基于改进的WOA-LSSVM樱桃番茄内部品质检测方法研究

doi:10.3964/j.issn.1000-0593(2023)11-3541-10

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摘要：基于近红外光谱技术与统计方法，提出了一种樱桃番茄内部品质快速、无损检测方法。首先采集样品的近红外光谱，采用多元散射校正（MSC）、Savitzky-Golay卷积平滑（SG）、Savitzky-Golay卷积一阶导数（SG 1^st）、去趋势化（De-trending）、变量标准化（SNV）5种预处理方法消除光谱干扰，筛选出最佳预处理方法；然后采用连续投影算法（SPA）、稳定性竞争性自适应重加权算法（SCARS）、遗传算法（GA），以及引入自动有序预测因子选择算法进行改进的遗传算法（IGA）4种特征波长提取方法减少变量冗余，选择最优特征波长提取方法；最后结合回归方法——将冯诺依曼拓扑结构、轮盘赌选择、锦标赛选择和自适应权重与鲸鱼算法相结合来对算法进行改进，采用改进的鲸鱼算法优化最小二乘支持向量机方法（IWOA-LSSVM），与基于粒子群算法优化的BP神经网络方法（PSO-BPNN）和基于鲸鱼算法优化的最小二乘支持向量机方法（WOA-LSSVM）进行对比，分别建立樱桃番茄内部品质含量的预测模型。结果表明：樱桃番茄内部品质中的可溶性固形物（SSC）含量使用De-trending-IGA-IWOA-LSSVM模型效果最佳，其中校正集决定系数和预测集决定系数分别是0.917 2和0.866 7，校正均方根误差和预测均方根误差为0.542 3和0.768 2，预测相对误差达到2.592 9；维生素C（V_C）含量使用SG-IGA-IWOA-LSSVM模型预测效果最准确，其中校正集决定系数和预测集决定系数分别为0.857 6和0.821 6，校正均方根误差和预测均方根误差分别是0.661 4和0.634 2，预测相对误差达到2.078 5。以上结果表明，采用近红外光谱技术与统计方法结合可实现对樱桃番茄内部品质的快速无损预测分析。

关键词：樱桃番茄；机器学习；鲸鱼算法；近红外光谱技术

Abstract：Based on near-infrared spectroscopy and statistical methods, a rapid and non-destructive testing method for the internal quality of cherry tomatoes was proposed. First, the near-infrared spectrum of the sample was collected, and five preprocessing methods, Multiplicative Scatter Correction, Savitzky-Golay convolution smoothing, Savitzky-Golay convolution first derivative, De-trending, Standard Normal Variate, and SNV were used to eliminate spectral interference and screen out the best preprocessing method; then use the Successive Projections Algorithm, Stability Competitive Adaptive Reweighted Sampling, Genetic Algorithm, and the introduction of automatic ordered predictor selection algorithm for Improved Genetic Algorithm Four characteristic wavelength extraction methods reduce variable redundancy and select the optimal characteristic wavelength extraction. Method; finally, combined regression method-combining von Neumann topology, roulette selection, tournament selection and adaptive weights with whale algorithm to improve the algorithm, using the Improved Whale Optimization Algorithm, and based on Particle Swarm Optimization-BP Neural Network was compared with the Whale Optimization Algorithm-Least Squares Support Vector Machine, and the prediction models for the internal quality content of cherry tomatoes were established respectively. The results showed that the De-trending-IGA-IWOA-LSSVM model was used for the best soluble solid content in the internal quality of cherry tomatoes, where the coefficient of determination of the calibration set and prediction set were 0.917 2 and 0.866 7, respectively, the corrected root mean square error and the predicted mean square The root error was 0.542 3 and 0.768 2, and the relative error of prediction reached 2.592 9; the SG-IGA-IWOA-LSSVM model was used to predict the Vitamins C content the most accurate, and the coefficient of determination of the calibration set and prediction set were 0.857 6 and 0.821 6, respectively, and the corrected root mean square The error and prediction root mean square error are 0.661 4 and 0.634 2, respectively, and the prediction relative error reaches 2.078 5. The above results show that the combination of near-infrared spectroscopy and statistical methods can achieve rapid and non-destructive prediction and analysis of the internal quality of cherry tomatoes.

Key words：Cherry tomatoes;Machine learning methods;Whale algorithm; Near-infrared spectroscopy technology

收稿日期: 2022-06-06 修订日期: 2022-09-19

中图分类号:

S661.1

基金资助: 广东省重点领域研发计划项目（2019B020214005），江苏省科技计划重点及面上项目（BE2021379）和国家重点研发计划项目（2017YFD0701205）资助

通讯作者: 罗斌 E-mail: luob@nercita.org.cn

作者简介: 康明月，女，1997年生，北京市农林科学院信息技术研究中心与中国地质大学（北京）数理学院硕士研究生
e-mail: 774404711@qq.com

引用本文:

康明月，王成，孙鸿雁，李作麟，罗斌. 基于改进的WOA-LSSVM樱桃番茄内部品质检测方法研究[J]. 光谱学与光谱分析, 2023, 43(11): 3541-3550.
KANG Ming-yue, WANG Cheng, SUN Hong-yan, LI Zuo-lin, LUO Bin. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550.

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