基于PCA-ELM和光谱技术预测香蕉成熟度

Banana Maturity Characteristic Prediction Based on Hyperspectral and PCA-ELM

本文利用高光谱成像技术(Hyperspectral imaging)对常温下贮存的450个未剥皮香蕉样本光谱数据进行采集,首先检测样本果肉可溶性固形物含量(TSS)、坚实度(FIM),采用SPSS单因素方差分析,然后运用线性优化岭回归分析-偏最小二乘法(RR-i PLS)建立了香蕉成熟度理化指标的光谱和图像特征分类模型,结果表明通过实验平台获取光谱数据预测香蕉可溶性固形物含量以及坚实度的相关系数R2值分别为0.92和0.94。再通过连续投影法(successive projections algorithm,SPA)法以及主成分分析法(principal component analysis,PCA)分别选取特征波长,建立基于特征波长的极限学习机(extreme learning machine,ELM)对光谱数据进行建模交叉验证。通过比较RR-i PLS,SPA-ELM与PCA-ELM三种分类预测模型,表明基于特征波长的PCA-ELM分类模型具有较好的预测性能。交叉验证准确率达到99%。为能快速无损识别香蕉果实品质提供一种有效的预测研究,基本满足对香蕉成熟度分类检测且显示出有效建模分析,且能达到有效的经济效益。

Spectral data of 450 unbarked banana samples, stored at room temperature, were collected by hyperspectral imaging tecchique. The single factor analysis of variance was used to measure the soluble solid content（TSS） and firmness（FIM）, and then a spectral and image feature classification model of banana maturity physicochemical index was established using partial least squares linear regression analysis method of ridge optimization（RR-i PLS）. The results showed that the correlation coefficient values（R2） of the soluble solid content of spectral data predicting banana and the firmness were 0.92 and 0.94. Then, characteristic wavelengths were selected by continuous projection method（successive projections algorithm, SPA） and principal component analysis（principal component, analysis, PCA） and the extreme learning machine（extreme learning machine, ELM） was established for the cross validation of spectral data based on characteristic wavelengths., The PCA-ELM classification model based on characteristic wavelength had a better prediction performance with a high accuracy by comparing the RR-i PLS, SPA-ELM and PCA-ELM classification prediction models. The accuracy of cross validation reached to 99%, which could provide an effectively predictive study for the rapid and non-destructive identification of banana quality. The proposed method basically fulfilled the classification and detection of banana maturity and could achieve effective economic benefits.