基于可见/短波近红外光谱检测结球甘蓝维生素C含量

维生素C是人类必需的营养素,结球甘蓝作为主要蔬菜品种之一富含维生素C。该试验利用可见/短波近红外光谱分析技术,开展结球甘蓝维生素C含量的快速检测方法研究。首先通过Kennard-Stone(K-S)法将样本按照6:1划分为校正集(60个样本)和验证集(11个样本),分别利用反射率和吸光度的原始光谱、一阶导数(first derivative,FD)和二阶导数(second derivative,SD)光谱预处理后对应的6个数据集,建立偏最小二乘(partial least squares,PLS)回归模型。针对最优光谱预处理方法处理后的光谱,设置5个置信水平(0.95,0.975,0.99,0.995,0.999 5),利用逐步回归(stepwise regression,SR)进行建模波长选择,以各置信水平对应的各组优选波长进行多元线性回归建模。结果表明:利用FD光谱预处理方法可以提高PLS回归模型精度,验正集R~2从处理前的0.85提高到0.96,是该研究的最佳光谱数据预处理方法。利用降维后的7个主成分继续建立PLS回归模型,校正集R~2为0.92,交互验证均方根误差(root mean squared error of cross validation,RMSECV)为0.658 0 mg/100 g,验证集R~2为0.96,预测均方根误差(root mean squared error of prediction,RMSEP)为1.620 4 mg/100 g。PLS回归模型预测维生素C含量,检测精度高,可以代替传统检测方法,为结球甘蓝的品质评定提供一种新的途径。进一步分别应用8,6,5个优选波长进行多元线性回归建模,校正集R~2平均为0.78,RMSECV平均为3.760 9 mg/100 g,验证集R~2平均为0.73,RMSEP平均为2.879 2 mg/100 g,虽然R~2有所降低,但波长点少,利用较少的波长变量来预测维生素C含量,降低模型复杂度,可以为便携式检测仪器开发提供技术支持,以提高结球甘蓝内部品质评定作业效率。     

基于近红外光谱的脐橙产地溯源研究

为研究近红外光谱分析技术鉴别脐橙产地的可行性,该文采用江西、重庆和湖南3个产地脐橙样品1140～1170nm波段的近红外光谱经一阶导数(9点平滑)预处理,分别建立了簇类独立软模式法脐橙产地鉴别模型。在5%显著水平下,模型对3个产地训练集样品的识别率均为100%,拒绝率分别为85.7%、83.3%、100%;对验证集样品的识别率均为100%,拒绝率分别为100%、89.5%、100%,表明簇类独立软模式法模型基本能够判别脐橙产地。将江西、重庆和湖南3个产地的脐橙样品分别赋值0、1、?1,在全波段范围内建立原始光谱脐橙产地的偏最小二乘判别模型,其预测值与真实值的决定系数为0.973,校正标准差为0.110,预测标准差为0.159,模型对训练集和验证集样品的识别率达到100%。因此,应用近红外光谱分析技术可准确、快速地追溯脐橙产地来源。     

基于近红外光谱的土壤全氮含量估算模型

土壤全氮是诊断土壤肥力水平和指导作物精确施肥所需的重要信息,建立土壤全氮的近红外光谱估测模型并对建模波段进行优化选择对于土壤养分信息快速获取和精确农业发展具有重要意义。该研究以中国中、东部地区5种主要类型土壤为研究对象,利用近红外光谱仪采集土壤样品的光谱信息,结合近红外区域分子振动特点选取全谱、合频、一倍频、二倍频和N-H基团及其组合的8个波段,采用多元散射校正等多种预处理方法组合进行处理,结合偏最小二乘法(PLS)对每个波谱区域进行定标建模。结果表明,利用4000～5500cm-1波谱区域结合附加散射校正处理过的原始光谱建立的模型精度表现最好,其内部互验证决定系数达到0.90,均方根误差为0.16。经不同类型土壤的观测资料检验,模型验证决定系数为0.91,均方根误差为0.15,相对分析误差RPD为3.40,表明模型具有极好的预测能力。因此,利用近红外光谱可以实现土壤全氮的快速估测,且以合频波段(4000～5500cm-1)为建模区域可以得到更好的预测效果。     

