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1.
《Cereal Chemistry》2017,94(4):677-682
Deoxynivalenol (DON) levels in harvested grain samples are used to evaluate the Fusarium head blight (FHB) resistance of wheat cultivars and breeding lines. Fourier transform near‐infrared (FT‐NIR) calibrations were developed to estimate the DON level and moisture content (MC) of bulk wheat grain samples harvested from FHB screening trials. Grains in a rotating glass petri dish were scanned in the 10,000–4,000 cm−1 (1,000–2,500 nm) spectral range using a Perkin Elmer Spectrum 400 FT‐IR/FT‐NIR spectrometer. The DON calibration predicted the DON levels in test samples with R 2 = 0.62 and root mean square error of prediction (RMSEP) = 8.01 ppm. When 5–25 ppm of DON was used as the cut‐off to classify samples into low‐ and high‐DON groups, 60.8–82.3% of the low‐DON samples were correctly classified, whereas the classification accuracy of the high‐DON group was 82.3–94.0%. The MC calibration predicted the MC in grain samples with R 2 = 0.98 and RMSEP = 0.19%. Therefore, these FT‐NIR calibrations can be used to rapidly prescreen wheat lines to identify low‐DON lines for further evaluation using standard laboratory methods, thereby reducing the time and costs of analyzing samples from FHB screening trials.  相似文献   

2.
Visible and near infrared (VIS/NIR) transmission spectroscopy and chemometric methods were utilized for the fast determination of soluble solids content (SSC) and pH of cola beverage. A total of 180 samples were used for the calibration set, whereas 60 samples were used for the validation set. Some preprocessing methods were applied before developing the calibration models. Several PLS factors, extracted by partial least squares (PLS) analysis, were used as the inputs of least squares-support vector machine (LS-SVM) model according to their accumulative reliabilities. The correlation coefficient (r), root mean square error of prediction (rmsEP), bias, and RPD were 0.959, 1.136, -0.185, and 3.5 for SSC, whereas 0.973, 0.053, 0.017, and 4.1 for pH, respectively. An excellent prediction precision was achieved by LS-SVM compared with PLS. The results indicated that VIS/NIR spectroscopy combined with LS-SVM could be applied as a rapid and alternative way for the fast determination of SSC and pH of cola beverage.  相似文献   

3.
The development of accurate calibration models for selected soil properties is a crucial prerequisite for successful implementation of visible and near infrared (Vis‐NIR) spectroscopy for soil analysis. This paper compares the performance of calibration models developed for individual farms with that of general models valid for three farms in three European countries. Fresh soil samples collected from farms in the Czech Republic, Germany and Denmark were scanned with a fibre‐type Vis‐NIR spectrophotometer. After dividing spectra into calibration (70%) and validation (30%) sets, spectra in the calibration set were subjected to partial least squares regression (PLSR) with leave‐one‐out cross‐validation to establish calibration models of soil properties. Except for the Czech Republic farm, individual farm models provided successful calibration for total carbon (TC), total nitrogen (TN) and organic carbon (OC), with coefficients of determination (R2) of 0.85–0.93 and 0.74–0.96 and residual prediction deviations (RPD) of 2.61–3.96 and 2.00–4.95 for the cross‐validation and independent validation respectively. General calibration models gave improved prediction accuracies compared with models of farms in the Czech Republic and Germany, which was attributed to larger ranges in the variation of soil properties in general models compared with those in individual farm models. The results revealed that larger standard deviations (SDs) and wider variation ranges have resulted in larger R2 and RPD, but also larger root mean square errors of prediction (RMSEP). Therefore, a compromise solution, which also results in small RMSEP values, should be found when selecting soil samples for Vis‐NIR calibration to cover a wide variation range.  相似文献   

