Analyses of Forest Foliage III: Determining Nitrogen, Lignin and Cellulose in Fresh Leaves Using near Infrared Reflectance Data

1994 ◽  
Vol 2 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Mary E. Martin ◽  
John D. Aber
Keyword(s):  
Near Infrared ◽  
Linear Regression Analysis ◽  
Laboratory Data ◽  
Multiple Linear Regression Analysis ◽  
Near Infrared Reflectance ◽  
Fresh Leaf ◽  
Near Infrared Spectra ◽  
Calibration Equations ◽  
Reflectance Data ◽  
Leaf Biochemistry

Near infrared laboratory data of whole fresh leaves were evaluated with respect to leaf chemical composition. Near infrared spectra were measured for 211 foliage samples including both broad- and needle-leaved species. A multiple linear regression analysis was used to determine if reflectance data from fresh leaf samples contains information on nitrogen, lignin and cellulose concentrations. Calibration equations were developed for all three leaf constituents, indicating that information on leaf biochemistry is present in the spectra of fresh as well as dried, ground leaf samples.

Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy

1991 ◽  
Vol 31 (2) ◽  
pp. 205 ◽  
Author(s):  
KF Smith ◽  
PC Flinn
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Linear Regression Analysis ◽  
Spectral Characteristics ◽  
Nir Spectroscopy ◽  
Cost Effective ◽  
Multiple Linear Regression Analysis ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance

Near infrared reflectance (NIR) spectroscopy is a rapid and cost-effective method for the measurement of organic constituents of agricultural products. NIR is widely used to measure feed quality around the world and is gaining acceptance in Australia. This study describes the development of an NIR calibration to measure crude protein (CP), predicted in vivo dry matter digestibility (IVDMD) and neutral detergent fibre (NDF) in temperate pasture species grown in south-western Victoria. A subset of 116 samples was selected on the basis of spectral characteristics from 461 pasture samples grown in 1987-89. Several grass and legume species were present in the population. Stepwise multiple linear regression analysis was used on the 116 samples to develop calibration equations with standard errors of 0.8,2.3 and 2.2% for CP, NDF and IVDMD, respectively. When these equations were tested on 2 independent pasture populations, a significant bias existed between NIR and reference values for 2 constituents in each population, indicating that the calibration samples did not adequately represent the new populations for these constituents. The results also showed that the H statistic alone was inadequate as an indicator of equation performance. It was confirmed that it was possible to develop a broad-based calibration to measure accurately the nutritive value of closed populations of temperate pasture species. For the resulting equations to be used for analysis of other populations, however, they must be monitored by comparing reference and NIR analyses on a small number of samples to check for the presence of bias or a significant increase in unexplained error.

Determination of Moisture in Cheddar Cheese by Near Infrared Reflectance Spectroscopy

1988 ◽  
Vol 71 (3) ◽  
pp. 571-574 ◽  
Author(s):  
Randy L Wehling ◽  
Michelle M Pierce
Keyword(s):  
Near Infrared ◽  
Spectral Response ◽  
Linear Regression Analysis ◽  
Nir Spectroscopy ◽  
Cheddar Cheese ◽  
Multiple Linear Regression Analysis ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Wide Range ◽  
Validation Set

Abstract Near infrared reflectance (NIR) spectroscopy was used to determine the moisture content of Cheddar cheese. Through multiple linear regression analysis, a 3-wavelength calibration was developed for use with a commercial filter monochromator instrument. For a validation set of 47 samples, the correlation coefficient squared (r2) between the NIR and oven moisture methods was 0.92, with a standard error of performance (SEP) of 0.38%. Sample temperature was found to significantly affect the spectral response; therefore, it was necessary to equilibrate all samples to a uniform temperature prior to NIR analysis. Aging may also affect the NIR characteristics of cheese, although it was possible to develop a successful calibration that encompassed a wide range of aging times

Factors affecting the intake of grass silage by cattle and prediction of silage intake

