Exploring the Use of near Infrared Reflectance Spectroscopy to Study Physical Properties and Microelements in Soils

2003 ◽  
Vol 11 (2) ◽  
pp. 145-154 ◽  
Author(s):  
A. Moron ◽  
D. Cozzolino
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Agricultural Soils ◽  
Low Cost ◽  
Scatter Correction ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Second Derivative ◽  
Near Infrared Reflectance ◽  
Nir Reflectance Spectroscopy

Near infrared (NIR) reflectance spectroscopy was used to predict the content of silt, sand, clay, iron (Fe), copper (Cu), manganese (Mn) and zinc (Zn) in soil. A total of 332 samples from agricultural soils (0–15 cm depth) in Uruguay (South America) were used. The samples were scanned in a monochromator instrument (NIRSystems 6500, Silver Spring, MD, USA). Two mathematical treatments (first and second derivative) with SNVD (scatter normal variate and detrend) and without scatter correction were studied. Modified partial least squares (mPLS) was used to develop the calibration models. The coefficient of determination in calibration ( R2cal) and the standard error in calibration ( SEC) using the second derivative were 0.81 ( SEC: 5.1), 0.83 ( SEC: 5.3), 0.92 ( SEC: 2.6) for percent sand, silt and clay, respectively. The R2cal and standard error of cross-validation ( SECV) were for Cu 0.87 ( SEC: 0.7), for Fe 0.92 ( SEC: 21.7), for Mn 0.72 ( SEC: 83.0) and for Zn 0.72 ( SEC: 1.2) on mg kg−1 dry matter. It was concluded that NIR reflectance spectroscopy has a great potential as an analytical method for routine analysis of soil texture, Fe, Zn and Cu due the speed and low cost of analysis.

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.

The potential of near-infrared reflectance spectroscopy to analyse soil chemical and physical characteristics

2003 ◽  
Vol 140 (1) ◽  
pp. 65-71 ◽  
Author(s):  
D. COZZOLINO ◽  
A. MORÓN
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Silver Spring

Near-infrared reflectance spectroscopy (NIRS) was used for the analysis of soil samples for silt, sand, clay, calcium (Ca), potassium (K), sodium (Na), magnesium (Mg), copper (Cu) and iron (Fe). A total of 332 samples of different soils from Uruguay (South America) were used. The samples were scanned in a NIRS 6500 (NIRSystems, Silver Spring, MD, USA) in reflectance. Cross validation was applied to avoid overfitting of the models. The coefficient of determination in calibration (R^2_{\rm cal}) and the standard errors in cross validation (SECV) were 0·80 (SECV: 6·8), 0·84 (SECV: 6·0), 0·90 (SECV: 3·6) in per cent for sand, silt and clay respectively. For both macro and microelements the R^2_{\rm cal} and SECV were 0·80 (SECV: 0·1), 0·95 (SECV: 2·9), 0·90 (SECV 0·8), for K, Ca, Mg in g/kg respectively, and 0·86 (SECV: 0·82) and 0·92 (SECV: 25·5) for Cu and Fe in mg/kg. It was concluded that NIRS has a great potential as an analytical method for soil routine analysis due to the speed and low cost of analysis.

Prediction of the Neuro-Toxin Beta-N-Oxalyl-Amino-L-Alanine in Lathyrus Species, Using near Infrared Reflectance Spectroscopy

1998 ◽  
Vol 6 (A) ◽  
pp. A93-A96 ◽  
Author(s):  
F. Jaby El-Haramein ◽  
A. Abd-El Moneim ◽  
H. Nakkoul ◽  
P.C. Williams
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Lower Limbs ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Northern India ◽  
Drought Tolerant ◽  
Large Numbers ◽  
Analysis Application ◽  
Nir Reflectance Spectroscopy

Grasspea or chickling vetch ( Lathyrus spp.) is a common food legume, widely grown and eaten in northern India, Bangladesh, Pakistan, Nepal and Ethiopia. It contains the neurotoxin beta-N-oxalyl-amino-L-alanine (BOAA), which can cause the disease known as “neuro-lathyrism”, an irreversible paralysis of the lower limbs, if BOAA-rich seeds form a large proportion of the diet. Lathyrus is a drought-tolerant crop, and ICARDA seeks to breed high-yielding lines that are low in BOAA. Conventional methods for determination of BOAA are time-consuming, expensive, and not practicable for screening large numbers of genotypes. Near infrared (NIR) reflectance spectroscopy offers a rapid, inexpensive method of analysis. Application of NIR reflectance spectroscopy to the prediction of BOAA in Lathyrus has been achieved by developing NIR reflectance spectroscopy equations involving 88 samples, which represented three species: L. sativus, L. cicera and L. ochrus. Both intact and ground seeds were studied. Content of BOAA ranged from 0.09 to 0.83%. Seeds of L. cicera were significantly lower than those of the other two species. The best results were obtained from whole seeds, using multiple linear regression. The standard error of prediction of 0.05% and coefficient of determination ( r2) of 0.94 are considered quite adequate for use in the Lathyrus breeding programme.

scholarly journals Analysis of the Acid Detergent Fibre Content in Turnip Greens and Turnip Tops (Brassica rapa L. Subsp. rapa) by Means of Near-Infrared Reflectance

Foods ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 364 ◽  
Author(s):  
Sara Obregón-Cano ◽  
Rafael Moreno-Rojas ◽  
Ana María Jurado-Millán ◽  
María Elena Cartea-González ◽  
Antonio De Haro-Bailón
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Mathematical Treatment ◽  
Second Derivative ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
First Derivative ◽  
Acid Detergent Fibre ◽  
Turnip Greens ◽  
Turnip Tops

