Ability of Vis-PIR spectroscopy to monitor changes in organic carbon of loamy soils at two depths

2021 ◽  
Author(s):  
Hayfa Zayani ◽  
Youssef Fouad ◽  
Didier Michot ◽  
Zeineb Kassouk ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Near Infrared ◽  
Full Range ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Calibration Data ◽  
Least Squares Regression ◽  
Data Set ◽  
Soil Carbon Stocks ◽  
The Mean

<p>Visible-Near Infrared (Vis-NIR) spectroscopy has proven its efficiency in predicting several soil properties such as soil organic carbon (SOC) content. In this preliminary study, we explored the ability of Vis-NIR to assess the temporal evolution of SOC content. Soil samples were collected in a watershed (ORE AgrHys), located in Brittany (Western France). Two sampling campaigns were carried out 5 years apart: in 2013, 198 soil samples were collected respectively at two depths (0-15 and 15-25 cm) over an area of 1200 ha including different land use and land cover; in 2018, 111 sampling points out of 198 of 2013 were selected and soil samples were collected from the same two depths. Whole samples were analyzed for their SOC content and were scanned for their reflectance spectrum. Spectral information was acquired from samples sieved at 2 mm fraction and oven dried at 40°C, 24h prior to spectra acquisition, with a full range Vis-NIR spectroradiometer ASD Fieldspec®3. Data set of 2013 was used to calibrate the SOC content prediction model by the mean of Partial Least Squares Regression (PLSR). Data set of 2018 was therefore used as test set. Our results showed that the variation ∆SOC<sub>obs</sub><sub></sub>obtained from observed values in 2013 and 2018 (∆SOC<sub>obs</sub> = Observed SOC (2018) - Observed SOC (2013)) is ranging from 0.1 to 25.9 g/kg. Moreover, our results showed that the prediction performance of the calibrated model was improved by including 11 spectra of 2018 in the 2013 calibration data set (R²= 0.87, RMSE = 5.1 g/kg and RPD = 1.92). Furthermore, the comparison of predicted and observed ∆SOC between 2018 and 2013 showed that 69% of the variations were of the same sign, either positive or negative. For the remaining 31%, the variations were of opposite signs but concerned mainly samples for which ∆SOCobs is less than 1,5 g/kg. These results reveal that Vis-NIR spectroscopy was potentially appropriate to detect variations of SOC content and are encouraging to further explore Vis-NIR spectroscopy to detect changes in soil carbon stocks.</p>

Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

2021 ◽  
Author(s):  
Iva Hrelja ◽  
Ivana Šestak ◽  
Igor Bogunović
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Data ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Macro Scale ◽  
Sentinel 2

<p>Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.</p><p>The study is performed in Mediterranean Croatia (44° 05’ N; 15° 22’ E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed <em>Quercus ssp.</em> and <em>Juniperus ssp.</em> forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22<sup>nd</sup> 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31<sup>st</sup> and September 5<sup>th </sup>2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSE<sub>p</sub>) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.</p><p>The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSE<sub>p </sub>(Spectroscopy dataset RMSE<sub>p</sub> = 1.91; Sentinel-2 dataset RMSE<sub>p</sub> = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.</p><p><strong>Keywords:</strong> remote sensing, soil spectroscopy, wildfires, soil organic matter</p><p><strong>Acknowledgment: </strong>This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Perčin is acknowledged for her cooperation during the laboratory work.</p>

Application of near-infrared spectroscopy for the detection of internal insect infestation inPiceaabiesseed lots

2004 ◽  
Vol 34 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Mulualem Tigabu ◽  
Per Christer Odén ◽  
Tong Yun Shen
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Absorption Bands ◽  
Classification Rate ◽  
Data Set ◽  
Storage Lipids ◽  
Nir Absorption ◽  
Seed Origin ◽  
Single Seeds

The use of near-infrared (NIR) spectroscopy to discriminate between uninfested seeds of Picea abies (L.) Karst and seeds infested with Plemeliella abietina Seitn (Hymenoptera, Torymidae) larva is sensitive to seed origin and year of collection. Five seed lots collected during different years from Sweden, Finland, and Belarus were used in this study. Initially, seeds were classified as infested or uninfested with X-radiography, and then, NIR spectra from single seeds were collected with a NIR spectrometer from 1100 to 2498 nm with a resolution of 2 nm. Discriminant models were derived by partial least squares regression using raw and orthogonal signal corrected spectra (OSC). The resulting OSC model developed on a pooled data set was more robust than the raw model and resulted in 100% classification accuracy. Once irrelevant spectral variations were removed by using OSC pretreatment, single-lot calibration models resulted in similar classification rates for the new samples irrespective of origin and year of collection. Dis criminant analyses performed with selected NIR absorption bands also gave nearly 100% classification rate for new samples. The origin of spectral differences between infested and uninfested seeds was attributed to storage lipids and proteins that were completely depleted in the former by the feeding larva.

