Analyzing the performance of statistical models for estimating leaf nitrogen concentration of Phragmites australis based on leaf spectral reflectance

Abstract

Nitrogen is an essential nutrient for plant growth and development. Rapid and nondestructive monitoring of nitrogen nutrition in plants using hyperspectral remote sensing is important for accurate diagnosis and quality evaluation of plant growth status. The sensitive bands of leaf nitrogen concentration varied in different plants. However, most of the current studies are concentrated on crops, and only a few studies focused on wetland plants. This study investigated the accuracy of the most common univariate, stepwise multiple linear regression, and partial least squares regression models for predicting leaf nitrogen content in a wetland plant reed (Phragmites australis) by testing the accuracy of all the models through leave-one-out cross validation coefficient of determination, root mean square error and relative error. It found that: (i) sensitive bands responding to leaf nitrogen concentration were concentrated in the green and red regions of visible light; (ii) for univariate regression models, the quadratic polynomial model based on the difference spectral index composed of 665?nm and 680?nm had the highest predictive accuracy (the validation coefficient of determination was 0.7535); (iii) for multivariate regression models, the stepwise multiple linear regression models had superior predictive accuracy to the partial least squares regression models, and the stepwise multiple linear regression model with first derivative reflectance was optimal for predicting leaf nitrogen concentration (the validation coefficient of determination was 0.7746, the validation root mean square error was 0.2925, and the validation relative error was 0.0804). The findings provide a scientific basis for rapid estimation and monitoring of leaf nitrogen concentration in P. australis in a nondestructive manner.     

Nondestructive Quantification of Foliar Chlorophyll in an Apple Orchard by Visible/Near-Infrared Reflectance Spectroscopy and Partial Least Squares

Chlorophylls respond rapidly to the current physiological status of a tree and reflect nutrient availability. Visible/near-infrared spectroscopy was attempted to determine foliar chlorophyll content in an apple orchard. Backward interval partial least squares and genetic algorithms were sequentially applied to select an optimized spectral interval and an optimized combination of spectral regions selected from informative regions in model calibration. Backward interval partial least squares was used to remove the noninformative regions, which significantly reduced the number of variables. The subsequent application of genetic algorithms-partial least squares to this reduced domain could lead to an efficient and refined model. The performance of the final model was back-evaluated according to root mean square error of calibration (RMSEC) and the correlation coefficient (R _c ) in the calibration set, and was then tested by root mean square error of prediction (RMSEP) and the correlation coefficient (R _p ) in the prediction set. The optimal backward interval partial least squares-genetic algorithms model was obtained with 5 partial least squares factors with 3 spectral regions and 71 variables selected. The measurement results of the final model were achieved as follows: RMSEC = 0.26, R _c  = 0.91 in the calibration set; and RMSEP = 0.22, R _p  = 0.91 in the prediction set. This experiment showed that visible/near-infrared spectroscopy and backward interval partial least squares-genetic algorithms are useful tools for nondestructively assessing foliar chlorophyll content and may have potential application for field assessments in decision-making and operational fertilizer management programs for apple orchards.     

估测田间烟叶色素含量的光谱模型研究

田间烟叶色素含量的光谱无损快速测量,对烟草营养生长期的营养诊断与长势监测、成熟期的烟叶品质评判具有重要的生产指导意义。该研究的目的是利用烟叶田间光谱估测烟叶的叶绿素和类胡萝卜素含量。研究采集了营养生长期和成熟期烟叶田间反射光谱,测量了样品烟叶的色素含量,利用支持向量机(SVM)和光谱指数法,对营养生长期和成熟期烟叶样品用分期建模和混合建模两种方法建立色素含量估测模型,并对模型的预测性能进行比较。研究结果表明,分期建模和混合建模对于烟叶色素含量的估测效果差异不显著。对于叶绿素含量,SVM和光谱指数法均有较好的估测效果;对于类胡萝卜素含量,SVM方法比光谱指数法具有更高的估测精度。采用SVM方法对烟叶样品的叶绿素含量分期建模得到的估测决定系数和均方根误差分别为0.862 9和0.015 5,对叶绿素含量混合建模得到的估测决定系数和均方根误差分别为0.898 5和0.012 3;采用SVM方法对烟叶样品类胡萝卜素含量分期建模得到的估测决定系数和均方根误差分别为0.873 0和0.002 4,对类胡萝卜素含量混合建模得到的估测决定系数和均方根误差分别为0.852 7和0.002 4。该研究的创新点是通过支持向量机和光谱指数法采用分期建模以及混合建模的方式建立了烟叶样品色素含量的估测模型,为烟草田间生产的质量控制、烟叶的采收品质保证提供科学依据和技术支持。     