二维相关光谱结合偏最小二乘法测定牛奶中的掺杂尿素

为了检验牛奶中是否掺杂尿素并将其量化测定,配置含有尿素质量浓度范围为1～20g/L之间40个牛奶样品,以掺杂物尿素浓度为外扰,分别研究了掺杂尿素牛奶的二维相关(近红外-近红外,中红外-中红外,近红外-中红外)光谱特性,在此基础上,分别选择随浓度变化大的4200～4800cm-1和1400～1704cm-1为建模区间,采用偏最小二乘方法建立定量分析模型。研究结果表明:4200～4800cm-1建模分析效果优于1400～1704cm-1建模结果,其交叉验证均方根误差为0.266g/L,对未知样品集预测相关系数达到0.999,预测均方根误差为0.219g/L,这表明所建模型具有较好的预测效果。该方法无需样品处理,成本低,为快速判别牛奶是否掺杂提供了一种新的可能的方法。     

Rapid Analysis of Apple Leaf Nitrogen using Near Infrared Spectroscopy and Multiple Linear Regression

A quick method was developed for diagnosis of nitrogen (N) in apple trees based on multiple linear regressions to establish the relationship between near-infrared reflectance spectra (NIRS) and the N contents of fresh and dry tissue. Spectral pretreatment methods such as derivatives, smoothing, and normalization were used. The derivatives appeared to be the most effective. The best calibration for fresh leaf gave 0.842 for the correlation coefficient of validation (R_v), 1.119 g kg^?1 for the root mean square error of prediction (RMSEP), and 8.311 for the ratio of the range in reference data from the validation samples to the root mean square error of prediction (RER). The best calibration for dried ground samples was obtained with R_v = 0.952, RMSEP = 0.633 g kg^?1, the ratio performance deviation (RPD) = 3.27, and RER = 13.728. The results showed that calibrations of dry-apple leaf are robust enough for an accurate prediction of N.     

基于可见-近红外光谱识别氧乐果污染的脐橙

该文对喷施过不同浓度氧乐果农药的脐橙样品采集可见-近红外光谱进行识别。采用多元散射校正（MSC）、标准正交变量变换（SNV）、一阶导数（FD）和二阶导数（SD）4种不同光谱预处理方法时,分别选取430～1 000、1 000～1 800和430～1 800 nm 3种波谱范围建立偏最小二乘法（PLS）农药污染预测模型。比较实验结果表明：波谱范围取430～1 000 nm,采用一阶导数的预处理方法时应用建立的PLS预测模型最优,其验证组脐橙表面氧乐果污染程度的实际类别与预测类别的相关系数Rpred为0.9817,预测样本均方根误差RMSEP是0.1564。     

Influence of Soil Particle Size on the Measurement of Sodium by Near-Infrared Reflectance Spectroscopy

This study evaluates the effect of soil particle size (SPS) on the measurement of exchangeable sodium (Na) (EXC-Na) by near-infrared reflectance (NIR) spectroscopy. Three hundred thirty-two (n = 332) top soil samples (0–10 cm) were taken from different locations across Uruguay, analyzed by EXC-Na using emission spectrometry, and scanned in reflectance using a NIR spectrophotometer (1100–2500 nm). Partial least squares (PLS) and principal component regression (PCR) models between reference chemical data and NIR data were developed using cross validation (leaving one out). The coefficient of determination in calibration (R²) and the root mean square of the standard error of cross validation (RMSECV) for EXC-Na concentration were 0.44 (RMSECV: 0.12 mg kg^–1) for soil with small particle size (SPS-0.053) and 0.77 (RMSECV: 0.09 mg kg^–1) for soils with particle sizes greater than 0.212 mm (SPS-0.212), using the NIR region after second derivative as mathematical transformation. The R² and RMSECV for EXC-Na concentration using PCR were 0.54 (RMSECV: 0.07 mg kg^–1) and 0.80 (RMSECV: 0.03 mg kg^–1) for SPS-0.053 and SPS-0.212 samples, respectively.     

