Research On The Shelf Life Prediction Model For Fresh-Cutlettuce

Fresh cut vegetables and fruits are fresh produce that has been processed using different techniques to create ready-to-eat fresh produce products with high nutritional value.They are widely loved by consumers and have a broad range of processing and utilization prospects.In the food industry,the shelf-life prediction model for fresh cut vegetables is an important research direction that has significant implications for ensuring product quality,reducing food waste,and improving supply chain efficiency.However,users have difficulty in building models due to the long learning curve and the discrete analysis results,which make it difficult to accumulate sustained research achievements.To efficiently support research activities and create a research database,this article constructs a shelf-life prediction model for fresh cut lettuce and explores the optimal process parameters for maximizing the preservation of fresh cut lettuce under different processing techniques.In this paper,the experimental data of lettuce processing and preservation provided by an institute of agricultural product processing and food nutrition were studied,the experimental data included physical,chemical,biological and sensory characteristics of fresh-cut lettuce after heat shock treatment,ethanol treatment and pulsed light treatment,the parameters under different processes were evaluated.Firstly,the sensitivity of physical,chemical,biological and sensory characteristics of fresh-cut lettuce treated with heat shock,ethanol and pulsed light is different.In this paper,through the significance test of different indexes and the principal component analysis of 10 flavor indexes,the changes of indexes in different treatment groups with time were investigated,combined with the apriori knowledge of each index of fresh-cut lettuce,the optimum technological parameters of fresh-cut lettuce preservation were determined.Secondly,LASSO regression and principal component regression were used to screen and model-fit the characteristics of fresh-cut lettuce under the treatment of the three processing parameters,so as to solve the serious Multicollinearity problem among the variables,the evaluation indexes of R~2,RMSE,AIC and BIC were compared,and the optimal model was selected as the shelf life prediction model.Thirdly,the independent variable selected by the shelf life prediction model was used as the quality evaluation index of fresh-cut lettuce,and the TOPSIS algorithm based on the entropy weight method was adopted,the synthetic distances of the process parameters under heat shock,ethanol immersion and pulsed light treatment were calculated respectively,and the optimal process parameters were determined according to the synthetic distances.In this paper,the principle and process of data analysis,modeling and evaluation algorithm for fresh-cut lettuce are discussed in detail,and the experimental results of each stage are given,the change of fresh-cut lettuce characteristics with time was revealed,which provided a scientific method to predict the shelf life of fresh-cut lettuce.It provides a model basis for the development of food shelf life prediction software.