宽度学习的虾新鲜度检测方法

Shrimp Freshness Detection Method Based on Broad Learning System

为了提升虾新鲜度判别的准确性,提出了一种基于宽度学习(BLS)的虾新鲜度检测方法。首先采用多元散射校正(MSC)、标准正态变量校正(SNV)和直接正交信号校正(DOSC)对不同冷藏天数虾的原始高光谱进行预处理,再使用t分布随机邻域嵌入(t-SNE)将预处理之后的数据可视化,可视化结果表明DOSC聚类效果最佳。然后使用随机森林(RF)、主成分分析(PCA)和二维相关光谱分析(2D-COS)对经DOSC预处理之后的光谱数据进行特征选择。最后基于选择的特征波长对虾新鲜度进行建模分析。将宽度学习(BLS)首次用于虾新鲜度建模,同时与偏最小二乘判别(PLS-DA)和极限学习机(ELM)等经典判别模型做比较。研究结果表明RF方法最大限度地消除了光谱中的冗余信息,而BLS与线性建模方法PLS-DA以及非线性建模方法ELM相比,准确率更高并且判别时间更短,因此RF-BLS组合模型获得了最佳新鲜度判别效果,表明高光谱成像技术结合宽度学习识别虾的新鲜度是可行的,可以为在线检测虾新鲜度系统的开发提供理论依据。

To improve the accuracy of shrimp freshness discrimination,we proposed a shrimp freshness detection method based on a broad learning system in this paper.In this study,firstly,multivariate scatter correction(MSC),standard normal variate(SNV),and direct orthogonal signal correction(DOSC)were used to preprocess the raw hyperspectral data of shrimp with different days of refrigeration.And secondly,t-distributed stochastic neighbor embedding(t-SNE)was used to visualize the data after preprocessing,and the visualization results showed that the DOSC-processed data had the best clustering effect.Then,the spectral data after DOSC preprocessing were used for feature extraction using random forest(RF),principal component analysis(PCA),and two-dimensional correction spectroscopy(2D-COS).Finally,the shrimp freshness was modeled based on the characteristic wavelength,and the broad learning system(BLS)was used in shrimp freshness modeling for the first time in this paper and compared with the classical discriminant models such as partial least squares discrimination analysis(PLS-DA)and extreme learning machine(ELM).The results indicated that the RF method minimized the redundant information in the spectra,while the BLS had high accuracy and shorter discrimination time than the linear modeling method PLS-DA and the nonlinear modeling method ELM,and thus the combined RF-BLS model obtained the best freshness discrimination performance.The experimental results indicated that the hyperspectral imaging technology combined with broad learning system to identify shrimp freshness is feasible.The method proposed in this paper can provide a theoretical basis for developing an online shrimp freshness detection system.