Application of near Infrared Reflectance Spectroscopy for the Analysis of Organic C, Total N and pH in Soils of Uruguay

2002 ◽  
Vol 10 (3) ◽  
pp. 215-221 ◽  
Author(s):  
A. Morón ◽  
D. Cozzolino
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Reflectance Spectroscopy ◽  
Calibration Coefficient ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Total N ◽  
Organic C ◽  
Silver Spring ◽  
Validation Set

Near infrared (NIR) reflectance spectroscopy was used to analyse samples ( n = 332) from different soils from Uruguay (South America) for organic carbon (OC), total nitrogen (N) and pH. One set ( n = 200) of samples randomly selected was used to develop the NIR calibrations while the remaining ( n = 132) samples were used as the validation set. The samples were scanned in a small circular cup in reflectance mode (400–2500 nm), using a Foss NIRSystems 6500 (Silver Spring, MD, USA). Modified partial least squares (MPLS) was used to produce the calibration models and cross-validation was used to avoid collinearity effects among variables. Three mathematical treatments and four scatter corrections were also applied. The calibration coefficient of determination ( R2CAL) and the standard error in cross-validation ( SECV) were 0.94 ( SECV: 1.9) for OC; 0.91 ( SECV: 0.19) for total N in g kg−1 and 0.93 ( SECV: 0.18) for pH, respectively. The simple correlation coefficient of validation ( rVAL) and the standard errors of prediction ( SEP) were 0.74 and 5; 0.73 and 0.4; 0.84 and 0.28 for OC, total N and pH, 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.

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.

Effect of Sample Presentation and Animal Muscle Species on the Analysis of Meat by near Infrared Reflectance Spectroscopy

2002 ◽  
Vol 10 (1) ◽  
pp. 37-44 ◽  
Author(s):  
D. Cozzolino ◽  
I. Murray
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Cross Validation ◽  
Infrared Region ◽  
Coefficient Of Determination ◽  
Meat Industry ◽  
Near Infrared Reflectance ◽  
Silver Spring ◽  
Meat Samples ◽  
Beef Muscle

The useful wavelengths in both the visible and the near infrared region as well as two sample presentations (intact and minced) were evaluated to assess moisture (M), crude protein (CP) and intra muscular fat (IMF) in lamb ( n = 300), beef ( n = 100) and chicken ( n = 48) muscle samples. Samples were scanned in reflectance in a NIRSystems 6500 (NIRSystems, Silver Spring, MD, USA). Predictive equations were performed using modified partial least squares (MPLS) with internal cross-validation. The coefficient of determination in calibration ( R2CAL) and the standard error in cross-validation ( SECV) were calculated for each chemical parameter. For moisture, crude protein and fat (each expressed as g kg−1), R2CAL and SECV for beef muscle were 0.98, 0.81 and 0.96, respectively, and SECV was 33.1, 21.8 and 44.8 for beef muscle; for chicken muscle the comparable statistics were 0.99, 0.97 and 0.95 and SECV was 6.9, 2.4 and 33.1; while for lamb muscle R2CAL was 0.76, 0.83 and 0.73 and SECV 10.3, 5.5 and 4.7. It was concluded that the minced presentation is the best way to analyse muscle samples. On the other hand, intact presentation could have a great potential for use in the meat industry, although more research will be needed in order to determine quality attributes on meat samples.

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.

Near Infrared Reflectance Spectroscopy Coupled to Chemometrics as a Cost-Effective, Rapid and Non-Destructive Tool for Fish Fraud Control: Monitoring Source, Condition and Nutritional Value of Five Common Whitefish Species

2020 ◽  
Author(s):  
Diogo B Gonçalves ◽  
Carla S P Santos ◽  
Teresa Pinho ◽  
Rafael Queirós ◽  
Pedro D Vaz ◽  
...  
Keyword(s):  
Near Infrared ◽  
Atlantic Cod ◽  
Cost Effective ◽  
Reflectance Spectroscopy ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

