Open AccessThe application of NIRS method for non-destructive measurement of fat and carbohydrates in pumpkin seeds
Author(s) -
Ifmalinda,
Andasuryani,
. Santosa,
Indri Mardian Eka Putri
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/922/1/012024
Subject(s) - partial least squares regression , calibration , near infrared spectroscopy , absorbance , second derivative , chemistry , mathematics , analytical chemistry (journal) , chromatography , optics , statistics , physics , mathematical analysis
Pumpkin is one of the agricultural products that can be used as an alternative food ingredient. Chemical content such as carbohydrates and fats can be obtained by the near infrared spectroscopy (NIRS) method by using NIR light that is penetrated from the material so that the reflectance and absorbance spectra will be obtained. This spectral data is the value of the reflectance intensity and is the raw data that still contains noise. Furthermore, it is processed with the NIRS data processing method to reduce the influence of wave inference and noise on the spectral data in order to obtain more accurate results. The purpose of this study was to obtain the best calibration mode to estimate the fat and carbohydrate content of pumpkin seeds using the NIRS method. Pre-treatment of the spectrum data was carried out with GapDerivarive and Derivative Savitzky-Golay. NIRS spectra data were processed using the multivariate Partial Least Squares (PLS) calibration method. The results showed the best calibration model for carbohydrate and fat content using DerivativeGap data processing with values of r = 0.95, R2 = 0.98, SEC = 1.25, and RMSEC = 1.23. and using Latent Variable (LV) factor 3, while for fat content r = 0.99, R2 = 0.89, SEC = 0.17 and RMSEC = 0.17. and using Latent Variable (LV) factor 4.