基于高光谱技术的灰霉病胁迫下番茄叶片SPAD值检测方法研究

对灰霉病胁迫下番茄叶片中叶绿素含量(SPAD)的高光谱图像信息进行了研究。首先获取380～1 030 nm波段范围内健康和染病番茄叶片的高光谱图像,然后基于ENVI软件处理平台提取高光谱图像中感兴趣区域的光谱信息,经平滑(Smoothing)、标准化(Normalize)等预处理后,建立了基于Normalize预处理的偏最小二乘回归(PLSR)和主成分回归(PCR)模型。再基于PLSR获得的4个变量建立反向传播神经网络(BPNN)和最小二乘-支持向量机(LS-SVM)模型。4个模型中,LS-SVM的预测效果最好,其决定系数R2为0.901 8,预测集均方根误差RMSEP为2.599 2。结果表明,基于健康和染病番茄叶片的高光谱图像响应特性检测叶绿素含量(SPAD)是可行的。     

Near-infrared spectroscopy to assess typhaneoside and isorhamnetin-3-O-glucoside in different processed products of pollen typhae

With the ever increasing importance of testing drug quality, rapid analytical methods are needed for supervision of Chinese herbal medicines. Near-infrared spectroscopy is one of the most powerful tools in quality assessment of Chinese herbal medicines. In this work, near-infrared spectroscopy was applied to develop a rapid method for quantitative determination of typhaneoside and isorhamnetin-3-O-glucoside in different processed products of Pollen Typhae. A total of 71 batches of samples were collected from different regions in China. After acquisition of near-infrared spectra, different pre-processing methods were compared, and a competitive adaptive reweighted sampling algorithm was used to perform the variable selection. Then a partial least squares regression algorithm was applied to build the quantitative models. The root mean square error of calibration, root mean square error of cross validation, and root mean square error of prediction were 0.0190, 0.0364, and 0.0158%, respectively, for a quantitative model of typhaneoside. The root mean square error of calibration, root mean square error of cross validation, and root mean square error of prediction were 0.0190, 0.0377, and 0.0170%, respectively, for a quantitative model of isorhamnetin-3-O-glucoside. Moreover, the relative prediction deviation values of both quantitative models were larger than 3, indicating good performance of the partial least squares (PLS) models. The results demonstrated that high accuracy prediction of typhaneoside and isorhamnetin-3-O-glucoside could be obtained by near-infrared spectroscopy, to allow an alternative method for quality assessment of different processed products of Pollen Typhae.     