Measurement of Phosphorus in Soils by Near Infrared Reflectance Spectroscopy: Effect of Reference Method on Calibration

Abstract

The use of ultraviolet (UV), visible (VIS), near infrared reflectance (NIR), and midinfrared (MIR) spectroscopy techniques have been found to be successful in determining the concentration of several chemical properties in soils. The aim of this study was to evaluate the effect of two reference methods, namely Bray and Resins, on the VIS and NIR calibrations to predict phosphorus in soil samples. Two hundred (n=200) soil samples were taken in different years from different locations across Uruguay with different physical and chemical characteristics due to different soil types and management. Soil samples were analyzed by two reference methods (Bray and Resins) and scanned using an NIR spectrophotometer (NIRSystems 6500). Partial least square (PLS) calibration models between reference data and NIR data were developed using cross‐validation. The coefficient of determination in calibration (R²) and the root mean square of the cross validation (RMSECV) were 0.58 (RMSECV: 3.78 mg kg^?1) and 0.61 (RMSECV: 2.01 mg kg^?1) for phosphorus (P) analyzed by Bray and Resins methods, respectively, using the VIS and NIR regions. The R² and RMSECV for P using the NIR region were 0.50 (RMSECV: 3.78 mg kg^?1) and 0.58 (RMSECV: 2.01 mg kg^?1). This study suggested that differences in accuracy and prediction depend on the method of reference used to develop an NIR calibration for the measurement of P in soil.     

Dose detection of radiated rice by infrared spectroscopy and chemometrics

Infrared spectroscopy based on sensitive wavelengths (SWs) and chemometrics was proposed to discriminate the nine different radiation doses (0, 250, 500, 750, 1000, 1500, 2000, 2500, and 3000 Gy) of rice. Samples ( n = 16 each dose) were selected randomly for the calibration set, and the remaining 36 samples ( n = 4 each dose) were selected for the prediction set. Partial least-squares (PLS) analysis and least-squares-support vector machine (LS-SVM) were implemented for calibration models. PLS analysis was implemented for calibration models with different wavelength bands including near-infrared (NIR) regions and mid-infrared (MIR) regions. The best PLS models were achieved in the MIR (400-4000 cm (-1)) region. Furthermore, different latent variables (5-9 LVs) were used as inputs of LS-SVM to develop the LV-LS-SVM models with a grid search technique and radial basis function (RBF) kernel. The optimal models were achieved with six LVs, and they outperformed PLS models. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights. The optimal LS-SVM model was achieved with SWs (756, 895, 1140, and 2980 cm (-1)) selected by ICA and had better performance than PLS and LV-LS-SVM with the parameters of correlation coefficient ( r), root-mean-square error of prediction, and bias of 0.996, 80.260, and 5.172 x 10 (-4), respectively. The overall results indicted that the ICA was an effective way for the selection of SWs, and infrared spectroscopy combined with LS-SVM models had the capability to predict the different radiation doses of rice.     

Determination of alcohol and extract concentration in beer samples using a combined method of near-infrared (NIR) spectroscopy and refractometry

A new approach of combination of near-infrared (NIR) spectroscopy and refractometry was developed in this work to determine the concentration of alcohol and real extract in various beer samples. A partial least-squares (PLS) regression, as multivariate calibration method, was used to evaluate the correlation between the data of spectroscopy/refractometry and alcohol/extract concentration. This multivariate combination of spectroscopy and refractometry enhanced the precision in the determination of alcohol, compared to single spectroscopy measurements, due to the effect of high extract concentration on the spectral data, especially of nonalcoholic beer samples. For NIR calibration, two mathematical pretreatments (first-order derivation and linear baseline correction) were applied to eliminate light scattering effects. A sample grouping of the refractometry data was also applied to increase the accuracy of the determined concentration. The root mean squared errors of validation (RMSEV) of the validation process concerning alcohol and extract concentration were 0.23 Mas% (method A), 0.12 Mas% (method B), and 0.19 Mas% (method C) and 0.11 Mas% (method A), 0.11 Mas% (method B), and 0.11 Mas% (method C), respectively.     

