Studies to Measure Cotton Fibre Length, Strength, Micronaire and Colour by Vis/NIR Reflectance Spectroscopy. Part I: Descriptive Statistics of Fibre Properties and Reflectance Spectra

1993 ◽  
Vol 1 (3) ◽  
pp. 153-173 ◽  
Author(s):  
Joseph G. Montalvo ◽  
Sherman E. Faught ◽  
Harmon H. Ramey ◽  
Steven E. Buco
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
A Priori ◽  
Fibre Length ◽  
High Volume ◽  
Reflectance Spectroscopy ◽  
Reflectance Spectra ◽  
Fibre Property ◽  
Property Data ◽  
Nir Reflectance Spectroscopy

Fibre property data representing the 1989 and 1990 crop years and its reflectance spectra are analysed using standard error, regression and correlation analysis. The six properties of interest are upper-half mean length, uniformity index, strength and micronaire measured on two high volume instrument systems placed side-by-side, and colour (Rd and +b) measured by the traditional lab system. Visible (vis) and near infrared (NIR) reflectance spectra are observed on a scanning spectrophotometer, and span the 400–2500 nm range. Three findings highlight the research. One, a diagnostic test is presented to decide, a priori of reflectance spectroscopy, the degree to which the mean property values have reduced random error. Two, the standard error of replicate spectra provides a way to probe the fibre mass in the diffuse reflectance optical path. The spectral error is strongly influenced by both how the cotton is packed into the spectrophotometric cell and the non-homogeneity of the sample. And three, correlations between the spectra confirm that some visible and NIR wavelength regions contain mutually exclusive information about the properties of this natural staple.

Studies to Measure Cotton Fibre Length, Strength, Micronaire and Colour by vis/NIR Reflectance Spectroscopy. Part II: Principal Components Regression

1994 ◽  
Vol 2 (4) ◽  
pp. 185-198 ◽  
Author(s):  
Joseph G. Montalvo ◽  
Steven E. Buco ◽  
Harmon H. Ramey
Keyword(s):  
Principal Components ◽  
Fibre Length ◽  
Reflectance Spectroscopy ◽  
Cotton Fibre ◽  
Reflectance Spectra ◽  
Principal Components Regression ◽  
Validation Data ◽  
Data Set ◽  
Fibre Property ◽  
The Press

In Part I of this series, both cotton fibre property and reflectance spectra data on 185 US cottons including four Pimas were analysed by descriptive statistics. In this paper, principal components regression (PCR) models for measuring six properties from the cotton's vis/NIR reflectance spectra are critically examined. These properties are upper-half mean length (UHM), uniformity index (UI), bundle strength (STR), micronaire (MIC) and colour (Rd and +b). The spectra were recorded with a scanning spectrophotometer in the wavelength range from 400 to 2498 nm. A variety of spectral processing options, some of which give improved PCR analysis results, were applied prior to the regressions and allowed for testing of over 100 PCR models. All PCR model results are based on the PRESS statistic by one-out-rotation, a fast approximation of the PRESS statistic (to reduce computer time) or on cluster analysis using separate calibration and validation data sets. The standard error of prediction (SEP) of all the properties except UHM compared well to the reference method precision. The precision of the UHM measure by reflectance spectroscopy was strongly influenced by the sample repack error. The SEP of UHM, UI and STR was improved by excluding the Pimas from the data set.

Exploring the Use of near Infrared Reflectance Spectroscopy to Study Physical Properties and Microelements in Soils

2003 ◽  
Vol 11 (2) ◽  
pp. 145-154 ◽  
Author(s):  
A. Moron ◽  
D. Cozzolino
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Agricultural Soils ◽  
Low Cost ◽  
Scatter Correction ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Second Derivative ◽  
Near Infrared Reflectance ◽  
Nir Reflectance Spectroscopy

