A quantitative method for measuring simultaneously the flavor and water contents in model spray-dried flavor delivery systems was developed using spectroscopic techniques and chemometrics. Nine encapsulated systems were prepared, consisting of a solid carrier (maltodextrin and gum arabic) and varying the amounts of water and flavor. The model flavors used in this work were a hydrophobic (limonene) and a more hydrophilic (2,5-dimethylpyrazine) single components. Near-infrared (NIR) and low-field timedomain nuclear magnetic resonance (low field TD-NMR) data were acquired on each system and analyzed using multivariate chemometric techniques to develop optimal prediction models. Partial least squares regression models showed good predictive ability, with coefficients of determination (R 2 ) between 0.81 and 1.00 and low root mean square error of cross-validation values compared to the range of concentrations. The predictive ability of the chemometric models computed using the NIR spectra improved significantly when data were pre-processed using multiplicative signal correction. The development of good prediction models (i.e., robust models resulting in accurate predictions for water and flavor content) from the NMR relaxation data spectra was successful only for the hydrophobic limonene systems, yielding prediction models whose performance was better than the models obtained using the NIR data. Overall, NIR spectroscopy and NMR relaxometry were identified as complementary techniques rather than competitive methods in the characterization of encapsulated flavor systems.

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The influence of temperature on near-infrared (NIR) and nuclear magnetic resonance (NMR) spectroscopy complicates the industrial applications of both spectroscopic methods. The focus of this study is to analyze and model the effect of temperature variation on NIR spectra and NMR relaxation data. Different multivariate methods were tested for constructing robust prediction models based on NIR and NMR data acquired at various temperatures. Data were acquired on model spray-dried limonene systems at five temperatures in the range from 20 °C to 60 °C and partial least squares (PLS) regression models were computed for limonene and water predictions. The predictive ability of the models computed on the NIR spectra (acquired at various temperatures) improved significantly when data were preprocessed using extended inverted signal correction (EISC). The average PLS regression prediction error was reduced to 0.2%, corresponding to 1.9% and 3.4% of the full range of limonene and water reference values, respectively. The removal of variation induced by temperature prior to calibration, by direct orthogonalization (DO), slightly enhanced the predictive ability of the models based on NMR data. Bilinear PLS models, with implicit inclusion of the temperature, enabled limonene and water predictions by NMR with an error of 0.3% (corresponding to 2.8% and 7.0% of the full range of limonene and water). For NMR, and in contrast to the NIR results, modeling the data using multi-way N-PLS improved the models' performance. N-PLS models, in which temperature was included as an extra variable, enabled more accurate prediction, especially for limonene (prediction error was reduced to 0.2%). Overall, this study proved that it is possible to develop models for limonene and water content prediction based on NIR and NMR data, independent of the measurement temperature.

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