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1.
The calibration of soil organic C (SOC) and hot water‐extractable C (HWE‐C) from visible and near‐infrared soil reflectance spectra is hindered by the complex spectral interaction of soil chromophores that usually varies from one soil or soil type to another. The exploitation of spectral variables from spectroradiometer data is further affected by multicollinearity and noise. In this study, a set of soil samples (Fluvisols, Podzols, Cambisols and Chernozems; n = 48) representing a wide range of properties was analysed. Spectral readings with a fibre‐optics visible to near‐infrared instrument were used to estimate SOC and HWE‐C contents by partial least squares regression (PLS). In addition to full‐spectrum PLS, spectral feature selection techniques were applied with PLS (uninformative variable elimination, UVE‐PLS, and a genetic algorithm, GA‐PLS). On the basis of normalized spectra (mean centring + vector normalization), the order of prediction accuracy was GA‐PLS ? UVE‐PLS > PLS for SOC; for HWE‐C, it was GA‐PLS > UVE‐PLS, PLS. With GA‐PLS, acceptable cross‐validated (cv) prediction accuracies were obtained for the complete dataset (SOC, , RPDcv = 2.42; HWE‐Ccv, , RPDcv = 2.13). Splitting the soil data into two groups with different basic properties (Podzols compared with Fluvisols/Cambisols; n = 21 and n = 23, respectively) improved SOC predictions with GA‐PLS distinctly (Podzols, , RPDcv = 3.14; Fluvisols/Cambisols, , RPDcv = 3.64). This demonstrates the importance of using stratified models for successful quantitative approaches after an initial rough screening. GA selection frequencies suggest that the spectral region over 1900 nm, and in particular the hydroxyl band at 2200 nm are of great importance for the spectral prediction of both SOC and HWE‐C.  相似文献   

2.
This study investigated the potential of visible/near‐infrared reflectance spectroscopy (Vis‐NIRS) to predict soil water repellency (SWR). The top 40 mm of soils (n = 288) across 48 sites under pastoral land‐use in the North Island of New Zealand, which represented 10 soil orders and covered five classes of drought proneness, were analysed by standard laboratory methods and Vis‐NIRS. Soil WR was measured by using the molarity of ethanol droplet (MED) and the water drop penetration time (WDPT) tests. Soil organic carbon content (%C) was also measured to examine a possible relationship with SWR. A partial least squares regression (PLSR) model was developed by using Vis‐NIRS spectral data and the reference laboratory data. In addition, we explored the power of discrimination based on WDPT classes using partial least squares discriminant analysis (PLS‐DA). The PLSR of the processed spectra produced moderately accurate prediction for MED (R2val = 0.61, RPDval = 1.60, RMSEval = 0.59) and good prediction for %C (R2val = 0.82, RPDval = 2.30, RMSEval = 2.72). When the data from the 10 soil orders were considered separately and based on soil order rather than being grouped, the prediction of MED was further improved except for the Allophanic, Brown, Organic and Ultic soil orders. The PLS‐DA was successful in classifying 60% of soil samples into the correct WDPT classes. Our results indicate clearly that Vis‐NIRS has the potential to predict SWR. Further improvement in the prediction accuracy of SWR is envisaged by increasing the understanding of the relationship between Vis‐NIRS and the SWR of all New Zealand soil orders as a function of their physical properties and chemical constituents such as hydrophobic compounds.  相似文献   

3.
为了测量从橄榄油中分提的高、低熔点油脂的脂肪酸成分,在4 000～600 cm-1 的范围测量了31个含有不同脂肪酸成分植物油的傅里叶变换红外光谱,用于建立偏最小二乘（PLS）回归分析校正模型。在油脂的傅里叶变换红外光谱变量和脂肪酸组成变量之间建立了交叉验证的PLS校正模型。为了校正油酸和亚油酸含量,在4?000～600 cm-1 的频率范围,经平滑,二阶导数,规范化处理的红外光谱获得了最好的交叉验证校正模型和最佳的预测结果。PLS校正模型预测结果表明,与高熔点橄榄油（油酸,72.29%,亚油酸,9.98%）相比,低熔点橄榄油含有较高的油酸含量和亚油酸含量（油酸,77.46%,亚油酸,12.51%）,预测的结果与气相色谱测量的结果有很好的一致性。建立的PLS校正模型预测橄榄油的不饱和脂肪酸含量具有较好的相关性。该研究为分提油脂质量的判别评价提供了便捷的方法。  相似文献   

