Study on the moisture content detection method of fresh milk based on dielectric property

doi:10.6048/j.issn.1001-4330.2025.04.022

Abstract

Abstract:

【Objective】 Explore the relationship between milk dielectric characteristics and moisture content,In order to scientifically and effectively predict the moisture content of raw milk, realize rapid detection of moisture content. 【Methods】 The dielectric constant (ε') and dielectric loss factor (ε″) of milk samples between 2 and 20 GHz were measured using vector network analyzer and coaxial probe. PLSR, SVR and PSO-LSVR were used to use ε'or ε″ as variables to predict the milk from 86.9% to 92.9% and select the best, so as to achieve the accurate prediction of milk moisture content. 【Results】 The results showed that as the frequency increased, ε' gradually decreased, while ε'' gradually increased. Among the six models, ε″ based on the PSO-LSSVR method had the best water content prediction performance, with R² and RMSE of 0.996,3 and 0.001,3, respectively. 【Conclusion】 The results can provide methodological guidance for predicting other nutritional indicators of milk and a reference for studying the quality of other natural emulsions.

Key words: milk; moisture content; dielectric property; particle swarm algorithm; support vector regression; partial least squares regression

摘要：

【目的】探索牛奶介电特性和含水率之间的关系,为有效预测生鲜牛奶含水率、实现含水率快速检测提供参考。【方法】采用矢量网络分析仪和同轴探头测量2~20 GHz频率范围内牛奶样品的介电常数(ε')和介质损耗因数(ε″)。采用偏最小二乘回归(PLSR)、支持向量回归(SVR)和基于粒子群优化的最小二乘支持向量回归(PSO-LSSVR)3种建模方法,依次以ε'和ε″为变量建立6种数学模型对牛奶含水率进行预测并选优,精准预测牛奶含水率。【结果】随着频率的增加,ε'呈逐渐减小的趋势,ε″呈逐渐增大的趋势。6种模型中基于PSO-LSSVR方法下以ε″为变量建立的模型具有最好的含水率预测性能,其R²和RMSE分别为0.996 3和0.001 3。【结论】在2~20 GHz,随着频率的增加,ε'呈逐渐减小的趋势,而ε″则逐渐增加,介电特性可有效地预测牛奶的含水率。

关键词: 牛奶, 含水率, 介电特性, 粒子群算法, 支持向量回归, 偏最小二乘回归

CLC Number:

WANG Huan, ZHANG Yiteng, WANG Bin, ZHOU Tong, LIANG Qing, ZHANG Hong. Study on the moisture content detection method of fresh milk based on dielectric property[J]. Xinjiang Agricultural Sciences, 2025, 62(4): 975-981.

王欢, 张轶腾, 王斌, 周童, 梁清, 张宏. 基于介电特性的生鲜牛奶含水率检测方法[J]. 新疆农业科学, 2025, 62(4): 975-981.

Figures/Tables 7

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参数 Parameter	最小值 Minimum(%)	最大值 Maximum(%)	平均值±标准偏差 Average value± standard deviation(%)
含水率Water content	86.900	92.900	89.900± 2.16
非脂乳固体质量分数Non-fat milk solid mass fraction	4.390	8.100	6.245±1.336
碳水化合物质量分数Carbohydrate mass fraction	2.710	5.000	3.855±0.824
脂肪质量分数Fat mass fraction	1.897	3.500	2.698±0.577
蛋白质质量分数Protein mass fraction	1.789	3.300	2.544±0.544

参数 Parameter	最小值 Minimum(%)	最大值 Maximum(%)	平均值±标准偏差 Average value± standard deviation(%)
含水率Water content	86.900	92.900	89.900± 2.16
非脂乳固体质量分数Non-fat milk solid mass fraction	4.390	8.100	6.245±1.336
碳水化合物质量分数Carbohydrate mass fraction	2.710	5.000	3.855±0.824
脂肪质量分数Fat mass fraction	1.897	3.500	2.698±0.577
蛋白质质量分数Protein mass fraction	1.789	3.300	2.544±0.544

建模方法 Modeling method	建模变量 Mode- ling variable		训练阶段 Training phase				预测阶段 Prediction phase
建模方法 Modeling method	建模变量 Mode- ling variable		R²		RMSE		R²		RMSE
PLSR		ε'		0.994 4		0.001 0		0.867		0.002 4
PLSR		ε″		0.999 3		0.000 4		0.901 5		0.001 8
SVR		ε'		0.993 6		0.001 8		0.978 1		0.004 0
SVR		ε″		0.997 9		0.001 5		0.792 2		0.010 2
PSO- LSSVR		ε'		0.991 7		0.001 7		0.992 0		0.002 6
PSO- LSSVR		ε″		0.995 3		0.001 4		0.996 3		0.001 3

建模方法 Modeling method	建模变量 Mode- ling variable		训练阶段 Training phase				预测阶段 Prediction phase
建模方法 Modeling method	建模变量 Mode- ling variable		R²		RMSE		R²		RMSE
PLSR		ε'		0.994 4		0.001 0		0.867		0.002 4
PLSR		ε″		0.999 3		0.000 4		0.901 5		0.001 8
SVR		ε'		0.993 6		0.001 8		0.978 1		0.004 0
SVR		ε″		0.997 9		0.001 5		0.792 2		0.010 2
PSO- LSSVR		ε'		0.991 7		0.001 7		0.992 0		0.002 6
PSO- LSSVR		ε″		0.995 3		0.001 4		0.996 3		0.001 3