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.

Prediction of the type of milk and degree of ripening in cheeses by means of artificial neural networks with data concerning fatty acids and near infrared spectroscopy.

The present study addresses the prediction of the time of ripening and type of mixtures of milk (cow's, ewe's and goat's) in cheeses of varying composition using artificial neural networks (ANN). To accomplish this aim, neural networks were designed using as input data the content of 19 fatty acids obtained with GC-FID of the cheese fat and scores obtained from principal component analysis (PCA) of NIR spectra. The best model of neuronal networks for the identification of the type of mixtures of milk was obtained using the information concerning the fatty acid concentration (80% of correct results in the training phase and 75% in the validation phase). Regarding the information of the near-infrared (NIR) spectra a neural network was designed. The aforesaid neural network predicted the ripening of cheeses with 100% accuracy in both training and in validation.

Multivariate calibration by near infrared spectroscopy for the determination of the vitamin E and the antioxidant properties of quinoa.

Quinoa is a pseudocereal that is grown mainly in the Andes. It is a functional food supplement and ingredient in the preparation of highly nutritious food. In this paper we evaluate the potential of near infrared spectroscopy (NIR) for the determination of vitamin E and antioxidant capacity in the quinoa as total phenol content (TPC), radical scavenging activity by DPPH (2,2-diphenyl-2-picryl-hydrazyl) and cupric reducing antioxidant capacity (CUPRAC) expressed as gallic acid equivalent (GAE). For recording NIR a fiber optic remote reflectance probe applied directly on the quinoa samples without treatment was used. The regression method used was modified partial least squares (MPLS). The multiple correlation coefficients (RSQ) and the standard prediction error corrected (SEP(C)) were for the vitamin E (0.841 and 1.70 mg 100 g(-1)) and for the antioxidants TPC (0.947 and 0.08 mg GAE g(-1)), DPPH radical (0.952 and 0.23 mg GAE g(-1)) and CUPRAC ( 0.623 and 0.21 mg GAE g(-1)), respectively. The prediction capacity of the model developed measured by the ratio performance deviation (RPD) for vitamin E (2.51), antioxidants TPC (4.33), DPPH radical (4.55) and CUPRAC (1.55) indicated that NIRS with a fiber optic probe provides an alternative for the determination of vitamin E and antioxidant properties of the quinoa, with a lower cost, higher speed and results comparable with the chemical methods.