Performance comparison of UV and FT-Raman spectroscopy in the determination of conjugated linoleic acids in cow milk fat

The determination of conjugated linoleic acids (CLA) in cow milk fat was studied by using UV (210-250 nm) and Fourier transform (FT)-Raman (900-3400 cm (-1)) spectroscopy in order to determine the best spectrophotometric technique for routine analysis of milk fat. A collection of 57 milk fat samples was randomly divided into two sets, a calibration set and a validation set, representing two-thirds and one-third of the samples, respectively. All calculations were performed on the calibration set and then applied to the validation set. The CLA content ranged from 0.56 to 4.70%. A comparison of various spectral pretreatments and different multivariate calibration techniques, such as partial least-squares (PLS) and multiple linear regression (MLR), was done. This paper shows that UV spectroscopy is as reliable as FT-Raman spectroscopy to monitor CLA in cow milk fat. The best calibration for FT-Raman was given by a PLS model of seven factors with a standard error of prediction (SEP) of 0.246. For UV spectroscopy, PLS models were also better than MLR models. The most robust PLS model was constructed with only one factor and with SEP=0.288.     

用辐热积法模拟温室黄瓜果实生长

为了提高预测温室黄瓜产量的能力,该研究根据温室黄瓜（品种为：戴多星Cucumis sativus cv Deltestar）果实对温度和辐射的响应,建立了以辐热积（Product of thermal effectiveness and PAR,TEP）为尺度的温室黄瓜果实模型,并用独立的试验数据进行了检验。模型对温室黄瓜各节位果实果长、果径和鲜质量的模拟值与实测值的符合度较好,模型对温室黄瓜果长和果径的模拟值与实测值之间的决定系数（R2）分别为0.7325和0.5885;回归标准误差（RMSE）分别为1.64 cm和0.35 cm,而以有效积温（Growing degree days,GDD）为尺度构建的果实生长模型对果长和果径的预测结果与实测值之间的决定系数（R2）分别为0.5768和0.4893;回归标准误差（RMSE）分别为1.83 cm和0.40 cm;本模型对果实鲜质量的模拟结果与实测值之间的回归标准误差（RMSE）和决定系数（R2）分别为25.04 g 和0.6782。而基于有效积温的果实生长模型对果实鲜质量的模拟结果与实测值之间的回归标准误差（RMSE）和决定系数（R2）分别为28.52 g和0.6068。模拟精度提高了12.21%。本研究建立的辐热积模型能较准确地预测温室黄瓜各节位的果实生长,模型的实用性较强,可以为温室黄瓜生产提供理论依据和决策支持。     

冬小麦根层土壤水量平衡的系统动力学模型

为获取冬小麦根系层水量转化情况,该文采用系统动力学的建模思想和Vensim软件构建了冬小麦一维逐日土壤水量平衡模型。模型将2m土层概化为十个串联的水箱,计算了灌溉降雨后的土壤水分下渗、土壤蒸发、作物蒸腾、毛管上升补给和水分重分配等物理过程。利用河北省石津灌区军齐干渠北二支一斗渠2007－2009年两季冬小麦的田间试验资料对模型进行了率定和验证,结果显示率定期和验证期的平均残差比例和分散均方根比例均在15%以内。三种极端条件测试和六种参数的敏感性测试以及与Hydrus-1模型的比较表明模型假定合理,没有发生结构性错误。对灌区两季冬小麦生育期的土壤水分转化进行模拟,结果表明降雨和灌溉是主要供水水源,毛管水上升量很小,底部渗漏较大,而土壤储水量变化很小。     

NIR spectroscopy and partial least-squares regression for determination of natural alpha-tocopherol in vegetable oils

Near-infrared (NIR) spectroscopy and partial least-square regression were used for determination of alpha-tocopherol in edible oils after extraction with ethanol. The standard error of calibration and the standard error of prediction were calculated for evaluation of the calibration models. The chemometric calibration model was prepared in spectral region 6500-4500 cm(-1) for standard alpha-tocopherol solutions (0.54-53.54 mg/mL). Obtained mean concentrations of natural alpha-tocopherol in different types of oils varied from 17.53 to 57.10 mg/100 g. Net analyte signal calculation was used to estimate detection limit (DL = 0.12 mg/mL), quantification limit (QL = 0.40 mg/mL), sensitivity (SEN = 0.045 mg/mL), and selectivity (SEL ranged between 0.24 and 0.54% of the measured reflectance signal) of the proposed NIR method. The comparable precision (RSD = 0.68-2.80% and 0.79-3.06%) and accuracy (recovery, 97.2-102.4% and 96.8-103.2%) for the proposed NIR and standard HPLC methods, demonstrate the benefit of the NIR method in the routine analysis of alpha-tocopherol in vegetable oils.     

