Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy

This study explored the use of near-infrared spectroscopy (NIRS) for the rapid analysis of organic C (Corg) and organic N (Norg) in the A horizon of soil within a single field. Soil was sampled throughout a field in Manitoba, Canada to capture soil variability associated with topography. The soil samples were oven-dried and treated with acid to remove carbonates, after which C and N were determined by dry combustion. In this study, portions of the dried soil samples not treated with acid were scanned with a near-infrared scanning spectrophotometer between 1100 and 2500 nm. Correlating the spectral and the chemical analytical data using multiple linear regression or principal component analysis/partial least squares regression gave useful correlations for Corg. Over the range of 0–40 mg g-1 Corg, NIR-predicted values explained 75–78% of the variance in the chemical results. Results were improved to 80% for calibrations developed for the 0–20 mg g-1 organic C range. Useful results were not obtained for Norg although the literature shows that total N in soil is predictable using NIRS. It is likely that the acid treatment altered the composition of the samples in an inconsistent manner such that the chemically analyzed samples and those scanned by NIRS were different from each other in Norg concentration or composition. Extrapolation of these Corg results to the landscape scale implies that NIRS has potential to be a suitable method for mapping C for the purposes of monitoring C sequestration. Key words: Near-infrared spectroscopy, soil, carbon, nitrogen, topography, soil monitoring

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Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds

Animal Production Science ◽

10.1071/an17240 ◽

2019 ◽

Vol 59 (6) ◽

pp. 1190 ◽

Cited By ~ 2

Author(s):

A. Bahri ◽

S. Nawar ◽

H. Selmi ◽

M. Amraoui ◽

H. Rouissi ◽

...

Keyword(s):

Principal Component Analysis ◽

Infrared Spectroscopy ◽

Random Forest ◽

Near Infrared Spectroscopy ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Principal Component ◽

Least Squares Regression ◽

Random Forest Regression ◽

Milk Samples

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.

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Penerapan Teknologi Visible-Near Infrared Spectroscopy untuk Prediksi Cepat dan Simultan Kadar Air Buah Melon (Cucumis melo L.) Golden

Agroteknika ◽

10.32530/agroteknika.v3i2.83 ◽

2020 ◽

Vol 3 (2) ◽

pp. 67-74

Author(s):

Yuda Hadiwijaya ◽

Kusumiyati Kusumiyati ◽

Agus Arip Munawar

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Cucumis Melo ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Principal Component ◽

Least Squares Regression ◽

Orthogonal Signal Correction ◽

Signal Correction ◽

Cucumis Melo L

Kadar air merupakan salah satu atribut kualitas yang penting pada komoditas hortikultura. Penetapan kadar air buah melon dengan metode konvensional memakan waktu yang lama dan perlu merusak sampel buah. Penelitian ini bertujuan untuk memprediksi kadar air buah melon golden menggunakan teknologi visible-near infrared spectroscopy (Vis-NIRS). Metode koreksi spektra orthogonal signal correction (OSC) diterapkan pada spektra original untuk meningkatkan kehandalan model kalibrasi. Partial least squares regression (PLSR) digunakan sebagai metode pendekatan regresi untuk mengekstraksi data spektra Vis-NIRS. Hasil penelitian membuktikan bahwa Vis-NIRS dapat diandalkan untuk memprediksi kadar air buah melon golden. Metode koreksi spektra OSC mampu memperkecil jumlah principal component (PC) pada spektra original. Linieritas pada model kalibrasi menggunakan spektra OSC tercatat memperoleh nilai tertinggi sebesar 0,92. Ratio of performance to deviation (RPD) pada spektra OSC menampilkan nilai tertinggi pula yaitu 3,63. Model kalibrasi yang diperoleh pada penelitian ini dapat ditransfer ke dalam spektrometer Vis-NIRS untuk prediksi kadar air melon golden secara cepat dan simultan.

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

Air Soil and Water Research ◽

10.4137/aswr.s40173 ◽

2016 ◽

Vol 9 ◽

pp. ASWR.S40173 ◽

Cited By ~ 1

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.

