柑橘叶片叶绿素含量的准确检测对柑橘营养状况和生长态势具有极其重要的意义。 研究了快速无损诊断柑橘叶片中叶绿素含量的方法, 以期为拉曼光谱检测技术用于柑橘叶片叶绿素含量检测提供参考。 采集不同冠层高度和不同地理分布的柑橘叶片120片, 拭去叶片表面的灰尘, 用去离子水对其清洗、 晾干装入密封袋中并用标签分类标注。 然后对柑橘叶片进行拉曼光谱采集, 参数设置如下: 分辨率为3 cm-1, 积分时间为15 s; 激光功率为50 mW。 分别采用BaselineWavelet、 迭代限制最小二乘（IRLS）和不对称最小二乘(ALS)三种算法对柑橘叶片的拉曼光谱背景进行扣除, 使用偏最小二乘（PLS）方法建立定量模型; 四种光谱预处理方法归一化(Normalization), Savitzky-Golay卷积平滑(SG smoothing, SG平滑)、 多元散射校正(MSC)和Savitzky-Golay一阶导数(SG 1st Der)对扣除背景后的光谱进行进一步的优化处理。 结果表明: 采用原始光谱、 BaselineWavelet、 IRLS、 ALS背景扣除处理后的光谱建立PLS模型, 模型的相关系数r分别为0.858, 0.828, 0.885和0.862, 交互验证均方根误差(RMSECV)分别为5.392, 5.870, 4.934和5.336, 最佳因子数分别为8, 3, 8和8; IRLS背景扣除处理后的PLS模型的RMSECV最小, 相关系数最高, 建模效果最好。 分别采用SG平滑、 归一化、 MSC和SG 1st Der预处理方法对IRLS背景扣除后光谱进行预处理并建立PLS模型, 结果表明: IRLS光谱及其结合SG平滑、 归一化、 MSC和SG 1st Der四种预处理方法的PLS模型的R分别为0.885, 0.897, 0.852, 0.863和0.888, RMSECV分别为4.934, 4.715, 5.595, 5.182和4.962; 最佳因子数分别为8, 8, 8, 8和5; IRLS-SG平滑后PLS模型的RMSECV最小, 模型效果最优。 对IRLS-SG平滑预处理后的PLS模型展开验证, 预测相关系数r为0.844, 预测均方根误差(RMSEP)为5.29, 预测精确度较高。 采用拉曼光谱结合三种光谱背景扣除方法和四种预处理方法对柑橘叶片叶绿素含量进行定量分析表明: 采用IRLS背景扣除结合SG平滑预处理后的PLS模型最优, 建模集r为0.897, RMSECV为4.715; 预测集r为0.844, RMSEP为5.29, 预测精度较高。 拉曼光谱结合背景扣除方法可以为柑橘叶片叶绿素含量的定量分析提供一种快速简便的分析方法。

The accurate detection of the content of chlorophyll in citrus leaves is of great significance to the nutritional status and the growth trend of citrus. A rapid and non-destructive method for diagnosing chlorophyll content in citrus leaves was studied in order to provide a reference for the detection of chlorophyll content in citrus leaves by Raman spectroscopy. A hundred and twenty slices of citrus leaves with different canopy heights and different geographical distributions were collected. The dust on the surface of the leaves was wiped off. The deionized water was used in the laboratory to clean it, dried in a sealed bag, and labeled with a label. The Raman spectra of citrus leaves were then collected. The parameters were set as follows: resolution 3 cm-1, integration time 15 s; laser power 50 mW. Three methods were used, such as baseline wavelet, iterative restricted least squares （IRLS）and asymmetric least squares (ALS), for background correction of Raman spectroscopy. After that, Partial least squares (PLS) method was used to establish the quantitative model. Subsequently, four methods of spectral pretreatment, like Savitzky-Golay convolution smoothing (SG smoothing), normalization, multiplicative scatter correction (MSC) and the Savitzky-Golay 1st derivative, were used to further optimize the spectra which had been treated by the background correction. The research process showed that the PLS model was established by the spectra of the original spectrum, Baseline Wavelet, IRLS, and ALS preprocessing. The correlation coefficients of the models were 0.858, 0.828, 0.885, and 0.862, respectively. The root mean square error cross validation, RMSECV were 5.392, 5.870, 4.934, and 5.336, respectively. The best principal component factors were 8, 3, 8 and 8 respectively. The RMSECV of the pre-processed PLS model deducted from the IRLS background was the smallest, the correlation coefficient was the highest, and the modeling effect was the best. SG smoothing, normalization, MSC and SG 1st Der preprocessing methods were used to preprocess IRLS background correction spectrum and establish PLS model. The results showed that: IRLS spectrum and its combination of SG smoothing, normalization, MSC and SG 1st Der The PLS of the four pretreatment methods of r were 0.885, 0.897, 0.852, 0.863, and 0.888, respectively. The RMSECV were 4.934, 4.715, 5.595, 5.182, and 4.962, respectively. The best principal component factors were 8, 8, 8, 8 and 5, respectively; the RMSECV of the PLS model after IRLS-SG smoothing was the smallest, and the model had the best effect. After verifying the PLLS model preprocessed by IRLS-SG, the predictive correlation coefficient r of the prediction set was 0.844, the root mean square error of prediction (RMSEP) was 5.29, and the prediction accuracy was high. Three kinds of background correction methods combined with four kinds of spectral pretreatment methods were used to quantitatively model the Raman spectra of citrus leaves. It can be concluded that the experimental results after IRLS background correction combined with the SG smoothing are optimal. The modeling set r is 0.897, the RMSECV is 4.715, the prediction set r is 0.844, and the RMSEP is 5.29, and the prediction accuracy is high. Studies have shown that Raman spectroscopy combined with background correction methods can provide a quick and easy analytical method for quantitative analysis of chlorophyll content in citrus leaves.