The Potential of near Infrared Reflectance Spectroscopy as a Tool for the Chemical Characterisation of Agricultural Soils

2001 ◽  
Vol 9 (2) ◽  
pp. 123-131 ◽  
Author(s):  
M. Confalonieri ◽  
F. Fornasier ◽  
A. Ursino ◽  
F. Boccardi ◽  
B. Pintus ◽  
...  
Keyword(s):  
Total Nitrogen ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Available Phosphorus ◽  
Exchangeable Potassium ◽  
Near Infrared Reflectance ◽  
Nir Reflectance Spectroscopy ◽  
Soil Constituents

The feasibility of near infrared (NIR) reflectance spectroscopy in determining various soil constituents such as total organic carbon, total nitrogen, exchangeable potassium and available phosphorus has been investigated, to monitor their concentration during a long-term agronomic trial. Soil samples previously analysed by conventional chemical methods were scanned using a NIRSystems 5000 monochromator and spectra were treated using several algorithms. The first derivative of each NIR spectrum was used for all statistical analyses. Step-up, stepwise and modified partial least squares (MPLS) regression methods were applied to develop reliable calibration models between the NIR spectral data and the results of wet analyses. MPLS almost always gave the most successful calibrations. The results demonstrated that NIR reflectance spectroscopy can be used to determine accurately two important soil constituents, namely total nitrogen and carbon content. This technique could be employed as a routine testing method in estimating, rapidly and non-destructively, these constituents in soil samples, demonstrating soil variations within a long-term field experiment. For other determinations, such as exchangeable potassium and available phosphorus content, our results were less successful but may be useful for separation of samples into groups.

scholarly journals 363 Near-infrared Reflectance Spectroscopy for the Determination of Total Nitrogen, Amino Acid, and Nonstructural Carbohydrates in Apple and Almond Samples

HortScience ◽  
2000 ◽  
Vol 35 (3) ◽  
pp. 455A-455
Author(s):  
Pinghai Ding ◽  
Shufu Dong ◽  
Lailiang Cheng ◽  
Guihong Bi ◽  
Leslie H. Fuchigami
Keyword(s):  
Amino Acid ◽  
Chemical Composition ◽  
Total Nitrogen ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Composition Analysis ◽  
Near Infrared Reflectance ◽  
Total N ◽  
Regression Methods ◽  
Nir Reflectance Spectroscopy

Near-infrared (NIR) reflectance spectroscopy was used to determine the chemical composition of fruit and nut trees. Potted almond and bench-grafted Fuji/M26 trees were fertigated during the growing season with different N levels by modifying the Hoagland to create different levels of nitrogen and carbohydrates in plant tissues during dormancy. Dried, ground, and sieved shoot, shank, and root samples were uniformly packed into NIR cells and scanned with a Foss NIRSystem 6500 monochromator from 400 to 2500 nm. Statistical and multiple linear regression methods were used to derive a standard error of performance and the correlation between NIR reading and standard chemical composition analysis (anthrone, Kjedahl and Ninhydrin methods for carbohydrate, total N, and amino acid analysis, respectively) were determined. The multiple determination coefficients (R2) of apple and almond tissues were 0.9949 and 0.9842 for total nitrogen, 0.9971 and 0.9802 for amino acid, and 0.8889 and 0.8687 for nonstructural carbohydrate, respectively.

Authentication of Whole and Ground Coffee Beans by near Infrared Reflectance Spectroscopy

1994 ◽  
Vol 2 (2) ◽  
pp. 85-92 ◽  
Author(s):  
Gerard Downey ◽  
Jerôme Boussion ◽  
Dominique Beauchêne
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Correct Identification ◽  
Correct Classification Rate ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Identification Rate ◽  
Coffee Beans ◽  
Correct Identification Rate ◽  
Nir Reflectance Spectroscopy

The potential of NIR reflectance spectroscopy for discriminating between pure Arabica and pure Robusta coffees and blends of these two was investigated. Studies were performed on whole and ground beans using a factorial discriminant procedure. For whole beans, in the absence of blended samples, a correct classification rate of 96.2% was achieved. Inclusion of blended samples reduced this figure to between 82.9 and 87.6%. In the case of ground samples, including blends, a correct identification rate of 83.02% was achieved. The molecular basis for discrimination is discussed.

