The aim of this study was to develop chemometric models for
protein, fat, ashes and carbohydrates contents of quinoa flour using
Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa flour
obtained from grains of 70 different cultivars were scanned while dietary constituents
were determined by reference AOAC methods. As a pre-treatment,
spectra were subjected to extended multiplicative signal correction (EMSC) with
polynomial degree 0, 1 or 2. Next, the Canonical Powered Partial Least Squares
(CPPLS) algorithm was applied, and models were compared in terms of accuracy
and predictability. For all models, root mean square errors of
cross-validation (RMSECV), root meat square errors of prediction (RMSEP)
and coefficient of correlation of cross-validation (RCV) were computed. Robust
models were obtained when quinoa spectra were pre-processed using EMSC of
polynomial degree 2 for both fat (RMSECV: 0.268% and RMSEP: 0.256%) and
carbohydrates (RMSECV: 0.641% and RMSEP: 0.643%) following extraction
of five CPPLS latent variables. Good coefficients of correlation of prediction
(RP: 0.690–0.821) were found for all constituents when models were validated
on a test data set consisting of 13 quinoa flour spectra. Thus, good predictions of
the dietary constituents of quinoa flour could be achieved by using NIT technology,
as implied by the low coefficient of variation of prediction (CVP):
5.64% for protein, 3.88% for fat 7.32% for ashes and 0.80% for carbohydrates
contents.
