Factors Influencing the Calibration of near Infrared Reflectometry Applied to the Assessment of Total Nitrogen in Potato Tissues. I. Particle Size, Milling Speed and Leaf Senescence

1995 ◽  
Vol 3 (3) ◽  
pp. 155-166 ◽  
Author(s):  
Donald K.L. MacKerron ◽  
Mark W. Young ◽  
Howard V. Davies
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Important Variable ◽  
Calibration Model ◽  
Size Classes ◽  
Factors Influencing ◽  
Large Numbers ◽  
Different Tissues ◽  
Separate Calibration

Several factors thought to modify the calibration between nitrogen concentration, [N]NIR and [N]Dumas, were examined including particle size using size-classes from the distribution produced by milling through a standard screen. The standard milling procedure produced differing distributions of particle size from different tissues, [N] differed between size classes and the pattern of [N] with particle size differed between tissues. In leaf and stem the smallest particles had the highest [N], in tuber material they had the lowest. Samples for analysis are generally milled in batches and analysed later. These findings show that it is important to ensure that samples are well-mixed and that they are not allowed to stratify. Only well-mixed samples should be used to fill the sample cups on a near infrared (NIR) analyser. A divergence in the relations between [N]NIR and [N]Dumas for stem samples implies that the calibration model used for that material might be particularly sensitive to particle size distribution. Within the range examined, milling speed was not an important variable in the preparation of leaf and tuber material. There is little to be gained from treating small quantities of senescent leaves separately from the remainder of a sample. However, if [N] is to be assessed in large numbers of samples of leaves that are mostly senescent then a separate calibration should be derived.

Factors Influencing the Calibration of near Infrared Reflectometry Applied to the Assessment of Total Nitrogen in Potato. II. Operator, Moisture and Maturity Class

1995 ◽  
Vol 3 (3) ◽  
pp. 167-174 ◽  
Author(s):  
Mark W. Young ◽  
Donald K.L. MacKerron ◽  
Howard V. Davies
Keyword(s):  
Field Experiment ◽  
Spectral Data ◽  
Total Nitrogen ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Moisture Contents ◽  
Factors Influencing ◽  
Maturity Class ◽  
The One ◽  
Low Nitrogen

Oven dried samples of leaf stem and tuber material taken from a nitrogen field experiment were analysed by Dumas combustion when fresh and by near infrared (NIR) then, and in the next two years, by a number of operators who made estimates of nitrogen concentration, [N]NIR, with differing degrees of error. The errors differed between years in the case of the one operator who made estimates in two years. Leaf, stem and tuber material of high and low nitrogen concentration were treated to produce samples at various moisture contents. These samples were scanned by NIR and the spectral data were examined. Higher moisture was found to decrease the reflectance at all the wavelengths used and would, therefore, introduce error into [N]NIR estimates. The NIR calibration used was found to be applicable to cultivars in a range of maturity classes. Several recommendations are made that will help to minimise the error introduced into [N]NIR estimates from various sources.

Particle counting as a tool to predict filterability in membrane bioreactors activated sludge?

2011 ◽  
Vol 64 (1) ◽  
pp. 139-146 ◽  
Author(s):  
M. Lousada-Ferreira ◽  
A. Moreau ◽  
J. B. van Lier ◽  
J. H. J. M. van der Graaf
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Activated Sludge ◽  
Membrane Bioreactors ◽  
Submicron Particles ◽  
Factors Influencing ◽  
Particle Counting ◽  
Large Numbers ◽  
Flux Decline ◽  
Major Factors

