The application of near infrared spectroscopy to predict faecal nitrogen and phosphorus in multiple ruminant herbivore species

2020 ◽  
Vol 42 (6) ◽  
pp. 415
Author(s):  
D. R. Tolleson ◽  
J. P. Angerer
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Coefficient Of Determination ◽  
Individual Species ◽  
Nitrogen And Phosphorus ◽  
Faecal Nitrogen ◽  
Herbivore Species ◽  
The Individual ◽  
Leave One Out

Near infrared spectroscopy (NIRS) was applied to determine faecal nitrogen and phosphorus using a temporo-spatially diverse dataset derived from multiple ruminant herbivore species (i.e. cattle, bison, deer, elk, goats, and sheep). Single-species NIRS calibrations have previously been developed to predict faecal constituents. Multi-species NIRS calibrations have previously been developed for herbivore faecal nitrogen but not for faecal phosphorus. Faecal samples representing a herd or flock composite were analysed via NIRS (400–2498nm). Calibration sets for faecal nitrogen and phosphorus were developed from: (1) all samples from all six species, (2) all cattle samples only, (3) all samples except those from bison, (4) all samples except those from deer, (5) all samples except those from elk, (6) all samples except those from goats, and (7) all samples except those from sheep. Validation sample sets included: (1) each of the individual species (predicted with a cattle only-derived calibration), and (2) each of the individual species (other than cattle) predicted with a multi-species calibration constructed from all cattle samples plus those samples from the remaining four species (i.e. ‘leave-one-out’). All multiple coefficient of determination (R2) values for faecal nitrogen calibrations were ≥0.97. Corresponding standard error of cross validation (SECV) values were ≤0.13. Validation simple coefficient of determination (r2) and standard error of prediction (SEP) of each alternate species using the cattle-derived calibration ranged from 0.76 to 0.84, and 0.28 to 0.5 respectively. Similar values for the sequential species leave-one-out validation for faecal nitrogen were 0.67 to 0.89, and 0.17 to 0.47 respectively. All R2 values for faecal phosphorus calibrations were ≥0.79; corresponding SECV were ≤0.14. Validation r2 and SEP of each alternate species using the cattle-derived phosphorus calibration were ≤0.63 and ≥0.13 respectively. Similar values for the sequential species leave-one-out validation were ≤0.66 and ≥0.22 respectively for faecal phosphorus. Multi-species faecal NIRS calibrations can be developed for monitoring applications in which determination of faecal nitrogen is appropriate, e.g. free-ranging herbivore nutrition, nitrogen deposition from animal faeces on rangelands with declining forage quality, or runoff from confined animal feeding operations. Similar calibrations for faecal phosphorus require additional research to ascertain their applicability.

scholarly journals OTIMIZAÇÃO DE CALIBRAÇÕES BASEADAS EM ESPECTROSCOPIA NO INFRAVERMELHO PRÓXIMO PARA ESTIMATIVA DE PROPRIEDADES DA MADEIRA DE Eucalyptus

FLORESTA ◽  
2010 ◽  
Vol 40 (3) ◽  
Author(s):  
Paulo Ricardo Gherardi Hein ◽  
José Tarcísio Lima ◽  
Gilles Chaix Gilles Chaix
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Laboratory Data ◽  
Wood Properties ◽  
Coefficient Of Determination ◽  
Mathematical Treatment ◽  
Least Squares Regression ◽  
Eucalyptus Wood ◽  
Prediction Density

A espectroscopia no infravermelho próximo (NIRS) é uma técnica não-destrutiva, rápida e utilizada para avaliação, caracterização e classificação de materiais, sobretudo de origem biológica. A obtenção de informações contida nos espectros NIR é complexa e requer a utilização de métodos quimiométricos. Assim, por meio de regressão multivariada, os espectros de absorbância podem ser associados às propriedades da madeira, tornando possível a sua predição em amostras desconhecidas. Existem algumas ferramentas quimiométricas que melhoram o ajuste dos modelos preditivos. Assim, o objetivo deste trabalho foi simular regressões dos mínimos quadrados parciais baseados nas informações espectrais e de laboratório e estudar a influência da aplicação de tratamentos matemáticos, do descarte de amostras anômalas e da seleção de comprimentos de onda no ajuste dos modelos para estimativa da densidade básica e do módulo de elasticidade em ensaio de compressão paralela às fibras da madeira de Eucalyptus. A aplicação da primeira e segunda derivada nos espectros, o descarte de amostras anômalas e a seleção de algumas das variáveis espectrais melhorou significativamente o ajuste do modelo, reduzindo o erro padrão e aumentando o coeficiente de determinação e a relação de desempenho do desvio.Palavras-chave:  Espectroscopia no infravermelho próximo; predição; densidade básica; MOE; madeira; Eucalyptus. AbstractOptimization of calibrations based on near infrared spectroscopy for estimation of Eucalyptus wood properties. Near infrared spectroscopy (NIRS) is a non-destructive technique used for rapid evaluation, characterization and classification of biological materials. The extraction of the information contained in the NIR spectrum is complex and requires the use of chemo metric methods. Thus, by means of multivariate regression, the absorbance spectra are correlated to wood properties, making possible the prediction in unknown samples. There are some chemo metric tools that can improve the adjustment of the predictive models. The aim of this work was to simulate partial least squares regression based on NIR spectra and laboratory data and to study the influence of the application of mathematical treatment, the removal of outliers and the wavelengths selection in the adjustment of models to estimate the density and modulus of elasticity in Eucalyptus wood. The use of the first and second derivative spectra, the disposal of outliers, and the variables selection improved significantly the model fit, reducing the standard error and increasing the coefficient of determination and the ratio of performance to deviation.Keywords: Near infrared; spectroscopy; prediction; density; MOE; wood; Eucalyptus.