Abstract Fish fraud is a problematic issue for the industry that to be properly addressed requires the use of accurate, rapid and cost-effective tools. In this work, near infrared reflectance spectroscopy (NIRS) was used to predict nutritional values (protein, lipids and moisture) as well as to discriminate between source (farmed vs. wild fish) and condition (fresh, defrosted or frozen fish). Five whitefish species consisting of Alaskan pollock (Gadus chalcogrammu), Atlantic cod (Gadus morhua), European plaice (Pleuronectes platessa), Common sole (Solea solea) and Turbot (Psetta maxima), including farmed, wild, fresh and frozen ones, were scanned by a low-cost handheld near infrared reflectance spectrometer with a spectral range between 900 nm and 1700 nm. Several machine learning algorithms were explored for both regression and classification tasks, achieving precisions and coefficient of determination higher than 88% and 0.78, respectively. Principal component analysis (PCA) was used to cluster samples according to classes where good linear discriminations were denoted. Loadings from PCA reveal bands at 1150, 1200 and 1400 nm as the most discriminative spectral regions regarding classification of both source and condition, suggesting the absorbance of OH, CH, CH2 and CH3 groups as the most important ones. This study shows the use of NIRS and both linear and non-linear learners as a suitable strategy to address the fish fraud problematic and fish quality control.

Prediction of Chemical Composition in Sunflower Whole Plant and Silage (Helianthus Annus L.) by near Infrared Reflectance Spectroscopy

2007 ◽  
Vol 15 (3) ◽  
pp. 201-207 ◽  
Author(s):  
A. Fassio ◽  
A. Gimenez ◽  
E. Fernandez ◽  
D. Vaz Martins ◽  
D. Cozzolino
Keyword(s):  
Chemical Composition ◽  
Near Infrared ◽  
Cross Validation ◽  
Reference Method ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Whole Plant ◽  
Calibration Models

The aim of this study was to investigate the potential use of near infrared (NIR) reflectance spectroscopy to predict chemical composition in both sunflower whole plant (WPSun) and sunflower silage (SunS). Samples of both WPSun ( n = 73) and SunS ( n = 50) were analysed by reference method and scanned in reflectance using a NIR monochromator instrument (400–2500 nm). Calibration models were developed between NIR data and reference values for dry matter (DM), crude protein (CP), ash, acid detergent fibre (ADFom), neutral detergent fibre (aNDFom), in vitro organic matter digestibility (OMD), ether extract (EE) and pH using partial least squares regression (PLS). Due to the limited number of samples full cross-validation was used to test the calibration models. The best correlations (R 2cal) and lowest standard errors in cross-validation (SECV) were obtained for DM (R 2cal > 0.82, SECV: 27.0 and 35.8 g kg−1), CP (R 2cal> 0.85, SECV: 9.9 and 10.1 g kg−1) and ash (R2cal> 0.85, SECV 11.2 and 8.2 g kg−1) in both WPSun and SunS samples, respectively. For ADFom, aNDFom and OMD the calibrations were considered to be poor (R 2cal < 0.85). In SunS samples a good correlation was found for EE (R 2cal = 0.94, SECV: 15.3 g kg−1).

Determination of the Sum of Dimer and Polymer Triglycerides and of Acid Value of Used Frying Fats and Oils by Near Infrared Reflectance Spectroscopy

1994 ◽  
Vol 77 (5) ◽  
pp. 1184-1189 ◽  
Author(s):  
Andries J Boot ◽  
Andrbes J Speek
Keyword(s):  
Near Infrared ◽  
Acid Value ◽  
Fats And Oils ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Frying Fats ◽  
Value Determination ◽  
Validation Set

