scholarly journals 11 The effect of tissue depth and day of healing on effectiveness of near infrared spectroscopy to predict quantitative differences in histological imagery of superficial burn scars in cattle

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 14-15
Author(s):  
Douglas R Tolleson ◽  
Laura Bryan ◽  
Canaan Whitfield ◽  
Thomas Hairgrove
Keyword(s):  
Near Infrared ◽  
Bos Taurus ◽  
Healing Process ◽  
Scatter Correction ◽  
Diagnostic Technique ◽  
Predictive Ability ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Burn Scar ◽  
Near Infrared Spectra

Abstract Hot-iron branding is a common method of cattle identification. The branding procedure results in a burn scar that once healed, provides a permanent mark. Near infrared reflectance spectroscopy (NIRS) has been applied to monitor healing and determine age in cattle burn scars. The ability of NIRS to quantify histological characteristics of cattle burn scars has not been reported. The objective of this study was to evaluate the effectiveness of portable NIRS as a non-invasive chute-side diagnostic tool to detect histological changes in hot-iron brand burn scars during the healing process. Near infrared spectra (1100–1700nm) were obtained from burn scars on 15 Bos taurus cross steers (~ 270 d old; 238 ± 7 kg) at 21, 32, and 51 d post-branding. Spectra were obtained on branded (n = 3) and unbranded (n = 3) skin from each animal, each date. Skin punch biopsies (8 mm, n = 3 per animal) were obtained from a unique subset (n = 5) of animals at each date (n = 44 total). Spectra were analyzed as log 1/reflectance with 1st derivative and scatter correction applied. Skin tissue was preserved in ethanol and subjected to standard histological staining techniques at cutaneous, transitional, and sub-cutaneous layers. Relationships between spectra and histological values were determined by partial least squares regression. NIRS successfully predicted (P < 0.001) Gomori values for the total calibration (RSQ = 0.80; RMSE = 0.20). Success of Gomori calibrations varied by day of healing (d 21, NS; d 32, P < 0.001; d 51 NS). Other staining calibrations were also significant (P < 0.05) but not deemed useful for predicting quantitative histological values (RSQ < 0.5). Predictive ability generally declined with depth. Portable NIRS may prove useful as a chute-side histological diagnostic technique but requires further evaluation.

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.

scholarly journals 121 Non-invasive monitoring of hot-iron brands on growing cattle

2019 ◽  
Vol 97 (Supplement_1) ◽  
pp. 49-50
Author(s):  
Douglas R Tolleson ◽  
Erika Campbell ◽  
Nick Garza ◽  
Robert Moen
Keyword(s):  
Near Infrared ◽  
Bos Taurus ◽  
Normal Skin ◽  
Healing Process ◽  
Type Iii Collagen ◽  
Type I ◽  
Invasive Monitoring ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Non Invasive

Abstract Hot-iron branding is a traditional form of permanent cattle identification that produces a scar. There is a need for science-based determination of cattle brand age through non-invasive monitoring of the healing process. Near infrared reflectance spectroscopy (NIRS) has been used by medical forensic scientists to obtain such information. Healing of cutaneous injury involves inflammatory (1 to 3 d), proliferative (4 to 21 d), and remodeling (22 to 365 d) phases. Collagen changes as burn scars mature, i.e. there is an increase in the Type I/ Type III collagen ratio compared to normal skin. The altered ratio is evident in a transformation of collagen from a basketweave arrangement to small parallel bundles. During the remodeling phase, due largely to the Type I/III collagen ratio, scar tissue becomes visibly different than un-injured skin. Previous research has examined the differences in hot-iron brands applied to nursing (~30 d old) Bos taurus cross calves at 0, 33, and 153 d post-branding. Our objective was to continue this research by obtaining near infrared spectra (11001700nm) on hot-iron brands applied to 15 weaned (~ 270 d old; 238 ± 7kg) Bos taurus cross steers at 21, 32, and 51 d post-branding. Spectra were obtained on branded (n = 3) and unbranded (n = 3) skin tissue from each animal, each date. Hair was clipped to < 5mm on unbranded skin. Spectra were analyzed as log 1/reflectance with 1st derivative and scatter correction applied. Linear discriminant analysis and regression procedures were applied to distinguish between brand treatment and date post-branding. Spectra from branded and unbranded skin were correctly (P < 0.05) identified at ~95%. Brand age was predicted successfully (P < 0.05) but not accurately enough for forensic application (RSQ = 0.50; RMSE = 9.75 d). The NIRS technique can discriminate differences in the age of cattle brand scars but numerical prediction requires further investigation.

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.

