Original article Rapid instrumental methods and chemometrics for the determination of pre-crystallization in chocolate

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1 International Journal of Food Science and Technology 25, 4, Original article Rapid instrumental methods and chemometrics for the determination of pre-crystallization in chocolate Gitte Svenstrup, 1 Hanne Heimdal 2 & Lars Nørgaard 1 * 1 Chemometrics Group, Department of Food Science, The Royal Veterinary and Agricultural University, Rolighedsvej 3, DK-1958 Frederiksberg C, Denmark 2 Toms Confectionery Group, DK-275 Ballerup, Denmark (Received 6 June 24; Accepted in revised form 1 March 25) Summary Keywords Three rapid instrumental methods for the determination of the pre-crystallization stage in six types of chocolate were studied. The methods were near-infrared (NIR) spectroscopy, fluorescence spectroscopy and tri stimulus colour measurements. The chocolates were tempered into five categories: two levels of under-tempered, two levels of over-tempered and one level of well-tempered chocolate. A temper meter was used as the reference method. NIR and fluorescence data were orthogonalized before modelling in order to remove the chocolate type characteristics. NIR spectroscopy was capable of discriminating between the five tempering groups when the principal component analysis (PCA) model was used on all chocolate types. A partial least squares discriminant analysis on the NIR spectra with the three main tempering groups (over-, well-, and under-tempered) as the dependent variable showed perfect separation of the groups. Using fluorescence spectroscopy it was possible to separate the chocolates into the three main tempering groups in a PCA model, while the colour measurements did not reflect the degree of pre-crystallization. Colour measurement, eliminating product differences, fluorescence spectroscopy, gloss, near-infrared spectroscopy, tempering. Introduction Tempering pre-crystallization is one of the main processes influencing product quality in chocolate manufacturing. The aim of the precrystallization step is to produce crystal seeds in the preferred V (b 2 ) crystal lattice of cocoa butter. Cocoa butter, cocoa mass and sugar are the three main ingredients in dark chocolate: cocoa butter is a polymorphic fat, a mixture of different crystalline phases. Generally, triglycerides have three main crystallization structures: a, b and b, where a is the least stable, b the most stable and b shows stability in between a and b (Schlichter-Aronhime & Garti, 1988). Cocoa butter has been found to *Correspondent: Fax: ; lan@kvl.dk have six different solid phases (I VI) (ille & Lutton, 1966) and, consequently, it also has six different melting points. The six solid phases correspond to the a, b, and b structures shown in Table 1. Some authors claim that phase III is a mix of phase II and phase IV (ille & Lutton, 1966; Huyghebaert & Hendrickx, 1971; Schlichter- Aronhime & Garti, 1988). In chocolate manufacturing the optimal crystal lattice of cocoa butter is phase V; in this phase the chocolate has a glossy surface, a clean break and, as a consequence of the relatively higher melting point, the chocolate does not Ômelt in the hand but in the mouthõ (Loisel et al., 1997b; Voltz & Beckett, 1997; Talbot, 1999). ith time (>12 months) or if the chocolate is stored at too high a temperature (>21 C) phase V may recrystallize to crystal VI because this solid phase is more stable than V. doi:1.1111/j x Ó 25 Institute of Food Science and Technology Trust Fund

2 954 Rapid determination of chocolate tempering G. Svenstrup et al. Table 1 The different solid phases and melting points of cocoa butter (ille & Lutton, 1966) I (17.3 C) II (23.3 C) III (25.5 C) IV (27.5 C) V (33.8 C) VI (36.3 C) (Larsson, 1966) Sub a a b 2 b 1 b 2 b 1 This recrystallization causes undesired blooming of the chocolate (Schlichter-Aronhime & Garti, 1988; Hachiya et al., 1989; Pszczola, 1997; Hartel et al., 1999). In order to obtain a large number of the smallest possible crystals in the right crystalline form the chocolate has to be well tempered. This is performed in a machine called a temperer. First all the crystal seeds in the chocolate must be completely melted (reheated); i.e. the temperature is increased to about 5 C. Secondly, the chocolate