4.
The classification of cereals using near‐infrared Fourier transform Raman (NIR‐FT/Raman) spectroscopy was accomplished. Cereal‐based food samples (n = 120) were utilized in the study. Ground samples were scanned in low‐iron NMR tubes with a 1064 nm (NIR) excitation laser using 500 mW of power. Raman scatter was collected using a Ge (LN2) detector over the Raman shift range of 202.45~3399.89 cm‐1. Samples were classified based on their primary nutritional components (total dietary fiber [TDF], fat, protein, and sugar) using principle component analysis (PCA) to extract the main information. Samples were classified according to high and low content of each component using the spectral variables. Both soft independent modeling of class analogy (SIMCA) and partial least squares (PLS) regression based classification were investigated to determine which technique was the most appropriate. PCA results suggested that the classification of a target component is subject to interference by other components in cereal. The Raman shifts that were most responsible for classification of each component were 1600~1630 cm‐1 for TDF, 1440 and 2853 cm‐1 for fat, 2910 and 1660 cm‐1 for protein, and 401 and 848 cm‐1 for sugar. The use of the selected spectral region (frequency region) for each component produced better results than the use of the entire region in both SIMCA and PLS‐based classifications. PLS‐based classification performed better than SIMCA for all four components, resulting in correct classification of samples 85~95% of the time. NIR‐FT/Raman spectroscopy represents a rapid and reliable method by which to classify cereal foods based on their nutritional components.  相似文献   

5.
Carbon 13 nuclear magnetic resonance spectroscopy (13C NMR) is a powerful technique for studying the structure and turnover of soil organic matter, but is time consuming and expensive. It is therefore worth seeking swifter and cheaper methods. Diffuse reflectance FT‐IR spectroscopy (DRIFT), along with partial least squares (PLS) algorithms, provides statistical models to quantify soil properties, such as contents of C, N and clay. I have applied DRIFT?PLS to quantify soil organic C species, as measured by solid state 13C NMR spectroscopy, for several bulk soils and physical soil fractions. Calibration and prediction models for organic C and for particular NMR regions, namely alkyl C, O?alkyl C and carboxyl C, attained R2 values of between 0.94 and 0.98 (calibration) and 0.70–0.93 (cross‐validation). The prediction of unknown soil samples, after pre‐selection by statistical indices, confirmed the applicability of DRIFT?PLS. The prediction of aromatic C failed, probably because of superimposition of aromatic bands by signals from minerals. Results from fractions of particulate organic matter suggest that the chemical homogeneity of the material hampers the quantification of its constituting C species by DRIFT?PLS. For alkyl C, prediction of carbon species by DRIFT?PLS was better than direct peak‐area quantification in the IR spectra, but advantageous in parts only compared with a linear model correlating C species with soil C contents. In conclusion, DRIFT?PLS calibrated with NMR data provides quantitative information on the composition of soil organic matter and can therefore complement structural studies by its application to large numbers of samples. However, it cannot replace the information provided by more specific methods. The actual potential of DRIFT?PLS lies in its capacity to predict unknown samples, which is helpful for classification and identification of environmental outliers or benchmarks.  相似文献   

6.
通过田间试验,研究了采用猪粪堆肥、中药渣堆肥和鸡粪堆肥为原料制成的有机无机复合肥与无机复合肥等氮量施入对油菜产量、氮素利用率、土壤供氮特征以及土壤微生物多样性的影响。结果表明:各施肥处理油菜籽产量均显著高于对照的油菜籽产量。与化肥处理比较,三种堆肥原料的有机无机复合肥的油菜籽产量显著高于化肥处理,较化肥增产12.7%～33.2%。各有机无机复合肥处理均能增加油菜单株有效角果数。三种有机无机复合肥处理均能显著促进油菜对氮素的吸收,从而提高了氮素利用率。与化肥处理和不施肥处理比较,三种堆肥原料的有机无机复合肥处理能够明显提高土壤有效态氮的含量,调节土壤氮素的释放速度。采用邻接法分析各处理土壤DNA条带表明:5个处理土壤样品的细菌群落共分为三大族群,化肥处理与对照处理为一种族群,中药渣处理为一种族群,猪粪处理和鸡粪处理属一种族群。说明施入外源有机物质(猪粪、鸡粪与中药渣)可能改变土壤的细菌群落结构,而施入化肥对土壤的细菌群落结构影响较小。  相似文献   