scholarly journals Environmental assessment of soil quality indices using near infrared reflectance spectroscopy

2021 ◽  
Vol 922 (1) ◽  
pp. 012009
Author(s):  
D Devianti ◽  
Sufardi ◽  
S Syafriandi ◽  
A A Munawar
Keyword(s):  
Spectral Data ◽  
Soil Quality ◽  
Environmental Assessment ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Infrared Reflectance ◽  
Quality Indices ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance

Abstract The main purpose of this preset study is to assess soil quality indices in form of potassium (K) and phosphorus (P) contents using a non-invasive and environmental friendly approach namely near infrared reflectance spectroscopy. Soil samples were obtained from Aceh Besar district in rice field land-use. Near infrared spectral data of soil samples were acquired and recorded as absorbance in wavelength range from 1000 to 2500 nm. On the other hand, actual P and K were measured using standard laboratory procedures by means of Kjeldahl methods. Spectral data were corrected and pre-treated using mean centering approach and applied to all dataset. Prediction models were developed using principal component regression and validated using leverage cross validation. The results showed that both soil quality indices can be predicted with maximum correlation coefficient (r) of 0.98 and ratio prediction to deviation (RPD) index of 3.47 for P, and r of 0.91, RPD of 2.68 for K respectively. It may conclude that environmental assessment, particularly for soil quality determination can be conducted rapidly and non-invasively using near infrared spectroscopy approach.

Use of near Infrared Reflectance Spectroscopy to Analyse Bovine Faecal Samples

2002 ◽  
Vol 10 (4) ◽  
pp. 309-314 ◽  
Author(s):  
D. Cozzolino ◽  
A. La Manna ◽  
D. Vaz Martins
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Dry Weight ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Faecal Samples ◽  
Acid Detergent Fibre ◽  
Nir Reflectance Spectroscopy ◽  
Calibration Equations

Near infrared (NIR) reflectance spectroscopy was used to predict nitrogen (N), acid detergent fibre (ADF), neutral detergent fibre (NDF) and chromium (Cr) in beef faecal samples. One hundred and twenty faecal samples were scanned in a NIRSystems 6500 monochromator instrument over the wavelength range of 400–2500 nm in reflectance. Calibration equations were developed using modified partial least squares (MPLS) with internal cross validation to avoid overfitting. The coefficient of determination in calibration ( R2cal) and the standard error in cross validation ( SECV) were 0.80 (0.74) for N, 0.92 (12.04) for ADF, 0.86 (13.5) for NDF and 0.56 (0.07) for Cr in g kg−1 dry weight, respectively. Results for validation were 0.78 ( SEP: 0.1) for N, 0.74 ( SEP: 7.5) for ADF, 0.85 ( SEP: 8.5) for NDF and 0.10 (0.09) for Cr in g kg−1 dry weight, respectively.

Predicting Elements in White Pine and Red Oak Foliage with Visible-Near Infrared Reflectance Spectroscopy

1997 ◽  
Vol 5 (2) ◽  
pp. 77-82 ◽  
Author(s):  
R.A. Hallett ◽  
J.W. Hornbeck ◽  
M.E. Martin
Keyword(s):  
Forest Canopy ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Red Oak ◽  
Acid Digestion ◽  
White Pine ◽  
Near Infrared Reflectance ◽  
Nir Reflectance Spectroscopy ◽  
Calibration Equations ◽  
Mn Concentrations

Near infrared (NIR) reflectance spectroscopy was evaluated for its effectiveness at predicting Al, Ca, Fe, K, Mg and Mn concentrations in white pine ( Pinus strobus L.) and red oak ( Quercus rubra L.) foliage. A NIR spectrophotometer was used to scan 470 dried, ground foliage samples. These samples were used to develop calibration equations using a modified partial least squares (MPLS) regression technique. For the calibration equations, concentrations of Al, Ca, Fe, K, Mg and Mn as determined by acid digestion and laboratory analysis were regressed against second-difference absorbance values measured from 400 to 2498 nm. The regression models developed by NIR reflectance spectroscopy were unable to predict Fe. Predictions were satisfactory for Al, Ca, K, Mn and Mg. It still is uncertain which mineral/organic associations are being detected by NIR reflectance spectroscopy. Future applications may include prediction of element concentrations in the forest canopy via remote sensing.

Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy

Soil Research ◽  
2003 ◽  
Vol 41 (6) ◽  
pp. 1101 ◽  
Author(s):  
Kamrunnahar Islam ◽  
Balwant Singh ◽  
Alex McBratney
Keyword(s):  
Soil Properties ◽  
Water Content ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Simultaneous Estimation ◽  
Near Infrared Reflectance ◽  
Free Iron ◽  
Rapid Acquisition ◽  
Gravimetric Water Content

Fast and convenient soil analytical techniques are needed for soil quality assessment and precision soil management. Spectroscopy in the ultraviolet (UV, 250–400 nm), visible (VIS, 400–700 nm), and near-infrared (NIR, 700–2500 nm) ranges allows rapid acquisition of soil information at quantitative, and qualitative or indicator, levels for use in agriculture and environmental monitoring. The main objective of this study was to evaluate the ability of reflectance spectroscopy in the UV, VIS, and NIR ranges to predict several soil properties simultaneously. Soil samples (161 surface and subsurface) were used for simultaneous estimation of pH, electrical conductivity (EC), air-dry gravimetric water content, organic carbon (OC), free iron, clay, sand, and silt contents, cation exchange capacity (CEC), and exchangeable calcium (Ca), magnesium (Mg), potassium (K), and sodium (Na). Principal component regression analyses (PCA) were used to develop calibration equations between the reflectance spectral data and measured values for the above soil properties obtained by traditional laboratory methods. By using randomly selected calibration and validation sets of samples, PCA models were able to successfully predict pH, OC, air-dry gravimetric water content, clay, CEC, exchangeable Ca, and exchangeable Mg of soil samples. The predictions, however, were poor for EC, free iron, sand, silt, exchangeable K, and exchangeable Na. The study shows that reflectance spectroscopy in the UV–VIS–NIR range has the potential for the rapid simultaneous prediction of several soil properties.

Quality Control of Technical Casein—Near Infrared Reflectance Spectroscopy Method

1998 ◽  
Vol 6 (A) ◽  
pp. A303-A306 ◽  
Author(s):  
Henryk W. Czarnik-Matusewicz ◽  
Adolf Korniewicz
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Spectroscopy Method ◽  
Total Acidity ◽  
Near Infrared Reflectance ◽  
Significant Saving ◽  
Wet Chemistry ◽  
Large Numbers ◽  
Nir Reflectance Spectroscopy

The near infrared (NIR) reflectance spectroscopy method can be used in the routine checking of the technical casein. All the chemical and physical characteristics of the product that influence the NIR spectrum affect the qualification. In order to monitor possible deviations in the preparation, it is advisable to carry out some test during the different manufacturing stages. These test are: determination of water, fat, ash, free and total acidity. A set of 66 ground casein samples was used to calibrate the output from NIR instrument InfraAlyzer 500 (Bran+Luebbe GmbH), taking reflectance readings every 2 nm between 1100 nm and 2500 nm. As soon as the spectral scanning had been completed, the casein samples were subjected to the standard wet chemistry analysis. The spectral data from this calibration set was then statistically manipulated using MLR method with the aid of the software SESAME ver. 2.10 (Bran+Luebbe GmbH) to generate calibration models. These calibrations were then applied to a separate set of 20 samples which, for validation purposes, were also analysed by wet chemistry. The casein samples analysis predictions compared with the wet chemistry results on these samples, with standard errors of determination of 0.1%, 0.2%, 0.2%, 0.2% and 0.5% for water, fat, ash, free and total acidity, respectively. The use of NIR instrumentation and appropriate calibrations is able to result in a significant saving of laboratory resources when large numbers of the technical casein samples are being processed for analysis.

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