Near infrared (NIR) reflectance spectroscopy was used to predict the content of silt, sand, clay, iron (Fe), copper (Cu), manganese (Mn) and zinc (Zn) in soil. A total of 332 samples from agricultural soils (0–15 cm depth) in Uruguay (South America) were used. The samples were scanned in a monochromator instrument (NIRSystems 6500, Silver Spring, MD, USA). Two mathematical treatments (first and second derivative) with SNVD (scatter normal variate and detrend) and without scatter correction were studied. Modified partial least squares (mPLS) was used to develop the calibration models. The coefficient of determination in calibration ( R2cal) and the standard error in calibration ( SEC) using the second derivative were 0.81 ( SEC: 5.1), 0.83 ( SEC: 5.3), 0.92 ( SEC: 2.6) for percent sand, silt and clay, respectively. The R2cal and standard error of cross-validation ( SECV) were for Cu 0.87 ( SEC: 0.7), for Fe 0.92 ( SEC: 21.7), for Mn 0.72 ( SEC: 83.0) and for Zn 0.72 ( SEC: 1.2) on mg kg−1 dry matter. It was concluded that NIR reflectance spectroscopy has a great potential as an analytical method for routine analysis of soil texture, Fe, Zn and Cu due the speed and low cost of analysis.

Theoretical and Experimental Profiles of Fiber Fineness and Maturity Using the Shirley Micromat and the Zellweger Uster Advanced Fiber Information System Module

1997 ◽  
Vol 67 (8) ◽  
pp. 545-555 ◽  
Author(s):  
Stuart G. Gordon ◽  
Joseph G. Montalvo ◽  
Sherman E. Faught ◽  
Robert T. Grimball ◽  
Terry A. Watkins
Keyword(s):  
Wall Thickness ◽  
Near Infrared ◽  
High Volume ◽  
Past Research ◽  
Fundamental Properties ◽  
Property Data ◽  
Fiber Fineness ◽  
System Module ◽  
Replicate Measurements ◽  
Southern Regional

Past research has demonstrated that the fundamental properties of wall thickness and perimeter, computed from the fmt (Micromat model) readings, produce better correlations with the Southern Regional Research Center (SRRC) near-infrared high volume instrumentation (nir hvi) than similar property data from the afis fineness and maturity module. (The nir hvi analyzes about 3,000,000 fibers or 30 g, the Micromat 400,000 fibers or 4 g, and the afis 5000 fibers or 0.05 g, a single fiber at a time.) To help understand these differences in correlation, we probe the random and systematic variations in the Micromat and afis data using appropriate data analysis techniques. We present descriptive statistics and an internal correlation of paired means from replicate measurements, and we compare the fit of wall thickness versus perimeter with the expected theoretical fit. Preliminary results suggest that the Micromat is more robust in representing changes in mean wall thickness and perimeter values. Direct comparison of wall thickness and perimeter from each method shows that although the relationships are highly significant, the values are different.

Near Infrared Assessment of Vegetable Quality

1998 ◽  
Vol 6 (A) ◽  
pp. A107-A110 ◽  
Author(s):  
Christopher N.G. Scotter
Keyword(s):  
Product Quality ◽  
Industrial Application ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Quality Factors ◽  
Sensory Assessment ◽  
Research Consortium ◽  
The Uk ◽  
Nir Reflectance Spectroscopy ◽  
Vegetable Quality

NIR reflectance spectroscopy has been applied to the assessment of product quality factors for vining peas and sweetcorn. A CCFRA Industrial Research Club and an EU research consortium led by CCFRA, have been conducting the work since 1994. Sample sets of peas have been collected which are broadly representative of commercial material in the UK, Hungary, and Bulgaria All sweetcorn samples were from Hungary. The calibration results, at this stage, for alcohol insoluble solids (AIS) in peas and sweetcorn, and Tenderometer measurement and sensory assessment for both vegetables indicate that the work will find industrial application.

scholarly journals Noninvasive Prediction of Glucose by Near-Infrared Diffuse Reflectance Spectroscopy

