Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging
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Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging Shuxiang Fan & Wenqian Huang & Zhiming Guo & Baohua Zhang & Chunjiang Zhao
Received: 8 October 2014 / Accepted: 22 December 2014 # Springer Science+Business Media New York 2015
Abstract Hyperspectral imaging technique was investigated to determine the soluble solids content (SSC) and firmness of pears. A total of 160 pear samples were prepared for the calibration (n=120) and prediction (n=40) sets. A hyperspectral imaging system was used to acquire hyperspectral reflectance image from each pear in visible and near infrared (400– 1000 nm) regions. Mean spectra were extracted from the regions of interest for the hyperspectral image of each pear. Spectral data were first pretreated with different preprocessing techniques and analyzed using partial least square (PLS) to establish calibration models. However, the large size of spectral data contains a large number of redundant variables that lead to complexity and poor predicting ability of calibration models. Several variable selection methods were investigated to select effective wavelength variables for the determination of SSC and firmness of pear. In this study, the variables selected by successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS) and the combination of CARS and SPA were used for PLS regression. The CARSSPA-PLS models based on 25 and 22 variables achieved the optimal performance for two internal quality indices compared with full-spectrum PLS, CARS-PLS, and SPA-PLS models. The correlation coefficient (rpre) and root mean square error of prediction (RMSEP) by CARS-SPA-PLS were 0.876, 0.491 for SSC and 0.867, 0.721 for firmness, respectively. The overall results indicated that the CARS-SPA was a powerful way for the selection of effective variables and the hyperspectral imaging system together with CARS-SPAS. Fan : W. Huang : Z. Guo : B. Zhang : C. Zhao (*) Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China e-mail: [email protected] S. Fan : C. Zhao College of Mechanical and Electronic Engineering, Northwest Agricultural and Forestry University, Yangling, Shaanxi 712100, China
PLS model could be applied as a fast and potential method for the determination of SSC and firmness of pear. Keywords Hyperspectral imaging . Soluble solids content . Firmness . Pear . Variable selection
Introduction Pear is one of the most popular fruits in the global fresh produce market. Today, consumers are demanding pear with high internal quality standards, rather than pear which looks mouthwatering but actually tastes insipid or has an undesirable texture (Paz et al. 2009). In turn, improvements in pear quality lead to an increased purchases by the consumers and increase profit margins for the fruit industry through price differentiations for different quality grades of pears (Mendoza et al. 2014). Soluble solids content (SSC) and firmness are importa
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