Open AccessNIR reflectance spectroscopy and SIMCA for classification of crops flour
Author(s) -
Rudiati Evi Masithoh,
I Yuliyanda
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/355/1/012004
Subject(s) - principal component analysis , mathematics , chemometrics , reflectivity , food science , chemistry , chromatography , statistics , physics , optics
The potential of SIMCA technique for crops flour classification was studied based on FT-NIR spectroscopy in this research. A total of 72 spectra of flour samples taken from 6 types of crops, i.e. of banana, breadfruit, taro, arrowroot, purple sweet potato, and modified cassava (mocaf). The reflectance data were measured using the NIRFlex N500 Fiber Optic Solids Cell at 4000 – 10, 000 cm −1 . The spectral obtained were pre-processed and analyzed using The Unscrambler X version 10.5.1. A 2 nd derivative Savitzky-Golay (polynomial order 2, 25 smoothing points) followed by a Standard Normal Variate (SNV) were used for pre-treatment data. Characterization of the flours was done using chemometric models based on principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) explained for each group of flour samples of banana, breadfruit, taro, arrowroot, purple sweet potato, and modified cassava (mocaf). SIMCA calibration models were constructed using 6 spectral measurements for each type of flours; classification set were constructed using 6 spectral measurements. The SIMCA accuracy classification were 100% for mocaf, banana, arrowroot, bread fruit, and taro, and 67% for purple sweet potato.