Process Evaluation of Key Parameters during Plant-Field Composting Using Genetic Algorithms and Near-Infrared Spectroscopy

2012 ◽  
Vol 532-533 ◽  
pp. 202-207
Author(s):  
Guang Qun Huang ◽  
Lu Jia Han ◽  
Xiao Yan Wang
Keyword(s):  
Genetic Algorithms ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Electronic Conductivity ◽  
Chicken Manure ◽  
Total Carbon ◽  
Coefficient Of Determination ◽  
Full Spectrum ◽  
Validation Set

The nondestructive estimation of key parameters during plant-field chicken manure composting is of great importance for quality evaluation. In the process of developing regression models using near-infrared spectroscopy (NIRS), methods used for wavelength selection significantly influence on the efficiency of the calibration. This study explored the method of genetic algorithms (GAs) for selecting highly related wavelengths to improve NIRS models for moisture (Miost), pH and electronic conductivity (EC), total carbon (TC), total nitrogen (TN) and C/N ratio determination in chicken manure during composting. Based on the values of coefficient of determination in the validation set (R2) and root mean square error of prediction (RMSEP), the prediction results were evaluated as excellent for Miost, TC and TN, good for pH and EC, and approximate for C/N ratio. But GAs had better performance than using full spectrum for near-infrared spectroscopy model construction in the process of evaluating key parameters during plant-field chicken manure composting.

scholarly journals Prediction of water content and soluble solids content of ‘manalagi’ apples using near infrared spectroscopy

2021 ◽  
Vol 922 (1) ◽  
pp. 012062
Author(s):  
K Kusumiyati ◽  
Y Hadiwijaya ◽  
D Suhandy ◽  
A A Munawar
Keyword(s):  
Infrared Spectroscopy ◽  
Water Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Quality Attributes ◽  
Soluble Solids ◽  
Coefficient Of Determination ◽  
Soluble Solids Content ◽  
Orthogonal Signal Correction ◽  
Solids Content

Abstract The purpose of the research was to predict quality attributes of ‘manalagi’ apples using near infrared spectroscopy (NIRS). The desired quality attributes were water content and soluble solids content. Spectra data collection was performed at wavelength of 702 to 1065 nm using a Nirvana AG410 spectrometer. The original spectra were enhanced using orthogonal signal correction (OSC). The regression approaches used in the study were partial least squares regression (PLSR) and principal component regression (PCR). The results showed that water content prediction acquired coefficient of determination in calibration set (R2cal) of 0.81, coefficient of determination in prediction set (R2pred) of 0.61, root mean squares error of calibration set (RMSEC) of 0.009, root mean squares of prediction set (RMSEP) of 0.020, and ratio performance to deviation (RPD) of 1.62, while soluble solids content prediction displayed R2cal, R2pred, RMSEC, RMSEP, and RPD of 0.79, 0.85, 0.474, 0.420, and 2.69, respectively. These findings indicated that near infrared spectroscopy could be used as an alternative technique to predict water content and soluble solids content of ‘manalagi’ apples.

