fitFTIRc {cryst}

Relative Crystallinity Calculation of FTIR Spectrum of Starch by SUN Method

Description

Allow to calculate the relative crystallinity of starch by FTIR. The basic concept of SUN approach involves obtaining a gaussian holocrystalline-peak in the 800-1300 cm-1 region of FTIR spectrum of starch which is divided into amorphous region and crystalline region.

Usage

fitFTIRc(spectrum, mu = 1180, sigma = 60, k = 1, lim = c(1190, 1160,
  985, 950))

Arguments

spectrum

matrix. The matrix of FTIR spectrum baseline-corrected by drawing a tangentline in the 800-1300 cm-1 region. The first row corresponds to wavelength; the second row corresponds to intensity.

mu

numeric. Gaussian mean of holocrystalline-peak. Defaults to 1180.

sigma

numeric. Standard deviation of holocrystalline-peak. Defaults to 60.

k

numeric. Arbitrary scaling parameter. Defaults to 1.

lim

vector. Fitting points of holocrystalline-peak. Defaults to c(1190, 1160, 985, 950).

Details

Calculate the relative starch crystallinity of FTIR spectrum by SUN method.

Value

An object of class fitFTIRc, which is a list with the following components:

original

Original matrix of FTIR spectrum.

gauss

Gaussian curve fit.

fit

Summary of Non-Linear Least-Squares Model Fits.

summary

Summary calculation of crystallinity. Total area under the curve of the diffraction spectrum (A.U.); Amorphous area (A.U.); Crystalline area (A.U.); Relative crystallinity (%).

Author(s)

Claudio Pozo Valenzuela [aut, cre] and Saddys Rodriguez-llamazares [aut]

References

Sun, Y., et al. (2014). "A new method for determining the relative crystallinity of chickpea starch by Fourier-transform infrared spectroscopy." Carbohydrate Polymers 108: 153-158.

Examples

# Convert data frame to matrix, select A-type starch
spectrum <- as.matrix(t(FTIR[, c('wavelength','A')]))
# List of crystallinity components
crs <- fitFTIRc(spectrum = spectrum, mu = 1180, sigma = 60, k = 1, lim = c(1190, 1160, 985, 955))
# Original matrix
original <- crs$original
# Gaussian curve fit
gauss <- crs$gauss
# Summary of Non-Linear Least-Squares Model Fits
fit <- crs$fit
# Summary calculation of crystallinity
summary <- crs$summary