7.
Spectral stress strain analysis was used in combination with partial least squares (PLS) regression and artificial neural networks (ANN) to predict nine sensory texture attributes of cooked rice. The models calculated with ANN were significantly more accurate in predicting most of the sensory texture characteristics evaluated than the PLS models. Furthermore, ANN models were more robust and discriminative than PLS models.  相似文献   

8.
《Cereal Chemistry》2017,94(4):760-769
The interrelationships between flour quality and the variability in the dough physical properties and bread loaf characteristics were investigated under reduced salt conditions using partial least squares (PLS) regression analysis. Seventy‐two percent of the variability in dough physical properties was explained by the flour quality using a three‐factor PLS model. Damaged starch content (DS), protein content, and farinograph dough development time (DDT) explained the variability of dough creep‐recovery behavior along PLS‐1. Farinograph absorption (FAB), located along PLS‐2, was strongly related to dough adhesiveness, in which adhesiveness was highly correlated to dough stickiness (r = 0.91). Eighty‐nine percent of the variability in bread loaf characteristics was explained by the flour quality using a four‐factor PLS model; the first two PLS factors explained 66% of the variability. The loaf volume was related to a high number of loaf cells, whose expansion resulted in a greater loaf height. The relation between loaf volume and loaf height was expressed more in PLS‐3 than PLS‐1 and PLS‐2. Mean cell wall thickness and mean cell diameter were closely related negatively along PLS‐1, for which DS and farinograph dough stability explained much of the variability in these loaf characteristics. Along the third PLS factor, FAB explained the variability in loaf weight.  相似文献   

9.
Fat content in rice is one of the most important nutritional quality properties. But the chemical analysis of fat content is time‐consuming and costly and could result in poor reproduction between replicates. Near‐infrared spectroscopy (NIRS) can solve those problems by providing a rapid, nondestructive, and quantitative analysis. Based on the NIRS technique and partial least squares (PLS) algorithm, four calibration models were established to quantitatively analyze fat content in brown rice grain and flour and milled rice grain and flour with 248 representative samples. The determination coefficients (R2) of these calibration models were 0.79, 0.84, 0.89, and 0.91, respectively, with the corresponding root mean square errors 0.16, 0.14, 0.09, and 0.08%. The R2 were 0.73, 0.81, 0.81, and 0.89 with the corresponding root mean square errors 0.17, 0.15, 0.12, and 0.09%, respectively, in cross validation. The R2 were 0.62, 0.80, 0.81, and 0.87, respectively, with the root mean square errors 0.25, 0.31, 0.28, and 0.30% in external validation. These results indicate that the method of NIRS has relatively high accuracy in the prediction of rice fat content. The four calibration models established in the present study should be useful for nutrient quality improvement in rice breeding.  相似文献   

10.
The backward interval partial least squares(Bi PLS)and the synergy interval partial least squares(Si PLS)were applied to select the characteristic spectral regions representing the germination rate of 84 wheat seeds and build the near infrared(NIR)quantitative analysis model of wheat seed germination rate.Results from comparison showed that the models built by two variable selection methods had better predictive ability than full-spectral partial least squares(PLS)model.The optimal model was obtained by Si PLS with the calibration and prediction correlation coefficient(R)at 0.902 and 0.967 respectively,and ratio of performance to standard deviate(RPD)at 3.75.Based on this,the physical chemistry significance of characteristic spectral regions was analyzed.The characteristic spectral of wheat seed germination rate contained characteristic peaks of water,protein,starch,fiber,which were the internal nutrients of the seed that influence the germination ability,thus explaining the mechanism of measuring wheat seed germination rate using NIR to a certain extent.  相似文献   