Foliar Damage Assessment and Selection of Cold Resistant Genotypes Using near Infrared Spectra of Eucalyptus Globulus Leaves

2009 ◽  
Vol 17 (4) ◽  
pp. 223-231 ◽  
Author(s):  
Rosario del P. Castillo ◽  
David Contreras ◽  
Matthias Otto ◽  
Jaime Baeza ◽  
Juanita Freer
Keyword(s):  
Eucalyptus Globulus ◽  
Near Infrared ◽  
Cold Resistance ◽  
External Validation ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Near Infrared Spectra ◽  
Orthogonal Signal Correction ◽  
Foliar Damage ◽  
Reference Parameters

Near infrared (NIR) spectroscopy was used to predict cold resistance in Eucalyptus globulus genotypes after acclimatation treatments at low temperatures. Branches of the genotypes were maintained during 31 days in three cold chambers and the NIR spectra of milled leaves were obtained. The samples were subsequently exposed to artificial freezing (with some branches exposed to −2°C) and the foliar damage was assessed by visually estimating the necrotic area of each leaf. These values were used as reference parameters to evaluate cold resistance in the genotypes. A partial least squares (PLS) method was performed using the foliar damage and the NIR spectra of leaves. Spectra were treated with multiplicative scatter correction (MSC) and orthogonal signal correction (OSC). An excellent model was achieved which predicted foliar damage in the genotypes with a low standard error of prediction (3.5%), a high regression coefficient in cross-validation and external validation ( r > 0.9) and a high percentage of the variance explained by the spectra (95.4%). Furthermore, a pattern recognition method, using a regularised discriminant analysis (RDA) of the scores matrix obtained in PLS, in a denominated PLS/RDA on scores strategy, was applied directly to the spectra to classify each genotype as tolerant or sensitive; 100% of the genotypes were correctly assigned. These results demonstrate the advantages of using the NIR spectra of leaves as a rapid, nondestructive tool to evaluate cold resistance in genotypes.

scholarly journals Using Near-infrared reflectance spectroscopy (NIRS) to predict glucobrassicin concentrations in cabbage and brussels sprout leaf tissue

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Ilse E. Renner ◽  
Vincent A Fritz
Keyword(s):  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Leaf Tissue ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Least Squares Regression ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Brussels Sprouts ◽  
Freeze Dried

Abstract Background Glucobrassicin (GBS) and its hydrolysis product indole-3-carbinol are important nutritional constituents implicated in cancer chemoprevention. Dietary consumption of vegetables sources of GBS, such as cabbage and Brussels sprouts, is linked to tumor suppression, carcinogen excretion, and cancer-risk reduction. High-performance liquid-chromatography (HPLC) is the current standard GBS identification method, and quantification is based on UV-light absorption in comparison to known standards or via mass spectrometry. These analytical techniques require expensive equipment, trained laboratory personnel, hazardous chemicals, and they are labor intensive. A rapid, nondestructive, inexpensive quantification method is needed to accelerate the adoption of GBS-enhancing production systems. Such an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties. Near-infrared reflectance spectroscopy (NIRS) paired with partial least squares regression (PLSR) could be a useful tool to develop such a method. Results Here we demonstrate that GBS concentrations of freeze-dried tissue from a wide variety of cabbage and Brussels sprouts can be predicted using partial least squares regression from NIRS data generated from wavelengths between 950 and 1650 nm. Cross-validation models had R2 = 0.75 with RPD = 2.3 for predicting µmol GBS·100 g−1 fresh weight and R2 = 0.80 with RPD = 2.4 for predicting µmol GBS·g−1 dry weight. Inspections of equation loadings suggest the molecular associations used in modeling may be due to first overtones from O–H stretching and/or N–H stretching of amines. Conclusions A calibration model suitable for screening GBS concentration of freeze-dried leaf tissue using NIRS-generated data paired with PLSR can be created for cabbage and Brussels sprouts. Optimal NIRS wavelength ranges for calibration remain an open question.

Prediction of diet quality for sheep from faecal characteristics: comparison of near-infrared spectroscopy and conventional chemistry predictive models

2015 ◽  
Vol 55 (1) ◽  
pp. 1 ◽  
Author(s):  
D. G. Kneebone ◽  
G. McL. Dryden
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Predictive Ability ◽  
Validation Dataset ◽  
Least Squares Regression ◽  
Prediction Equations ◽  
Faecal Excretion ◽  
Excretion Rates

This study evaluated the ability of equations developed from the analysis of faecal material by conventional chemistry (F.CHEM), and by near-infrared spectroscopy (F.NIRS), to predict intake and digestibility of forages fed with or without supplements. In vivo datasets were obtained using 30 sheep and 25 diets to provide 124 diet–faecal pairs, with each sheep fed four or five of the diets. The diets were five forages fed alone or with urea, molasses, cottonseed meal or sorghum grain supplements. Ninety-nine diet–faecal pairs were selected at random, but ensuring that all diets were represented and both the F.CHEM and F.NIRS prediction equations were developed from this dataset. The remaining 25 diet–faecal pairs were used as a validation dataset. Regressions for F.CHEM were developed by stepwise regression, and F.NIRS prediction equations were developed by partial least-squares regression. Prediction equations based solely on faecal analyte concentrations (F.CHEMc) had poor predictive ability, and models incorporating faecal constituent excretion rates (F.CHEMe) were the best at predicting feed constituent intakes. These models had slightly lower standard errors of prediction (SEP) for organic matter (OM) intake and digestible OM intake compared with the F.NIRS models that did not include faecal excretion rates. However, F.NIRS models had lower SEP for protein intake and OM digestibility. Good agreement between the F.CHEMe and F.NIRS methods was evident (according to the 95% limits-of-agreement test), and both predicted the reference values precisely and with small bias. Equations derived from a dataset that included representatives of all diets used in the experiment gave much better prediction of diet characteristics than those developed from a dataset constructed entirely at random. Equations for F.NIRS developed in this way successfully predicted the characteristics of diets that included forages fed alone and with the type of supplements used in tropical Australia.

