生菜镉污染可见-近红外光谱分析模型

Visible/Near Infrared Spectroscopic Modeling for Cadmium Contaminated Lettuce

为了快速无损监测生菜受镉污染的程度,利用可见-近红外光谱进行生菜镉污染的分类监测。将土壤镉污染样品设置为0(CK,对照组)、 5、 10和20 mg·kg^(-1),以不同污染程度下种植的生菜为研究对象,采集生菜叶片的可见-近红外反射光谱,分析镉污染下生菜叶片可见-近红外光谱反射率的变化规律。光谱信息经分析表明,在510~730 nm波段之间,随着土壤中镉含量的增加,生菜叶片的可见-近红外光谱反射率表现为先降低后增加;在730~799.53 nm波段之间,5和20 mg·kg^(-1)镉胁迫下生菜叶片反射率高于CK组,10 mg·kg^(-1)镉胁迫下生菜叶片反射率低于CK组;且在762.199 nm处出现了一个吸收谷。首先采用平滑(SG)、多元散射校正(MSC)、标准正态化(SNV)、平均归一化(MN)、 SG+MSC、 SG+SNV、 SG+MN、 SG+一阶导数(FD)、 SG+二阶导数(SD)方法对原始光谱进行预处理,以提高信噪比。然后通过主成分分析(PCA)对原始光谱和各种预处理的光谱进行降维处理,最后将降维处理后的数据按照4∶1的比例划分训练集和测试集,分别与粒子群优化随机森林(PSO-RF)、遗传算法优化支持向量机(GA-SVM)、 BP神经网络(BP-NN)、极限学习机(ELM)、朴素贝叶斯(Naive Bayes)结合建立生菜镉污染的分类监测模型,并进行分析比较。结果表明,在不同的模型中,PSO-RF(SG)模型的识别效果最佳,其次是GA-SVM(SG+FD)模型和ELM(MSC)模型,PSO-RF(SG)、 GA-SVM(SG+FD)、 ELM(MSC)模型训练集的准确率均为100%,而测试集的准确率分别为100%、 83.33%和79.17%;BP-NN模型和Naive Bayes模型的效果较差,BP-NN(SNV)模型训练集的准确率为42.72%,测试集准确率为50%;Naive Bayes(SG+FD)模型训练集准确率为71.84%,测试集准确率为83.33%。说明采用可见-近红外光谱结合粒子群优化随机森林建模能够为生菜重金属污染监测提供一种新思路。

To quickly and non-destructively monitor the degree of cadmium contamination in lettuce,visible-near infrared spectroscopy is used to classify cadmium contamination in lettuce.Lettuce leaves'visible-near infrared reflectance spectra were collected to analyze the variation in reflectance spectra under different cadmium pollution levels(0,5,10,20 mg·kg^(-1))in soil,with lettuce as the research subject.The spectral analysis reveals that within the wavelength range of 510 to 730 nm,the reflectance of lettuce leaves in the visible-near infrared spectrum decreases and then increases with the increase in cadmium content in the soil.Within the wavelength range of 730 to 799.53 nm,the reflectance of lettuce leaves under 5 and 20 mg·kg^(-1) cadmium stress is higher than the CK,while under 10 mg·kg^(-1) cadmium stress,the reflectance is lower than the control group.Additionally,an absorption valley was observed at 762.199 nm.In establishing a cadmium pollution monitoring model for lettuce,various preprocessing methods were applied to the raw spectra to improve the signal-to-noise ratio.These methods include smoothing(SG),multiplicative scatter correction(MSC),standard normal variate(SNV),mean normalization(MN),SG+MSC,SG+SNV,SG+MN,SG+first derivative(FD),and SG+second derivative(SD).Based on the principal component analysis(PCA),dimensionality reduction was performed on the original spectra and spectra subjected to various preprocessing methods.Subsequently,the reduced data was divided into training and testing sets in a 4∶1 ratio.These sets were then used to establish classification monitoring models for cadmium pollution in lettuce by combining particle swarm optimization-random forest(PSO-RF),genetic algorithm-optimized support vector machine(GA-SVM),backpropagation neural network(BP-NN),extreme learning machine(ELM),and Naive Bayes,followed by analysis and comparison.The results demonstrate that among the different models,the PSO-RF(SG)model achieves the best recognition performance,followed by the GA-SVM(SG+FD)model and the ELM(MSC)model.The training accuracy of the PSO-RF(SG),GA-SVM(SG+FD),and ELM(MSC)models is 100%,while their testing accuracies are 100%,83.33%,and 79.17%respectively.On the other hand,the BP-NN model and the Naive Bayes model perform relatively poorly.The training accuracy of the BP-NN(SNV)model is 42.72%with a testing accuracy of 50%.The Naive Bayes(SG+FD)model achieves a training accuracy of 71.84%and a testing accuracy of 83.33%.It indicates that applying visible-near infrared spectroscopy combined with particle swarm optimization random forest modeling can provide a novel approach for studying the monitoring of heavy metal contamination in lettuce.