Opportunities for, and limitations of, near infrared reflectance spectroscopy applications in soil analysis: A review

2009 ◽  
Vol 89 (5) ◽  
pp. 531-541 ◽  
Author(s):  
C Nduwamungu ◽  
N Ziadi ◽  
L -É Parent ◽  
G F Tremblay ◽  
L Thuriès
Keyword(s):  
Sample Preparation ◽  
Standard Error ◽  
Near Infrared ◽  
Soil Analysis ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Reference Methods

Near infrared reflectance spectroscopy (NIRS) is a cost- and time-effective and environmentally friendly technique that could be an alternative to conventional soil analysis methods. In this review, we focussed on factors that hamper the potential application of NIRS in soil analysis. The reported studies differed in many aspects, including sample preparation, reference methods, spectrum acquisition and pre-treatments, and regression methods. The most significant opportunities provided by NIRS in soil analysis include its potential use in situ, the determination of various biological, chemical, and physical properties using a single spectrum per sample, and an estimated reduction of analytical cost of at least 50%. Contradictory results among studies on NIRS utilisation in soil analysis are partly related to variations in sample preparation and reference methods. The following calibration statistics appear to be most appropriate for comparing NIRS performance across soil attributes: (i) coefficient of determination (r2), (ii) ratio of performance deviation (RPD), (iii) coefficient of regression (b), and (iv) ratio of the standard error of prediction (SEP) to the standard error of the reference method (SER), i.e., the ratio of standard errors (RSE). Further investigations on issues such as (i) RSE guidelines, (ii) correlation between NIRS spectrophotometers, (iii) correlation of different reference methods for a given attribute to soil spectra, (iv) identification of key factors affecting the accuracy of NIRS predictions, and (v) efficient use of spectral libraries are required to enhance the acceptability of NIRS as a soil analysis technique and to make it more user-friendly. Standardized guidelines are proposed for the assessment of the accuracy of NIRS predictions of soil attributes.Key words: Near infrared reflectance spectroscopy, soil analysis, calibration

scholarly journals Analysis of Fermented Milk Products by Near-Infrared Reflectance Spectroscopy

1996 ◽  
Vol 79 (3) ◽  
pp. 817-821 ◽  
Author(s):  
José L Rodríguez-Oxero ◽  
Marià Hermtoa
Keyword(s):  
Near Infrared ◽  
Fermented Milk ◽  
Reflectance Spectroscopy ◽  
Standard Errors ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Total Solids ◽  
Infrared Reflectance Spectroscopy ◽  
Reference Methods

Abstract Fat, protein, and total solids in fermented milk were determined by near-infrared reflectance spectroscopy (NIRS). A set of 107 samples from diverse types of fermented milk (whole, skimmed, with added flavors, and without added flavors) were used to calibrate the instrument by modified partial least-square regression. Global calibration, using all samples, was more effective than specific calibrations using fewer samples of each type. Standard errors of a calibration were 0.071 for fat, 0.075 for protein, and 0.083 for total solids. Values of the correlation coefficient square (R2) were 0.997 for fat, 0.981 for protein, and 1.000 for total solids. Calibration was validated with an independent set of 34 samples of the same types. Standard errors of validation were 0.08,0.14, and 0.25 for fat, protein, and total solids, respectively, and values of R2 for the regression of measurements by NIRS versus reference methods were 1.00 for fat and total solids and 0.94 for protein. When Standard 128 of the International Dairy Federation was used to compare NIRS results with those from reference methods, no significant differences were found (p = 0.05).

