title: How to predict the sugariness and hardness of melons: A near-infrared hyperspectral imaging method.
abstract: Hyperspectral imaging (HSI) in the near-infrared (NIR) region (900-1700nm) was used for non-intrusive quality measurements (of sweetness and texture) in melons. First, HSI data from melon samples were acquired to extract the spectral signatures. The corresponding sample sweetness and hardness values were recorded using traditional intrusive methods. Partial least squares regression (PLSR), principal component analysis (PCA), support vector machine (SVM), and artificial neural network (ANN) models were created to predict melon sweetness and hardness values from the hyperspectral data. Experimental results for the three types of melons show that PLSR produces the most accurate results. To reduce the high dimensionality of the hyperspectral data, the weighted regression coefficients of the resulting PLSR models were used to identify the most important wavelengths. On the basis of these wavelengths, each image pixel was used to visualize the sweetness and hardness in all the portions of each sample.
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
authors.forename: Meijun
authors.initials: M
authors.lastname: Sun
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Zhang
authors.affiliation: School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China.
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.affiliation: School of Computer Software, Tianjin University, Tianjin 300072, China. Electronic address: wzheng@tju.edu.cn.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Wang
mesh_terms: D019662:Cucurbitaceae / Q000737:chemistry*; D006244:Hardness; D016018:Least-Squares Analysis; D025341:Principal Component Analysis; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D060388:Support Vector Machine; D013649:Taste
keywords: Hardness; Hyperspectral image; Melon; Non-intrusive quality measurement; Sweetness