title: Real-time evaluation of polyphenol oxidase (PPO) activity in lychee pericarp based on weighted combination of spectral data and image features as determined by fuzzy neural network.
abstract: A novel method of using hyperspectral imaging technique with the weighted combination of spectral data and image features by fuzzy neural network (FNN) was proposed for real-time prediction of polyphenol oxidase (PPO) activity in lychee pericarp. Lychee images were obtained by a hyperspectral reflectance imaging system operating in the range of 400-1000nm. A support vector machine-recursive feature elimination (SVM-RFE) algorithm was applied to eliminating variables with no or little information for the prediction from all bands, resulting in a reduced set of optimal wavelengths. Spectral information at the optimal wavelengths and image color features were then used respectively to develop calibration models for the prediction of PPO in pericarp during storage, and the results of two models were compared. In order to improve the prediction accuracy, a decision strategy was developed based on weighted combination of spectral data and image features, in which the weights were determined by FNN for a better estimation of PPO activity. The results showed that the combined decision model was the best among all of the calibration models, with high R(2) values of 0.9117 and 0.9072 and low RMSEs of 0.45% and 0.459% for calibration and prediction, respectively. These results demonstrate that the proposed weighted combined decision method has great potential for improving model performance. The proposed technique could be used for a better prediction of other internal and external quality attributes of fruits.
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China.
authors.forename: Yi-Chao
authors.initials: YC
authors.lastname: Yang
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China; Food Refrigeration and Computerized Food Technology, Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland. Electronic address: dawen.sun@ucd.ie.
authors.forename: Da-Wen
authors.initials: DW
authors.lastname: Sun
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China.
authors.forename: Nan-Nan
authors.initials: NN
authors.lastname: Wang
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China.
authors.forename: Anguo
authors.initials: A
authors.lastname: Xie
mesh_terms: D000465:Algorithms; D002138:Calibration; D004156:Catechol Oxidase / Q000032:analysis* / Q000378:metabolism*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D032125:Litchi / Q000737:chemistry*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D060388:Support Vector Machine
chemical_list: D004156:Catechol Oxidase
keywords: Fuzzy neural network (FNN); Image feature; Lychee; Multiband imaging; Support vector machine-recursive feature elimination (SVM-RFE)