Analysis of oligomer proanthocyanidins in different barley genotypes using high-performance liquid chromatography-fluorescence detection-mass spectrometry and near-infrared methodologies.

Proanthocyanidins are a class of polyphenols present in many foodstuffs (i.e., tea, cocoa, berries, etc.) that may reduce the risk of several chronic diseases. Barley, with sorghum, rice, and wheat, are the only cereals that contain these compounds. Because of that, two barley genotypes, named waxy and non-waxy, were analyzed by normal-phase high-performance liquid chromatography-fluorescence detection-mass spectrometry (NP-HPLC-FLD-MS). Total proanthocyanidin content ranged between 293.2 and 652.6 µg/g of flour. Waxy samples reported the highest content (p < 0.05) of proanthocyanidins. Dimer compounds were the principal proanthocyanidin constituents of barley samples. Moreover, the possibility to use near-infrared (NIR) spectroscopy as a rapid method to discriminate between waxy and non-waxy samples and to predict quantitatively proanthocyanidins in barley samples was evaluated. Partial least squares (PLS) models were built to predict the proanthocyanidin constituent, obtaining determination coefficients (R(2)) ranging from 0.92 to 0.97, in test set validation. Because of that, this study highlights that NIR spectroscopy technology with multivariate calibration analysis could be successfully applied as a rapid method to determine proanthocyanidin content in barley.

Evaluation of coffee roasting degree by using electronic nose and artificial neural network for off-line quality control.

UNLABELLED: An electronic nose (EN) based on an array of 10 metal oxide semiconductor sensors was used, jointly with an artificial neural network (ANN), to predict coffee roasting degree. The flavor release evolution and the main physicochemical modifications (weight loss, density, moisture content, and surface color: L*, a*), during the roasting process of coffee, were monitored at different cooking times (0, 6, 8, 10, 14, 19 min). Principal component analysis (PCA) was used to reduce the dimensionality of sensors data set (600 values per sensor). The selected PCs were used as ANN input variables. Two types of ANN methods (multilayer perceptron [MLP] and general regression neural network [GRNN]) were used in order to estimate the EN signals. For both neural networks the input values were represented by scores of sensors data set PCs, while the output values were the quality parameter at different roasting times. Both the ANNs were able to well predict coffee roasting degree, giving good prediction results for both roasting time and coffee quality parameters. In particular, GRNN showed the highest prediction reliability. PRACTICAL APPLICATION: Actually the evaluation of coffee roasting degree is mainly a manned operation, substantially based on the empirical final color observation. For this reason it requires well-trained operators with a long professional skill. The coupling of e-nose and artificial neural networks (ANNs) may represent an effective possibility to roasting process automation and to set up a more reproducible procedure for final coffee bean quality characterization.