title: [Nondestructive test on predicting sugar content and valid acidity of mango by spectroscopy technology].
abstract: Mango is a kind of popular tropic fruit in the word, and its quality will affect the health of consumers. Unsaturated acid is an important component in mango. So it is very important and necessary to detect the sugar content and valid acidity in mango fast and non-destructively. Visible and short-wave near-infrared reflectance spectroscopy (VIS/SWNIRS) was applied in the present study to predict sugar content and valid acidity of mango. Because of the non-linear information in spectral data characteristics of the pattern were analyzed by neural network optimized by genetic algorithm (GA-BP). Spectral data were compressed by the partial least squares (PLS). The best number of principal components (PCs) was selected according the accumulative reliabilities (AR). PCs could be used to replace the complex spectral data. After some preprocessing and through full cross validation, 17 principal components presenting important information of spectra were confirmed as the best number of principal components for valid acidity, and 18 PCs as best number of principal components for sugar content. Then, these best principal components were taken as the input of GA-BP neural network. One hundred thirty five samples were randomly collected as modeling, and the remaining 45 as samples to check the forecast results by the model. For the sake of testing the GA-BP model, at the same time we took the BP neural network on the same PCs. The quality of the calibration model was evaluated by the correlation coefficients (R) and standard error of calibration (SECV), and the prediction results were assessed by correlation coefficients (R) and standard error of prediction (SEP). Comparing PLS-BP model with PLS-GA-BP model, the coefficients of determination (R) of 0.788/0.83699 and standard errors of prediction (SEP) of 0.133312/0.109447 were calculated in valid acidity. The sugar content result was calculated by the coefficients of determination (R) = 0.75705/0.85409 and standard errors of prediction (SEP)0.864676/0.60934. Thus, it is obvious that this model is reliable and practicable. And the PLS-GA-BP model based on the spectroscopy technology is a better pattern to predict sugar content and valid acidity of mango, giving a new method for detecting fruit's sugar content and valid acidity.
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
authors.forename: Jia-jia
authors.initials: JJ
authors.lastname: Yu
authors.forename: Yong
authors.initials: Y
authors.lastname: He
authors.forename: Yi-dan
authors.initials: YD
authors.lastname: Bao
mesh_terms: D002241:Carbohydrates / Q000032:analysis / Q000737:chemistry; D005638:Fruit / Q000737:chemistry; D031022:Mangifera / Q000737:chemistry*; D016571:Neural Networks, Computer; D013057:Spectrum Analysis
chemical_list: D002241:Carbohydrates