Using different sample holders in determining protein and moisture content in whole wheat flour by means of Fourier transform near infrared (FT-NIR) spectroscopy

Journal of Near Infrared Spectroscopy

Zyl

Osborne

2002

Self Cite

Fourier transform near infrared (FT-NIR) spectroscopy was used successfully to determine the kernel hardness, protein and moisture content of a single sample set of whole wheat flour. The hardness calibration was derived by performing partial least square (PLS) regression on baseline corrected data and using the particle size index (PSI) test as reference method. Both the protein and moisture calibrations were derived by performing PLS regression on multiplicative scatter corrected (MSC) and baseline corrected data. In all cases, the calibration models were subsequently tested by means of independent validation procedures. Good calibration and prediction results were obtained for all three characteristics. Standard error of prediction (SEP), root mean standard error of prediction (RMSEP) and correlation coefficient (r) values of 2.13%, 4.53% and 0.42, respectively, were obtained for the hardness calibration. Corresponding results for the protein and moisture calibrations were 0.51%, 1.16%, 0.81 and 0.15%, 0.38% and 0.85, respectively. The FT-NIR protein and moisture results of this study compare satisfactorily with results achieved in previous FT-NIR studies. No other FT-NIR wheat hardness results have been reported elsewhere, but results of this study compare well to those achieved in other NIR wheat hardness studies.

Section: Fourier Transform Near Infrared Measurementsmentioning

confidence: 99%

Using Fourier Transform near Infrared Spectroscopy in Determining Kernel Hardness, Protein and Moisture Content of Whole Wheat Flour

Journal of Near Infrared Spectroscopy

Zyl

Osborne

2002

Self Cite

“…The wheat grains from the 10 cultivars and five localities were harvested and milled using a Cyclone Sample Mill (UDY Corporation, Fort Collins, CO) fitted with a 1 mm sieve to obtain the flour. The flour samples were packed in individual borosilicate glass vials [13] and measured by Fourier transform near infrared (FT-NIR) using a Perkin Elmer Spectrum IdentiCheck (The Perkin Elmer Corporation, Wellesley, MA) and the Spectrum IdentiCheck version 2.0 software programme. Spectra were collected in diffuse reflectance mode from 10 000 À 4000 cm À1 at 4 cm À1 intervals at a resolution of 16 cm À1 resulting in 1501 wavenumbers.…”

Section: Methodsmentioning

confidence: 99%

Three‐way data analysis of a wheat growing experiment using near infrared spectral data

Geladi

Journal of Chemometrics

2008

a An agricultural growing experiment cultivating 10 cultivars at five localities produced whole wheat flour samples that were analysed by Fourier transform near infrared (FT-NIR) spectroscopy. This produced a three-way data array: locality T cultivar T wavelength. By taking smoothing first derivatives and removing a noisy part of the spectra meaningful PARAFAC (PARAllel FACtor analysis) models could be produced. Two factors could be interpreted spectroscopically, showing that cultivar properties are related in a not so trivial way to average wheat hardness values and that locality variations are straightforwardly related to average protein content.

“…The reason why Figure 2 gives the correct geometrical interpretation is that the basis (p 1 and p 2 from Equation (1)) for the figure is orthonormal and that it is made with identical scales. Figure 4 shows a PCA score plot for a more complicated situation: a 112 Â 1501 data matrix of FT-NIR spectra of wheat samples [10]. PCA was done on mean-centered data and gave four components explaining 79%, 18%, 1.6% and 0.8% of the total sum of squares, a total of 99.4%.…”

Section: Plotting In Component Modelsmentioning

confidence: 99%

Scatter plotting in multivariate data analysis

Geladi

Journal of Chemometrics

Lestander

2003

In data analysis, many situations arise where plotting and visualization are helpful or an absolute requirement for understanding. There are many techniques of plotting data/parameters/residuals. These have to be understood and visualization has to be made clearly and interpreted correctly. In this paper the classical favourites in chemometrics, scatter plots, are looked into more deeply and some criticism based on recent literature references is formulated for situations of principal component analysis, PARAFAC three-way analysis and regression by partial least squares. Biplots are also afforded some attention. Examples from near-infrared spectroscopy are given as illustrations.