Artificial nose refrigerator food freshness detection method and system based on machine learning

Abstract

The invention discloses an artificial nose refrigerator food freshness detection method and system based on machine learning, and belongs to the field of refrigerator food detection. Obtaining an original response data set X of food volatile gas in the refrigerator; carrying out median filtering processing to obtain a sample set Xm; dividing the sample set into a training set Xtrain and a test set Xtest according to a ratio of 3: 1; standardizing the training set and the test set; carrying out feature dimension reduction on the processed training set Xtrain std and the test set Xtestd to generate new feature subsets Xtrain pca and Xtestpca, and carrying out feature dimension reduction on the processed training set Xtrain std and the test set Xteststd to generate new feature subsets Xtrain pca and Xtestpca; and training the training set Xtrain pca by using a machine learning algorithm to obtain an optimal parameter of the model, classifying and identifying the freshness of the food in the test set Xtestpca, and verifying the prediction performance of the model. The freshness of food in the refrigerator can be quickly predicted, the hardware cost of the system is reduced through a software algorithm, and the prediction precision is improved.

Description

technical field [0001] The invention relates to the field of refrigerator food detection, in particular to a method and system for detecting the freshness of refrigerator food with an artificial nose based on machine learning. Background technique [0002] Food freshness directly affects people's health and safety, and is a major issue related to the national economy and people's livelihood. Monitoring food freshness is a necessary means to ensure food safety. In daily life, the refrigerator is a commonly used low-temperature food preservation method. As time goes on, the food stored in the refrigerator will also rot and deteriorate. Simultaneously due to the relatively sealed environment inside the refrigerator, it is difficult for people to find the deterioration of food in time. Therefore, it is of great significance to develop a fast and accurate detection technology for the freshness of food in the refrigerator. [0003] Existing food freshness detection methods mai...

AI Technical Summary

Problems solved by technology

[0003] Existing food freshness detection methods mainly rely on expensive analytical instruments, which are costly, complicated to operate, long in analysis time, and require a strict laboratory environment, which can damage samples and is not convenient for daily use

However, the traditional method of sensory evaluation is mainly completed by well-trained professionals, which is easily affected by external factors such as age, gender, mood, experience, etc., has strong subjectivity, poor repeatability, and is prone to olfactory fatigue

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