基于高光谱成像技术的脐橙叶片的叶绿素含量及其分布测量

为实现脐橙叶片叶绿素含量无损检测及其分布可视化表征,采用高光谱成像技术,结合自适应重加权算法(CARS)和连续投影算法(SPA),筛选特征光谱变量,进行脐橙叶片叶绿素含量及可视化分布研究。选取叶绿素测量位置的7×7矩形感兴趣区域,提取并计算脐橙叶片平均光谱。基于Kennard-ston方法,将148个脐橙叶片样品划分成建模集和预测集(111∶37)。采用CARS和SPA算法分别筛选出了32个和6个叶绿素特征光谱变量,用于建立偏最小二乘(PLS)回归模型。采用37个未参与建模的脐橙叶片样品评价模型的预测能力,经比较,CARS-PLS和SPA-PLS模型均优于变量筛选前的PLS模型,且CARS-PLS和SPA-PLS模型的预测能力几乎相同,其预测集相关系数分别为0.90和0.91,均方根误差分别为1.53和1.60。SPA-PLS模型计算脐橙叶片每个像素点的叶绿素含量,经伪彩色变换,绘制了脐橙叶片叶绿素含量可视化分布图。实验结果表明:变量筛选方法结合高光谱成像技术,能够实现脐橙叶片叶绿素含量无损检测及叶绿素分布可视化表达,并简化了数学模型。     

Hyperspectral estimation of soil organic matter based on different spectral preprocessing techniques

In this study, the potentiality of visible and near-infrared reflectance spectroscopy to estimate soil organic matter was assessed. Six preprocessing methods were implemented to process the original spectra. The partial least-squares regression approach was also applied to construct predictive models and evaluate the optimal spectral preprocessing method. The significant wavelengths of soil organic matter were determined by using the correlation analysis and the partial least-squares regression analysis. The results were: (i) visible and near-infrared reflectance spectroscopy was proved to be an ideal approach in the soil organic matter estimation; (ii) different preprocessed spectra could improve their correlation with soil organic matter; the combination of first-order derivative and Savitzky–Golay smoothing method outperformed other preprocessing methods; (iii) the soil organic matter predictive models based on spectra processed by derivatives and Savitzky–Golay smoothing together presented a satisfactory accuracy, yielding the determination coefficient and root mean square error values of 0.986 and 0.077, respectively, for first-order derivative; and 0.973 and 0.105, respectively, for second-order derivative. The combination of first-order derivative and Savitzky–Golay smoothing was ultimately recommended the preferable preprocessing method; and (iv) the wavelengths of 417, 1853, 1000, and 2412?nm were determined as the significant wavelengths associated with soil organic matter. The study will provide a reference for the site specific management of agricultural inputs by using the visible and near-infrared reflectance spectroscopy technology.     

光谱重构油菜叶绿素含量快速检测方法及设备研制

为了实现油菜叶片中叶绿素含量的快速无损检测,开发了手持式多光谱成像系统用于采集油菜叶片在460,520,660,740,840和940 nm 六个波段的光谱图像。将一台能够采集可见光/近红外（380～1 023 nm）512个波段光谱图像但是价格高昂且体积大的室内高光谱成像系统作为参考仪器,将手持式多光谱成像系统作为目标仪器后,采用伪逆法（pseudo-inverse method）求得高光谱成像系统和多光谱成像系统两台仪器之间的转换矩阵F,从而实现6个波段的多光谱图像向512个波段的高光谱图像的重构,提高了手持式设备的光谱分辨率。运用偏最小二乘回归算法（PLSR）建立了重构的光谱与油菜叶片的叶绿素含量之间的关系模型。结果表明,重构的可见光范围内的光谱反射率与叶绿素浓度之间具有很强的相关性,PLSR回归模型建模集的决定系数R2_c为0.82,建模集均方根误差RMESC为1.98,预测集的决定系数R2_p为0.78,预测集均方根误差RMESP为1.50,RPD为2.14。虽然应用本文开发的手持式成像系统结合PLSR模型实现油菜叶绿素含量快速无损预测的精度低于基于室内高光谱成像系统获得的高光谱图像建立的PLSR模型（R2_c,RMESC,R2_p,RMESP和RPD分别为0.90,1.41,0.82,1.36和2.37）,但是明显优于基于原始多光谱成像系统4个波段（460,520,660和740 nm）反射率建立的PLSR模型得到的结果（R2_c,RMESC,R2_p,RMESP和RPD分别为0.78,2.06,0.72,1.85和1.88）。表明光谱重构技术可提高多光谱成像预测油菜叶绿素含量的精度,并且与室内高光谱成像系统相比,开发的手持式设备具有体积小、成本低廉和操作简便等优点,可为田间油菜叶片的生理状态和养分检测及可视化表达提供技术支持。     