scholarly journals Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection

2020 ◽  
Vol 12 (20) ◽  
pp. 3394
Author(s):  
Lu Xu ◽  
Yongsheng Hong ◽  
Yu Wei ◽  
Long Guo ◽  
Tiezhu Shi ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Variable Selection ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Jianghan Plain ◽  
Model Accuracy ◽  
Near Infrared Reflectance ◽  
Soc Estimation ◽  
Anthropogenic Soil

Visible and near-infrared reflectance (VIS-NIR) spectroscopy is widely applied to estimate soil organic carbon (SOC). Intense and diverse human activities increase the heterogeneity in the relationships between SOC and VIS-NIR spectra in anthropogenic soil. This fact results in poor performance of SOC estimation models. To improve model accuracy and parsimony, we investigated the performance of two variable selection algorithms, namely competitive adaptive reweighted sampling (CARS) and random frog (RF), coupled with five spectral pretreatments. A total of 108 samples were collected from Jianghan Plain, China, with the SOC content and VIS-NIR spectra measured in the laboratory. Results showed that both CARS and RF coupled with partial least squares regression (PLSR) outperformed PLSR alone in terms of higher model accuracy and less spectral variables. It revealed that spectral variable selection could identify important spectral variables that account for the relationships between SOC and VIS-NIR spectra, thereby improving the accuracy and parsimony of PLSR models in anthropogenic soil. Our findings are of significant practical value to the SOC estimation in anthropogenic soil by VIS-NIR spectroscopy.

Estimating the Composition of Tomato Juice Products by near Infrared Spectroscopy

2003 ◽  
Vol 11 (2) ◽  
pp. 123-136 ◽  
Author(s):  
Athanasia M. Goula ◽  
Konstantinos G. Adamopoulos
Keyword(s):  
Near Infrared ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Tomato Juice ◽  
Calibration Data ◽  
Multilinear Regression ◽  
Statistical Parameters ◽  
Data Set ◽  
Prediction Ability ◽  
Specific Product

The use of near infrared (NIR) reflectance spectroscopy for the rapid and accurate measurement of moisture, sugar, acid, protein and salt was explored in a diverse group of tomato juice products. Partial and overall calibrations were performed on four different tomato juice products. Partial calibrations for each product included samples of the specific product, whereas overall calibration used samples of all the products. Samples were analysed employing traditional chemical methods and scanned using an Instalab 600-Dickey-John NIR apparatus to obtain NIR spectra. Calibrations were achieved with the use of multilinear regression between chemical and spectral data from each calibration data set. A separate set of samples was used to validate the calibrations. Linear regression was applied to compare the results obtained by NIR spectroscopy for all constituents of the validation set with those obtained by the reference methods. In addition, the root mean square error of prediction ( RMSEP), the bias and the correlation coefficients ( r and r′) were calculated. All of the statistical parameters were better with overall than with partial calibrations. Prediction ability of overall calibration was very good for all the constituents. r and r′ values were higher than 0.9488 and 0.9453, respectively, RMSEP values were smaller than 0.1067, whereas bias varied from −0.020 to 0.016. The partial calibrations are considerable less variable with the correlation coefficients r and r′ ranged from 0.8890 to 0.9477 and from 0.7202 to 0.8518, respectively, RMSEP varied from 0.0647 to 0.4942 and bias from −0.365 to 0.071. NIR measurement as performed by the Dickey-John Analyser was proved a rapid and accurate method for analysis of tomato juice samples and may be used as a replacement for conventional expensive and time-consuming wet chemistry methods.

scholarly journals Laboratory Spectroscopy Assessments of Rainfed Paddy Soil Samples on Visible and Near-Infrared Spectroscopy Reflectance for Estimating Soil Organic Carbon

