Estimation of Moisture and Protein Content in Pumpkin Seeds Using NIRS with Partial Least Square (PLS) Method

Abstract

Most people do not use pumpkin seeds and only fruit for food production. Meanwhile, pumpkin seeds contain antioxidants, protein, carbohydrates, and other vitamins. Measurement of the chemical content of a material is usually carried out destructively (conventional methods) in the laboratory. This method is expensive, takes a long time to repair samples, and creates chemical waste. One method that is currently being developed to detect the chemical content of a material is the near-infrared spectroscopy (NIRS) method. Near-infrared spectroscopy (NIRS) is a non-destructive method used to quickly analyze and obtain information on a material's chemical content without using chemicals. The NIRS data calibration technique using partial least squares (PLS) is a step carried out to build a model that relates the response spectra of each sample at each wavelength to chemical concentrations known from laboratory analysis and protein content in pumpkin seeds with NIRS using PLS calibration. Spectrum data pre-treatment was carried out with GapDerivarive and Derivative Savitzky-Golay. The results showed that the best calibration model for moisture and protein content was obtained using DerivativeGap data processing with values of r = 0.92, R2 = 0.84, SEC = 0.11%, and RMSEC = 0.11%. RPD = 2.66 and using Latent Variable (LV) factor 2, while for protein content r = 0.95, R2 = 0.89, SEC = 0.71%, and RMSEC = 0.70%. RPD value = 3.33 and using Latent Variable (LV) factor 3.