is slowly cooled; thereby a saturated solution of triglycerides with high melting points is obtained. In the next zone of the temperer the chocolate is cooled further and in this phase the stable V and the unstable II, III, and IV polymorphic forms are formed (JovanovicÕ et al., 1995). Finally, the temperature is raised to enhance the formation of the polymorphic form of V by melting the unstable crystals II, III and IV (JovanovicÕ et al., 1995; Stapley et al., 1999). If the chocolate is not well-tempered it is either over-tempered, under-tempered or un-tempered. Over-tempered chocolate contains a large amount of crystals in different sizes and forms (Allen, 1995; Loisel et al., 1997a). The reason may be that one or more of the temperatures have been too low, resulting in unstable crystals not melting or leading to crystals which are too big (Loisel et al., 1997a). Under-tempered chocolate contains too few crystals (Allen, 1995; Loisel et al., 1997a) and the reason for this may be too high a temperature in one or several zones of the temperer, causing some of the stable V crystals to melt along with the unstable crystals. A third possibility is that the chocolate is un-tempered, i.e. there are no crystals in the chocolate. Different methods have been used to classify the pre-crystallized chocolate, but the temper meter is the most commonly used method in the chocolate industry. The method is based on a calorimetric principle where the chocolate sample is cooled in a cooling cell with a temperature sensor inserted in the chocolate sample. The sensor measures changes in temperature of the sample during cooling. According to the pre-crystallization state of the chocolate the appearance of the temper curve varies. The method is at-line and the measurement takes approximately 1 min (Allen, 1995; Bolliger et al., 1998; Talbot, 1999). Differential scanning calorimetry (DSC) is an alternative method for analysing the pre-crystallization of the chocolate and this method is primarily used in research laboratories. To date, only a very limited number of studies have been published introducing on-line/at-line methods to determine pre-crystallization; e.g. near-infrared spectroscopy (NIR) has been shown to correlate with DSC and viscosity with a squared correlation coefficient (r 2 ) of.96 and.95 respectively (indhab & Bolliger, 1996; Bolliger et al., 1999). The aim of this paper was to investigate NIR, fluorescence spectroscopy and tri stimulus colour measurements in relation to classification of the pre-crystallization of chocolate by means of multivariate data analysis. The temper meter was used as the reference method. Materials and methods Materials Six different types of chocolate (A F) were examined: two types of milk chocolate and four types of plain chocolate. The different chocolates all contained cocoa mass, cocoa butter, sugar, lecithin, and ethyl vanillin. The differences between the chocolates were, besides the milk in milk chocolate, different kinds and amounts of fat and the emulsifier polyglycerol polyricinoleate (PGPR), which is used to decrease the viscosity of a chocolate. In Table 2 the chocolates and the different kinds of fat and emulsifier in the products are listed. Methods Chocolate was pre-crystallized using an Aasted temper machine AMK 1 (Aasted-Mikroverk, Farum, Denmark) with three tempering zones and a reheating container. The tempering temperatures International Journal of Food Science and Technology 25, 4, Ó 25 Institute of Food Science and Technology Trust Fund

3 Rapid determination of chocolate tempering G. Svenstrup et al. 955 Table 2 The ingredients of the analysed chocolates. Fats and emulsifiers (PGPR) are responsible for the main differences Products are dependent on the individual chocolate type, but the procedure for the temperature adjustments was the same for all the products. Each type of chocolate was pre-crystallized into five categories (the temperature refers to the water temperature): Initially, a well-tempered chocolate was made. The overtempered level 1 samples () were made by means of adjusting reheating temperature to 4 C (from 5 C) with the rest of the adjustments kept stable. For over-tempered level 2 samples (I), the temperature in zone 3 was set approximately 2 C lower than for a well-tempered chocolate (reheating