利用高光谱扫描技术检测小麦叶片叶绿素含量

利用高光谱扫描技术对小麦叶片进行无损检测试验,探索精确测定小麦叶绿素含量的方法,为农作物生长状况、植物病理诊断等提供科学依据。研究选取90个样本作为校正集,30个样本作为预测集,获取叶片的高光谱反射图像,同时用传统的分光光度计方法测定其叶绿素含量。选取波长491～887 nm范围光谱,用多元散射校正、一阶导数、二阶导数3种方法处理,利用偏最小二乘法和逐步线性回归法分别建立了小麦叶片叶绿素含量与光谱信号间的数学模型。研究发现多元散射校正(MSC)结合二阶导数光谱的多元线性回归(SMLR)模型的效果较优,模型校正集和预测集决定系数分别为0.82和0.79,校正均方根误差和预测均方根误差分别为0.69和0.71。研究结果表明可以利用高光谱扫描技术检测小麦叶片叶绿素含量。     

Quantification of Ethanol in Ethanol-Petrol and Biodiesel in Biodiesel-Diesel Blends Using Fluorescence Spectroscopy and Multivariate Methods

Ethanol blended petrol and biodiesel blended diesel are being introduced in many countries to meet the increasing demand of hydrocarbon fuels. However, technological limitations of current vehicle engine do not allow ethanol and biodiesel percentages in the blended fuel to be increased beyond a certain level. As a result quantification of ethanol in blended petrol and biodiesel in blended diesel becomes an important issue. In this work, calibration models for the quantification of ethanol in the ethanol-petrol and biodiesel in the biodiesel-diesel blends of a particular batch were made using the combination of synchronous fluorescence spectroscopy (SFS) with principal component regression (PCR) and partial least square (PLS) and excitation emission matrix fluorescence (EEMF) with N-way Partial least square (N-PLS) and unfolded-PLS. The PCR, PLS, N-PLS and unfolded-PLS calibration models were evaluated through measures like root mean square error of cross-validation (RMSECV), root mean square error of calibration (RMSEC) and square of the correlation coefficient (R ²). The prediction abilities of the models were tested using a testing set of ethanol-petrol and biodiesel-diesel blends of known ethanol and biodiesel concentrations, error in the predictions made by the models were found to be less than 2%. The obtained calibration models are highly robust and capable of estimating low as well as high concentrations of ethanol and biodiesel.     

铜胁迫下玉米污染特征波段提取与程度监测

我国农田重金属污染形势不容乐观。土壤中的重金属被作物根系吸收后会影响作物正常的生长发育,降低农产品质量,进而通过食物链进入人体,危害人体健康。高光谱遥感为实时动态高效监测作物重金属污染提供了可能。设置不同浓度Cu2+胁迫梯度的玉米盆栽实验,并采集苗期、拔节期和穗期玉米老、中、新叶片光谱数据,测定不同生长时期叶片叶绿素含量、叶片Cu2+含量。基于所获取的光谱数据、叶绿素含量和叶片Cu2+含量,结合相关分析法、最佳指数法(OIF)和偏最小二乘法(PLS)构建OIF-PLS法提取含有Cu2+污染信息的特征波段。首先依据苗期、拔节期和穗期叶片叶绿素含量及穗期叶片Cu2+含量与相应叶片光谱的相关系数初步筛选特征波段;然后,从中选取三个波段计算最佳指数因子,并以该三个波段为自变量,对玉米叶片Cu2+含量进行偏最小二乘回归分析,计算均方根误差;最后根据最佳指数因子最大、均方根误差最小的原则选取最佳特征波段。基于OIF-PLS法所选取的特征波段构造植被指数OIFPLSI监测重金属铜污染,并与常规的红边归一化植被指数(NDVI 705)、改进红边比值植被指数(mSR 705)、红边植被胁迫指数(RVSI)和光化学指数(PRI)监测结果做比较,验证OIFPLSI的有效性和优越性。另外利用在相同的实验方法下获取的不同年份的数据对OIFPLSI进行检验,验证OIFPLSI的适用性和稳定性。实验结果表明,基于OIF-PLS法提取的特征波段(542,701和712 nm)比基于OIF法提取的特征波段(602,711和712 nm)能更好地反映Cu2+污染信息;植被指数OIFPLSI与叶片Cu2+含量显著正相关,相关性优于NDVI 705,mSR 705,RVSI和PRI;OIFPLSI与叶片叶绿素含量显著负相关,与土壤中Cu2+含量显著正相关;不同生长时期OIFPLSI与土壤中Cu2+含量的相关性高低依次为拔节期、穗期、苗期。基于不同年份数据验证结果表明,OIFPLSI与叶片Cu2+含量显著正相关,OIFPLSI具有较强的稳定性。基于OIF-PLS法所提取的特征波段构建的OIFPLSI能够较好地诊断分析玉米叶片铜污染水平,可为作物重金属污染监测提供一定的技术参考。     