11.
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   

12.
This article describes a proof-of-concept exercise to examine the ability of near infrared spectroscopy (NIRS)–based methods to predict the major nutrient properties of sugar mill by-products, particularly mill mud, ash, and mixtures of mud and ash. Sixty mill mud, mixed mud/ash, and ash samples were subsampled three times and analyzed using traditional analytical techniques for carbon (C), nitrogen (N), silicon (Si), phosphorus (P), and potassium (K), and the NIR spectra were recorded. Two different partial least squares (PLS) regression models were constructed, one using all samples and the other without the ash samples included in the model development. Three mud, one mixed mud/ash, and two ash samples were retained for predictive purposes and were not included in the model development process. R2 values in the range of 0.77 to 0.98 were obtained for all constituents across both sets of PLS models. The standard errors of prediction (SEP) were similar for both models for N (0.10 and 0.08), P (0.17 and 0.16), and K (0.05 and 0.05). However, the SEP obtained for Si (3.53 and 1.04) and C (1.92 and 1.00) varied between the two models. These preliminary results are very encouraging. Future research will extend to robust NIRS calibrations for these nutrients and develop applications for their use within laboratory or field situations to permit nutrient monitoring in various sugar mill by-products.  相似文献   

13.
The contents of nitrogen and organic carbon in an agricultural soil were analyzed using reflectance measurements （n = 52） performed with an ASD FieldSpee-Ⅱ spectroradiometer. For parameter prediction, empirical models based on partial least squares （PLS） regression were defined from the measured reflectance spectra （0.4 to 2.4 μm）. Here, reliable estimates were obtained for nitrogen content, but prediction accuracy was only moderate for organic carbon. For nitrogen, the real spatial pattern of within-field variability was reproduced with high accuracy. The results indicate the potential of this method as a quick screening tool for the spatial assessment of nitrogen and organic carbon, and therefore an appropriate alternative to time- and cost-intensive chemical analysis in the laboratory.  相似文献   

14.
ABSTRACT

Accurate monitoring of crop moisture content is very important for irrigation scheduling and yield increase. This study aims to construct an optimal estimation model of winter wheat leaf moisture content (LMC) through spectral data processing and feature band selection. LMC and spectral reflectance were measured in 2017-2018 to construct models using simple linear regression (SLR), principal components regression (PCR), and partial least square regression (PLSR); feature bands for modelling were selected through correlation analysis and the effects of feature band number on estimation accuracy were compared. The results showed that data transformation significantly enhanced the correlation between spectral features and LMC. However, the band position corresponding to the maximum correlation coefficient for each transformation was not fixed. The accuracy of PLSR models were significantly higher than that of PCR and SLR models. The comparison of relative percent deviation (RPD) values indicated that the RPD values increased rapidly and then tended to be stable with the increase of feature band number. The R′′ -PLSR model constructed with 28 feature bands (R2c = 0.8517; RPD > 2.0) estimated the LMC more accurately than other models. This study provides a good method for non-destructive monitoring of crop moisture content.  相似文献   

15.
Vegetational changes during the restoration of cutover peatlands leave a legacy in terms of the organic matter quality of the newly formed peat. Current efforts to restore peatlands at a large scale therefore require low cost and high throughput techniques to monitor the evolution of organic matter. In this study, we assessed the merits of using Fourier transform infrared (FTIR) spectra to predict the organic matter composition in peat samples at various stages of peatland regeneration from five European countries. Using predictive partial least squares (PLS) analyses, we were able to reconstruct peat C:N ratio and carbohydrate signatures with reasonable accuracy, but not the micromorphological composition of vegetation remains. Despite utilising different size fractions, both carbohydrate (<200 μm fraction) and FTIR (bulk soil) analyses report on the composition of plant cell wall constituents in the peat and therefore essentially reveal the composition of the parent vegetational material. The accuracy of the FTIR-based PLS models for C:N ratios and carbohydrate signatures was adequate to allow for their use as initial screening tools in the evaluation of the present and future organic matter composition of peat during monitoring of restoration efforts.  相似文献   

16.
The rheological properties of fresh gluten in small amplitude oscillation in shear (SAOS) and creep recovery after short application of stress was related to the hearth breadbaking performance of wheat flours using the multivariate statistics partial least squares (PLS) regression. The picture was completed by dough mixing and extensional properties, flour protein size distribution determined by SE‐HPLC, and high molecular weight glutenin subunit (HMW‐GS) composition. The sample set comprised 20 wheat cultivars grown at two different levels of nitrogen fertilizer in one location. Flours yielding stiffer and more elastic glutens, with higher elastic and viscous moduli (G′ and G″) and lower tan δ values in SAOS, gave doughs that were better able to retain their shape during proving and baking, resulting in breads of high form ratios. Creep recovery measurements after short application of stress showed that glutens from flours of good breadmaking quality had high relative elastic recovery. The nitrogen fertilizer level affected the protein size distribution by an increase in monomeric proteins (gliadins), which gave glutens of higher tan δ and flatter bread loaves (lower form ratio).  相似文献   

