基于荧光光谱的生菜农药残留检测

Detection of pesticide residues in lettuce based on fluorescence spectra

为了研究采用荧光光谱技术对生菜农药残留快速无损定性鉴别的可行性,该文通过采集180个生菜样品(3个浓度农药残留生菜,每个浓度农残生菜样本数为60,其中农药与水配比为1:500、1:1 000、1:1 200,即重度超标、轻微超标、标准农残)的荧光发射光谱,结合Savitzky-Golay(SG)、标准正态变量变换(standard normalized variable,SNV)、标准正态变量变换结合去趋势(standard normalized variable detrending,SNV detrending)、SG与SNV算法组合(SG-SNV)、SG与SNV detrending算法组合(SG-SNV detrending)对提取的荧光光谱进行预处理,基于全光谱、荧光特征峰值、小波特征建立支持向量机(Support Vector Machine,SVM)分类模型。其中,小波特征通过小波变换对原始光谱以及预处理后光谱进行特征选择获取,分别采用db4、db6、sym5、sym7作为小波基函数。试验结果表明:基于小波特征、荧光特征峰值建立的SVM模型预测集识别率要高于基于全光谱建立的SVM模型。以sym5作为小波基函数,基于SG-SNV detrending预处理光谱选择的小波特征建立的SVM模型取得最优的预测集识别率93.33%,最佳小波分解层数为4。结果表明应用荧光光谱技术对生菜农药残留鉴别是可行的,为生菜农药残留快速、无损检测分析提供了参考。

Lettuce is a kind of important food for people all over the world, which contains a lot of protein, carbohydrate, vitamin and some nutrient elements. However, pesticide residue in lettuce leaves is becoming one of the most urgent problems and it needs to be solved as soon as possible in the world. Therefore, fast and efficient nondestructive detection of pesticide residues with different concentration in lettuce leaves plays a key role in the growth of lettuce and also improves food safety supervision. In this paper, we aimed to identify pesticide residues with different concentration in lettuce leaves in a novel and rapid nondestructive way by using fluorescence spectra technology. The fluorescence spectral data of 180 lettuce leaf samples with pesticide residues of 3 concentrations （the ratio of pesticides to water was 1：500, 1：1000 and 1：1200） were obtained using Cary Eclipse fluorescence spectrometer （at an excitation wavelength of 245 nm, an emission wavelength in the range of 300-510 nm and discharge voltage of 600 V）. Besides, the actual concentrations of pesticide sprayed were 12.77, 2.55 and 1.28 mg/L, respectively. Moreover, Savitzky-Golay （SG）, Standard Normalized Variable （SNV）, Standard Normalized Variable detrending （SNV detrending）, Savitzky-Golay coupled with Standard Normalized Variable （SG-SNV）, Savitzky-Golay coupled with Standard Normalized Variable detrending （SG-SNV detrending） were used to preprocess the raw spectra, respectively. Finally, support vector machine （SVM） classification models were established based on full spectra （FS）, fluorescence characteristic peak （FCP） and wavelet feature （WF）, respectively. The Monte Carlo Cross Validation algorithm （MCCV） was used to select a total of 120 lettuce leaves samples as the training set, the remaining 60 lettuce leaves samples were taken as the prediction set, and the number of MCCV cycle was set as 1000. Moreover, the sample selection would be completed when the training set and the prediction set accuracy were less than 2%. Besides, FCP mainly contained the band including 371.07, 424, 440, 460 and 486.96 nm. Furthermore, the WF was selected by wavelet transform using db4, db6, sym5 and sym7 as wavelet basis functions, respectively. The results showed that the prediction set identification rate of SVM model based on WF obtained the best results compared with the SVM models based on the FS and FCP. SG-SNV detrending - FS-SVM model obtained the best performance among all FS-SVM models, with an identification rate of 100% in the calibration set and 63.33% in the prediction set. Furthermore, SG-SNV detrending -FCP-SVM model obtained the best performance among all FCP-SVM models with an identification rate of 95% in the calibration set and 70% in the prediction set. Moreover, SG-SNV detrending -WF-SVM model obtained the best performance among all SVM models with an identification rate of 98.33% in the calibration set and 93.33% in the prediction set, in which sym5 was used as wavelet basis function and the optimal wavelet decomposition level was 4. The results indicated that it was feasible to use fluorescence spectra technology to identify different concentration of pesticide residues in lettuce leaves.