4.
可见/近红外光谱技术无损检测果实坚实度的研究   总被引：9，自引：2，他引：7  
该研究的目的是建立可见/近红外光谱与梨果实坚实度之间的数学模型,评价可见/近红外光谱技术无损测量梨果实坚实度的应用价值.在可见/近红外光谱区域(350～1800nm),试验对比分析了不同测量部位、不同光谱预处理方法和不同校正建模算法的梨果实坚实度校正模型.结果表明:赤道部位吸光度一阶微分光谱的偏最小二乘回归所建梨果实坚实度校正模型的预测性能较优,其校正和预测相关系数分别为0.8779和0.8087,校正和预测均方误差分别为1.0804N和1.4455N.研究表明:可见/近红外光谱技术无损检测梨果实坚实度是可行的.  相似文献   

5.
This paper reports on the influence of the number of samples used for the development of farm‐scale calibration models for moisture content (MC), total nitrogen (TN) and organic carbon (OC) on the prediction error expressed as root mean square error of prediction (RMSEP) for visible and near infrared (vis‐NIR) spectroscopy. Fresh (wet) soil samples collected from four farms in the Czech Republic, Germany, Denmark and the UK were scanned with a fibre‐type vis‐NIR, AgroSpec spectrophotometer with a spectral range of 305–2200 nm. Spectra were divided into calibration (two thirds) and prediction (one third) sets and the calibration spectra were subjected to a partial least squares regression (PLSR) with leave‐one‐out cross‐validation using Unscrambler 7.8 software. The RMSEP values of models with a large sample number (46–84 samples from each farm) were compared with those of models developed with a small sample number (25 samples selected from the large sample set of each farm) for the same variation range. Both large‐set and small‐set models were validated by the same prediction set for each property. Further PLSR analysis was carried out on samples from the German farm, with different sample numbers of the calibration set of 25, 50, 75 and 100 samples. Results showed that the large‐size dataset models resulted in smaller RMSEP values than the small‐size dataset models for all the soil properties studied. The results also demonstrated that with the increase in sample number used in the calibration set, RMSEP decreased in almost linear fashion, although the largest decrease was between 25 and 50 samples. Therefore, it is recommended that the number of samples should be chosen according to the accuracy required, although 50 soil samples is considered appropriate in this study to establish calibration models of TN, OC and MC with smaller expected prediction errors as compared with smaller sample numbers.  相似文献   

6.
Near-infrared spectroscopy (NIRS) is a well-established technique for determining the components of foods. Sample preparation for NIRS is easy, making it suitable for breeding and/or quality evaluation, for which a large number of samples should be analyzed. We aimed to assess the feasibility of NIRS to estimate parameters that seem to influence consumers' perception of the seed coat of common beans: dietary fiber (DF), uronic acids (UA), ashes, calcium, and magnesium. We used reference methods to analyze ground seed coats of 90 common bean samples with a wide range of genetic variability and cultivated at many locations. We registered the NIR spectra on intact beans and ground seed coat samples. We derived partial least-squares (PLS) regression equations from a set of calibration samples and tested their predictive power in an external validation set. For intact beans, only RER values for ashes and calcium are good enough for very rough screening. For ground seed coat samples, the RPD and RER values for ashes (3.49 and 14.09, respectively) and calcium (3.57 and 12.70, respectively) are good enough for screening. RPD and RER values for DF (2.60 and 9.15, respectively) and RER values for magnesium (6.57) also enable rough screening. A poorer correlation was achieved for UA. We conclude that NIRS can help in common bean breeding research and quality evaluation.  相似文献   

7.
基于dbiPLS-SPA变量筛选的固态发酵湿度近红外光谱检测   总被引：2，自引：1，他引：1  
为了提高基于近红外光谱技术的固态发酵关键过程参数——湿度快速检测的精度和稳定性,研究采用动态反向区间偏最小二乘（dbiPLS）法结合连续投影算法（SPA）进行最佳光谱子区间和特征组合变量的筛选,通过交互验证法确定偏最小二乘（PLS）模型的主成分因子数,并以预测均方根误差（RMSEP）和相关系数（Rp）作为模型的评价标准。试验结果显示,最佳dbiPLS-SPA模型筛选的组合变量个数为8,其RMSEP和Rp分别为1.1795%（质量分数）和0.9430。试验结果表明,dbiPLS-SPA是一个有效的波长组合变量筛选方法,可简化模型结构、增强模型精度和稳健性。  相似文献   