temperature 4 C). The under-tempered samples have a reheating temperature at 5 C. The temperature in zone 2 was set approximately 2 C higher than for the well-tempered, to make undertempered level 1 samples (). For under-tempered level 2 samples (I) the temperature in zone 3 was set approximately 1 C higher than for the welltempered chocolate the temperature in the zone 2 was the same as for. An example of the adjustments for chocolate D is shown in Table 3. For every tempering experiment five determinations were measured with the temper meter, NIR spectroscopy, fluorescence spectroscopy and the colour instrument. After the chocolate pre-crystallization had gained equilibrium, a sample was taken, and after 4 s measurements (temper meter, fluorescence, NIR, and colour instrument) were taken. Temper meter Specifics Main ingredients in every chocolate Chocolate A Milk powder Cocoa mass, Chocolate B Milk powder and PGPR cocoa butter, Chocolate C PGPR sugar, lecithin, Chocolate D Butter fat ethyl vanillin Chocolate E Untreated cocoa butter Chocolate F 5% cocoa butter substitute PGPR, polyglycerol polyricinoleate. To determine the temper curves of the pre-crystallized chocolates an Aasted Temper meter Type PC II, (Aasted-Mikroverk) was used. The temper Table 3 The settings of the temper for the five different levels of pre-crystallization for chocolate D Tempering Zone 1 Zone 2 Zone 3 Reheating ell ater Chocolate Over I ater Chocolate Over II ater Chocolate Under I ater Chocolate Under II ater Chocolate Values are expressed in C. curve is a temperature vs. time curve, which is obtained from uniform cooling (8 C cooling cell in this case) of the pre-crystallized sample, while the temperature in the sample (14 ml) is measured precisely. The sampling time was 6 min. Near-infrared spectroscopy The reflectance NIR analysis principle was used. Measurements were made with an MB16 FTIR for Near-IR Analysis, (Bomen Inc., Quebec, Canada). The software was in-bomen Easy tm Version 3.4. Measurements were performed with a Diffus-ir NIR Diffuse Reflectance Accessory and the resolution was 16 cm )1 (sixty-four scans) in the interval from 1 to 4 cm )1. Fluorescence spectroscopy Fluorescence measurements were made using an industrial BioViewÒ System (Delta Light & Optics, Hørsholm, Denmark). The system has fifteen fixed excitation wavelengths (in the range of nm) and fifteen emission wavelengths (in the range of nm) and a neutral density (ND) range (white light measured against every emission wavelength). The slit width of the filters was estimated to be around 2 nm. A xenon lamp is the source of light through an optic fibre. The fibre optic probe was placed directly in the chocolate for measurement. Ó 25 Institute of Food Science and Technology Trust Fund International Journal of Food Science and Technology 25, 4,

4 956 Rapid determination of chocolate tempering G. Svenstrup et al. Colour measuring Colour was measured by using a Minolta Cromameter CR-3 (Minolta, Osaka, Japan) and the parameters obtained were the L, C and h values. The surface of the samples was measured at a distance of 4.5 cm. Every sample was measured ten times. the capability of the temper meter to distinguish between temperings, a PCA was performed. Figure 1 shows the temper curves transformed by MSC and score plot for those as a function of PC1 and PC2, which explain 97.2% of the variance. In the score plot it seems clear that the temperings of the chocolate are completely separated, as they are grouped by the tempering. Data treatment Measurements were evaluated by multivariate data analysis. Principal component analysis (PCA) and partial least squares regression analysis (PLS) (Martens & Næs, 1993), and the temper curves were transformed by multiplicative signal correlation (MSC) to remove the off-set (Martens & Næs, 1993). PCA and PLS models were based on mean-centred data and evaluated using crossvalidation. Multivariate data analysis was performed by using The Unscrambler, Version 8. (CAMO, Trondheim, Norway). To remove the chocolate type characteristics, NIR and fluorescence data were orthogonalized according to equation 1 before further modelling. X ortho ¼ X X YðY YÞ 1 Y ð1þ The Y matrix was of the dimension 3 samples 6 chocolate types (with and 1 ones in the columns), and the X matrix was the original NIR or