Development of a Sample Set for Soya Bean Calibration of near Infrared Reflectance Spectroscopy

1994 ◽  
Vol 2 (4) ◽  
pp. 223-227 ◽  
Author(s):  
T.L. Hong ◽  
S.-J. Tsai ◽  
S.C.S. Tsou
Keyword(s):  
Near Infrared ◽  
Analytical Data ◽  
Storage Period ◽  
Nir Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Soya Bean ◽  
Chemical Compositions ◽  
Near Infrared Reflectance ◽  
Sample Set ◽  
Calibration Equations

The potential application of near infrared (NIR) spectroscopy is limited since its calibration equations are not always transferable from one instrument to another. Hence, an attempt was made to develop a selected sample set of soya beans with analytical data, which could be distributed to collaborators to calibrate their instruments. Sixty soya bean samples, (1 kg each) were selected and packed (200 g each) in laminated film bags after thorough mixing. During their storage at 4°C, the soya bean samples were periodically evaluated by chemical analysis as well as by NIR spectroscopy. Chemical compositions (i.e. moisture, protein and fat) were determined using conventional methods. Experimental results showed that no significant differences were found in the compositions of interest as well as in the reflectance spectra over a storage period of up to three years, and that the NIR spectroscopy method is independent of the location and model of the instruments. The experiment demonstrated that it is possible practically to use a pre-packed sample set with chemical data for calibration purposes.

On-line application of visible and near infrared reflectance spectroscopy to predict physical and sensory characteristics of beef quality

2009 ◽  
Vol 2009 ◽  
pp. 135-135
Author(s):  
N Prieto ◽  
D W Ross ◽  
E A Navajas ◽  
G Nute ◽  
R I Richardson ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Sensory Characteristics ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Beef Quality ◽  
Near Infrared Reflectance ◽  
On Line ◽  
Meat Samples

Visible and near infrared reflectance spectroscopy (Vis-NIR) has been widely used by the industry research-base for large-scale meat quality evaluation to predict the chemical composition of meat quickly and accurately. Meat tenderness is measured by means of slow and destructive methods (e.g. Warner-Bratzler shear force). Similarly, sensory analysis, using trained panellists, requires large meat samples and is a complex, expensive and time-consuming technique. Nevertheless, these characteristics are important criteria that affect consumers’ evaluation of beef quality. Vis-NIR technique provides information about the molecular bonds (chemical constituents) and tissue ultra-structure in a scanned sample and thus can indirectly predict physical or sensory parameters of meat samples. Applications of Vis-NIR spectroscopy in an abattoir for prediction of physical and sensory characteristics have been less developed than in other fields. Therefore, the aim of this study was to test the on-line Vis-NIR spectroscopy for the prediction of beef quality characteristics such as colour, instrumental texture, water holding capacity (WHC) and sensory traits, by direct application of a fibre-optic probe to the M. longissimus thoracis with no prior sample treatment.

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.

Determination of Soil Separates with near Infrared Reflectance Spectroscopy

1996 ◽  
Vol 4 (1) ◽  
pp. 201-212 ◽  
Author(s):  
A. Couillard ◽  
A.J. Turgeon ◽  
M.O. Westerhaus ◽  
J.S. Shenk
Keyword(s):  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Transformation Model ◽  
Natural State ◽  
Peat Moss ◽  
Particle Sizes ◽  
Near Infrared Reflectance ◽  
Laboratory Values

The use of near infrared (NIR) reflectance spectroscopy to evaluate soil properties has started to receive more attention in recent years. The technology is evolving and research on NIR spectroscopic analysis using natural state samples is increasing. There is no method available today, besides NIR spectroscopy, that could simultaneously evaluate physical and chemical properties of a soil sample without processing the sample and affecting the visual quality of the site. More samples can be scanned in their natural undisturbed form resulting in a variety of particle sizes. Research on the effect of scanning products with different particle sizes is essential. The differences in the particle size of the soil separates may lower the prediction accuracy of NIR spectroscopy. In this study, we evaluated the ability of NIR spectroscopy to predict soil separates from artificial soil samples. Feldspar and silica sands and silts, kaolinite and montmorillonite clays, and reed sedge and Canadian sphagnum peat moss organic matters were used as separates. They were scanned alone, and in different mixture percentages, from 400 to 2500 nm with a total of 116 samples. The absence of linearity in the binary mixtures, preventing accurate calibration, was noticed and required the development of a transformation model to generate new laboratory values from a laboratory weight scaling factor generated for each soil separate. The adjustment of the laboratory values improved the prediction accuracy of the mixtures. The coefficient of determination ranged from 0.95 to 0.99. The standard error of cross-validation ranged from 2.09 to 5.82%.