Study of dissected lamb muscles by visible and near infrared reflectance spectroscopy for composition assessment

2000 ◽  
Vol 70 (3) ◽  
pp. 417-423 ◽  
Author(s):  
D. Cozzolino ◽  
I. Murray ◽  
J. R. Scaife ◽  
R. Paterson
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Cross Validation ◽  
Rectus Femoris ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Fresh Weight Basis ◽  
Infrared Reflectance Spectroscopy

AbstractNear infrared reflectance spectroscopy (NIRS) was used to study the reflectance properties of intact and minced lamb muscles in two presentations to the instrument to predict their chemical composition. A total of 306 muscles were examined from 51 lambs, consisting of the following muscles: longissimus dorsi, supraspinatus, infraspinatus, semimembranosus, semitendinosus and rectus femoris. Modified partial least squares (MPLS) regression models of chemical variables yielded R2 and standard error of cross-validation (SECV) of 0·76 (SECV: 10·4), 0·83 (SECV: 5·5) and 0·73 (SECV: 4·7) for moisture, crude protein and intramuscular fat in the minced samples expressed as g/kg on a fresh-weight basis, respectively. Calibrations for intact samples had lower R2 and higher standard error of cross validation (SECV) compared with the minced samples.

Moisture Analysis of Forage by Near Infrared Reflectance Spectroscopy: Preliminary Collaborative Study and Comparison Between Karl Fischer and Oven Drying Reference Methods

1988 ◽  
Vol 71 (2) ◽  
pp. 256-262
Author(s):  
William R Windham ◽  
Franklin E Barton ◽  
James A Robertson
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Collaborative Study ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Karl Fischer ◽  
Infrared Reflectance Spectroscopy ◽  
Moisture Analysis

Abstract A collaborative study of moisture analysis by neai infrared reflectance spectroscopy (NIRS) has been completed involving 5 laboratories and 20 forage samples. Near infrared reflectance spectroscopy calibrations for moisture were developed in the Associate Referee's laboratory from Karl Fischer (KF) and AOAC air oven (AO) (135°C for 2 h) moisture methods, respectively, and transferred to each collaborating laboratory's NIRS instrument. NIRS moisture data were validated with KF data from the Associate Referee's laboratory and AO data from each collaborating laboratory. The standard error of analysis of KF data by NIRS KF determination and AO data by NIRS AO determination ranged from 0.25 to 0.48% and from 0.74 to 1.88%, respectively. The standard errors between laboratories for NIRS KF and NIRS AO determinations were 0.2" and 0.39%, respectively. The standard error between moisture analyses by NIRS KF and NIRS AO calibrations, averaged across laboratories, was 0.42%. In addition, the standard error between laboratories for the AOAC AO method was 0.63%. The increase in standard error for the AOAC AO method was due to the random and systematic errors associated with the gravimetric techniques. The results indicate that NIRS analysis can accurately and precisely deterrr ine the moisture content of forages and forage crops because of th« very strong absorbance of water in the near infrared region.

scholarly journals Effect of Sample Preparation Methods on the Prediction Performances of Near Infrared Reflectance Spectroscopy for Quality Traits of Fresh Yam (Dioscorea spp.)

2020 ◽  
Vol 10 (17) ◽  
pp. 6035
Author(s):  
Emmanuel Oladeji Alamu ◽  
Michael Adesokan ◽  
Asrat Asfaw ◽  
Busie Maziya-Dixon
Keyword(s):  
Sample Preparation ◽  
Dry Matter ◽  
Near Infrared ◽  
Starch Content ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Quality Traits ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

High throughput techniques for phenotyping quality traits in root and tuber crops are useful in breeding programs where thousands of genotypes are screened at the early stages. This study assessed the effects of sample preparation on the prediction accuracies of dry matter, protein, and starch content in fresh yam using Near-Infrared Reflectance Spectroscopy (NIRS). Fresh tubers of Dioscorearotundata (D. rotundata) and Dioscoreaalata (D. alata) were prepared using different sampling techniques—blending, chopping, and grating. Spectra of each sample and reference data were used to develop calibration models using Modified Partial Least Square (MPLS). The performance of the model developed from the blended yam samples was tested using a new set of yam samples (N = 50) by comparing their wet laboratory results with the predicted values from NIRS. Blended samples had the highest coefficient of prediction (R2pre) for dry matter (0.95) and starch (0.83), though very low for protein (0.26), while grated samples had the lowest R2pre of 0.87 for dry matter and 0.50 for starch. Results showed that blended samples gave a better prediction compared with other methods. The feasibility of NIRS for the prediction of dry matter and starch content in fresh yam was highlighted.