8.
新疆艾比湖湿地土壤有机碳含量的光谱测定方法对比   总被引：2，自引：3，他引：2  
干旱半干旱地区湿地土壤中的有机碳是影响土壤质量,制约植物生长的重要因素之一,其含量的变化会影响生态系统的安全和稳定。为快速估测湿地土壤有机碳含量,在新疆艾比湖湿地保护区采集140个荒漠土壤样品,利用土壤可见/近红外光谱数据以及化学分析获取的土壤有机碳数据,在对土壤原始光谱反射率进行卷积平滑的基础上,获取了一阶微分、倒数对数一阶微分2种光谱预处理指标,采用蚁群-区间偏最小二乘法、基于支持向量机的回归特征消去法,选择土壤有机碳含量近红外光谱特征波长,在此基础上构建土壤有机碳含量偏最小二乘回归、支持向量回归模型。结果表明:1)利用原始一阶微分建立的模型,预测能力优于倒数对数一阶微分建立的模型。2)4种建模结果比较显示,利用原始一阶微分经基于支持向量机的回归特征消去法进行特征变量选择后建立的土壤有机碳含量模型,预测精度最高。训练集的相关系数以及均方根误差分别为0.9687、0.158%;测试集的相关系数和均方根误差分别为0.9091以及0.268%。因此,经过卷积平滑以及一阶微分预处理、并利用基于支持向量机的回归特征消去法建立的模型具有较高的预测精度和较好的稳健性,可以作为有效手段估算荒漠湿地土壤有机碳含量。  相似文献   

9.
We investigate the potential of near-infrared (NIR) spectroscopy to predict some heavy metals content (Zn, Cu, Pb, Cr and Ni) in several soil types in Stara Zagora Region, South Bulgaria, as affected by the size of calibration set using partial least squares (PLS) regression models. A total of 124 soil samples from the 0–20 and 20–40 cm layers were collected from fields with different cropping systems. Total Zn, Cu, Pb, Cr and Ni concentrations were determined by Atomic Absorption Spectrometry. Spectra of air dried soil samples were obtained using an FT-NIR Spectrometer (spectral range 700–2,500 nm). PLS calibration models were developed with full-cross-validation using calibration sets of 90 %, 80 %, 70 % and 60 % of the 124 samples. These models were validated with the same prediction set of 12 samples. The validation of the NIR models showed Cu to be best predicted with NIR spectroscopy. Less accurate prediction was observed for Zn, Pb and Ni, which was classified as possible to distinguish between high and low concentrations and as approximate quantitative. The worst model performance in cross-validation and prediction was for Cr. Results also showed that values of root mean square error in cross-validation (RMSEcv) increased with decreasing number of samples in calibration sets, which was particularly clear for Cu, Pb, Ni and Cr content. A similar tendency was observed in the prediction sets, where RMSEP values increased with a decrease in the number of samples, particularly for Pb, Ni and Cr content. This tendency was not clear for Zn, while even an increase in RMSEP for Cu with the sample size was observed. It can be concluded that NIR spectroscopy can be used to measure heavy metals in a sample set with different soil type, when sufficient number of soil samples (depending on variability) is used in the calibration set.  相似文献   