Activated sludge quality is one of the major factors influencing flux decline in membrane bioreactors (MBRs). Sludge filterability is a recognized parameter to characterize the physical properties of activated sludge. Decrease in filterability is linked to a higher number of submicron particles. In our present research we studied whether particle counting techniques can be used to indicate deflocculation of the sludge suspended fraction to submicron particles, causing the aforementioned filterability decrease. A total number of 105 activated sludge samples were collected in four full scale municipal MBRs. Samples were tested for filterability and particle counting in the range 2–100 μm. In 88% of the membrane tank samples the filterability varied between good and poor, characterized by the ΔR20, being 0 < ΔR20 < 1. Filterability varied following the season of the year, stability of the MBR operation and recirculation ratio. The membrane tank filterability can be improved by applying low recirculation ratio between MBR tanks. The applied particle counting methodology generated reproducible and reliable results in the range 10–100 μm. Results show that differences in filterability cannot be explained by variations in particle size distribution in the range 10–100 μm. However, measurable deflocculation might be masked by the large numbers of particles present. Therefore, we cannot exclude the suspended particles as a possible source of submicron particles that are subsequently responsible for MBR sludge filterability deterioration.

scholarly journals Robust PLS Prediction Model for Saikosaponin A inBupleurum chinenseDC. Coupled with Granularity-Hybrid Calibration Set

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Zhisheng Wu ◽  
Min Du ◽  
Xinyuan Shi ◽  
Bing Xu ◽  
Yanjiang Qiao
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Model Performance ◽  
Nir Spectroscopy ◽  
Particle Size Effect ◽  
Calibration Model ◽  
Near Infrared Reflectance ◽  
Prediction Ability ◽  
Partial Least Squares Model ◽  
Saikosaponin A

This study demonstrated particle size effect on the measurement of saikosaponin A inBupleurum chinenseDC. by near infrared reflectance (NIR) spectroscopy. Four types of granularity were prepared including powder samples passed through 40-mesh, 65-mesh, 80-mesh, and 100-mesh sieve. Effects of granularity on NIR spectra were investigated, which showed to be wavelength dependent. NIR intensity was proportional to particle size in the first combination-overtone and combination region. Local partial least squares model was constructed separately for every kind of samples, and data-preprocessing techniques were performed to optimize calibration model. The 65-mesh model exhibited the best prediction ability with root mean of square error of prediction (RMSEP) = 0.492 mg·g−1, correlation coefficientRP=0.9221, and relative predictive determinant (RPD) = 2.58. Furthermore, a granularity-hybrid calibration model was developed by incorporating granularity variation. Granularity-hybrid model showed better performance than local model. The model performance with 65-mesh samples was still the most accurate with RMSEP = 0.481 mg·g−1,RP=0.9279, and RPD = 2.64. All the results presented the guidance for construction of a robust model coupled with granularity-hybrid calibration set.

Factors Influencing the Robustness and Accuracy of Near Infrared Reflectance Spectroscopy (NIRS) Calibrations for Ps5edicting the Digestibility of Grass Silages

1993 ◽  
Vol 1993 ◽  
pp. 50-50
Author(s):  
C W Baker ◽  
D I Givens
Keyword(s):  
Particle Size ◽  
Organic Matter ◽  
Size Effects ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Factors Influencing

Earlier studies showed the superiority of NIRS, over fibre measurements, for predicting organic matter digestibility (OMD) in vivo of grass silages. (Barber et al 1990). This system was put into routine use in ADAS in 1989 and after some initial doubts, due to the wider range of the predicted data seen, is now accepted as the best system available for routine use. However, occasional erroneous values were predicted for which there were no obvious explanations, and which resulted in occasional relatively poor repeatability. In common with all NIRS applications it was likely that the sources of the problems contributing to the errors were i) instrumental and environmental noise, ii) sample particle size effects and iii) variable moisture content of the samples. A course of investigation was undertaken with the objective of determining the effects of these on the predicted data.

scholarly journals Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland

2021 ◽  
Vol 13 (11) ◽  
pp. 2045
Author(s):  
Anaí Caparó Bellido ◽  
Bradley C. Rundquist
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Important Variable ◽  
Field Station ◽  
Near Surface ◽  
Fractional Snow Cover ◽  
Rgb Images ◽  
Phenological Metrics

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability.

scholarly journals Factors influencing Lp[a]- particle size as determined by gradient gel electrophoresis.