Quantification of protein in wheat using near infrared hyperspectral imaging: Performance comparison with conventional near infrared spectroscopy

2018 ◽  
Vol 26 (3) ◽  
pp. 186-195 ◽  
Author(s):  
Ana Morales-Sillero ◽  
Juan A. Fernández Pierna ◽  
George Sinnaeve ◽  
Pierre Dardenne ◽  
Vincent Baeten
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Wavelength Range ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Performance Comparison ◽  
Imaging Instrument ◽  
Leave One Out

Hyperspectral imaging is a powerful technique that combines the advantages of near infrared spectroscopy and imaging technologies. Most hyperspectral imaging studies focus on qualitative analysis, but there is growing interest in using such technique for the quantitative analysis of agro-food products in order to use them as universal tools. The overall objective of this study was to compare the performance of a hyperspectral imaging instrument with a classical near infrared instrument for predicting chemical composition. The determination of the protein content of wheat flour was selected as example. Spectra acquisition was made in individual sealed cells using two classical near infrared instruments (NIR-DS and NIR-Perstop) and a near infrared hyperspectral line-scan camera (NIR-HSI). In the latter, they were also acquired in open cells in order to study the possibility of accelerating the measurement process. Calibration models were developed using partial least squares for the full wavelength range of each individual instrument and for the common range between instruments (1120–2424 nm). The partial least squares models were validated using the “leave-one-out” cross-validation procedure and an independent validation set. The results showed that the NIR-HSI system worked as well as the classical near infrared spectrometers when a common wavelength range was used, with an r2 of 0.99 for all instruments and Root Mean Square Error in Prediction (RMSEP) values of 0.15% for NIR-HSI and NIR-DS and 0.16% for NIR-Perstop. The high residual predictive deviation values obtained (8.08 for NIR-DS, 7.92 for NIR-HSI, and 7.56 for NIR-Perstop) demonstrate the precision of the models built. In addition, the prediction performance with open cells was almost identical to that obtained with sealed cells.

Determination of gel time of prepreg in copper clad laminate industry by near infrared spectroscopy

2020 ◽  
pp. 096703352096379
Author(s):  
Qian-Fa Liu ◽  
Dan Li ◽  
Yao-De Zeng ◽  
Wei-Zhuang Huang
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Manufacturing Process ◽  
Near Infrared ◽  
Reference Method ◽  
Coefficient Of Determination ◽  
Resin Matrix ◽  
Calibration Model ◽  
Gel Time ◽  
Significant Difference

Gel time of prepreg is an important quality determinant in the manufacturing process of Copper Clad Laminate (CCL). Prepreg consists of a glass fiber reinforcement impregnated to a predetermined level with a resin matrix. In this work, near infrared spectroscopy associated with partial least squares (PLS) regression has been applied to analyse the gel time of prepreg samples in the manufacturing process. A total of 250 prepreg samples were randomly divided into a calibration set and a validation prediction set with a ratio of 4:1. The values of Root Mean Square Error of leave-one-out Cross-Validation (RMSECV) and the coefficient of determination (R2) of the calibration model was 2.95 s and 0.92 respectively, with eight PLS factors used. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. The analytical result showed that, NIR spectroscopy was a rapid, nondestructive, and accurate method for real-time prediction of prepreg quality in the CCL manufacturing process.

scholarly journals Near-Infrared Adaptive Optics Spectroscopy of Binary Brown Dwarfs HD 130948B and HD 130948C

2003 ◽  
Vol 211 ◽  
pp. 269-270
Author(s):  
M Goto ◽  
A.T. Tokunaga ◽  
M. Cushing ◽  
D. Potter ◽  
N. Kobayashi ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Binary System ◽  
Adaptive Optics ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Brown Dwarfs ◽  
Brown Dwarf ◽  
Low Mass ◽  
The Individual

We present near-infrared spectroscopy of low-mass companions in the HD 130948 system (Goto et al. 2002a). Adaptive optics on the Subaru Telescope allowed for spectroscopy of the individual components of the 0″.13 binary system. Based on a direct comparison with a series of template spectra, we determined the spectral types of HD 130948B and C to be L4 ± 1. We find they are most likely a binary brown dwarf system.

scholarly journals Using near-infrared spectroscopy to discriminate closely related species: A case study of neotropical ferns

2020 ◽  
Author(s):  
Darlem Nikerlly Amaral Paiva ◽  
Ricardo de Oliveira Perdiz ◽  
Thaís Elias Almeida
Keyword(s):  
Infrared Spectroscopy ◽  
Plant Species ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Herbarium Specimens ◽  
Correct Identification ◽  
Fern Species ◽  
Identification Of Species ◽  
Leave One Out