10.
For 30 years, near‐infrared (NIR) spectroscopy has routinely been applied to the cereal grains for the purpose of rapidly measuring concentrations of constituents such as protein and moisture. The research described herein examined the ability of NIR reflectance spectroscopy on harvested wheat to determine weather‐related, quality‐determining properties that occurred during plant development. Twenty commercial cultivars or advanced breeding lines of hard red winter and hard white wheat (Triticum aestivum L.) were grown in 10 geographical locations under prevailing natural conditions of the U.S. Great Plains. Diffuse reflectance spectra (1,100–2,498 nm) of ground wheat from these samples were modeled by partial least squares one (PLS1) and multiple linear regression algorithms for the following properties: SDS sedimentation volume, amount of time during grain fill in which the temperature or relative humidity exceeded or was less than a threshold level (i.e., >30, >32, >35, <24°C; >80%, <40% rh). Rainfall values associated with four pre‐ and post‐planting stages also were examined heuristically by PLS2 analysis. Partial correlation analysis was used to statistically remove the contribution of protein content from the quantitative NIR models. PLS1 models of 9–11 factors on scatter‐corrected and (second order) derivatized spectra produced models whose dimensionless error (RPD, ratio of standard deviation of the property in a test set to the model standard error for that property) ranged from 2.0 to 3.3. Multiple linear regression models, involving the sum of four second‐derivative terms with coefficients, produced models of slightly higher error compared with PLS models. For both modeling approaches, partial correlation analysis demonstrated that model success extends beyond an intercorrelation between property and protein content, a constituent that is well‐modeled by NIR spectroscopy. With refinement, these types of NIR models may have the potential to provide grain handlers, millers, and bakers a tool for identifying the cultural environment under which the purchased grain was produced.  相似文献   

11.
Fourier transform infrared (FTIR) spectroscopy combined with chemometric multivariate methods was proposed to discriminate the type (unfermented and fermented) and predict the age of tuocha tea. Transmittance FTIR spectra ranging from 400 to 4000 cm(-1) of 80 fermented and 98 unfermented tea samples from Yunnan province of China were measured. Sample preparation involved finely grinding tea samples and formation of thin KBr disks (under 120 kg/cm(2) for 5 min). For data analysis, partial least-squares (PLS) discriminant analysis (PLSDA) was applied to discriminate unfermented and fermented teas. The sensitivity and specificity of PLSDA with first-derivative spectra were 93 and 96%, respectively. Multivariate calibration models were developed to predict the age of fermented and unfermented teas. Different options of data preprocessing and calibration models were investigated. Whereas linear PLS based on standard normal variate (SNV) spectra was adequate for modeling the age of unfermented tea samples (RMSEP = 1.47 months), a nonlinear back-propagation-artificial neutral network was required for calibrating the age of fermented tea (RMSEP = 1.67 months with second-derivative spectra). For type discrimination and calibration of tea age, SNV and derivative preprocessing played an important role in reducing the spectral variations caused by scattering effects and baseline shifts.  相似文献   

12.
Visible and near-infrared spectroscopy (VIS/NIR) has been used to detect economic adulteration of crab meat samples. Atlantic blue and blue swimmer crab meat samples were adulterated with surimi-based imitation crab meat in 10% increments. Waveform evaluation revealed that the main features seen in the spectral data arise from water absorptions with a decrease in sample absorbance with increasing adulteration level. Prediction and quantitative analysis was done using raw data, a 15-point smoothing average, a first derivative, a second derivative, and 150 wavelength spectral data gathered from a correlogram. Regression analysis included partial least squares (PLS) and principal component analysis (PCR). Both models were able to perform similarly in predicting crab meat adulteration. The best model for both PLS and PCR used the first derivative spectral data gathered from the correlogram, with a standard error of prediction (SEP) of 0.252 and 0.244, respectively. The results suggest that VIS/NIR technology can be successfully used to detect adulteration in crab meat samples adulterated with surimi-based imitation crab meat.  相似文献   

13.
基于近红外光声光谱的土壤有机质含量定量建模方法   总被引：13，自引：7，他引：6  
该研究的目的在于应用近红外光声光谱技术结合不同的定量分析方法实现5种不同类型土壤有机质含量的快速估测。对中国中、东部地区5种不同类型土壤风干样本进行光谱扫描,经过多元散射校正、一阶导数、二阶导数及平滑等预处理后,应用逐步多元回归(SMLR)、主成分分析(PCR)、偏最小二乘法(PLS)和偏最小二乘法-反向传播神经网络(PLS-BPNN)等方法建立土壤有机质含量的定量估测模型。结果显示,不同预处理方法对所建土壤有机质含量估测模型的预测精度有较大影响,总体表现为多元散射校正+Norris一阶导数>多元散射校正>Norris一阶导数>标准正态化>Norris二阶导数>吸光度>Savitzky-Golay平滑后一阶导数>Savitzky-Golay平滑后二阶导数。对于4种不同建模方法,均以多元散射校正+Norris一阶导数滤波平滑后的光谱建模精度最高,其中采用PLS-BPNN方法建模效果最好,其次是PLS、SMLR和PCR。采用PLS-BPNN建立有机质校正模型具有极高的预测精度,建模决定系数和均方根偏差分别为0.97和1.88,模型测试决定系数和均方根偏差分别为0.97和1.72。因此,基于多元散射校正+Norris一阶导数光谱建立的PLS-BPNN模型可能是土壤有机质含量估测建模的最优方法。  相似文献   

