基于光谱技术的芒果糖度酸度无损检测方法研究

提出了一种用近红外光谱技术结合遗传算法和人工神经网络模型的芒果糖度酸度快速无损检测的新方法。首先用偏最小二乘法计算芒果糖度酸度光谱数据的主成分得分值,以此获取芒果的近红外指纹图谱,再结合遗传算法优化人工神经网络技术(GA-BP)进行检测。PLS分析表明,主因子选取18时对糖度具有较好的聚类作用,而主因子数17个时对酸度的聚类效果好。选取最佳主因子作为芒果糖度酸度的神经网络的输入,建立三层GA-BP人工神经网络模型。用135个芒果样本的糖度酸度用来建立遗传算法优化神经网络的芒果糖度酸度检测模型,对未知的45个芒果样本进行糖度酸度的预测。结果表明,提出的遗传算法和人工神经网络模型相结合的光谱分析方法具有很好的预测能力,为芒果糖度酸度检测方法提供了一种新方法。

Nondestructive Test on Predicting Sugar Content and Valid Acidity of Mango by Spectroscopy Technology

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 0.788/0.836 99 and standard errors of prediction(SEP) of 0.133 312/0.109 447 were calculated in valid acidity.The sugar content result was calculated by the coefficients of determination(R)=0.757 05/0.854 09 and standard errors of prediction(SEP)=0.864 676/0.609 34.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.