Chemical characteristics and mineral composition of quinoa by near-infrared spectroscopy.

BACKGROUND: Quinoa (Chenopodium quinoa Willd.) has been traditionally used as a foodstuff in the Andes and it has gained increasing interest in recent years owing to its high nutritional value. The potential of near-infrared spectroscopy (NIRS) for estimating chemical compounds in quinoa was studied because of the possibility of conducting direct measurement without prior sample treatment. RESULTS: The use of NIRS technology with a remote reflectance fiber-optic probe for the analysis of major compounds and mineral composition of 48 quinoa samples was studied. The calibration of the NIRS instrument was conducted using modified partial least squares (MPLS) analysis. This allowed the determination of protein (16.0-20.2 g 100g⁻¹), crude fiber (1.8-3.1 g 100g⁻¹), fat (4.4-7.5 g 100g⁻¹), calcium (298.8-1164.5 mg kg⁻¹), iron (0-948.5 mg kg⁻¹) and phosphorus (2735.0-4543.3 mg kg⁻¹). The correlation coefficients (RSQ) were 0.83 for protein, 0.73 for crude fiber, 0.93 for fat; 0.60 for calcium; 0.76 for iron and 0.75 for phosphorus. The robustness of the equations obtained was verified by external validation on unknown quinoa samples. CONCLUSIONS: NIRS with fiber-optic probe provides an alternative for the determination of chemical compounds of quinoa, faster and at lower cost, with results comparable with chemical methods.

Chilean flour and wheat grain: tracing their origin using near infrared spectroscopy and chemometrics.

Instrumental techniques such a near-infrared spectroscopy (NIRS) are used in industry to monitor and establish product composition and quality. As occurs with other food industries, the Chilean flour industry needs simple, rapid techniques to objectively assess the origin of different products, which is often related to their quality. In this sense, NIRS has been used in combination with chemometric methods to predict the geographic origin of wheat grain and flour samples produced in different regions of Chile. Here, the spectral data obtained with NIRS were analysed using a supervised pattern recognition method, Discriminat Partial Least Squares (DPLS). The method correctly classified 76% of the wheat grain samples and between 90% and 96% of the flour samples according to their geographic origin. The results show that NIRS, together with chemometric methods, provides a rapid tool for the classification of wheat grain and flour samples according to their geographic origin.