1999 ◽  
Vol 45 (9) ◽  
pp. 1651-1658 ◽  
Author(s):  
Stephen F Malin ◽  
Timothy L Ruchti ◽  
Thomas B Blank ◽  
Suresh N Thennadil ◽  
Stephen L Monfre
Keyword(s):  
Blood Glucose ◽  
Wavelength Range ◽  
Standard Error ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Calibration Model ◽  
Calibration Models ◽  
Blood Glucose Concentrations

Abstract Background: Self-monitoring of blood glucose by diabetics is crucial in the reduction of complications related to diabetes. Current monitoring techniques are invasive and painful, and discourage regular use. The aim of this study was to demonstrate the use of near-infrared (NIR) diffuse reflectance over the 1050–2450 nm wavelength range for noninvasive monitoring of blood glucose. Methods: Two approaches were used to develop calibration models for predicting the concentration of blood glucose. In the first approach, seven diabetic subjects were studied over a 35-day period with random collection of NIR spectra. Corresponding blood samples were collected for analyte analysis during the collection of each NIR spectrum. The second approach involved three nondiabetic subjects and the use of oral glucose tolerance tests (OGTTs) over multiple days to cause fluctuations in blood glucose concentrations. Twenty NIR spectra were collected over the 3.5-h test, with 16 corresponding blood specimens taken for analyte analysis. Results: Statistically valid calibration models were developed on three of the seven diabetic subjects. The mean standard error of prediction through cross-validation was 1.41 mmol/L (25 mg/dL). The results from the OGTT testing of three nondiabetic subjects yielded a mean standard error of calibration of 1.1 mmol/L (20 mg/dL). Validation of the calibration model with an independent test set produced a mean standard error of prediction equivalent to 1.03 mmol/L (19 mg/dL). Conclusions: These data provide preliminary evidence and allow cautious optimism that NIR diffuse reflectance spectroscopy using the 1050–2450 nm wavelength range can be used to predict blood glucose concentrations noninvasively. Substantial research is still required to validate whether this technology is a viable tool for long-term home diagnostic use by diabetics.

Authentication of Whole and Ground Coffee Beans by near Infrared Reflectance Spectroscopy

1994 ◽  
Vol 2 (2) ◽  
pp. 85-92 ◽  
Author(s):  
Gerard Downey ◽  
Jerôme Boussion ◽  
Dominique Beauchêne
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Correct Identification ◽  
Correct Classification Rate ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Identification Rate ◽  
Coffee Beans ◽  
Correct Identification Rate ◽  
Nir Reflectance Spectroscopy

The potential of NIR reflectance spectroscopy for discriminating between pure Arabica and pure Robusta coffees and blends of these two was investigated. Studies were performed on whole and ground beans using a factorial discriminant procedure. For whole beans, in the absence of blended samples, a correct classification rate of 96.2% was achieved. Inclusion of blended samples reduced this figure to between 82.9 and 87.6%. In the case of ground samples, including blends, a correct identification rate of 83.02% was achieved. The molecular basis for discrimination is discussed.

Determination of nitrogen, lignin, and cellulose content of decomposing leaf material by near infrared reflectance spectroscopy

1991 ◽  
Vol 21 (11) ◽  
pp. 1684-1688 ◽  
Author(s):  
Toni M. McLellan ◽  
John D. Aber ◽  
Mary E. Martin ◽  
Jerry M. Melillo ◽  
Knute J. Nadelhoffer
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Reflectance Spectra ◽  
Cellulose Content ◽  
Regression Equations ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

We report the results of a study of the near infrared reflectance spectra of decaying forest foliage. During the decay process, a broad absorbance feature develops in the 1100–2000 nm region of the near infrared spectrum. The magnitude of this feature is directly related to the age of the material (or to degree of decomposition) and may be useful in determining degree of decay in field samples. More specifically, multiple linear regression equations derived from second-derivative near infrared reflectance spectra are presented that predict the concentrations of nitrogen, lignin, and cellulose in decaying foliage. We conclude that near infrared reflectance spectroscopy is a very viable and attractive method for the simultaneous determination of these components in decaying foliage.