scholarly journals The Nature of Silanol Groups on the Surfaces of Silica, Modified Silica and Some Silica Based Materials

2014 ◽  
Vol 998-999 ◽  
pp. 3-10 ◽  
Author(s):  
Alfred A. Christy
Keyword(s):  
Silica Gel ◽  
Near Infrared ◽  
Water Molecules ◽  
Modified Silica ◽  
Near Infrared Reflectance ◽  
Modified Silica Gel ◽  
Silanol Groups ◽  
Near Infrared Spectra ◽  
Chemically Modified Silica ◽  
Adsorption Of Water

Silica gel, a material that is produced from the condensation polymerisation of silicic acid, contains surface silanol groups formed during the condensation. The silanol groups on the surface are mostly of free and vicinal silanol groups. These silanol groups can be modified in several different ways. Thermal treatment and hydrothermal treatment can be carried out to alter the concentration proportions between free and hydrogen bonded silanol groups on the surface. They can also be chemically treated with suitable chlorosilanes to modify the silanol groups into polar or non polar materials that can be used in separation science.This article explores the chemical nature of silanol groups on the surfaces of different materials. Near infrared reflectance spectroscopy was used as the instrumental technique in this study. The silanol groups classifications were made by analyzing the near infrared spectra obtained during the adsorption of water molecules. Absorption of the combination frequencies of water molecules in the region 5500- 5000 cm-1were used in characterizing the silanol groups on the surfaces. Second derivative technique was employed in the resolution and detailed analysis of these absorptions.The study reveals that the materials contain free, vicinal and gem silanol groups. Silica gel contains free and vicinal silanol groups, thermally treated silica gel contains fewer vicinal silanol groups compared to the base silica gel, and hydrothermally treated silica gel contains higher concentrations of vicinal silanol groups compared to the base silica gel. Furthermore, the chemically modified silica gel contains vicinal or geminal silanol groups depending on the type of functionality introduced.

Near-Infrared Reflectance Spectroscopy: Different Strategies for Local Calibrations in Analyses of Forage Quality

1993 ◽  
Vol 47 (4) ◽  
pp. 463-469 ◽  
Author(s):  
Are Halvor Aastveit ◽  
Petter Marum
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Calibration Method ◽  
Neutral Detergent Fiber ◽  
Optimum Number ◽  
Local Regression ◽  
Near Infrared Reflectance ◽  
Data Set ◽  
Positive Effects

This paper deals with the problem of how to utilize a large calibration set with 10 different analytes in order to make the best predictions possible on a routine basis. Ten different strategies of using the data set were studied with the use of numbers of principal components ranging from 4 to 12. We found positive effects of scatter correction for most of the analytes. On average, the local regression methods were superior to the others. The optimum number of samples for local regression seems to be between 50 and 100. The largest reduction in root mean square error of prediction (RMSEP), in comparison to results for the traditional method, was found on scatter-corrected spectra and a proposed local calibration with 50 calibration samples. The gain in RMSEP for neutral detergent fiber (NDF), acid detergent fiber (ADF), and crude fiber was about 25% and for protein and in vitro digestible dry matter digestibility (IVDMD) about 10%, compared to results for the traditional universal calibration method.

Discrete Wavelet Transform on Near-Infrared Spectra Analysis for Moisture Determination in Lignitic Coal Samples

2014 ◽  
Vol 513-517 ◽  
pp. 319-322
Author(s):  
Xiao Li Yang ◽  
Neng Bang Hou ◽  
Jun You Shi ◽  
Yan Fang Li
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Near Infrared ◽  
Processing Method ◽  
Discrete Wavelet ◽  
Moisture Determination ◽  
Least Squares Regression ◽  
Spectra Analysis ◽  
Near Infrared Spectra ◽  
Compressed Data

We studied moisture determination in lignitic coal samples through near-infrared (NIR) technique. This research was developed by applying partical least squares regression (PLS) and discrete wavelet transform (DWT). Firstly, the NIR spectra were pre-processed by DWT for fitting and compression. Then, the compressed data were used to build regression model with PLS for moisture determination in coal samples. Three type DWTs were investigated.Determination performance at different resolution scales was studied. The results show that DWT is a very efficient pre-processing method for NIR spectra analysis.

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