17.
This study evaluates the use of visible and near-infrared spectroscopy for rapid prediction of total carbon, total nitrogen, and total phosphorus concentrations in field crop samples. Two multivariate models (partial least squares regression and support vector machine regression) were compared. In addition, four spectral variable selection algorithms (competitive adaptive reweighted sampling, genetic algorithm, uninformative variable elimination, and variable importance for projection) were applied with support vector machine regression to determine the most accurate predictions. The results showed that support vector machine regression performed better than partial least squares regression for predicting the three chemical compositions. The combination of competitive adaptive reweighted sampling and support vector machine regression outperformed the other models for the predictions of total carbon and total nitrogen with high coefficients of determination of 0.91 and 0.90, respectively. For the determination of total phosphorus, the prediction accuracy of competitive adaptive reweighted sampling was comparable with the best result obtained from genetic algorithm with the coefficients of determination of 0.73 and 0.77, respectively. In conclusion, the support vector machine regression combined with competitive adaptive reweighted sampling has great potential to accurately determine the chemical composition of field crops using the visible and near-infrared spectroscopy.  相似文献   

18.
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19.
秦文虎  董凯月  邓志超 《土壤》2023,55(6):1347-1353
摘要：【目的】传统的基于近红外光谱数据预测土壤全氮的方法需要对原始光谱数据做复杂的预处理，筛选出与土壤全氮含量相关性高的敏感波长之后进行模型的回归拟合。本文提出一种一维卷积神经网络（1D-CNN）模型，可以在对数据进行简单预处理甚至无处理的情况下达到非常理想的结果，实现用近红外光谱技术对土壤全氮含量的预测。【方法】于江苏无锡采集410个土壤样品，利用半微量开氏法(NY/T 53-1987)测定土壤的全氮含量，并利用NIR Quest 512光谱仪，在室内环境下对每份土壤样品做光谱检测，并用均值中心化(CT)、标准正态变换(SNV)、趋势校正(DT)对光谱进行预处理，运用偏最小二乘回归（PLS）、BP神经网络、1D-CNN方法建立土壤全氮含量的回归预测模型。每种模型在采用不同预处理方法的数据集上做十折交叉验证，记录预测模型的决定系数(R2)和均方根误差(RMSE)的平均值，并对比三种预处理方法对模型精度的影响。【结果】证明了本文提出的1D-CNN模型基于土壤近红外光谱数据预测土壤全氮含量的可靠性。使用原始数据与经均值中心化、标准正态变换、趋势校正预处理的数据训练得到的1D-CNN模型的决定系数分别为0.907、0.931、0.922、0.964，构建的PLS回归模型决定系数为0.856、0.863、0.861、0.880，训练的BP神经网络的决定系数为0.874、0.907、0.901、0.911。【结论】本文提出的1D-CNN模型在原始数据和经预处理的光谱数据上的表现都优于PLS和BP神经网络，且可以证明，对光谱数据进行预处理能够有效提高1D-CNN模型的性能，尤其是趋势校正对模型的提升效果最明显。研究表明，1D-CNN能更好地提取光谱特征并建立其与含氮量的映射关系，有效地避免过拟合，在未经过预处理的光谱数据上依然能够达到一定的精度。  相似文献   

20.
北京典型耕作土壤养分的近红外光谱分析   总被引：7，自引：2，他引：5  
为研究土壤养分含量分布信息,以从北京郊区一块试验田采集的72个土壤样品为试验材料,应用傅里叶变换近红外光谱技术分析了土样的全氮、全钾、有机质养分含量和pH值。采用偏最小二乘法(PLS)对光谱数据与土壤养分实测值进行回归分析,建立预测模型,以模型决定系数(R2)、校正标准差(RMSECV)、预测标准差(RMSEP)和相对分析误差(RPD)作为模型精度的评价指标。结果表明,利用该模型与光谱数据对土壤全氮、全钾、有机质养分含量和pH值进行预测,结果与实测数据具有较好的一致性,最高决定系数R2达到0.9544。偏最小二乘回归方法建立的养分预测模型能准确地对北京地区褐土土质全氮、有机质、全钾和pH值4种养分进行预测。  相似文献   

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