ABSTRACTIdentifying plant species requires considerable knowledge and can be difficult without complete specimens. Fourier-transform near-infrared spectroscopy (FT-NIR) is an effective technique for discriminating plant species, especially angiosperms. However, its efficacy has never been tested on ferns. Here we tested the accuracy of FT-NIR at discriminating species of the genus Microgramma. We obtained 16 spectral readings per individual from the adaxial and abaxial surfaces of 100 specimens belonging to 13 species. The analyses included all 1557 spectral variables. We tested different datasets (adaxial+abaxial, adaxial, and abaxial) to compare the correct identification of species through the construction of discriminant models (LDA, PLS) and cross-validation techniques (leave-one-out, K-fold). All analyses recovered an overall high percentage (>90 %) of correct predictions of specimen identifications for all datasets, regardless of the model or cross-validation used. On average, there was > 95 % accuracy when using PLS-DA and both cross-validations. Our results show the high predictive power of FT-NIR at correctly discriminating fern species when using leaves of dried herbarium specimens. The technique is sensitive enough to reflect species delimitation problems and possible hybridization, and it has the potential of helping better delimit and identify fern species.

scholarly journals Estimate of the density of Eucalyptus grandis W. Hill ex Maiden using near infrared spectroscopy

CERNE ◽  
2013 ◽  
Vol 19 (4) ◽  
pp. 647-652 ◽  
Author(s):  
Silviana Rosso ◽  
Graciela Ines Bolzon de Muniz ◽  
Jorge Luis Monteiro de Matos ◽  
Clóvis Roberto Haselein ◽  
Paulo Ricardo Gherardi Hein ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Wood Density ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Eucalyptus Grandis ◽  
Basic Density ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Old Trees

This study aimed to analyze use of near infrared spectroscopy (NIRS) to estimate wood density of Eucalyptus grandis. For that, 66 27-year-old trees were logged and central planks were removed from each log. Test pieces 2.5 x 2.5 x 5.0 cm in size were removed from the base of each plank, in the pith-bark direction, and subjected to determination of bulk and basic density at 12% moisture (dry basis), followed by spectral readings in the radial, tangential and transverse directions using a Bruker Tensor 37 infrared spectrophotometer. The calibration to estimate wood density was developed based on the matrix of spectra obtained from the radial face, containing 216 samples. The partial least squares regression to estimate bulk wood density of Eucalyptus grandis provided a coefficient of determination of validation of 0.74 and a ratio performance deviation of 2.29. Statistics relating to the predictive models had adequate magnitudes for estimating wood density from unknown samples, indicating that the above technique has potential for use in replacement of conventional testing.

Analysis of Betaines Using near Infrared Spectroscopy

2005 ◽  
Vol 13 (3) ◽  
pp. 133-138 ◽  
Author(s):  
Emma Ibarra ◽  
Omar Valencia ◽  
Héctor Pérez
Keyword(s):  
Infrared Spectroscopy ◽  
Sodium Chloride ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Accurate Method ◽  
Coefficient Of Determination ◽  
Active Matter ◽  
Surface Active Properties ◽  
Surface Active ◽  
True Values

Cocoamidopropyl betaines are becoming increasingly important in cosmetic formulations because of their mild and effective surface active properties. A simple and accurate method is needed for quality control during the production of betaines. The aim of the present study was to determine if near infrared spectroscopy can replace wet methods for routine analysis of betaine. The calibration curve was obtained by partial least squares. The optimisation of calibration factors was guided by coefficient of determination ( R2) and the root mean square error of evaluation ( RMSEE). R2 was 0.99 or higher and RMSEE 0.025, 0.071, 0.03% and 0.007 units for active matter, sodium chloride, solids and pH, respectively. The method was validated with independent samples in the same manner on a different day and true values were obtained with R2 of 0.99 or higher and root mean standard error of prediction of 0.060, 0.074, 0.075% and 0.035 units for active matter, sodium chloride, solids and pH, respectively.

Modeling Wood Density of Larch by Near-Infrared Spectrometry and Support Vector Machine

2011 ◽  
Vol 55-57 ◽  
pp. 433-438
Author(s):  
Ya Zhao Zhang ◽  
Yao Xiang Li ◽  
Hong Fu Zhang ◽  
Hui Juan Zhang ◽  
Pai Li
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Wood Density ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Model Building ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Infrared Spectrometry ◽  
Support Vector

Model for predicting wood density of Larch was established using near-infrared spectroscopy (NIR) combined with support vector machine (SVM). A hundred and seventeen Larch samples were used in the study. Wood density of samples was measured according to standard test methods for physical and mechanical properties of wood. Support vector machines for regression (SVR) was used for model building. Radial basis function (RBF) was used as kernel function to establish a model for predicting wood density. For the train set, the coefficient of determination (R2) and the mean square error (MSE) were 0.8504 and 0.6460×10-3, while the R2 and MSE was 0.8520 and 0.4451×10-3, respectively, for the test set. Results showed that using SVM in near-infrared spectroscopy calibration could significantly improve the model performance in order to rapidly and accurately predict wood density.

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