Near-infrared spectroscopy for rapid evaluation of different processing products of Sophora japonica L.

To analyze the content of rutin in differently processed products of Sophora japonica L., a combination of near-infrared spectroscopy and chemometrics was used. Factors that affect near infrared spectroscopy modeling were investigated, and the optimal spectral conditions were determined. As a reference method, the rutin content was determined by high-performance liquid chromatography-diode array detection in 99 groups of products. Near-infrared spectra were collected by optimal near infrared acquisition conditions and the spectral features were enhanced by several preprocessing methods. Moreover, a near-infrared quantitative calibration model of rutin was established using partial least squares analysis. The root-mean-square standard error of cross validation and correlation coefficient (cross validation) were calculated as 0.800 and 0.9399, whereas the root-mean-square standard error of calibration and correlation coefficient (calibration) were 0.558 and 0.9709. This indicates that there is a good correlation between the predicted value and the actual measured value. Moreover, the root-mean-square standard error of prediction and correlation coefficient (validation) were calculated as 0.495 and 0.9785. And the ratio of the root-mean-square standard error of calibration and root-mean-square standard error of prediction was as small as 0.89 (≤1.2). The relative deviation between the predicted value and the reference value of the model was ?11.02 to 8.02%, and the ratio of performance to deviation was 4.70 (>3). Thus, these data indicate that the accuracy of the model used to predict the results was sufficient. In conclusion, the calibration model presented in this study provides satisfactory performance in a rapid, quantitative analysis of the rutin proportion by a simple, fast, and high accuracy of the prediction results.     

基于表面增强拉曼的饮料中山梨酸钾快速定量检测方法

基于实验室自行搭建的拉曼光谱点扫描系统,利用表面增强拉曼技术对橙味饮料中山梨酸钾的含量进行了定量快速检测研究。通过与山梨酸钾标准品拉曼光谱及其水溶液表面增强拉曼光谱等比较分析,确定了山梨酸钾1 648.4,1 389.3和1 161.8 cm-1处的表面增强特征拉曼位移。通过山梨酸钾橙味饮料平行样品的拉曼位移峰强重现性实验并计算其峰强的相对标准偏差证实了该表面增强拉曼方法具有较好的重复性。采集了山梨酸钾浓度范围为1.706～0.180 7 g·kg-1的33个橙味饮料样品的表面增强拉曼光谱,所有原始光谱经S-G 5点平滑及Baseline基线去除荧光背景预处理后分别用一元线性回归分析、多元线性回归分析和偏最小二乘回归分析方法,建立了山梨酸钾的定量预测模型。经比较,选取三个山梨酸钾拉曼特征位移1 161.8,1 389.3和1 648.4 cm-1所建立的多元线性回归模型校正集的相关系数(R2_C)和均方根误差(RMSEC)分别为0.983 7和0.051 7 g·kg-1,验证集的相关系数(R2_P)和均方根误差(RMSEP)分别为和0.969 9和0.052 8 g·kg-1,比一元线性回归模型和偏最小二乘回归模型误差小、精度高。基于表面增强拉曼完全可以实现橙味饮料中山梨酸钾的定量快速预测,为各类食品中山梨酸钾含量的快速监测奠定了技术基础。     

