# Load chemRmetrics package - If not installed, refer to the guide file in the ChemRmetrics repository on GitHub
library(chemRmetrics)

# Load data from jdx files in 'Inside-Outside-Filament Raw Data' folder
raw_data <- load_data('Inside-Outside-Filament Raw Data', ftype = 'jdx', fstruc = 'Brand_Filament-Type_Colour_Location_Replicate', delim = '_')

# Remove diamond signal from all spectra
trunc <- data_trunc(raw_data, 6, 2350, 1850)

# Interpolate the data between a range of 4000-400 cm-1, with a step size of 1
interp <- data_interp(trunc, 6, 4000, 400, 1)

# Normalise the spectra to unity
norm <- data_norm_mm(interp, 6)

# Perform principal component analysis (also exports scree plot and loadings plots for first 7 PCs)
pc_data <- pca(norm, 6, 7)

# Create a 3D PCA scores plot using PC1, PC2, and PC3 and the 'Location' variable for the colour and legend (marker size = 5)
plot_3D(pc_data, 'Location', 1, 2, 3, 5, colours = c('#A75529', '#8B0069'))

# Load data from jdx files in 'Printed-Filament Raw Data' folder
proj_data <- load_data('Printed Raw Data', ftype = 'jdx', fstruc = 'Brand_Filament-Type_Colour_Location_Replicate', delim = '_')

# Remove diamond signal from all spectra
trunc_proj <- data_trunc(proj_data, 6, 2350, 1850)

# Interpolate the data between a range of 4000-400 cm-1, with a step size of 1
interp_proj <- data_interp(trunc_proj, 6, 4000, 400, 1)

# Normalise the spectra to unity
norm_proj <- data_norm_mm(interp_proj, 6)

# Projecting the Printed-Filament data onto the previously established PCA model for the inside-outside data
pc_data_proj <- pca_proj(norm, norm_proj, 6, 7)

# Create a 3D PCA scores plot with the projected data set using PC1, PC2, and PC3 and the 'Location' variable for the colour and legend (marker size = 5)
plot_3D(pc_data_proj, 'Location', 1, 2, 3, 5, c('#A75529', '#8B0069', '#5DD291'), proj = TRUE)