基于可见/近红外光谱技术的土壤总磷分析研究

Overabundance of phosphorus (P) in soils and water is of great concern and has received much attention in Florida, USA. Therefore, it is essential to analyze and predict the distribution of P in soils across large areas. This study was undertaken to model the variation of soil total phosphorus (TP) in Florida. A total of 448 soil samples were collected from different soil types. Soil samples were analyzed by chemical reference method and scanned in the visible/near-infrared (VNIR) region of 350--2 500 nm. Partial least squares regression (PLSR) calibration model was developed between chemical reference values and VNIR values. The coefficient of determination (R²) and the root mean squares error (RMSE) of calibration and validation sets, and the residual prediction deviation (RPD) were used to evaluate the models. The R² in calibration and validation for log-transformed TP (log TP) were 0.69 and 0.65, respectively, indicating that VNIR calibration obtained in this study accounted for at least 65% of the variance in log TP using only VNIR spectra, and the high RPD of 2.82 obtained suggested that the spectral model derived in this study was suitable and robust to predict TP in a wide range of soil types, being representative of Florida soil conditions.     

青海三江源地区土壤水分常数转换函数的建立与比较

利用土壤理化性质数据建立转换函数是间接获得土壤水力参数的重要手段之一。基于测定的土壤理化性质和土壤水分常数数据,本文采用回归分析、BP神经网络和基于BP神经网络的Rosetta模型3种方式分别建立了青海三江源地区土壤饱和含水量、毛管持水量和田间持水量的转换函数,并对其预测精度进行了比较。结果表明:(1)回归分析方法总体预测效果比较理想,特别是田间持水量的平均误差(ME)和均方根误差(RMSE)都在3.397%以下,决定系数(R2)高达0.868;(2)BP神经网络方法的预测效果非常理想,各土壤水分常数平均误差和均方根误差都在4.685%以下,并且决定系数均在0.857以上;(3)Rosetta模型的预测效果相对较差,特别是饱和含水量和毛管持水量,平均误差(ME)和均方根误差(RMSE)相对较大,决定系数(R2)相对较小。3种方式中,BP神经网络方法所建立的毛管持水量和饱和含水量转换函数均为最佳,回归方法所建立的田间持水量的转换函数要好于BP神经网络方法和Rosetta模型,Rosetta模型对土壤水分常数的预测效果不如其他两种方式。研究可为青海三江源地区土壤水力特性参数研究以及区域尺度上土壤水分估算提供科学依据。     

不同品种间的猪肉含水率高光谱模型传递方法研究

针对目前的模型传递方法研究大多为不同仪器间的近红外光谱模型传递,该文采用高光谱技术建立猪肉含水率定量检测模型,并针对不同品种间的模型传递提出了一种分段直接校正结合线性插值(piecewise direct standardization combine with linear interpolation,PDS-LI)的传递算法。以杜长大、茂佳山黑猪和零号土猪3个品种为研究对象,以杜长大作为主品种,茂佳山黑猪和零号土猪作为从品种,采用偏最小二乘回归(partial least squares regression,PLSR)法建立猪肉含水率主模型,经PDS-LI算法对主模型进行传递后,主模型对茂佳山黑猪和零号土猪样品的预测决定系数R2p分别由传递前的0.263和0.507提高到0.832和0.848,预测均方根误差分别由传递前的1.151%和0.857%降低到0.470%和0.440%,剩余预测偏差(residual prediction deviation,RPD)分别由传递前的1.000和1.214提高到2.447和2.364。结果表明,PDS-LI传递算法能够实现杜长大对茂佳山黑猪和零号土猪样品的模型传递。研究结果为提高猪肉含水率模型适配性问题提供参考。     

Prediction of soil organic and inorganic carbon contents at a national scale (France) using mid‐infrared reflectance spectroscopy (MIRS)

