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.