Near Infrared Spectroscopy of Fuel Contaminated Sand and Soil. I. Preliminary Results and Calibration Study

1998 ◽  
Vol 6 (1) ◽  
pp. 189-197 ◽  
Author(s):  
Heinz W. Zwanziger ◽  
Heidrun Förster
Keyword(s):  
Near Infrared ◽  
Direct Detection ◽  
Scatter Correction ◽  
Reflectance Spectroscopy ◽  
Calibration Error ◽  
Particle Size Fractions ◽  
Cultivated Soil ◽  
Synthetic Hydrocarbon ◽  
Calibration Models ◽  
Nir Reflectance Spectroscopy

Regional areas may be contaminated by the past activities of the chemical and oil industries and of the military. Therefore, the present study was undertaken to test the possibilities of near infrared (NIR) reflectance spectroscopy for direct detection and determination of oil and fuel contaminations. If reliable results are obtained NIR reflectance spectroscopy could be a valuable part of land remediation processes. Preliminary investigations showed that it is possible to distinguish samples of stone chippings, sand, cultivated soil, humus and potting soil by multivariate data analysis. After spiking with gasoline, diesel, motor oil and synthetic hydrocarbon mixtures (BTEX) sand rather than cultivated soil shows obvious spectral absorptions due to contaminations higher than 1% (w/w). The influence of particle size fractions has been investigated in detail using dry sand sieved to < 500 μm (fine), 500–800 μm (medium) and >800 μm (coarse). Contaminations in fine and medium fractions often can be modelled with only one intensity at sufficiently low calibration error, SEC. With coarse fractions SEC is three times higher. Models based on derivative spectra have no significant advantage. In general, mean centring results in more pronounced error minima than multiplicative scatter correction (MSC). Partial least squares (PLS) models can be fitted to obtain any wanted SEC even by cross-validation. For comparable SEC, PLS models in general do not need more factors if samples become more inhomogeneous. Data pre-processing techniques such as Kubelka–Munk transformation, Saunderson correction, MSC and combinations thereof have been tested. Adequate sample variation of the diffuse reflectance fraction of detected light according to the Saunderson model could improve the performance of calibration models. The best values for standard error of prediction, SEP, are obtained if calibration models are derived from sets of spectra of sieved samples and used for contamination prediction of natural samples, and not vice versa. Spectra of contaminated soil and humus need cleverer spectral selection and pre-processing for better performance of calibration models.

The Use of near Infrared Reflectance Spectroscopy to Predict the Insoluble Dietary Fibre Fraction of Cereal Products

1998 ◽  
Vol 6 (1) ◽  
pp. 221-227 ◽  
Author(s):  
Sandra E. Kays ◽  
Franklin E. Barton
Keyword(s):  
Standard Error ◽  
Dietary Fibre ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Medical Community ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Cereal Products ◽  
Wide Range ◽  
Insoluble Dietary Fibre

The insoluble and soluble fractions of dietary fibre have different human physiological effects and their presence in foods is of interest to consumers, the medical community and the cereal product industry. The development of a model, using near infrared (NIR) reflectance spectroscopy, to predict insoluble dietary fibre in a wide range of dry-milled cereal products and grains is described. The products included breakfast cereals, crackers, brans, pastas and flours. Insoluble dietary fibre was measured by the AOAC enzymatic–gravimetric procedure (AOAC 991.43). The range in insoluble dietary fibre was 0–48%. Near infrared reflectance spectra were obtained with a scanning monochromator and data analysed with a commercial analysis program. A calibration ( n = 90) was developed for prediction of insoluble dietary fibre using preprocessed spectra and modified partial least squares regression. The standard error of cross validation and R2 were 1.34% and 0.99, respectively. The model was tested with independent validation samples ( n = 32) and the resulting standard error of performance and r2 were 1.13% insoluble dietary fibre and 0.99, respectively. The results show that NIR spectroscopy can be used to predict the insoluble dietary fibre content in a wide variety of processed and unprocessed cereal products.

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