Predicting the stage of decay of decomposing leaves by near infrared reflectance spectroscopy

1993 ◽  
Vol 23 (12) ◽  
pp. 2552-2559 ◽  
Author(s):  
Dominique Gillon ◽  
Richard Joffre ◽  
Pierre Dardenne
Keyword(s):  
Leaf Litter ◽  
Standard Error ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Litter Bags ◽  
Litter Mass ◽  
Infrared Reflectance Spectroscopy

To study mineral cycling in forest ecosystems, it is essential to know the decomposition rate of the litter. This study attempted to predict directly, by near infrared reflectance spectroscopy, the stage of decomposition of leaf litter expressed as the percentage of ash-free litter mass remaining (LMR). Leaf litter of 10 different species, with varied initial compositions and at different stages of decomposition produced by incubation in the laboratory under controlled conditions, were used in this study. The LMR calibrations were carried out on half of the samples of the various populations (all species, woody species, broad-leaved species, trees, broad-leaved trees, oaks, deciduous trees, and evergreen trees). The standard error of cross validation varied between 1.69 and 3.01. Predictions were carried out on the other half of the samples of each population; the standard error of prediction varied between 2.35 and 3.77, with a r2 (coefficient of determination) of 0.97 to 0.99. The calibration equations obtained from the laboratory samples were applied to samples that had decomposed in the field in litter bags. The standard error of prediction varied between 4.46 and 5.97, with a r2 of 0.90 to 0.93. Near infrared reflectance spectroscopy thus provides a direct prediction of the LMR in leaf litter of different species, during the decomposition stage studied (i.e., between 100 and 20% of litter mass remaining). The possibilities of using near infrared reflectance spectroscopy in decomposition studies are discussed.

Mehlich 3 extractable nutrients as determined by near-infrared reflectance spectroscopy

2009 ◽  
Vol 89 (5) ◽  
pp. 579-587 ◽  
Author(s):  
C Nduwamungu ◽  
N Ziadi ◽  
L -É Parent ◽  
G F Tremblay
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Soil Analysis ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Least Squares Regression ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

Near-infrared reflectance spectroscopy (NIRS) is a cost-effective and environmentally friendly technique of soil analysis that is particularly advantageous in intensive soil sampling and soil nutrient management as well. This study evaluated the potential of NIRS for predicting P, K, Ca, Mg, Cu, Zn, Mn, Fe, and Al extracted by Mehlich 3. We used 150 air-dried samples collected from a 15-ha site dominated by Orthic Humic Gleysol and Gleyed Dystric Brunisol soils. Calibration equations were developed using modified partial least squares regression. The accuracy of NIRS prediction was evaluated using the coefficient of determination (R2), the ratio of performance deviation (RPD), and the ratio of error range (RER). Reliable calibrations were found for Ca, Cu, and Mg (R2 ≥ 0.7, RPD ≥ 1.75, and RER ≥ 8). Less-reliable calibrations were found for Al, Fe, K, Mn, P, and Zn (R2 < 0.7, RPD < 1.75, and RER < 8). In the validation with independent samples, acceptable regression coefficients (i.e., 0.8 ≤ slope ≤ 1.2) were only found for Ca, Mg, and Mn. We presumed that the pH of the Mehlich 3 extractant (2.5 ± 0.1) may affect the solubility of most of these nutrients, regardless the soil texture and, consequently, the potential of NIRS to predict them. The more a nutrient was correlated to clay content, the more it was likely predictable by NIRS. The prediction models obtained for Al, Ca, Cu, Fe, K, Mg, and Mn could still be used for screening purposes in cases where high accuracy is not required. These NIRS prediction models should be validated across larger geographic areas of geological homogeneity. Key words: Soil analysis, Mehlich 3, near-infrared reflectance spectroscopy, calibration

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