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. 2023 Sep 8;12(18):3372.
doi: 10.3390/foods12183372.

Predictive Modeling Analysis for the Quality Indicators of Matsutake Mushrooms in Different Transport Environments

Yangfeng Wang  1 , Xinyi Jin  1 , Lin Yang  2 , Xiang He  1 , Xiang Wang  1
Affiliations

Predictive Modeling Analysis for the Quality Indicators of Matsutake Mushrooms in Different Transport Environments

Yangfeng Wang et al. Foods. .

Abstract

Matsutake mushrooms, known for their high value, present challenges due to their seasonal availability, difficulties in harvesting, and short shelf life, making it crucial to extend their post-harvest preservation period. In this study, we developed three quality predictive models of Matsutake mushrooms using three different methods. The quality changes of Matsutake mushrooms were experimentally analyzed under two cases (case A: Temperature control and sealing measures; case B: Alteration of gas composition) with various parameters including the hardness, color, odor, pH, soluble solids content (SSC), and moisture content (MC) collected as indicators of quality changes throughout the storage period. Prediction models for Matsutake mushroom quality were developed using three different methods based on the collected data: multiple linear regression (MLR), support vector regression (SVR), and an artificial neural network (ANN). The comparative results reveal that the ANN outperforms MLR and SVR as the optimal model for predicting Matsutake mushroom quality indicators. To further enhance the ANN model's performance, optimization techniques such as the Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient backpropagation algorithm techniques were employed. The optimized ANN model achieved impressive results, with an R-Square value of 0.988 and an MSE of 0.099 under case A, and an R-Square of 0.981 and an MSE of 0.164 under case B. These findings provide valuable insights for the development of new preservation methods, contributing to the assurance of a high-quality supply of Matsutake mushrooms in the market.

Keywords: Matsutake mushroom; cold chain; food control; gas conditioning; quality prediction.

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Conflict of interest statement

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
The architecture of the experiment. (a) Procedure for conducting the experiment. (b) Matsutake mushrooms are treated in two cases. (c) Using the model to guide Matsutake mushroom preservation.
Figure 2
Figure 2
Algorithm principle of SVR.
Figure 3
Figure 3
ANN model. (a) Structure of the ANN model. (b) Neuron in the hidden layer. (c) Neuron in the output layer.
Figure 3
Figure 3
ANN model. (a) Structure of the ANN model. (b) Neuron in the hidden layer. (c) Neuron in the output layer.
Figure 4
Figure 4
Radar plot of the correlation between quality indicators and storage time of Matsutake mushrooms under different preservation environments (MC stands for moisture content and SCC stands for soluble solids content). (a) Under different preservation temperature. (b) With/without cling film. (c) Under different oxygen concentrations. (d) With/without SO2 in pure air.
Figure 5
Figure 5
The training process of ANN model under Case A. (a) Gradient, Mu, and validation checks during training process. (b) Changes in MSE during the training process. (c) Predictions of the final ANN on the training datasets. (d) Predictions of the final ANN on the test datasets.
Figure 5
Figure 5
The training process of ANN model under Case A. (a) Gradient, Mu, and validation checks during training process. (b) Changes in MSE during the training process. (c) Predictions of the final ANN on the training datasets. (d) Predictions of the final ANN on the test datasets.
Figure 6
Figure 6
The training process of the ANN model under Case B. (a) Gradient, Mu, and validation checks during the training process. (b) Changes in MSE during the training process. (c) Predictions of the final ANN on the training datasets. (d) Predictions of the final ANN on the test datasets.
Figure 7
Figure 7
Comparison of regressions under Case A between MLR, SVR, and ANN models. (a) Actual-forecast chart of MLR as a representative graph. (b) Residual plot of MLR as a representative graph. (c) Actual-forecast chart of SVR as a representative graph. (d) Residual plot of SVR as a representative graph. (e) Actual-forecast chart of ANN. (f) Error distribution histogram of ANN.
Figure 7
Figure 7
Comparison of regressions under Case A between MLR, SVR, and ANN models. (a) Actual-forecast chart of MLR as a representative graph. (b) Residual plot of MLR as a representative graph. (c) Actual-forecast chart of SVR as a representative graph. (d) Residual plot of SVR as a representative graph. (e) Actual-forecast chart of ANN. (f) Error distribution histogram of ANN.
Figure 8
Figure 8
Comparison of regressions under Case B between MLR, SVR, and ANN models. (a) Actual-forecast chart of MLR as a representative graph. (b) Residual plot of MLR as a representative graph. (c) Actual-forecast chart of SVR as a representative graph. (d) Residual plot of MLR as a representative graph. (e) Actual-forecast chart of ANN. (f) Error distribution histogram of ANN.
Figure 8
Figure 8
Comparison of regressions under Case B between MLR, SVR, and ANN models. (a) Actual-forecast chart of MLR as a representative graph. (b) Residual plot of MLR as a representative graph. (c) Actual-forecast chart of SVR as a representative graph. (d) Residual plot of MLR as a representative graph. (e) Actual-forecast chart of ANN. (f) Error distribution histogram of ANN.
Figure 9
Figure 9
Performance parameters comparison. (a) Comparison of MSE and R-Square for MLR, SVR, and ANN. (b) Comparison of the time consumed by running MLR, SVR and ANN.
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