Characterization of Encapsulated Flavor Systems by NIR and Low-field TD-NMR: A Chemometric Approach

Andrade, Letícia; Farhat, Imad A.; Aeberhardt, Kasia; Normand, Valéry; Engelsen, Søren B.

doi:10.1007/s11483-007-9046-3

Cited by 6 publications

(8 citation statements)

References 34 publications

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“…This finding underlines the fact that in glassy encapsulation systems the observable NMR signals originate exclusively from the encapsulated oils. Due to the hydrophobicity of the flavours used in this study, the oils exist as discrete droplets and the interaction with the carbohydrate matrix is insignificant 5. In contrast, in the spectrum of yeast cells loaded with limonene (Fig.…”

Section: Resultsmentioning

confidence: 95%

“…A common technique for the determination of oil loading is time‐domain NMR (TD‐NMR). Compared to classical analytical methods, for example solvent extraction, NMR provides several advantages, as the method is non‐invasive, usually fast and requires minimal sample pre‐treatment 4–6. This technique is widely used in the food industry on relatively inexpensive benchtop NMR spectrometers for the assessment of the phase composition of lipid‐based food products 7, 8.…”

Section: Introductionmentioning

confidence: 99%

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Determination of flavour loading in complex delivery systems by time‐domain NMR

Hafner

Dardelle

Normand

et al. 2011

Euro J Lipid Sci & Tech

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The determination of the flavour content of a newly developed encapsulation system based on yeast was attempted using time-domain NMR (TD-NMR) and compared to thermo gravimetric analysis (TGA). Flavour content as measured by TD-NMR is systematically higher than by TGA. The constant discrepancy between the two techniques is attributed to the presence of the phospholipidic membrane inside the yeast. These phospholipids also contribute to the NMR signal as was revealed by high field NMR studies under magic angle spinning conditions. The signal obtained for the empty yeast matched the extra-contribution to the signal of the flavoured yeast encapsulation system. A classical glassy encapsulation system was used as a comparison to highlight further the presence of the phospholipids in the yeast system. A two step procedure is proposed to correct the reading of the flavoured yeast for the contribution of the yeast cells themselves.Practical applications: TD-NMR is routinely used for the determination of the liquid content encapsulated in a solid matrix, where the liquid can be any oil, including lipids, or flavour and fragrance oils. As shown in the present study, in order to obtain accurate values of the oil loading, the nature of the carrier material of the encapsulation system must be taken into account. For example, with the conventional TD-NMR method, flavour loading in encapsulation systems in yeast cells is systematically overestimated since the phospholipid bilayer contributes to the NMR signal of the flavour oil. With the two-step procedure presented in this article, by subtracting the baseline value obtained for empty yeast cells, accurate loading values for yeast cell carriers can be determined. The modified testing approach is highly accurate and widely applicable to a variety of liquids including flavours, perfumes or nutritive fish oils encapsulated in solid delivery systems. IntroductionFlavour encapsulation is widely used in the food industry since flavour oils are often volatile and therefore subjected to significant losses during processing of food materials. In general, encapsulation is a technique by which the active core material, e.g. flavour oils, perfumes or pharmaceuticals, is coated by, or entrapped within a second material, referred to as matrix, shell, wall material, carrier or encapsulant to form a core-shell composite structure. The active core can thus be protected from moisture, heat, oxidation or other extreme conditions during storage and processing [1,2]. The release of the active can be triggered by pH change, mechanical stress, temperature, enzymatic activity, time or osmotic forces [3], thereby allowing controlled release of the flavour from the shell at a specific time or under predefined conditions. Therefore encapsulation products are also referred to as delivery systems. Flavour delivery systems have enhanced flavour functionality and performance in applications where stability to long shelf life and heavy thermal processing are required. The determination of the flavour oil con...

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Section: Resultsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Determination of flavour loading in complex delivery systems by time‐domain NMR

Hafner

Dardelle

Normand

et al. 2011

Euro J Lipid Sci & Tech

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“…Retention of oil within encapsulate powders was determined by time-domain low-field nuclear magnetic resonance (TD-LF-NMR) as described by Andrade, Farhat, Aeberhardt, Normand, and Engelsen (2008). This technique measures the fraction of a complex sample that has sufficient molecular mobility to respond to a change in magnetic field (Hafner, Dardelle, Normand, & Fieber, 2011).…”

Section: Methodsmentioning

confidence: 99%

Spray chill encapsulation of flavors within anhydrous erythritol crystals

Sillick

Gregson

2012

LWT - Food Science and Technology

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“…A complete description of the samples, including preparation steps, has previously been reported. 18 The initial sample set consisted of five samples: one control (not containing limonene) and four others with 2.6%, 5.1%, 9.2%, and 10.7% (w/w) limonene, determined by steam distillation (modified method 2.8.12, European Pharmacopoeia, 1997). The initial water content of each system was determined by Karl-Fisher titration 19 as being 5.4%, 6.1%, 5.4%, 4.6%, and 4.7% for the control and the systems containing limonene, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, a quantitative method for measuring flavor and water content in spray-dried flavor delivery systems was proposed. 18 Low field TD-NMR and NIR data were acquired on spray dried flavor delivery systems at a constant temperature (20 8C). Simple PLS regression models computed on the data acquired, for the prediction of flavor and water content, showed excellent predictive ability, with correlation coefficients (r) between 0.96 and 1.00 and low prediction errors compared to the concentration range.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Temperature-Induced Near-Infrared and Low-Field Time-Domain Nuclear Magnetic Resonance Spectral Variation: Chemometric Prediction of Limonene and Water Content in Spray-Dried Delivery Systems

et al. 2009

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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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Characterization of Encapsulated Flavor Systems by NIR and Low-field TD-NMR: A Chemometric Approach

Cited by 6 publications

References 34 publications

Determination of flavour loading in complex delivery systems by time‐domain NMR

Determination of flavour loading in complex delivery systems by time‐domain NMR

Spray chill encapsulation of flavors within anhydrous erythritol crystals

Modeling of Temperature-Induced Near-Infrared and Low-Field Time-Domain Nuclear Magnetic Resonance Spectral Variation: Chemometric Prediction of Limonene and Water Content in Spray-Dried Delivery Systems

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