scholarly journals The Quality Control of Tea by Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ming-Zhi Zhu ◽  
Beibei Wen ◽  
Hao Wu ◽  
Juan Li ◽  
Haiyan Lin ◽  
...  
Keyword(s):  
Quality Control ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Chemical Compositions ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Control Quality ◽  
Tea Quality ◽  
Instrumental Technique

Tea is known to be one of the most popular beverages enjoyed by two-thirds of the world’s population. Concern of variability in tea quality is increasing among consumers. It is of great significance to control quality for commercialized tea products. As a rapid, noninvasive, and nondestructive instrumental technique with simplicity in sample preparation, near-infrared reflectance (NIR) spectroscopy has been proved to be one of the most advanced and efficient tools for the control quality of tea products in recent years. In this article, we review the most recent advances and applications of NIR spectroscopy and chemometrics for the quality control of tea, including the measurement of chemical compositions, the evaluation of sensory attributes, the identification of categories and varieties, and the discrimination of geographical origins. Besides, challenges and future trends of tea quality control by NIR spectroscopy are also presented.

Use of near Infrared Reflectance Spectroscopy to Analyse Bovine Faecal Samples

2002 ◽  
Vol 10 (4) ◽  
pp. 309-314 ◽  
Author(s):  
D. Cozzolino ◽  
A. La Manna ◽  
D. Vaz Martins
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Dry Weight ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Faecal Samples ◽  
Acid Detergent Fibre ◽  
Nir Reflectance Spectroscopy ◽  
Calibration Equations

Near infrared (NIR) reflectance spectroscopy was used to predict nitrogen (N), acid detergent fibre (ADF), neutral detergent fibre (NDF) and chromium (Cr) in beef faecal samples. One hundred and twenty faecal samples were scanned in a NIRSystems 6500 monochromator instrument over the wavelength range of 400–2500 nm in reflectance. Calibration equations were developed using modified partial least squares (MPLS) with internal cross validation to avoid overfitting. The coefficient of determination in calibration ( R2cal) and the standard error in cross validation ( SECV) were 0.80 (0.74) for N, 0.92 (12.04) for ADF, 0.86 (13.5) for NDF and 0.56 (0.07) for Cr in g kg−1 dry weight, respectively. Results for validation were 0.78 ( SEP: 0.1) for N, 0.74 ( SEP: 7.5) for ADF, 0.85 ( SEP: 8.5) for NDF and 0.10 (0.09) for Cr in g kg−1 dry weight, respectively.

Predicting Elements in White Pine and Red Oak Foliage with Visible-Near Infrared Reflectance Spectroscopy

1997 ◽  
Vol 5 (2) ◽  
pp. 77-82 ◽  
Author(s):  
R.A. Hallett ◽  
J.W. Hornbeck ◽  
M.E. Martin
Keyword(s):  
Forest Canopy ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Red Oak ◽  
Acid Digestion ◽  
White Pine ◽  
Near Infrared Reflectance ◽  
Nir Reflectance Spectroscopy ◽  
Calibration Equations ◽  
Mn Concentrations

Near infrared (NIR) reflectance spectroscopy was evaluated for its effectiveness at predicting Al, Ca, Fe, K, Mg and Mn concentrations in white pine ( Pinus strobus L.) and red oak ( Quercus rubra L.) foliage. A NIR spectrophotometer was used to scan 470 dried, ground foliage samples. These samples were used to develop calibration equations using a modified partial least squares (MPLS) regression technique. For the calibration equations, concentrations of Al, Ca, Fe, K, Mg and Mn as determined by acid digestion and laboratory analysis were regressed against second-difference absorbance values measured from 400 to 2498 nm. The regression models developed by NIR reflectance spectroscopy were unable to predict Fe. Predictions were satisfactory for Al, Ca, K, Mn and Mg. It still is uncertain which mineral/organic associations are being detected by NIR reflectance spectroscopy. Future applications may include prediction of element concentrations in the forest canopy via remote sensing.

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