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Non-destructive determination of the main chemical components of red dragon fruit peel flour by using Near-Infrared Reflectance Spectroscopy (NIRS)
Author(s) -
Y Hotmaida,
Sutrisno Sutrisno
Publication year - 2020
Publication title -
iop conference series earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/542/1/012007
Subject(s) - partial least squares regression , near infrared reflectance spectroscopy , absorbance , chemistry , calibration , reflectivity , chemical composition , near infrared spectroscopy , calibration curve , analytical chemistry (journal) , food science , mathematics , chromatography , biology , optics , detection limit , statistics , physics , organic chemistry , neuroscience
Red dragon fruit peel is still not optimally utilized and only becomes waste. According to previous research, it is known that red dragon fruit peel contains 72.1% carbohydrate, 0.7% fat, 3.2% protein, and 46.7% food fiber and contains betacyanin of 186.90 mg / 100g dry weight and 53.71% antioxidant. One of the solutions to use red dragon fruit peel is to process it into flour. The accurate and efficient determination of the chemical content of red dragon fruit peel flour can be realized through the development of the Near-Infrared Reflectance Spectroscopy (NIRS) technology. The sample that used was made directly from the waste of red dragon fruit peel with a process of flouring, then measured the spectra with NIRS and the chemical content of the sample destructively. The reflectance spectra as output were transformed into absorbance spectra. Data calibration uses a partial least squares (PLS) method with several variations in the number of factors and pretreatment data in the form of normalization and smoothing Savitzky-Golay smoothing (SGs), resulting in the best calibration regression model in estimating water, ash, carbohydrate, and protein content. In contrast, the estimation of fat cannot be done non-destructively by using the NIRS method.

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