Measurement of chlorophyll in water based on laser-induced fluorescence spectroscopy: Using spiral-wound unclad optical fiber system and partial least squares regression

The feasibility of laser-induced fluorescence (LIF) method for chlorophyll concentration determination in water was investigated in this study. The chlorophyll fluorescence spectra were received by a spiral-wound unclad optical fiber system. Baseline and smoothing correction methods were studied. Statistical equations were established between reference data and fluorescence spectra by linear regression and partial least squares (PLSs) regression. The PLS regression outperformed linear regression by higher correlation coefficient of 0.9996063 and root mean square error value of 0.0221807. Based on the results, it was concluded that laser-induced fluorescence method with a spiral-wound unclad optical fiber system was suitable for chlorophyll concentration determination in water, and PLS regression was a reliable method for fluorescence spectra analysis.     

GWLS-SVR模型的红枣树叶片叶绿素含量估算

叶绿素含量是红枣树光合作用能力、生长状况、营养状况的指示剂,不同地理位置种植的红枣树受到自然、人为等因素的影响,叶绿素含量分布有所不同,该研究实地测定了若羌县枣树叶片高光谱反射率及表征叶绿素含量的枣树叶片SPAD（soil plant analysis development)值。为了高效无损地估算红枣树叶片SPAD值,计算了红枣树叶片SPAD值全局莫兰指数,以SPAD值和高光谱波段之间的相关性为基础,通过C_P统计量计算重要程度高的特征波段,运用地理加权最小二乘支持向量回归GWLS-SVR（geographically weighted least squares-support vector regression）模型对红枣树叶片SPAD值进行预测,与多元线性回归（MLR）、支持向量机回归（SVR）模型比较并探讨GWLS-SVR模型估算红枣树叶片SPAD值的能力。结果表明：（1）光谱一阶导数可以有效去除噪声并突出光谱信息尤其是492~510,542~543,642~652,657~670和682~692 nm区间内显著的提高了与SPAD值的相关性。（2）C_P统计量方法能够有效的选择敏感区间的特征波段,进而提高模型估算精度,由统计量方法计算出原始光谱重要程度最高的两个变量为595与696 nm,光谱一阶导数的特征波段为688 nm。其中对于同一个敏感波段区间的波段组合总有单个波段的统计量低于多个波段组合的统计量,这可能是相近波段间的较强共线性导致的。（3）若羌县红枣树叶片SPAD值存在显著的空间聚集性,全局莫兰指数为0.125 8（p<0.1）,适合建立考虑空间位置的GWLS-SVR模型。（4）结合Bootstrap再抽样与t检验模型检验得到结合地理位置信息的GWLS-SVR模型总体上估算能力优于SVR和MLR模型,且结果高度显著（p<0.001）,其中基于光谱一阶导数的GWLS-SVR模型为最优的红枣树叶片SPAD值估算模型（R2为0.975,MSE为1.082）,能够为高光谱定量反演红枣树SPAD值进而快速无损的监测红枣生长状况提供一定参考。     