Standard wet chemistry analytical techniques currently used to determine plant fibre constituents are costly, time-consuming and destructive. In this paper the potential of near-infrared reflectance spectroscopy (NIRS) to analyse the contents of acid detergent fibre (ADF) in turnip greens and turnip tops has been assessed. Three calibration equations were developed: in the equation without mathematical treatment the coefficient of determination (R2) was 0.91, in the first-derivative treatment equation R2 = 0.95 and in the second-derivative treatment R2 = 0.96. The estimation accuracy was based on RPD (the ratio between the standard deviation and the standard error of validation) and RER (the ratio between the range of ADF of the validation as a whole and the standard error of prediction) of the external validation. RPD and RER values were of 2.75 and 9.00 for the treatment without derivative, 3.41 and 11.79 with first-derivative, and 3.10 and 11.03 with second-derivative. With the acid detergent residue spectrum the wavelengths were identified and associated with the ADF contained in the sample. The results showed a great potential of NIRS for predicting ADF content in turnip greens and turnip tops.

scholarly journals Fast, Low-Cost and Non-Destructive Physico-Chemical Analysis of Virgin Olive Oils Using Near-Infrared Reflectance Spectroscopy

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2642 ◽  
Author(s):  
Ana Garrido-Varo ◽  
María-Teresa Sánchez ◽  
María-José De la Haba ◽  
Irina Torres ◽  
Dolores Pérez-Marín
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Virgin Olive Oils ◽  
Olive Oils ◽  
Physico Chemical ◽  
Non Destructive

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.

Studies to Measure Cotton Fibre Length, Strength, Micronaire and Colour by Vis/NIR Reflectance Spectroscopy. Part I: Descriptive Statistics of Fibre Properties and Reflectance Spectra

1993 ◽  
Vol 1 (3) ◽  
pp. 153-173 ◽  
Author(s):  
Joseph G. Montalvo ◽  
Sherman E. Faught ◽  
Harmon H. Ramey ◽  
Steven E. Buco
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
A Priori ◽  
Fibre Length ◽  
High Volume ◽  
Reflectance Spectroscopy ◽  
Reflectance Spectra ◽  
Fibre Property ◽  
Property Data ◽  
Nir Reflectance Spectroscopy

Fibre property data representing the 1989 and 1990 crop years and its reflectance spectra are analysed using standard error, regression and correlation analysis. The six properties of interest are upper-half mean length, uniformity index, strength and micronaire measured on two high volume instrument systems placed side-by-side, and colour (Rd and +b) measured by the traditional lab system. Visible (vis) and near infrared (NIR) reflectance spectra are observed on a scanning spectrophotometer, and span the 400–2500 nm range. Three findings highlight the research. One, a diagnostic test is presented to decide, a priori of reflectance spectroscopy, the degree to which the mean property values have reduced random error. Two, the standard error of replicate spectra provides a way to probe the fibre mass in the diffuse reflectance optical path. The spectral error is strongly influenced by both how the cotton is packed into the spectrophotometric cell and the non-homogeneity of the sample. And three, correlations between the spectra confirm that some visible and NIR wavelength regions contain mutually exclusive information about the properties of this natural staple.

Development and Validation of an Analytical Method for Identification of Granulated Nicarbazin by near Infrared Reflectance Spectroscopy

1997 ◽  
Vol 5 (1) ◽  
pp. 41-65 ◽  
Author(s):  
K.B. Bradfield ◽  
R.A. Forbes
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Raw Material ◽  
Second Derivative ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Distance Method ◽  
Designed Experiments ◽  
Development And Validation

A rapid method was developed for verification of the identity of granulated nicarbazin raw material by near infrared reflectance. A library was developed from 30 second derivative near infrared reflectance spectra obtained from 15 representative batches of nicarbazin. Following identification, new batches were qualified by conformance testing according to the wavelength distance method. The identification/qualification method was validated for selectivity and for ruggedness utilising statistically designed experiments.

scholarly journals Applicability of Near Infrared Reflectance Spectroscopy to Predict Amylose Contents of Single-Grain Maize

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2463
Author(s):  
Qing Dong ◽  
Qianqian Xu ◽  
Jiandong Wu ◽  
Beijiu Cheng ◽  
Haiyang Jiang
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Near Infrared Reflectance ◽  
Single Grain ◽  
Single Seeds

Near infrared reflectance spectroscopy (NIRS) and reference data were used to determine the amylose contents of single maize seeds to enable rapid, effective selection of individual seeds with desired traits. To predict the amylose contents of a single seed, a total of 1069 (865 as calibration set, 204 as validation set) single seeds representing 120 maize varieties were analyzed using chemical methods and performed calibration and external validation of the 150 single seeds set in parallel. Compared to various spectral pretreatments, the regression of partial least squares (PLS) with mathematical treatment of Harmonization showed the final optimization. The single-seed amylose contents showed the root mean square error of calibration (RMSEC) of 2.899, coefficient of determination for calibration (R2) of 0.902, and root mean square error of validation (RMSEV) of 2.948. In external validations, the coefficient of determination in cross-validation (r2), root mean square error of the prediction (RMSEP) and ratio of the standard deviation to SEP (RPD) were 0.892, 2.975 and 3.086 in the range of 20–30%, respectively. Therefore, NIRS will be helpful to breeders for determining the amylose contents of single-grain maize.

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