The Potential Use of Near Infrared Spectroscopy (NIRS) to Determine the Heavy Metals and the Percentage of Blends in Tea.

The following study analyzed the potential of Near Infrared Spectroscopy (NIRS) to predict the metal composition (Al, Pb, As, Hg and Cu) of tea and for establishing discriminant models for pure teas (green, red, and black) and their different blends. A total of 322 samples of pure black, red, and green teas and binary blends were analyzed. The results showed that pure red teas had the highest content of As and Pb, green teas were the only ones containing Hg, and black teas showed higher levels of Cu. NIRS allowed to predict the content of Al, Pb, As, Hg, and Cu with ratio performance deviation values > 3 for all of them. Additionally, it was possible to discriminate pure samples from their respective blends with an accuracy of 98.3% in calibration and 92.3% in validation. However, when the samples were discriminated according to the percentage of blending (>95%, 95-85%, 85-75%, or 75-50% of pure tea) 100% of the samples of 10 out of 12 groups were correctly classified in calibration, but only the groups with a level of pure tea of >95% showed 100% of the samples as being correctly classified as to validation.

Prediction of Mineral Composition in Wheat Flours Fortified with Lentil Flour Using NIR Technology.

Lentil flour is an important source of minerals, including iron, so its use in food fortification programs is becoming increasingly important. In this study, the potential of near infrared technology to discriminate the presence of lentil flour in fortified wheat flours and the quantification of their mineral composition is evaluated. Three varieties of lentils (Castellana, Pardina and Guareña) were used to produce flours, and a total of 153 samples of wheat flours fortified with them have been analyzed. The results show that it is possible to discriminate fortified flours with 100% efficiency according to their lentil flour content and to discriminate them according to the variety of lentil flour used. Regarding their mineral composition, the models developed have shown that it is possible to predict the Ca, Mg, Fe, K and P content in fortified flours using near infrared spectroscopy. Moreover, these models can be applied to unknown samples with results comparable to ICP-MS determination of these minerals.

Effects of the Replacement of Pork Backfat with High Oleic Sunflower Oil on the Quality of the "Chorizo Zamorano" Dry Fermented Sausage.

The "Chorizo Zamorano" dry fermented sausage is a traditional Spanish product which is highly appreciated by consumers. This paper studies the reformulation of this product in order to improve its lipid composition and its fatty acid profile and to reduce its fat content. To achieve this, the fat used in the production of the product was partially replaced with high oleic sunflower oil in proportions of 12.5%, 20%, and 50% of the total fat content. Proximate analysis, fatty acid profiles, lipid oxidation, and sensory analysis were studied. The replacement of fat with oil showed a significant effect on the evolution of the parameters analyzed during ripening in all cases. The batches with sunflower oil presented higher levels of monounsaturated fatty acids (MUFA) and lower levels of saturated fatty acids (SFA) and a similar amount of polyunsaturated fatty acids (PUFA) to the control products. The replacement of up to 20% of oil showed no significant differences for most of the physicochemical and sensory parameters analyzed at the end of the ripening.

Carbon stable isotopes, fatty acids and the use of NIRS to differentiate IBERIAN pigs.

This study explores the viability of the application of Near Infrared Spectrometry (NIR) for the rapid prediction of the ratio of 13C/12C stable isotopes and fatty acid composition in Iberian pigs. The potential use of this technique for distinguishing samples according to the duration of the montanera period was also studied. Subcutaneous fat samples from 50% and 100% Iberian pigs allowed to feed freely during different montanera periods were analyzed: 24 biopsies were taken prior to the montanera and 106 samples were taken after this feeding period. The results show significant correlations between δ13C (‰) and several fatty acids. Furthermore, it is possible to differentiate samples taken from pigs reared using different feeding regimes by analyzing the data obtained from the NIR spectra or by applying an Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) on data on δ13C (‰) and fatty acids in subcutaneous fat.

Prediction of stable isotopes and fatty acids in subcutaneous fat of Iberian pigs by means of NIR: A comparison between benchtop and portable systems.

The potential of a portable Near Infrared spectrophotometer compared with that of NIR benchtop equipment is assessed to determine the13C/12C relationship of stable isotopes and the fatty acid content. 105 samples of subcutaneous fat of Iberian pigs collected at the time of their slaughter have been analyzed. The analysis of stable isotopes and gas chromatography were the methods of reference used. The samples were analyzed without prior handling (portable and benchtop NIR) and after extracting the fat (benchtop NIR). The results show that with the portable equipment it is possible to determine δ13C (‰), 12 fatty acids, and 5 summations of fatty acids (SFA, MUFA, PUFA, w3, and w6), while with the benchtop NIR equipment it is possible to measure δ13C (‰), 16 fatty acids, and the 5 summationsof fatty acids. The correlation coefficients of the portable equipment were slightly lower than those of the NIR benchtop equipment.

NIR Spectroscopy for Discriminating and Predicting the Sensory Profile of Dry-Cured Beef "Cecina".

For Protected Geographical Indication (PGI)-labeled products, such as the dry-cured beef meat "cecina de León", a sensory analysis is compulsory. However, this is a complex and time-consuming process. This study explores the viability of using near infrared spectroscopy (NIRS) together with artificial neural networks (ANN) for predicting sensory attributes. Spectra of 50 samples of cecina were recorded and 451 reflectance data were obtained. A feedforward multilayer perceptron ANN with 451 neurons in the input layer, a number of neurons varying between 1 and 30 in the hidden layer, and a single neuron in the output layer were optimized for each sensory parameter. The regression coefficient R squared (RSQ > 0.8 except for odor intensity) and mean squared error of prediction (MSEP) values obtained when comparing predicted and reference values showed that it is possible to predict accurately 23 out of 24 sensory parameters. Although only 3 sensory parameters showed significant differences between PGI and non-PGI samples, the optimized ANN architecture applied to NIR spectra achieved the correct classification of the 100% of the samples while the residual mean squares method (RMS-X) allowed 100% of non-PGI samples to be distinguished.

Prediction of Sensory Parameters of Cured Ham: A Study of the Viability of the Use of NIR Spectroscopy and Artificial Neural Networks.

Dry-cured ham is a high-quality product owing to its organoleptic characteristics. Sensory analysis is an essential part of assessing its quality. However, sensory assessment is a laborious process which implies the availability of a trained tasting panel. The aim of this study was the prediction of dry-ham sensory characteristics by means of an instrumental technique. To do so, an artificial neural network (ANN) model for the prediction of sensory parameters of dry-cured hams based on NIR spectral information was developed and optimized. The NIR spectra were obtained with a fiber-optic probe applied directly to the ham sample. In order to achieve this objective, the neural network was designed using 28 sensory parameters analyzed by a trained panel for sensory profile analysis as output data. A total of 91 samples of dry-cured ham matured for 24 months were analyzed. The hams corresponded to two different breeds (Iberian and Iberian x Duroc) and two different feeding systems (feeding outdoors with acorns or feeding with concentrates). The training algorithm and ANN architecture (the number of neurons in the hidden layer) used for the training were optimized. The parameters of ANN architecture analyzed have been shown to have an effect on the prediction capacity of the network. The Levenberg-Marquardt training algorithm has been shown to be the most suitable for the application of an ANN to sensory parameters.