利用电学参数和机器学习无损检测番茄可溶性固形物含量

doi:10.16420/j.issn.0513-353x.2023-0645

摘要/Abstract

摘要：

利用电学参数和机器学习建立番茄可溶性固形物含量的预测模型。使用LCR测试仪检测0.1、1、10、100、1 000 kHz频率下的番茄并联等效电容、并联等效电阻、品质因子等9项电学参数，利用皮尔逊相关系数分析并确定电学特性的特征变量，基于特征变量构建番茄可溶性固形物含量的3种无损检测模型：BP神经网络（BpNN）、多元线性回归（MLR）、支持向量回归（SVR）。结果表明，在10 kHz频段下，品质因子、损耗因子、偏转角、并联等效电容及并联等效电阻5个电学参数与番茄可溶性固形物含量相关性显著。将这5个电学参数作为模型输入变量，可溶性固形物含量作为输出变量，经验证SVR模型对可溶性固形物含量的预测效果最好，决定系数R²为0.951，均方根误差RMSE为0.122，平均绝对误差MAE为0.082。本研究为番茄采后可溶性固形物含量快速无损检测提供了一种新方法。

关键词: 番茄, 可溶性固形物含量, 电学特性, 机器学习, 无损检测

Abstract:

This study aims to establish a model for predicting the soluble solids content of tomatoes using electrical parameters and machine learning techniques. An LCR meter was employed to measure nine electrical parameters of tomatoes at frequencies of 0.1，1，10，100，and 1 000 kHz，including parallel equivalent capacitance，parallel equivalent resistance，and quality factor. Key electrical characteristic variables were identified through Pearson correlation analysis. Based on these variables，three non-destructive models for predicting the soluble solids content of tomatoes were constructed：a Back Propagation Neural Network（BpNN），Multiple Linear Regression（MLR），and Support Vector Regression （SVR）. The results indicate that at 10 kHz frequency，five electrical parameters-quality factor，loss factor，deflection angle，parallel equivalent capacitance，and parallel equivalent resistance-showed significant correlation with the soluble solids content of tomatoes. By using these five electrical parameters as input variables and the soluble solids content as the output variable，the SVR model exhibited the best predictive performance，coefficient of determination R² equal to 0.951，Root Mean Square Error equal to 0.122，Mean Absolute Error equal to 0.082）. This study provides a new method for the rapid and non-destructive detection of soluble solids content in post-harvest tomatoes.

Key words: tomato, soluble solids content, electrical characteristics, machine learning, non-destructive testing

王婷婷, 谭占明, 程云霞, 马新超, 王永明. 利用电学参数和机器学习无损检测番茄可溶性固形物含量[J]. 园艺学报, 2024, 51(2): 385-395.

WANG Tingting, TAN Zhanming, CHENG Yunxia, MA Xinchao, WANG Yongming. Nondestructive Detection of Soluble Solids Content in Tomatoes Using Electrical Parameters and Machine Learning[J]. Acta Horticulturae Sinica, 2024, 51(2): 385-395.

图/表 14

图1 番茄可溶性固形物含量电学参数检测原理图

Fig. 1 Electrical parameter detection schematic of soluble solid content in tomato

图2 BP神经网络信号传播

Fig. 2 BP neural network signal propagation

图3 SVR模型映射原理

Fig. 3 SVR model mapping principle

表1 皮尔逊相关系数

Table 1 Pearson correlation coefficient

\|r\|	相关程度 Degree of relevance	\|r\|	相关程度 Degree of relevance
0.8 ~ 1.0	极强相关 Extremely strongly correlated	0.2 ~ 0.4	弱相关 Weak correlation
0.6 ~ 0.8	强相关 Strong correlation	0.0 ~ 0.2	极弱相关或无相关 Very weak or no correlation
0.4 ~ 0.6	中等程度相关 Moderately relevant

图4 番茄在不同频段下的电学参数与可溶性固形物含量之间的皮尔逊相关系数

Fig. 4 Pearson correlation coefficients between electrical parameters and SSC of tomato at different frequency bands

图5 BpNN模型训练流程图 Y：是；N：否。下同。

Fig. 5 BpNN model training flowchart Y：Yes；N：No. The same below.

表2 BpNN模型训练流程图

Table 2 BpNN paragraph settings

网络参数 Network parameter	设置或取值 Set or take a value
Epoch	100
Batch_size	10
Learning_rate	0.1
Activation function	Relu
Number of layers	3

图6 番茄可溶性固形物含量的BpNN预测值与真实值对比图

Fig. 6 BpNN prediction value compared to true value of soluble solids content in tomato

表3 番茄可溶性固形物含量的MLR模型不同输入变量效果对比

Table 3 Comparison of the effects of different input variables in the MLR model of soluble solids content in tomato

输入变量 Input variables	R²	RMSE	MAE
Q	0.513	0.560	0.518
Q + D	0.562	0.480	0.424
Q + D + θ	0.680	0.387	0.358
Q + D + θ + R_p	0.813	0.324	0.372
Q + D + θ + R_p + C_p	0.856	0.252	0.116

图7 番茄可溶性固形物含量的MLR模型预测值与真实值对比图

Fig. 7 Comparison of predicted value and true value of MLR model of soluble solids content in tomato

图8 SVR模型训练流程图

Fig. 8 SVR model training flowchart

表4 SVR参数设置表

Table 4 SVR parameter settings

参数 Pparameter	设置或取值 Set or take a value
惩罚系数C	1.8
Kernel	Linear
Gamma	Auto
Tol	0.001
Shrinkin	Ture
Cache_size	200
Max_iter	-1

图9 番茄可溶性固形物含量的SVR模型预测值与真实值对比图

Fig. 9 Comparison of predicted value and true value of SVR model of soluble solids content in tomato

表5 预测番茄可溶性固形物含量的3种模型精准度对比

Table 5 Comparison of the three models of soluble solids content in tomato

模型 Model	R²	RMSE
BpNN	0.865	0.143	0.153
MLR	0.856	0.252	0.116
SVR	0.951	0.122	0.082

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