fluorescence data with dimensions 3 samples 78 measuring points or 3 samples 15 measuring points respectively. Both matrices (X and Y) were mean centred before performing the orthogonalization. The orthogonalization programme was written by the authors in MatLab, Version a Release 13 (The Math- orks, Inc., Natick, MA, USA). The averages of the five determinations of every pre-crystallization were used in the calculations. Results and discussion Temper meter It was not possible to distinguish between the variety of the chocolate types and pre-crystallization when analysing the raw temper curves, but after MSC transformation the curves grouped according to the original tempering. To analyse Near-infrared spectroscopy From the NIR spectra it was possible to distinguish between the six different chocolate types and cocoa butter. The cocoa butter spectrum had a different appearance than the spectra of the chocolates (not shown). Cocoa butter is a main ingredient in chocolate and besides cocoa butter, chocolate contains sugar and cocoa mass. These ingredients contribute to the spectral signals because of the content of peptide and carbohydrate bonds. Cocoa butter and chocolate had peaks at 5786 cm )1 (1728 nm), 5678 cm )1 (1761 nm), and 8247 cm )1 (1213 nm) in common, which match the C H bonds in CH 2 groups (Osborne et al., 1993). Besides the common peaks cocoa butter also had absorbance peaks comparable to C H bonds in both CH and CH 2 groups and, furthermore, C ¼ C and ¼ C H bonds. Moreover, cocoa butter also had amid II and amid III bands at 21 nm (4975 cm )1 ) and 25 nm (4878 cm )1 ) (Osborne et al., 1993), which means that cocoa butter contains a small amount of protein. Chocolate showed peaks, indicating that it contains sugar, e.g. O H in sucrose absorbs at 144 nm (6944 cm )1 ) and 28 nm (488 cm )1 ) (Osborne et al., 1993); these peaks were not observed in the cocoa butter spectrum. A PCA model was performed on the NIR spectra of the chocolate. In the score plot (Fig. 2) the samples appeared to be separated only by the type of chocolate. This was also expected from inspection of the raw spectra. In the score plot the two types of milk chocolate (A and B) were placed separately compared with the other four chocolate types. All six chocolate types had the same internal order of samples; first, the two levels of undertempered, then the two levels of over-tempered and finally, the well-tempered samples, as illustrated in Fig. 2. International Journal of Food Science and Technology 25, 4, Ó 25 Institute of Food Science and Technology Trust Fund

5 Rapid determination of chocolate tempering G. Svenstrup et al. 957 (a) 3 29 Temperature C Figure 1 (a) The multiplicative signal correlation (MSC)-transformed temper curves. (b) Score plot from a principal component analysis (PCA) on the MSC-transformed temper curves of six different chocolate types. PC1 and PC2 explain 78 and 2% of the total variation respectively. ell-tempered (), over-tempered level 1 (), over-tempered level 2 (I), under-tempered level 1 () and under-tempered level 2 (I). (b) PC2 I I I I I I I Time (s) I I I PC1 Figure 2 Score plot of the principal component analysis (PCA) model on the raw near-infrared (NIR) measurements. Arrows illustrate the overall order of tempering, under-tempered (U), over-tempered (O) and well-tempered (), for the six chocolate types (A F). PC1 and PC2 explain 69 and 27% of the total variation respectively PC2 D_U D_UD_O C_U D_O D_ C_UC_O C_OF_U F_U C_ F_O F_O F_ B_U B_O B_U A_U A_U E_U E_U E_O E_O E_ A_OB_O B_ A_O A_ PC1 Ó 25 Institute of Food Science and Technology Trust Fund International Journal of Food Science and Technology 25, 4,

6 958 Rapid determination of chocolate tempering G. Svenstrup et al. After the chocolate type characteristics were removed by orthogonalization according to equation 1, the spectra had a different appearance. The colours indicate the different types of chocolate in Fig. 3a and, as expected, it was not possible to distinguish between the types of chocolate from Fig. 3a, while in Fig. 3b the colours indicate different kinds of chocolate tempering and it was then possible to distinguish the different chocolate temperings. In the score plot of the PCA model performed on the orthogonalized NIR spectra it appeared that the samples were grouped by tempering (Fig. 4). It was possible to distinguish between the tempering groups and the levels within the three major groups. In order to develop a quantitative model a discriminant PLS was performed. In PCA the first score separates the groups and, because of that, the discriminant variable (y) was chosen to be 1 (well-tempered), 2 (over-tempered) and 3 (undertempered), referring to the order of the samples in the score plot (this was not the usual way to (a) avenumber (cm 1 ) (b) avenumber (cm 1 ) 5 4 Figure 3 Near-infrared (NIR) spectra after orthogonalization. The colours in (a) indicate the different chocolate types (blue: A; green: B; black: C; red: D; cyan: E; magenta: F), and the colours in (b) indicate the different temperings of the chocolate (blue: over-tempered I; green: over-tempered II; red: under-tempered I; cyan: undertempered II; magenta: welltempered). International Journal of Food Science and Technology 25, 4, Ó 25 Institute of Food Science and Technology Trust Fund

7 Rapid determination of chocolate tempering G. Svenstrup et al. 959 Figure 4 Score plot of the principal component analysis (PCA) model on the orthogonalized near-infrared (NIR) measurements. PC1 and PC2 explain 89. and 1% of the total variation respectively. elltempered (), over-tempered level 1 (), over-tempered level 2 (I), under-tempered level 1 () and under-tempered level 2 (I) PC2 I I I I I I I I I I I I PC Figure 5 Predicted vs. measured plot for the partial least squares regression analysis (PLS) discriminant model. To separate the three tempering groups five PCs are used and the model explains 9.% of the y-variation. The average prediction error (root mean square error of cross validation, RMSECV) is.14. Under-tempered (U), over-tempered (O) and well-tempered () Predicted Y U U U U U O O O O O Measured Y implement D-PLS, but it was a natural choice in this case as judged from the PCA score plot), and X was the orthogonalized NIR data. The model was fully cross-validated and five PCs were chosen as the optimum. The PLS model explained well the discriminant variable and the three tempering groups were perfectly separated (Fig. 5). Furthermore, it was possible to separate all five categories by a second PLS discriminant model (not shown). The dependent variable (y) was now 1 (well-tempered), 2 (over-tempered level 1), 3 (over-tempered level 2), 4 (under-tempered level 1) and 5 (under-tempered level 2). As shown previously, this referred to the order in the score plot, and X was the orthogonalized NIR data. The model was full cross validated. Except for one under-tempered sample it was possible to perfectly separate the samples by tempering. By means of the PLS discriminant model it was possible to use the discriminant variables as a value of pre-crystallization. This means that, if an unknown sample was measured to a predicted value at <1.5, then the sample was well-tempered, the sample was over-tempered if the predicted value was between 1.5 and 2.5, and undertempered if the predicted value was >2.5. This categorizing might be more graduated if the second PLS discriminant model was used instead. This observation might be useful in the industry to get an idea of what the condition of the chocolate is at any particular moment, just like the chocolate temper unit is used today. One might argue that the samples are separated because of different outlet temperatures. The chocolate temperature is different when it leaves the temperer, depending on tempering; the temperature for the under-tempered chocolate is higher than the temperature of the well- and over-tempered chocolate and the temperature of the well-tempered chocolate is higher than the Ó 25 Institute of Food Science and Technology Trust Fund International Journal of Food Science and Technology 25, 4,