1998 ◽  
Vol 66 (1) ◽  
pp. 115-127 ◽  
Author(s):  
R. W. J. Steen ◽  
F. J. Gordon ◽  
L. E. R. Dawson ◽  
R. S. Park ◽  
C. S. Mayne ◽  
...  
Keyword(s):  
Near Infrared ◽  
Linear Regression Analysis ◽  
Live Weight ◽  
Multiple Linear Regression Analysis ◽  
Ammonia Nitrogen ◽  
Beef Steers ◽  
Near Infrared Reflectance ◽  
Factors Affecting ◽  
Digestible Organic Matter

AbstractA partially balanced change-over design experiment involving 192 beef steers, which were initially 14 months old and 415 kg live weight, was carried out to determine the intakes of 136 silages from commercial farms in Northern Ireland. Each silage was offered ad libitum as the sole food to 10 animals, with eight silages offered in each of 17 periods over 2 years. A standard grass hay was offered to 16 animals in each period to enable period effects on intake to be removed. Detailed chemical and biological compositions of the silages were also determined. The ranges for pH and dry matter (DM), crude protein, ammonia-nitrogen and apparent digestible organic matter (in vivo) concentrations in the silages and silage dry DM intakes were 3·50 to 5·49 (s.d. 0·396); 155 to 413 (s.d. 43·1) g/kg; 79 to 212 (s.d. 24·4) g/kg DM; 45 to 384 (s.d. 63·2) g/kg total nitrogen; 528 to 769 (s.d. 58) g/kg DM and 4·3 to 10·9 (s.d. 1·13) kg/day respectively. Relationships between intake and individual parameters or groups of parameters have been developed using simple and multiple linear regression analysis and partial least-squares analyses. Silage intake was closely related to factors which influence the extent of digestion and rate of passage of the material through the animal, as indicated by the strong relationships (R2 of regressions = 0·28 to 0·50) with in vivo apparent digestibility and rumen degradability and the concentrations of the fibre and nitrogen factors. Intake was poorly correlated with factors such as pH, total acidity, buffering capacity and the concentrations of lactic, acetic and butyric acids (R2 of regressions = zero to 0·11). Near infrared reflectance spectrometry (NIRS) provided the best fit relationship with intake (R2 of relationship = 0·90). The results also indicate that the intake potential of silages can be directly predicted with a high degree of accuracy from the NIRS of both dried and undried samples of silage, provided the appropriate sample preparation and scanning methods are used.

Nondestructive Analysis for Nitrogens of Soils by near Infrared Reflectance Spectroscopy

1998 ◽  
Vol 6 (A) ◽  
pp. A87-A91 ◽  
Author(s):  
R. K. Cho ◽  
G. Lin ◽  
Y. K. Kwon
Keyword(s):  
Near Infrared ◽  
Linear Regression Analysis ◽  
Nir Spectroscopy ◽  
Multiple Linear Regression Analysis ◽  
Soil Samples ◽  
Near Infrared Reflectance ◽  
Available Nitrogen ◽  
Destructive Analysis ◽  
Filter Type ◽  
Non Destructive

Traditional wet analytical method such as the gravimetric, Kjeldahl or Walkley-Black method are still the most widely used for determining the organic matter (OM), moisture and notal nitrogen (T–N) content of soils. However, these are time-consuming, high in cost and labour intensive as well producing harmfull pollutants making the method undesirable for field measurement. Over three years we have been working on the development of a non-destructive on-site analyser for measuring OM, moisture and T–N. In this research we investigated the possibility of using near infrared (NIR) spectroscopy for the non-destructive analysis of T–N, inorganic and available nitrogen in domestic soil samples.85 soil samples of upland over the Kyungpook prefecture were colledted to make a calibration and validation. Dried soil samples were packed in the closed-cup and the NIR spectra data was measured from 1100–2500 nm using a scanning type NIR instrum, InfraAlyzer 500 and filter type NIR instrument, InfraAlyzer 400, which has a modified sample compartment. Multiple linear regression analysis between the content of soil properties determined by the traditional method and the NIR spectral data were conducted to develop an non-destructive analysing equation for T–N, inorganic and available nitrogen. In the case of the scanning type, the standard error or prediction were 0.028%. 1.7 mg−1 and 1.1 mg−1 for T–N, inorganic and available nitrogen respectively. The prediction results in the filter type appeared to have the same accuracy as the scanning type. It is concluded that NIR spectroscopy could be used to predict soil nitrogen compounds such as total nitrogen, inorganic and available nitrogen non-destructively.