2016 ◽  
Vol 9 ◽  
pp. ASWR.S40173 ◽  
Author(s):  
Sakda Homhuan ◽  
Wanwisa Pansak ◽  
Siam Lawawirojwong ◽  
Chada Narongrit
Keyword(s):  
Infrared Spectroscopy ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared Spectroscopy ◽  
Paddy Soil ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Soc Estimation

Visible and near-infrared spectroscopy is a rapid, less expensive, and nondestructive alternative to conventional methods of soil analysis. This study aimed to investigate appropriate soil sample preparations and particle sizes for estimating soil organic carbon (SOC) through the use of laboratory spectroscopy. Rainfed paddy soils were sampled from 240 sampling sites to record their spectral reflectance and to measure their SOC contents in the laboratory. Partial least squares regression was applied to select the best model to estimate SOC using soil spectra. The results showed that the highest accuracy of SOC estimation was gained from soil samples prepared by 2 mm sieving. A short-wave infrared region was the most appropriate spectral wavelength for SOC estimation of rainfed paddy soil. Although the model showed potential in SOC prediction, the accuracy of partial least squares regression prediction in each spectral region varied between sampling times. Therefore, these models and methods should be further tested in soils sampled from different seasons and other regions to prove consistent validity. However, these results are useful for wavelength selection and soil sample preparation in future laboratory spectroscopy.

scholarly journals Evaluating basic density calibrations based on NIR spectra recorded on the three wood faces and subject to different mathematical treatments

2021 ◽  
Vol 51 ◽  
Author(s):  
Evelize A. Amaral ◽  
Luana M. Dos Santos ◽  
Paulo R.G. Hein ◽  
Emylle V.S. Costa ◽  
Sebastião Carlos S. Rosado ◽  
...  
Keyword(s):  
Near Infrared ◽  
Eucalyptus Grandis ◽  
Basic Density ◽  
Nir Spectroscopy ◽  
Physical And Mechanical Properties ◽  
Integrating Sphere ◽  
Least Squares Regression ◽  
Immersion Method ◽  
Data Set ◽  
Eucalyptus Wood

Background: Near infrared (NIR) spectroscopy has been successfully applied to estimate the chemical, physical and mechanical properties of various biological materials, including wood. This study aimed to evaluate basic density calibrations based on NIR spectra collected from three wood faces and subject to different mathematical treatments. Methods: Diffuse reflectance NIR spectra were recorded using an integrating sphere on the transverse, radial and tangential surfaces of 278 wood specimens of Eucalyptus urophylla x Eucalyptus grandis. Basic density of the wood specimens was determined in the laboratory by the immersion method and correlated with NIR spectra by Partial Least Squares regression. Different statistical treatments were then applied to the data, including Standard Normal Variate, Multiplicative Scatter Correction, First and Second Derivatives, Normalization, Autoscale and MeanCenter transformations. Results: The predictive model based on NIR spectra measured on the transverse surface performed the best (R²cv = 0.85 and RMSE = 25.5 kg/m³) while the model developed from the NIR spectra measured on the tangential surface had the poorest performance (R²cv = 0.53 and RMSE = 46.8 kg/m³). The difference in performance between models based on original (untreated) and mathematically-treated spectra was minimal. Conclusions: Multivariate models fitted to NIR spectra were found to be efficient for predicting the basic density of Eucalyptus wood, especially when based on spectra measured on the transversal surface. For this data set, models based on the original spectra and mathematically treated spectra had similar performance. The reported findings show that mathematical transformations are not always able to extract more information from the spectra in the NIR.

Non-Traditional Applications of near Infrared Spectroscopy Based on the Optical Characteristic Models for a Biological Material Having Cellular Structure

1998 ◽  
Vol 6 (1) ◽  
pp. 41-46 ◽  
Author(s):  
Satoru Tsuchikawa
Keyword(s):  
Biological Material ◽  
Path Length ◽  
Cellular Structure ◽  
Process Model ◽  
Near Infrared ◽  
Traditional Approach ◽  
Nir Spectroscopy ◽  
Optical Path Length ◽  
Optical Path ◽  
The Mean

Non-destructive measurements, based on near infrared (NIR) spectroscopy, on biological material with a cellular structure like wood require a non-traditional approach. We have developed new concepts to model the optical properties of a sample having cellular structure, for the illumination conditions of the spectrometer available to us. A set of optical models, which consisted of the directional characteristics models, the light-path models and the equivalent surface roughness model was proposed to clarify the behaviour of light propagation in a wood sample. Furthermore, the mean optical path length, which was derived by incorporating the nth power cosine model of radiant intensity into the diffusion process model in consideration of the parallel beam component of incident light, was calculated. By introducing the concept of equivalent sample thickness, compatible with the mean optical path length, into the Kubelka–Munk theory, generalised input/output equations for radiation were constructed. In this non-traditional application of NIR spectroscopy, these optical concepts make it possible to analyse both the physical condition and chemical composition of a biological material with a cellular structure.