基于可见-近红外光谱技术预测茶鲜叶全氮含量

为快速无损监测茶树氮素营养及其生长状况,基于可见-近红外光谱技术建立了茶鲜叶全氮含量的预测模型。以茶鲜叶为对象,田间试验使用便携式光谱仪采集叶片漫反射光谱信息,通过不同预处理和统计分析,建立茶鲜叶全氮含量预测的光谱模型。试验共采集111个样品,其中86个样品作校正集,25个样品作预测集。通过一阶导数与滑动平均滤波相结合的预处理方法,用7个主成分建立的偏最小二乘模型最好,其校正集均方根误差(RMSEC)为0.097 3,预测集的相关系数为0.888 1,预测均方根误差(RMSEP)为0.130 4,预测的平均相对误差为4.339%。研究结果表明,利用可见-近红外光谱技术可以很好地预测茶鲜叶全氮含量,对于快速实时监测茶树长势和施肥管理具有重要指导意义。     

Predicting the contents of polysaccharides and its monosugars in Dendrobium huoshanense by partial least squares regression model using attenuated total reflectance Fourier transform infrared spectroscopy

An attenuated total reflectance Fourier transform infrared spectroscopy method combined with partial least square regression was established to rapidly quantify the total polysaccharide and its major monosugars in Dendrobium huoshanense. The results showed that the optimal predictive methods were the models established by the infrared spectroscopy files pretreated by standard normal transformation combined with the second derivative; the accuracy of the models selecting wavelength regions based on the assignments of Fourier transform infrared spectroscopy signals of polysaccharide was obviously higher than the models selecting wavelength regions suggested by Thermofisher Quantity Analyst software and other possible wavelength region selecting modes. The external validation and the complete external validation confirmed the robustness and reliability of the developed attenuated total reflectance Fourier transform infrared model. Our study might provide an efficient technique tool for the rapid, green, low-cost, and nondestructive quantification of the total polysaccharide and the main monosaccharides in Dendrobium huoshanense and other rich-in-polysaccharide plant food or medicines.     

基于二维相关光谱的水体叶绿素含量探测

利用Shimadzu UV2450分光光度计测量了含有不同叶绿素浓度的湖水样本在可见光和近红外区域的透射光谱,并使用实验室手段测量了水体的叶绿素含量。分析了湖水样本的透射光谱特性,同时引入二维相关光谱技术,利用叶绿素浓度值作为微扰量,得到水体叶绿素的动态光谱,进而结合二维同步谱图和异步谱图确定表征水体叶绿素浓度的特征波段。综合观察二维相关光谱中的同步谱图和异步谱图,更加精确地阐明了水体光谱特征,同时剔除水体中其他物质对于光谱信息的影响,更有效、全面地提取反映水体叶绿素信息的敏感波段。利用所选特征波段构建归一化水体叶绿素指数,将特征波段与叶绿素指数分别与水体叶绿素浓度建立线性预测模型。结果显示,归一化水体叶绿素指数的预测模型测定R2达到0.771 2,均方根误差是45.509 8 mg·L-1,预测R2达到0.765 8,均方根误差是39.503 8 mg·L-1。模型精度较利用特征波段建立的多元线性回归模型有了较大的提高,达到了实用水平。     

Rapid evaluation of the salt content of canned sardines in brine using near-infrared diffuse reflectance spectroscopy

ABSTRACT

In this study, the evaluation of the salt content of canned sardines in brine using near-infrared (NIR) spectroscopy with a Fourier transform NIR spectrometer in diffuse reflectance mode over a range of 12,500–4000?cm^?1 was investigated. The reference salt content of the samples was measured using an autotitrator. The optimum partial least squares regression model was developed using vector normalization spectra in the ranges of 9403.8–5446.3?cm^?1 and 4605.4–4242.9?cm^?1 with seven factors. The model provided a coefficient of determination for a prediction of 0.82, a root mean square error of prediction of 0.069%, a bias of ?0.019%, and a ratio of prediction deviation of 2.5. NIR spectroscopy has great potential as an alternative method for measuring the salt content of canned sardines in brine, and the calibration model is useful for the off-line quality control of canned sardines in brine processing. In addition, the oxygen–hydrogen bond stretch and deformation and the carbon–hydrogen bond stretch and deformation had obvious effects on the prediction of the salt content of canned sardines in brine.