scholarly journals Dermacozine N, the First Natural Linear Pentacyclic Oxazinophenazine with UV–Vis Absorption Maxima in the Near Infrared Region, Along with Dermacozines O and P Isolated from the Mariana Trench Sediment Strain Dermacoccus abyssi MT 1.1T

Marine Drugs ◽  
2021 ◽  
Vol 19 (6) ◽  
pp. 325
Author(s):  
Bertalan Juhasz ◽  
Dawrin Pech-Puch ◽  
Jioji N. Tabudravu ◽  
Bastien Cautain ◽  
Fernando Reyes ◽  
...  
Keyword(s):  
Linear Regression ◽  
Density Functional ◽  
Near Infrared ◽  
Linear Regression Analysis ◽  
2D Nmr ◽  
Multiple Linear Regression Analysis ◽  
Infrared Region ◽  
Cellular Level ◽  
Mariana Trench ◽  
Uv Visible

Three dermacozines, dermacozines N–P (1–3), were isolated from the piezotolerant Actinomycete strain Dermacoccus abyssi MT 1.1T, which was isolated from a Mariana Trench sediment in 2006. Herein, we report the elucidation of their structures using a combination of 1D/2D NMR, LC-HRESI-MSn, UV–Visible, and IR spectroscopy. Further confirmation of the structures was achieved through the analysis of data from density functional theory (DFT)–UV–Visible spectral calculations and statistical analysis such as two tailed t-test, linear regression-, and multiple linear regression analysis applied to either solely experimental or to experimental and calculated 13C-NMR chemical shift data. Dermacozine N (1) bears a novel linear pentacyclic phenoxazine framework that has never been reported as a natural product. Dermacozine O (2) is a constitutional isomer of the known dermacozine F while dermacozine P (3) is 8-benzoyl-6-carbamoylphenazine-1-carboxylic acid. Dermacozine N (1) is unique among phenoxazines due to its near infrared (NIR) absorption maxima, which would make this compound an excellent candidate for research in biosensing chemistry, photodynamic therapy (PDT), opto-electronic applications, and metabolic mapping at the cellular level. Furthermore, dermacozine N (1) possesses weak cytotoxic activity against melanoma (A2058) and hepatocellular carcinoma cells (HepG2) with IC50 values of 51 and 38 μM, respectively.

scholarly journals Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage

2020 ◽  
Vol 12 (12) ◽  
pp. 5050
Author(s):  
Katarzyna Szwedziak ◽  
Ewa Polańczyk ◽  
Żaneta Grzywacz ◽  
Gniewko Niedbała ◽  
Wiktoria Wojtkiewicz
Keyword(s):  
Total Protein ◽  
Near Infrared ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Quality Parameters ◽  
Neural Modeling ◽  
Infrared Spectrometry ◽  
Stored Grain ◽  
Stored Wheat

An important requirement in the grain industry is to obtain fast information on the quality of purchased and stored grain. Therefore, it is of great importance to search for innovative solutions aimed at the monitoring and fast assessment of quality parameters of stored wheat The results of the evaluation of total protein, water and gluten content by means of near infrared spectrometry are presented in the paper. Multiple linear regression analysis (MLR) and neural modeling were used to analyze the obtained results. The results obtained show no significant changes in total protein (13.13 ± 0.15), water (10.63 ± 0.16) or gluten (30.56 ± 0.54) content during storage. On the basis of the collected data, a model artificial neural network (ANN) MLP 52-6-3 was created, which, with the use of four independent features, allows us to determine changes in the content of water, protein and gluten in stored wheat. The chosen network returned good error values: learning, below 0.001; testing, 0.015; and validation, 0.008. The obtained results and their interpretation are an important element in the warehouse industry. The information obtained in this way about the state of the quality of stored grain will allow for a fast reaction in case of the threat of lowering the quality parameters of the stored grain.

