Quantitative Detection of Water Content of Winter Jujubes Based on Spectral Morphological Features

Yabei Di, Huaping Luo (), Hongyang Liu (), Huaiyu Liu, Lei Kang and Yuesen Tong
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Yabei Di: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China
Huaping Luo: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China
Hongyang Liu: College of Horticulture and Forestry, Tarim University, Alar 843300, China
Huaiyu Liu: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China
Lei Kang: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China
Yuesen Tong: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China

Agriculture, 2025, vol. 15, issue 5, 1-17

Abstract: The spectral information extracted from hyperspectral images is characterized by redundancy and complexity, while the spectral morphological features extracted from the spectral information help to simplify the data and provide rich information about the material composition. This study is based on using spectral morphological features to quantitatively detect the water content of winter jujubes, and it extends the research scope to the composite effect of spectral morphological features on the basis of previous research. Firstly, a multiple linear regression analysis was carried out on different characteristic bands. Secondly, the multiple regression terms with high significance levels were used as the characteristic variables to be fused with the extracted characteristic wavelength variables for the data fusion. Finally, a partial least squares model was established for the water content of the winter jujubes. The results of the study show that a quantitative relationship can be established between the spectral morphology characteristics and the water content of winter jujubes. The coefficients of determination of the regression equations under the characteristic bands with center wavelengths of 1024 nm, 1146 nm, 1348 nm, and 1405 nm were 0.8449, 0.7944, 0.7479, and 0.9477, respectively. After fusing the spectral morphological features, the partial least squares modeling effects were all improved. The optimal model was the fusion model at a center wavelength of 1146 nm with a correlation coefficient of 0.9942 for the calibration set and 0.8698 for the prediction set. The overall results showed that the wave valley is more reflective of the fruit quality, and the morphological characteristics of the wave valley are more suitable than those of the wave peak for the quantitative detection of the moisture content of winter jujubes.

Keywords: spectral morphological features; water content; quantitative detection; regression model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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