This work aimed to evaluate the potential of mid‐infrared reflectance spectroscopy (MIRS) to predict soil organic and inorganic carbon contents with a 2086‐sample set representative of French topsoils (0–30 cm). Ground air‐dried samples collected regularly using a 16 × 16‐km grid were analysed for total (dry combustion) and inorganic (calcimeter) carbon; organic carbon was calculated by difference. Calibrations of MIR spectra with partial least square regressions were developed with 10–80% of the set and five random selections of samples. Comparisons between samples with contrasting organic or inorganic carbon content and regression coefficients of calibration equations both showed that organic carbon was firstly associated with a wide spectral region around 2500–3500 cm^?1 (which was a reflection of its complex nature), and inorganic carbon with narrow spectral bands, especially around 2520 cm^?1. Optimal calibrations for both organic and inorganic carbon were achieved by using 20% of the total set: predictions were not improved much by including more of the set and were less stable, probably because of atypical samples. At the 20% rate, organic carbon predictions over the validation set (80% of the total) yielded mean R², standard error of prediction (SEP) and RPD (ratio of standard deviation to SEP) of 0.89, 6.7 g kg^?1 and 3.0, respectively; inorganic carbon predictions yielded 0.97, 2.8 g kg^?1 and 5.6, respectively. This seemed appropriate for large‐scale soil inventories and mapping studies but not for accurate carbon monitoring, possibly because carbonate soils were included. More work is needed on organic carbon calibrations for large‐scale soil libraries.     

Estimation of wood density and chemical composition by means of diffuse reflectance mid-infrared Fourier transform (DRIFT-MIR) spectroscopy

Sitka spruce (Picea sitchensis) samples (491) from 50 different clones as well as 24 different tropical hardwoods and 20 Scots pine (Pinus sylvestris) samples were used to construct diffuse reflectance mid-infrared Fourier transform (DRIFT-MIR) based partial least squares (PLS) calibrations on lignin, cellulose, and wood resin contents and densities. Calibrations for density, lignin, and cellulose were established for all wood species combined into one data set as well as for the separate Sitka spruce data set. Relationships between wood resin and MIR data were constructed for the Sitka spruce data set as well as the combined Scots pine and Sitka spruce data sets. Calibrations containing only five wavenumbers instead of spectral ranges 4000-2800 and 1800-700 cm(-1) were also established. In addition, chemical factors contributing to wood density were studied. Chemical composition and density assessed from DRIFT-MIR calibrations had R2 and Q2 values in the ranges of 0.6-0.9 and 0.6-0.8, respectively. The PLS models gave residual mean squares error of prediction (RMSEP) values of 1.6-1.9, 2.8-3.7, and 0.4 for lignin, cellulose, and wood resin contents, respectively. Density test sets had RMSEP values ranging from 50 to 56. Reduced amount of wavenumbers can be utilized to predict the chemical composition and density of a wood, which should allow measurements of these properties using a hand-held device. MIR spectral data indicated that low-density samples had somewhat higher lignin contents than high-density samples. Correspondingly, high-density samples contained slightly more polysaccharides than low-density samples. This observation was consistent with the wet chemical data.     

基于冠层光谱的锦橙叶片磷素营养监测研究

以盆栽蓬安100号锦橙施肥调控试验为基础,利用田间冠层光谱信息探索建立植株磷素营养监测技术与方法。通过采集蓬安100号锦橙95个单株样本的冠层光谱信息和室内检测分析叶片磷含量,随机选取76个作为建模样本,19个为检验样本,运用多种光谱预处理方法和偏最小二乘法(Partial least square method,PLS)及内部交叉验证方法建立校正模型与模型检验。结果表明,经多种光谱预处理方法的建模结果比较,冠层原始反射光谱经二阶求导和SNV处理后建立的蓬安100号锦橙叶片磷含量冠层光谱监测模型预测能力和稳健性最佳,其主成分数4个,能表达全波段63%的信息;校正模型相关系数为0.90,偏差Bias=2.45E-10,且RMSEC和RMSEP均最小。模型检验预测的决定系数R2=0.85。因此,利用二阶导数及标准归一化(Standard normal variate transformation,SNV)预处理的田间冠层光谱信息快速无损监测蓬安100号锦橙叶片磷含量具有一定的可行性。