Determination of Particle Size in Powders by Scatter Correction in Diffuse Near-Infrared Reflectance

1988 ◽  
Vol 42 (5) ◽  
pp. 722-728 ◽  
Author(s):  
J. L. Ilari ◽  
H. Martens ◽  
T. Isaksson
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Scatter Correction ◽  
Large Degree ◽  
Chemical Compositions ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Analytical Technique ◽  
Scattering Effects ◽  
Reflectance Data

Diffuse near-infrared reflectance spectroscopy has traditionally been an analytical technique for determining chemical compositions in a sample. We will, in this paper, focus on light scattering effects and their ability to determine the mean particle sizes of powders. The reflectance data of NaCl, broken glass, and sorbitol powders are linearized and submitted to the Multiplicative Scatter Correction (MSC), and the ensuing parameters are used in subsequent multivariate calibrations. The results indicate that particle size can, to a large degree, be determined from NIR reflectance data for a given type of powder. Up to 99% of the partical size variance was explained by the regression.

On the Scales Associated with Near-Infrared Reflectance Difference Spectra

1995 ◽  
Vol 49 (6) ◽  
pp. 765-772 ◽  
Author(s):  
M. S. Dhanoa ◽  
S. J. Lister ◽  
R. J. Barnes
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
The Other ◽  
Difference Spectra ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Near Infrared Spectra ◽  
Standard Normal ◽  
Relationship Of ◽  
The Relationship

Scale differences of individual near-infrared spectra are identified when set-independent standard normal variate (SNV) and de-trend (DT) transformations are applied in either SNV followed by DT or DT then SNV order. The relationship of set-dependent multiplicative scatter correction (MSC) to SNV is also referred to. A simple correction factor is proposed to convert derived spectra from one order to the other. It is suggested that the suitable order for the study of changes using difference spectra (when removing baselines) should be DT followed by SNV, which leads to all derived spectra on the scale of mean zero and variance equal to one. If baselines are identical, then SNV scale spectra can be used to calculate differences.

A comparison of sample preparation and calibration techniques for the estimation of nitrogen, oil and glucosinolate content of rapeseed by near infrared spectroscopy

1985 ◽  
Vol 104 (2) ◽  
pp. 317-323 ◽  
Author(s):  
Carol Starr ◽  
Janet Suttle ◽  
A. G. Morgan ◽  
D. B. Smith
Keyword(s):  
Stepwise Regression ◽  
Near Infrared ◽  
Glucosinolate Content ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Reflectance Analysis ◽  
Glucosinolate Concentration ◽  
Forward Stepwise ◽  
Calibration Techniques ◽  
Reflectance Data

SummaryPredictions of nitrogen, oil and glucosinolate concentration in rapeseed samples were made by near infrared reflectance analysis after various grinding treatments. Also examined were the effects of normalizing reflectance data and the possible advantage of using all combinations of two and three wavelengths in the calibration regression analysis over forward stepwise regression. The main conclusion was that drying the samples prior to a controlled grinding treatment gave the best results, although acceptable results for selection purposes could be obtained using whole seeds to predict nitrogen and oil. None of the treatments of the seed or reflectance data allowed acceptable prediction of glucosinolate content.

Measurement of the magnesium concentration in perennial ryegrass (Lolium perenne) using near infrared reflectance spectroscopy

1991 ◽  
Vol 42 (8) ◽  
pp. 1399 ◽  
Author(s):  
KF Smith ◽  
SE Willis ◽  
PC Flinn
Keyword(s):  
Lolium Perenne ◽  
Perennial Ryegrass ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Magnesium Concentration ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Calibration Equations

Near infrared reflectance spectroscopy (NIR) was used to develop calibration equations to measure the magnesium concentration in perennial ryegrass herbage (Lolium perenne). A subset of 72 samples was selected on the basis of spectral variation from 400 samples grown in 1988-1989. Three alternative equations were chosen using stepwise multiple linear regression, with standard errors ranging from 0.4 to 0.3 g/kg DM with corresponding squared multiple correlation coefficients ( R2) of 0.68 to 0.82. The equations had 2, 4 and 4 wavelength terms respectively. When these equations were tested on an independent population of perennial ryegrass samples, a significant bias existed when using the 4 term equations but there was no bias when the 2 term equation was used. We conclude that NIR can be used to screen large numbers of perennial ryegrass plants for magnesium concentration. However, for the calibration equations to be used for the analysis of other populations equation performance must be monitored by comparing reference and NIR analyses on a small number of samples.

Sign in / Sign up

Export Citation Format

Share Document