基于近红外光谱和SSA-ELM的苹果糖度预测  

Prediction of sugar content in apple based on near-infrared spectroscopy of SSA-ELM

作　　者：乔正明[1] 詹成 QIAO Zheng-ming;ZHAN Cheng(Changzhou Vocational Institute of Textile and Garment,Changzhou,Jiangsu 213164,China;Suzhou University of Science and Technology School of Business,Suzhou,Jiangsu 215000,China)

机构地区：[1]常州纺织服装职业技术学院,江苏常州213164 [2]苏州科技大学,江苏苏州215000

出　　处：《食品与机械》2021年第9期121-126,共6页Food and Machinery

基　　金：江苏省高校专项课题(编号:2020JDKT158)。

摘　　要：目的:剔除近红外光谱存在大量冗余信息以及提高苹果糖度预测模型的精度,建立快速无损检测苹果糖度的方法。方法:提出一种基于小波包变换的特征波长筛选和樽海鞘算法改进极限学习机的苹果糖度预测模型。针对苹果光谱数据具有维度高而复杂的特点,对光谱数据进行降维处理,分别对比全波段和偏最小二乘法、连续投影法和小波包变换等筛选特征波长的结果,确定苹果光谱特征波长筛选方法;针对极限学习机(extreme learning machine, ELM),模型性能受其初始权值和隐含层偏置选择的影响,运用樽海鞘群算法进行ELM模型的初始权值和隐含层偏置优化,提出一种基于樽海鞘算法改进极限学习机的苹果糖度预测模型。结果:与遗传算法(genetic algorithm, GA)改进ELM(GA-ELM)、粒子群算法改进ELM(PSO-ELM)和ELM相比,基于SSA-ELM的苹果糖度预测模型的预测精度最高。结论:通过智能算法优化ELM模型的参数可以有效提高ELM模型的苹果糖度预测精度。Objective: In order to eliminate a large amount of redundant information in near infrared spectrum, and improve the accuracy of sugar degree in apple of the prediction model, a method for fast and non-destructive testing of the sugar content of apples was established. Methods: A prediction model of apple sugar content based on wavelet packet transform and bottle ascidian algorithm was proposed. Firstly, according to the characteristics of apple spectral data with high and complex dimensions, the spectral data was reduced, and the characteristic wavelength screening method was determined by comparing the results of full band and partial least squares method, continuous projection method and wavelet packet transform. Secondly, in view of the influence of extreme learning machine(ELM), the model performance was affected by its initial weight and hidden layer bias selection. The bottle salp swarm algorithm was used to optimize the initial weight and hidden layer bias of ELM model, and an prediction model for sugar content in apple was proposed based on bottle ascidian algorithm improved extreme learning machine. Results: Compared with GA-ELM, PSO-ELM and ELM, the prediction model based on SSA-ELM had the highest accuracy. Conclusion: The parameters of ELM model optimized by intelligent algorithm can effectively improve the prediction accuracy of ELM, and provide a new method for the prediction of sugar content in apple.

关 键 词：近红外光谱 苹果 糖度 极限学习机 樽海鞘算法 特征波长 小波包变换 

分 类 号：TS255.7[轻工技术与工程—农产品加工及贮藏工程] O657.33[轻工技术与工程—食品科学与工程]