14.
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.  相似文献   

15.
A study was conducted to investigate methods of improving a near-infrared transmittance spectroscopy (NITS) amylose calibration that could serve as a rapid, nondestructive alternative to traditional methods for determining amylose content in corn. Calibrations were developed using a set of genotypes possessing endosperm mutations in single- and double-mutant combinations ranging in starch-amylose content (SAC) from -8.5 to 76%, relative to a standard curve. The influence of three factors were examined including comparing calibrations made against SAC versus grain amylose content (GAC), developing calibrations using partial least squares (PLS) analysis versus artificial neural networking (ANN), and using all samples in the calibrations set versus using progressively narrower ranges of SAC or GAC in the calibration set. Grain samples were divided into calibration and validation sets for PLS analysis while samples used in ANN were assigned to a training set, test set, and validation set. Performance statistics of the validation sets that were considered were the coefficient of determination (R), the standard error of prediction (SEP), and the ratio of the standard deviation of amylose values to the SEP (RPD), which were used to compare all NITS models. The study revealed an NITS prediction model for SAC (R = 0.96, SEP = 5.1%, RDP = 3.8) of similar precision to the best GAC model (R = 0.96, SEP = 2.7%, RPD = 3.5). Narrowing the amylose range of the calibration set generally did not improve performance statistics except for PLS models for SAC in which a decrease in SEP values was observed. In one model, the SEP improved while R and RPD remained constant (R = 0.94, SEP = 4.2%, RPD = 2.8) when samples with SAC values <20% were removed from the calibration set. Although the NITS amylose calibrations in this study are of limited precision, they may be useful when a rough screening method is needed for SAC. For example, NITS may be useful to detect severe contamination during transport and storage of specialty grains or to aid breeders when selecting for amylose content from large numbers of grain samples.  相似文献   

16.
不同品种间的猪肉含水率高光谱模型传递方法研究   总被引：1，自引：1，他引：0  
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。  相似文献   

17.
赵化兵  王洁  董彩霞  徐阳春 《土壤》2014,46(2):256-261
利用可见/近红外反射光谱定量分析技术对梨树鲜叶钾素含量进行快速测定研究。对150个梨树叶片样本进行光谱扫描,其中120个做建模集,30个做验证集。通过对样品的可见/近红外光谱进行多种预处理,并建立钾素预测模型,探讨了可见/近红外光谱数据预处理对预测精度的影响。结果表明,通过原始光谱与S-G(3)平滑相结合的预处理方法,用17个主成分建立的偏最小二乘法模型最好,其交叉验证集和预测集模型的决定系数(R2)分别为0.722 7和0.679 1,交叉验证均方根误差(RMSECV)为1.171,预测的平均相对误差为6.81%,能高效、快速地预测梨树叶片钾素含量,为梨树钾素快速测定提供了新的手段。  相似文献   