8 96 Rapid determination of chocolate tempering G. Svenstrup et al. over-tempered chocolate. Thus, if the temperature is the explanation for the positions of the samples in the score plot the order should be: undertempered, well-tempered, and over-tempered or the other way round but this was not the case. Another reason why the position in the score plot is not caused by temperature difference is that different types of chocolate were tempered at different temperatures. This means that the undertempered milk chocolate would have the same outlet temperature as over-tempered plain chocolate; thus, if this should be the explanation of the separation, the samples would not be placed so clearly into the tempering groups. The order of the samples in the score plots was not the same for the temper curves (Fig. 1) and the NIR spectra (Fig. 4). The reason may be that the two methods measure two different parameters concerning tempering; the temper meter measures heat generation due to crystallization formation and NIR spectroscopy measures the vibrational reflectance of the bonds. On inspection of the chocolate it was seen that the surface was different between well-tempered and under-/over-tempered chocolate, as the well-tempered chocolate had a different glossiness (data not shown). Previously, NIR spectroscopy experiments have shown a relationship with the pre-crystallization of chocolate. Bolliger et al. (1999) and indhab & Bolliger (1996) have reported a correlation between viscosity and DSC to NIR spectroscopy of.95 and.96 respectively. The present study is a first report of the PCA model being capable of predicting the pre-crystallization of six different chocolate types based on NIR in the same model instead of only one type of chocolate at a time. Fluorescence spectroscopy The unfolded concatenated fluorescence emission spectra of the chocolates recorded at excitation wavelengths from 25 to 55 nm are shown in Fig. 6. It can be observed that the A and B samples were more fluorescent than the other samples. A and B samples were milk chocolate and milk compounds contain different proteins compared with cocoa. Furthermore, milk chocolate contains a higher amount of riboflavin than plain chocolate, which may cause the strong fluorescence at k ex ¼ 44 nm and k em ¼ 52 nm (Duggan et al., 1957). In the PCA model performed on the fluorescence spectra (excluding the ND part) the model only separated the samples by the type of chocolate and not by the tempering categories. In a score plot (not shown) A and B samples were separated far from the rest of the samples, which was expected on inspection of the fluorescence spectra and the samples are, in general, separated by the type of chocolate and not by tempering. After orthogonalization, according to equation 1, i.e. variation by chocolate type was removed, a PCA model on the remaining variation in the fluorescence spectra was calculated. In this model the samples only seemed to have a tendency to group according to tempering Intensity A samples B samples C,D,E & F samples ND Figure 6 Unfolded concatenated fluorescence emission spectra. The abscissa labels are the excitation wavelengths in nm. At excitation 27 nm the emission is measured from 31 to 59 nm and at the last excitation wavelength 55 nm the emission is only measured at one wavelength, 59 nm. Phenylalanine gives rise to emission signals at excitation 27 nm and tryptophan and tyrosine give rise to signals at excitation nm while riboflavin emits when illuminated with light at excitation 27, 37 and 445 nm. In the right part of the figure the neutral density (ND) range is shown. International Journal of Food Science and Technology 25, 4, Ó 25 Institute of Food Science and Technology Trust Fund

9 Rapid determination of chocolate tempering G. Svenstrup et al. 961 of the chocolate: under-tempered samples, overtempered samples, and well-tempered samples and no clear separation is observed. The PCA model does not predict the different levels of over-/ under-tempered chocolate, possibly because the fluorescence signals only gave an indirect result of pre-crystallization. A PCA model calculated only on the data in the ND area further increased the tendency to grouping according to tempering. The reason may be that it is the reflection of the white light, which is related to the glossiness of the samples, at different emission wavelengths that vary according to the temperings, and not actually according to the fluorophores in the chocolate. Colour measurements A PCA model was performed on the colour measurement values of L, C and h. In the score plot it was seen that the samples could be grouped into chocolate types. Unlike the NIR spectroscopy data, it was not possible to predict the precrystallization of the samples, even if the model was made for only one sample type. Colour measurements do not measure