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Analysis of Selected Properties of Fibreboard Panels Manufactured from Wood and Leather Using the Near Infrared Spectroscopy

International Journal of Spectroscopy ◽

10.1155/2015/691796 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Kerstin Wagner ◽

Thomas Schnabel ◽

Marius-Catalin Barbu ◽

Alexander Petutschnigg

Keyword(s):

Infrared Spectroscopy ◽

Data Analysis ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Bending Strength ◽

Raw Materials ◽

Nir Spectroscopy ◽

Physical And Mechanical Properties ◽

Principal Component ◽

Least Squares Regression

This paper deals with the characterization of the properties of wood fibres leather shavings composite board by using the near infrared spectroscopy (NIRS) and multivariate data analysis. In this study fibreboards were manufactured with different leather amounts by using spruce fibres, as well as vegetable and mineral tanned leather shavings (wet white and wet blue). The NIR spectroscopy was used to analyse the raw materials as well as the wood leather fibreboards. Moreover, the physical and mechanical features of the wood leather composite fibreboards were determined to characterize their properties for the further data analysis. The NIR spectra were analysed by univariate and multivariate methods using the Principal Component Analysis (PCA) and the Partial Least Squares Regression (PLSR) method. These results demonstrate the potential of FT-NIR spectroscopy to estimate the physical and mechanical properties (e.g., bending strength). This phenomenon provides a possibility for quality assurance systems by using the NIRS.

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Evaluation of the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using near infrared spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033517728733 ◽

2017 ◽

Vol 25 (5) ◽

pp. 301-310 ◽

Cited By ~ 8

Author(s):

Jetsada Posom ◽

Panmanas Sirisomboon

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Model Development ◽

Volatile Matter ◽

Ash Content ◽

Least Squares Regression ◽

Fixed Carbon ◽

Bomb Calorimetry ◽

Heating Value

This research aimed to determine the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using Fourier transform near infrared spectroscopy as an alternative to bomb calorimetry and thermogravimetry. Bamboo culms used in this study had circumferences ranging from 16 to 40 cm. Model development was performed using partial least squares regression. The higher heating value, volatile matter, fixed carbon and ash content were predicted with coefficients of determination (r2) of 0.92, 0.82, 0.85 and 0.51; root mean square error of prediction (RMSEP) of 122 J g−1, 1.15%, 1.00% and 0.77%; ratio of the standard deviation to standard error of validation (RPD) of 3.66, 2.55, 2.62 and 1.44; and bias of 14.4 J g−1, −0.43%, 0.03% and −0.11%, respectively. This report shows that near infrared spectroscopy is quite successful in predicting the higher heating value, and is usable with screening for the determination of fixed carbon and volatile matter. For ash content, the method is not recommended. The models should be able to predict the properties of bamboo samples which are suitable for achieving higher efficiency for the biomass conversion process.

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Identification of Liquor Brands Based on near Infrared Spectroscopy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.834-836.935 ◽

2013 ◽

Vol 834-836 ◽

pp. 935-938

Author(s):

Lian Shun Zhang ◽

Chao Guo ◽

Bao Quan Wang

Keyword(s):

Principal Component Analysis ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Principal Components ◽

Near Infrared ◽

Scatter Correction ◽

Principal Component ◽

Component Analysis ◽

Correction Method ◽

Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

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Rapid Diagnosis of HIV-1 virus by Near Infrared Spectroscopy: based on Partial least squares regression

E3S Web of Conferences ◽

10.1051/e3sconf/202127103067 ◽

2021 ◽

Vol 271 ◽

pp. 03067

Author(s):

Xiaohong He ◽

Zhihong Song ◽

Haifei Shang ◽

Silang Yang ◽

Lujing Wu ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

False Negative ◽

Cost Effective ◽

Rapid Diagnosis ◽

Enzyme Linked Immunosorbent Assay ◽

Least Squares Regression ◽

Early Screening ◽

Hiv 1

Currently, the laboratory diagnostic tests available for HIV-1 viral infection are mainly based on serological testing which relies on enzyme-linked immunosorbent assay (ELISA) for blood HIV antigen detection and reverse transcription polymerase chain reaction (RT-PCR) for HIV specific RNA sequence identification. However, these methods are expensive and time-consuming, and suffer from false positive and/or false negative results. Thus, there is an urgent need for developing a cost effective, rapid and accurate diagnostic method for HIV-1 infection. In order to reduce the barriers for effective diagnosis, a near-infrared spectroscopy (NIR) method was used to detect the HIV-1 virus in human serum, specifically, three absorption peaks with dose-dependent at 1582nm, 1810nm and 2363nm were found by multiple FBiPLSR test analysis for HIV-nano and HIV-EGFP, but not for MLV. Therefore, we recommend the use of 1582nm, 1810nm and 2363nm as the characteristic spectrum peak, for early screening and rapid diagnosis of serum HIV.

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