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.

Determination of Acid-Detergent Fiber and Crude Protein in Forages by Near-Infrared Reflectance Spectroscopy: Collaborative Study

1988 ◽  
Vol 71 (6) ◽  
pp. 1162-1167 ◽  
Author(s):  
Franklin E Barton II ◽  
William R Windham
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Collaborative Study ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Acid Detergent Fiber ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

Abstract A Collaborative Study Was Conducted To Determine The Standard Error Of Difference Among Laboratories For Near-Infrared Reflectance Spectroscopic (Nirs) Determination Of Acid-Detergent Fiber (Adf) And Crude Protein In Forages. The 6 Participating Laboratories Were Members Of The Usda/Ars National Near-Infrared Reflectance Spectroscopy Forage Research Project. The Nirs Calibration Equations Were Developed In The Associate Referee's Laboratory For Crude Protein And Adf And Were Transferred To The Instrument In Each Of The Other Collaborating Laboratories. The Calibration Set Included Over 650 Diverse Forage Samples With Crude Protein And Adf Calibration Data; The Validation Set Included 94 Samples Of Bermudagrass. Amonglaboratory Reproducibility For The Nirs Method, Calculated As The Relative Standard Deviation For Reproducibility (Rsdr), Was 1.14% For Adf And 0.42% For Crude Protein. The Variance Component For Among-Laboratory Variation (Coefficient Of Variation) Was 2.54% For Adf And 2.89% For Crude Protein. These Results Confirm That It Is Possible To Calibrate, Validate, And Transfer (Nirs) Equations And Data Among Laboratories For The Accurate Determination Of Adf And Crude Protein, And Thereby Demonstrate That Nirs Can Be Used As A Standard Method For The Analysis Of Forages. The Method Has Been Adopted Official First Action

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.

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

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.

scholarly journals Analysis of Grass Silage from Northwestern Spain by Near-Infrared Reflectance Spectroscopy

2002 ◽  
Vol 85 (3) ◽  
pp. 541-545 ◽  
Author(s):  
Begoña Villamarín ◽  
Esperanza Fernández ◽  
Jesus Mendéz
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Neutral Detergent Fiber ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Grass Silage ◽  
Northwestern Spain

Abstract Near-infrared reflectance spectroscopy (NIRS) was evaluated for the determination of protein, crude fiber (CF), acid detergent fiber (ADF), and neutral detergent fiber (NDF) in grass silage. Calibration equations were based on analyses of 366 samples of grass silage produced in Northwestern Spain over 4 consecutive years (1992–1995) and validated by analyses of a set of 72 silage samples harvested during 1996. Dried and ground samples were analyzed by chemical and NIRS procedures. The spectral data were analyzed by regression against a range of chemical parameters, using modified partial least-squares (MPLS) multivariate analysis in conjunction with different mathematical treatments of the spectra. For each parameter, the optimum calibration was evaluated on the basis of the coefficient of multiple determination (R2), the coefficient of simple correlation (r2), the standard error of calibration (SEC), the standard error of cross-validation (SECV), and the standard error of validation (SEV). R2 and r2 were >0.90; SEC values were 0.58, 1.04, 1.40, and 1.75; SECV values were 0.64, 1.15, 1.50, and 2.04; and SEV values were 0.56, 1.02, 1.42, and 1.80 for protein, CF, ADF, and NDF, respectively. The ratio of the standard deviation of the reference data to the SEV was >3.0 for each of the 4 parameters, which indicates that the equations can be used in routine analysis.

Application of near infrared reflectance spectroscopy to the determination of carotenoid content in tritordeum for breeding purposes

2005 ◽  
Vol 56 (1) ◽  
pp. 85 ◽  
Author(s):  
S. G. Atienza ◽  
C. M. Avila ◽  
M. C. Ramírez ◽  
A. Martín
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Cross Validation ◽  
Reflectance Spectroscopy ◽  
Carotenoid Content ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Non Destructive

For pasta production, the yellow colour, mainly caused by carotenoids, is a worldwide requirement. Hexaploid tritodeums are the amphiploids derived from the cross between Hordeum chilense and Triticum turgidum. They show a higher carotenoid content than their wheat parents. This work aimed to develop a non-destructive method for carotenoid content determination to assist the tritordeum breeding program. We assessed the ability of near infrared reflectance spectroscopy (NIRS) to predict carotenoid content in whole grains of tritordeum. In total, 285 samples were scanned by NIRS. After non-destructive NIRS scanning, the seeds were analysed for carotenoid content and a calibration equation was developed. It is characterised by a coefficient of multiple determination (R2) of 0.85. This equation was initially evaluated by cross validation showing an r2 of 0.81 and a standard error of cross validation (SECV) of 1.49. It was further evaluated using external validation with a different set of samples not included in the calibration. This analysis showed an r2 of 0.81 and a standard error of performance (SEP) of 1.51. This equation allows discrimination between low and high carotenoid content lines in a non-destructive way. These results constitute a substantial advance for tritordeum breeding programs whose final aim is to develop high carotenoid content tritordeums useful for durum wheat breeding.

scholarly journals Near Infrared Reflectance Spectroscopy and Multivariate Analyses for Fast and Non-Destructive Prediction of Corn Seed Germination

2020 ◽  
Vol 8 (8) ◽  
pp. 1636-1642
Author(s):  
Nafiz Çeliktaş ◽  
Ömer Konuşkan
Keyword(s):  
Seed Germination ◽  
Standard Error ◽  
Near Infrared ◽  
Germination Rate ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Corn Seed ◽  
Infrared Reflectance Spectroscopy

The application of near-infrared reflectance spectroscopy (NIRS) and multivariate analysis for determining the seed germination rate of corn genotypes was assessed. Seed samples about 90 gr belong to commercial and local corn varieties at various ages were scanned with FT-NIRS on the reflectance mode from 1000 to 2500 nm wavelength. Filter paper technique showed the seed germination rates varied between 18-100% depending on the genotypes after 7 days at ±25°C. Partial least squares regression (PLSR) was applied to the reference values corresponding to the spectra. The best statistical results obtained from the pre-treatment combinations of Smooth Savitzky-Golay 9 Points (sg9), MSC full and normalization to unit length (nle). The regression coefficient of calibration (R2C) and prediction (R2P) of the created NIRS calibration via chemometric software NIRCal are realized 0.97 and 0.98 respectively for the property of corn germination rate. The standard error of both calibration (SEC) and prediction (SEP) were almost overlapping (4.17%, 4.61% respectively). The prediction accuracy of the final NIRS model was quite reasonable with the acceptable root mean standard error of prediction (RMSEP) as 8.88%. According to the residual predictive deviation (RPD) index (4.18), the accuracy of the NIRS model regarded as in the best category. Therefore, the NIRS model developed here is sufficient to predict the corn seed germination rate very fast and non-destructively without using any regents.

scholarly journals Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy

2021 ◽  
pp. 096703352110075
Author(s):  
Adou Emmanuel Ehounou ◽  
Denis Cornet ◽  
Lucienne Desfontaines ◽  
Carine Marie-Magdeleine ◽  
Erick Maledon ◽  
...  
Keyword(s):  
High Throughput ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Quality Traits ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Texture Attributes

Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.

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