Dataset of adulteration with water in coconut milk using FTIR spectroscopy

This paper presents the spectroscopic dataset, pre-processing,
calibration, and predicted model database of Fourier transform infrared (FTIR) spectroscopy used to detect adulterated coconut milk with water. Absorbance spectral data were
acquired and recorded in wavelength range from 2500 to
4000 nm for a total of 43 coconut milk samples. Coconut
milk ware prepared in three forms of adulteration. Coconut
milk comes from traditional markets and instant coconut
milk in Indonesia. Spectra data may also be pre-processed
to increase prediction accuracy, robustness performance using normalize, multiplicative scatter correction (MSC), standard normal variate (SNV), 1st derivative, 2nd derivative, and
combination of 1st derivative and MSC. Calibration models
and cross-validation to forecast those adulteration parameters use two regression algorithms, i.e., principal component
regression (PCR) and partial least square regression (PLSR).
By looking at its statistical metrics, prediction efficiency can be measured and justified (correlation coefficient (r), correlation of determination (R2), and root mean square error
(RMSE)). Obtained FTIR datasets and models can be used as
a non-invasive method to predict and determine adulteration
on coconut milk.