Determining peanut moisture content by scattering coefficient. (May 2023)

• Record Type: Journal Article
• Authors: Ma, Fangyan; Wang, Dongwei; Yin, Yuanyuan; Yin, Hang; Song, Chao; Xu, Xin; Sun, Ying; Xue, Yiwei; Zhao, Liqing
• Is Part Of: Journal of food engineering. Volume 344(2023)
• Publication Date: 2023-05
• Subjects: Microwave measurement -- Peanut -- Moisture content -- Nondestructive testing; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2022.111398
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: Moisture content is a fundamental parameter in peanut production and storage. Moisture content information is essential for crop analysis. Crop analysis is significant for production planning and improving yield. Thus, developing a moisture content measurement system with high precision and low cost can benefit peanut production, processing, and storage. To achieve accurate and rapid nondestructive testing of shelled peanuts and peanut pods, this study proposes a new method for measuring moisture content based on the microwave scattering coefficient. The microwave free-space method was used to measure the scattering parameters of peanut samples with different moisture contents (8.11–45.59% wet basis) at different temperatures and sample thicknesses. Moreover, a peanut moisture measurement model using S-parameters was developed. Using the amplitude, phase, and real part and imaginary part combinations of the S11 and S21 parameters of the peanut samples as the input and peanut moisture content as the output, moisture content and S-parameter prediction models based on the gradient boost regression trees (GBRT), extreme gradient boost (XGBoost), and fully connected deep neural network (FC-DNN) were established. Test results verified that the FC-DNN-based model had the best prediction performance, with a coefficient of determination (R 2 ) = 0.9998, mean absolute error (MAE) = 0.0891, mean square error (MSE) = 0.0254, and root mean square error (RMSE) = 0.1593. This study provides a new method for the nondestructive measurement of moisture content during peanut production and storage. Moreover, the proposed model can be applied for moisture content measurement of other agricultural products in the food processing industry. Highlights: A platform for measuring the moisture content of shelled peanuts was constructed, with a measurement range of 8–45% w. b. Moisture content, temperature and material thickness are inversely proportional to S21 amplitude. The accurate measurement of moisture is realized through fitting. The prediction model R 2 = 0.9998 based on FC-DNN can be used for the actual measurement of peanut moisture.