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

2022 ◽  
pp. 096703352110572
Author(s):  
Nicholas T Anderson ◽  
Kerry B Walsh
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Short Wave ◽  
Least Squares Regression ◽  
Machine Learning Methods ◽  
Transmission Geometry ◽  
Modelling Techniques ◽  
Test Sets ◽  
Model Robustness

Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

NIR Spectroscopy and Partial Least Squares Regression for the Determination of Phosphate Content and Viscosity Behaviour of Potato Starch

2001 ◽  
Vol 9 (2) ◽  
pp. 133-139 ◽  
Author(s):  
L.G. Thygesen ◽  
S.B. Engelsen ◽  
M.H. Madsen ◽  
O.B. Sørensen
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Potato Starch ◽  
Nir Spectroscopy ◽  
Peak Time ◽  
Phosphate Content ◽  
Least Squares Regression ◽  
Data Prediction

A set of 97 potato starch samples with a phosphate content corresponding to a phosphorus content between 0.029 and 0.11 g per 100 g dry matter was analysed using a Rapid Visco Analyzer (RVA) and near infrared (NIR) spectroscopy, (700–2498 nm). NIR-based prediction of phosphate content was possible with a root mean square error of cross-validation ( RMSECV) of 0.006% using PLSR (partial least squares regression). However, the NIR/PLSR model relied on weak spectral signals, and was highly sensitive to sample preparation. The best prediction of phosphate content from the RVA viscograms was a linear regression model based on the RVA variable Breakdown, which gave a RMSECV of 0.008%. NIR/PLSR prediction of the RVA variables Peak viscosity and Breakdown was successful, probably because they were highly related to phosphate content in the present data. Prediction of the other RVA variables from NIR/PLSR was mediocre (Through, Final Viscosity) or not possible (Setback, Peak time, Pasting temperature).

Milk Components by in-Line Fiber Optic Probe-Based FT-NIR: Commercial Scale Evaluation of a Potential Alternative Measurement Approach for Milk Payment

2020 ◽  
Author(s):  
Shuaikun Tang ◽  
J Chris Johnson ◽  
Iswandi Jarto ◽  
Bridgette Smith ◽  
Scott Morris
Keyword(s):  
Milk Fat ◽  
Bovine Milk ◽  
Industrial Process ◽  
Nir Spectroscopy ◽  
Third Party ◽  
Least Squares Regression ◽  
Fiber Optic Probe ◽  
Milk Components ◽  
Ft Ir ◽  
The Mean

Abstract Background Mid-infrared (MIR) spectroscopy has traditionally been used to determine the macronutrients in bovine milk, as the basis of milk payment. Recent studies have demonstrated that NIR/FT-NIR spectroscopic systems can not only achieve MIR measurement performance, but are also generally simpler, more robust, and thus much more amenable to actual industrial process applications. Objective The goal of this unique study was to investigate the feasibility of in-line FT-NIR spectroscopy for milk fat, protein and total solids (TS) determination in a large industrial dairy processing facility, as an alternative basis for milk payment. Method Multivariant chemometric models using partial least squares regression were built to predict the milk components. Over one thousand composite FT-NIR results gathered from the milk unloading process were compared directly to independent third-party FT-IR results. Results Accuracy, precision and linearity of the method were shown by Standard Error of Prediction (SEP) and Range/SEP of individual components. SEP for fat, protein and TS models were 0.09, 0.11 and 0.52, respectively. Range/SEP were 25.10, 12.60 and 6.40 for fat, protein and TS, respectively. Accuracy and precision for the three components were further evaluated by the mean differences (0.01, 0.05, and 0.51) from dairy FT-IR results and the standard deviations of the mean difference (0.09, 0.09 and 0.13). Robustness was demonstrated by evaluating milk with natural variation over six months and using multiple instrumentation setups. The repeatability was also evaluated. Conclusion Overall, the in-line FT-NIR technology was found to have accurate, reliable, consistent performance similar to dairy FT-IR technology.

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