Abstract Near infrared reflectance spectroscopy (NIRS) in the transaction mode was applied to determine the sum of dimer and polymer triglycerides (DPTG) contents and acid value of used frying fats and oils. A filter instrument and a calibration sample set were used to determine DPTG content and acid value. For each parameter, a 7-wavelength calibration was developed using multiple linear regression analysis. For a validation set comprising 44 samples for the NIRS-DPTG determination in the range of 2.2 to 32.7% m/m, the correlation coefficient between NIRS and liquid chromatography (LC) was 0.976, with a standard error of prediction (SEP) of 1.8% m/m. For a validation set comprising 36 samples for the NIRS-acid value determination in the range of 0.30 to 18.8 mg potassium hydroxide per gram of sample (mg KOH/g), the correlation coefficient between NIRS and titration was 0.996, with a SEP of 0.33 mg KOH/g. Validation after routine operation for 1 year provided SEPs of 2.3% m/m and 0.44 mg KOH/g for DPTG and acid value determination, respectively. NIRS screening of 1400 samples collected during 1992 precluded the need for 1149 DPTG determinations by LC (82.1%) and 1033 acid value determinations by titration (73.8%), which are methods the judicature in The Netherlands accepted, because those samples appeared to comply with legislation.

Determination of potentially mineralizable nitrogen and nitrogen in particulate organic matter fractions in soil by visible and near-infrared reflectance spectroscopy

2004 ◽  
Vol 142 (3) ◽  
pp. 335-343 ◽  
Author(s):  
A. MORON ◽  
D. COZZOLINO
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Calibration Models ◽  
Potentially Mineralizable Nitrogen ◽  
Mineralizable Nitrogen ◽  
Organic Matter Fractions

Visible (VIS) and near-infrared reflectance spectroscopy (NIRS) combined with multivariate data analysis was used to predict potentially mineralizable nitrogen (PMN) and nitrogen in particulate organic matter fractions (PSOM-N). Soil samples from a long-term experiment (n=24) as well as soils under commercial management (n=160) in Uruguay (South America) were analysed. Samples were scanned in a NIRS 6500 monochromator instrument by reflectance (400–2500 nm). Modified partial least square regression (MPLS) and cross validation were used to develop the calibration models between NIRS data and reference values. NIRS calibration models gave a coefficient of determination for the calibration (R2CAL)>0·80 and the standard deviation of reference data to standard error in cross validation (RPD) ratio ranging from 2 to 5·5 for the variables evaluated. The results obtained in the study showed that NIRS could have the potential to determine PMN and PSOM-N fractions in soils under different agronomic conditions. However, the relatively limited number of samples led us to be cautious in terms of conclusions and to extend the results of this work to similar conditions.

Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy

2006 ◽  
Vol 82 (1) ◽  
pp. 111-116 ◽  
Author(s):  
N. Barlocco ◽  
A. Vadell ◽  
F. Ballesteros ◽  
G. Galietta ◽  
D. Cozzolino
Keyword(s):  
Shear Force ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Intramuscular Fat ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Calibration Models ◽  
Intact Muscle

AbstractPartial least-squares (PLS) models based on visible (Vis) and near infrared reflectance (NIR) spectroscopy data were explored to predict intramuscular fat (IMF), moisture and Warner Bratzler shear force (WBSF) in pork muscles (m. longissimus thoracis) using two sample presentations, namely intact and homogenized. Samples were scanned using a NIR monochromator instrument (NIRSystems 6500, 400 to 2500 nm). Due to the limited number of samples available, calibration models were developed and evaluated using full cross validation. The PLS calibration models developed using homogenized samples and raw spectra yielded a coefficient of determination in calibration (R2) and standard error of cross validation (SECV) for IMF (R2=0·87; SECV=1·8 g/kg), for moisture (R2=0·90; SECV=1·1 g/kg) and for WBSF (R2=0·38; SECV=9·0 N/cm). Intact muscle presentation gave poorer PLS calibration models for IMF and moisture (R2<0·70), however moderate good correlation was found for WBSF (R2=0·64; SECV=8·5 N/cm). Although few samples were used, the results showed the potential of Vis-NIR to predict moisture and IMF using homogenized pork muscles and WBSF in intact samples.

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