glossiness and, as the gloss of the surface is directly correlated with pre-crystallization, it was not possible, by analysis of the colour measurements, to predict the pre-crystallization of the chocolates. The correlation between glossiness and pre-crystallization may also be the reason why fluorescence measurement predicts the pre-crystallization best in the ND area (because of the reflectance from the glossy surface). Conclusions Near-infrared spectroscopy perfectly predicts the division of the chocolate samples into five chocolate tempering groups and it is plausible that NIR can replace the temper meter in the chocolate manufacturing industry. NIR analysis time is more than ten times faster than the temper meter analysis time, and NIR has great on-line/in-line possibilities. This would not only save time but also reduce costs in terms of minimized production losses. Furthermore, it has also been demonstrated that it is possible to separate the three categories of tempering under-, over- and welltempered by means of fluorescence spectroscopy. References Allen, P.G. (1995). Chocolate temper measurement. The Manufacturing Confectioner, 75, Bolliger, S., Breitschuh, B., Stranxinger, M., agner, T. & indhab, E.J. (1998). Comparison of precrystallization of chocolate. Journal of Food Engineering, 35, Bolliger, S., Zeng, Y. & indhab, E.J. (1999). In-line measurement of tempered cocoa butter and chocolate by means of near-infrared spectroscopy. Journal of the American Oil ChemistsÕ Society, 76, Duggan, D.E., Bowman, R.L., Brodie, B.B. & Udenffriend, S. (1957). A spectrophotofluorometric study of compounds of biological interest. Archives of Biochemistry and Biophysics, 68, Hachiya, I., Koyano, T. & Sato, K. (1989). Seeding effects on solidification behaviour of cocoa butter and dark chocolate. I. Kinetics of solidification. Journal of the American Oil ChemistsÕ Society, 66, Hartel, R.., Bricknell, J. & Tietz, R. (1999). Chocolate: fat bloom during storage. The Manufacturing Confectioner, 79, Huyghebaert, A. & Hendrickx, H. (1971). Polymorphism of cocoa butter, shown by differential scanning calorimetry. Lebensmittel issenschaft und Technologie, 4, JovanovicÕ, O., KarlovicÕ, D.J. & JakovljevicÕ, J. (1995). Chocolate pre-crystallization: a review. Acta Alimentaria, 24, Larsson, K. (1966). Classification of glyceride crystal forms. Acta Chemica Scandinavica, 2, Loisel, C., Keller, G., Lecq, G., Launay, B. & Ollivon, M. (1997a). Tempering of chocolate in a scraped surface heat exchanger. Journal of Food Science, 62, Loisel, C., Lecq, G., Ponchel, G. & Ollivon, M. (1997b). Fat bloom and chocolate structure studied by mercury porosimetry. Journal of Food Science, 62, Martens, H. & Næs, T. (1993). Multivariate Calibration. Pp New York, USA: iley. Osborne, B.G., Fearn, T. & Hindle, P.H. (1993). Theory of near infrared spectrophotometry. In: Practical NIR Spectroscopy with Applications in Food and Beverage Analysis (edited by B.G. Osborne, T. Fearn & P.H. Hindle). Pp Singapore: Longman Scientific & Technical. Pszczola, D.E. (1997). The bloom is off the chocolate. Food Technology, 51, 28. Schlichter-Aronhime, J. & Garti, N. (1988). Solidification and polymorphism in cocoa butter and the blooming problems. In: Crystallization and Polymorphism of Fats and Fatty Acids (edited by K. Sato & N. Garti). Pp New York, USA: Marcel Dekker Inc. Stapley, A.G.F., Tewkewbury, H. & Fryer, P.J. (1999). The effects of shear and temperature history on the crystallization of chocolate. Journal of the American Oil ChemistsÕ Society, 76, Talbot, G. (1999). Chocolate temper. In: Industrial Chocolate Manufacture and Use (edited by S.T. Beckett). Pp York: Blackwell Science. Ó 25 Institute of Food Science and Technology Trust Fund International Journal of Food Science and Technology 25, 4,

10 962 Rapid determination of chocolate tempering G. Svenstrup et al. Voltz, M. & Beckett, S.T. (1997). Sensory of chocolate. The Manufacturing Confectioner, 77, ille, R.L. & Lutton, E.S. (1966). Polymorphism of cocoa butter. Journal of the American Oil ChemistsÕ Society, 43, indhab, E. & Bolliger, S. (1996). On-line use of NIRspectroscopy in food processing. The European Food & Drink Review, Autumn, International Journal of Food Science and Technology 25, 4, Ó 25 Institute of Food Science and Technology Trust Fund