1996 ◽  
Vol 37 (8) ◽  
pp. 1655-1663
Author(s):  
D O'Neal ◽  
G Grieve ◽  
D Rae ◽  
G Dragicevic ◽  
J D Best
Keyword(s):  
Particle Size ◽  
Gel Electrophoresis ◽  
Factors Influencing ◽  
Gradient Gel Electrophoresis

Dryer Effluent Monitoring in a Chemical Pilot Plant via Fiber-Optic Near-Infrared Spectroscopy

1998 ◽  
Vol 52 (5) ◽  
pp. 717-724 ◽  
Author(s):  
Charity Coffey ◽  
Alex Predoehl ◽  
Dwight S. Walker
Keyword(s):  
Real Time ◽  
Pilot Plant ◽  
Near Infrared ◽  
Fiber Optic ◽  
Tunable Filter ◽  
Calibration Model ◽  
Gas Cell ◽  
Rotary Dryer ◽  
Vapor Stream ◽  
Effluent Stream

The monitoring of the effluent of a rotary dryer has been developed and implemented. The vapor stream between the dryer and the vacuum is monitored in real time by a process fiber-optic coupled near-infrared (NIR) spectrometer. A partial least-squares (PLS) calibration model was developed on the basis of solvents typically used in a chemical pilot plant and uploaded to an acousto-optic tunable filter NIR (AOTF-NIR). The AOTF-NIR is well suited to process monitoring as it electrically scans a crystal and hence has no moving parts. The AOTF-NIR continuously fits the PLS model to the currently collected spectrum. The returned values can be used to follow the drying process and determine when the material can be unloaded from the dryer. The effluent stream was monitored by placing a gas cell in-line with the vapor stream. The gas cell is fiber-optic coupled to a NIR instrument located 20 m away. The results indicate that the percent vapor in the effluent stream can be monitored in real time and thus be used to determine when the product is free of solvent.

Calibration Transfer between Miniature Photodiode Array-Based Spectrometers in the near Infrared Assessment of Mandarin Soluble Solids Content

2002 ◽  
Vol 10 (1) ◽  
pp. 27-35 ◽  
Author(s):  
C.V. Greensill ◽  
K.B. Walsh
Keyword(s):  
Near Infrared ◽  
Model Updating ◽  
Soluble Solids ◽  
Soluble Solid Content ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Orthogonal Signal Correction ◽  
Significant Difference ◽  
Photodiode Array ◽  
Direct Standardisation

The transfer of predictive models among photodiode array based, short wave near infrared spectrometers using the same illumination/detection optical geometry has been attempted using various chemometric techniques, including slope and bias correction (SBC), direct standardisation (DS), piecewise direct standardisation (PDS), double window PDS (DWPDS), orthogonal signal correction (OSC), finite impulse transform (FIR) and wavelet transform (WT). Additionally, an interpolation and photometric response correction method, a wavelength selection method and a model updating method were assessed. Calibration transfer was attempted across two populations of mandarin fruit. Model performance was compared in terms of root mean squared error of prediction ( RMSEP), using Fearn's significance testing, for calibration transfer (standardisation) between pairs of spectrometers from a group of four spectrometers. For example, when a calibration model (Root Mean Square Error of Cross-Validation [ RMSECV = 0.26% soluble solid content (SSC)], developed on one spectrometer, was used with spectral data collected on another spectrometer, a poor prediction resulted ( RMSEP = 2.5% SSC). A modified WT method performed significantly better (e.g. RMSEP = 0.25% SSC) than all other standardisation methods (10 of 12 cases), and almost on a par with model updating (MU) (nine cases with no significant difference, one case and two cases significantly better for WT and MU, respectively).

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