18.
This study investigated the suitability of mid‐infrared diffuse reflectance Fourier transform (MIR‐DRIFT) spectroscopy, with partial least squares (PLS) regression, for the determination of variations in soil properties typical of Italian Mediterranean off‐shore environments. Pianosa, Elba and Sardinia are typical of islands from this environment, but developed on different geological substrates. Principal components analysis (PCA) showed that spectra could be grouped according to the soil composition of the islands. PLS full cross‐validation of soil property predictions was assessed by the coefficient of determination (R2), the root mean square error of cross‐validation and prediction (RMSECV and RMSEP), the standard error (SECV for cross‐validation and SEP for prediction), and the residual predictive deviation (RPD). Although full cross‐validation appeared to be the most accurate (R2 = 0.95 for organic carbon (OC), 0.96 for inorganic carbon (IC), 0.87 for CEC, 0.72 for pH and 0.74 for clay; RPD = 4.4, 6.0, 2.7, 1.9 and 2.0, respectively), the prediction errors were considered to be optimistic and so alternative calibrations considered to be more similar to ‘true’ predictions were tested. Predictions using individual calibrations from each island were the least efficient, while predictions using calibration selection based on a Euclidian distance ranking method, using as few as 10 samples selected from each island, were almost as accurate as full cross‐validation for OC and IC (R2 = 0.93 for OC and 0.96 for IC; RPD = 3.9 and 4.7, respectively). Prediction accuracy for CEC, pH and clay was less accurate than expected, especially for clay (R2 = 0.73 for CEC, 0.50 for pH and 0.41 for clay; RPD = 1.8, 1.5 and 1.4, respectively). This study confirmed that the DRIFT PLS method was suitable for characterizing important properties for soils typical of islands in a Mediterranean environment and capable of discriminating between the variations in soil properties from different parent materials.  相似文献   

19.
Arid soil is common worldwide and has unique properties that often limit agronomic productivity, specifically, salinity expressed as soluble salts and large amounts of calcium carbonate and gypsum. Currently, the most common methods for evaluating these properties in soil are laboratory‐based techniques such as titration, gasometry and electrical conductivity. In this research, we used two proximal sensors (portable X‐ray fluorescence (PXRF) and visible near‐infrared diffuse reflectance spectroscopy (Vis–NIR DRS)) to scan a diverse set (n = 116) of samples from arid soil in Spain. Then, samples were processed by standard laboratory procedures and the two datasets were compared with advanced statistical techniques. The latter included penalized spline regression (PSR), support vector regression (SVR) and random forest (RF) analysis, which were applied to Vis–NIR DRS data, PXRF data and PXRF and Vis–NIR DRS data, respectively. Independent validation (30% of the data) of the calibration equations showed that PSR + RF predicted gypsum with a ratio of performance to interquartile distance (RPIQ) of 5.90 and residual prediction deviation (RPD) of 4.60, electrical conductivity (1:5 soil : water) with RPIQ of 3.14 and RPD of 2.10, Ca content with RPIQ of 2.92 and RPD of 2.07 and calcium carbonate equivalent with RPIQ of 2.13 and RPD of 1.74. The combined PXRF and Vis–NIR DRS approach was superior to those that use data from a single proximal sensor, enabling the prediction of several properties from two simple, rapid, non‐destructive scans.  相似文献   

20.
The feasibility of hyperspectral imaging (HSI) to detect deoxynivalenol (DON) content and Fusarium damage in single oat kernels was investigated. Hyperspectral images of oat kernels from a Fusarium‐inoculated nursery were used after visual classification as asymptomatic, mildly damaged, and severely damaged. Uninoculated kernels were included as controls. The average spectrum from each kernel was paired with the reference DON value for the same kernel, and a calibration model was fitted by partial least squares regression (PLSR). To correct for the skewed distribution of DON values and avoid nonlinearities in the model, the DON values were transformed as DON* = [log(DON)]3. The model was optimized by cross‐validation, and its prediction performance was validated by predicting DON* values for a separate set of validation kernels. The PLSR model and linear discriminant analysis classification were further used on single‐pixel spectra to investigate the spatial distribution of infection in the kernels. There were clear differences between the kernel classes. The first component separated the uninoculated and asymptomatic from the severely damaged kernels. Infected kernels showed higher intensities at 1,925, 2,070, and 2,140 nm, whereas noninfected kernels were dominated by signals at 1,400, 1,626, and 1,850 nm. The DON* values of the validation kernels were estimated by using their average spectra, and the correlation (R) between predicted and measured DON* was 0.8. Our results show that HSI has great potential in detecting Fusarium damage and predicting DON in oats, but it needs more work to develop a model for routine application.  相似文献   

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