizing maps (SOM) based on sensory evaluation. The texture parameters and color data of 99 sausage samples were collected and correlated versus the sensory evaluation results using linear regression analysis. Principal component analysis (PCA) was used to eliminate the redundant data. An SOM model with the competition layer of 6 neurons and the output layer of 36 neurons was established. The results showed that the accuracy rate was 100% by extracting and classifying the eigenvalues of sausage samples. At this time, the root mean square error (RMSE) of the prediction set was 0.118 4, which implies that the model showed good generalization ability. This study aims to establish an accurate and efficient method for predicting food preference, which will provide useful data for new food product development and market preference prediction.

Key words: self-organizing maps, sausage, preference, prediction

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