Journal of Thermal Analysis and Calorimetry

, Volume 133, Issue 1, pp 619–632 | Cite as

Quantitative and qualitative use of thermal analysis for the investigation of the properties of granules during fluid bed melt granulation



This study describes a novel approach for the use of thermal analysis to study the aftermath of the fluid bed melt granulation process and to depict the growth mechanism of the granules by quantifying the enthalpies of the granules at every physicochemical change using DSC and their mass loss through TG coupled to MS for a qualitative determination of the composition and amount of the evolved gases for the corresponding fragment ion. The experiments were made in situ with lactose monohydrate and two viscosity grades of PEG (2000 and 6000) as meltable binders with different contents and size fractions. DSC showed the presence of a beta lactose endotherm peak after the melting of alpha lactose and a proportional increase in its intensity with the increase in the particle size and the content of the binder, which suggested a relation with the agglomeration growth. Interestingly, TG and MS showed a larger reduction in the water content from lactose with the increase in the binder particle size, making it possible to evaluate the dehydration during the melt granulation. Indeed, during the distribution mechanism the low binder particle size and viscosity exposed lactose to a high heat transfer from the fluidizing air. However, a high binder particle size results in lactose immersed in the PEG particles, causing water to be trapped inside the granules and hence a larger reduction in water mass loss indicating the immersion mechanism. Therefore, thermal analysis is a promising tool for granulation growth control.


Fluid bed melt granulation Mass spectrometry TG–MS DSC Lactose monohydrate PEG 



The authors would like to thank the Ministry of Higher Education and Scientific Research of Algeria and the Tempus Public Foundation for the Stipendium Hungaricum scholarship provided to Yasmine Korteby for her PhD studies.


  1. 1.
    Szűts A, Sorrenti M, Catenacci L, Bettinetti G, Szabó-Révész P. Investigation of the thermal and structural behavior of diclofenac sodium–sugar ester surfactant systems. J Therm Anal Calorim. 2009;95:885–90.CrossRefGoogle Scholar
  2. 2.
    Sekiguchi K, Obi N. Studies on absorption of eutectic mixtures. I. A comparison of the behavior of eutectic mixtures of sulfathiazole and that of ordinary sulfathiazole in man. Chem Pham Bull. 1961;9:866–87.CrossRefGoogle Scholar
  3. 3.
    Maniruzzaman M, Boateng JS, Snowden MJ, Douroumis D. A review of hot-melt extrusion: process technology to pharmaceutical product. ISRN Pharm. 2012;. Scholar
  4. 4.
    Monteyne T, Heeze L, Mortier STFC, Oldörp K, Cardinaels R, Nopens I, Vervaet C, Remon JP, De Beer T. The use of rheology combined with differential scanning calorimetry to elucidate the granulation mechanism of an immiscible formulation during continuous twin-screw melt granulation. Pharm Res. 2016;510:271–84.Google Scholar
  5. 5.
    Roumeli E, Tsiapranta A, Pavlidou E, Vourlias G, Kachrimanis K, Bikiaris D, Chrissafis K. Compatibility study between trandolapril and natural excipients used in solid dosage forms. J Therm Anal Calorim. 2013;111:2109–15.CrossRefGoogle Scholar
  6. 6.
    Ansari MA, Stepanek F. Formation of hollow core granules by fluid bed in situ melt granulation: modelling and experiments. Int J Pharm. 2006;321:108–16.CrossRefGoogle Scholar
  7. 7.
    Kidokoro M, Sasaki K, Haramiishi Y, Matahira N. Effect of crystallization behavior of polyethylene glycol 6000 on the properties of granules prepared by fluidized hot-melt granulation (FHMG). Chem Pham Bull. 2003;51:487–93.CrossRefGoogle Scholar
  8. 8.
    Mangwandi C, Zainal NA, JiangTao L, Glocheux Y, Albadarin AB. Investigation of influence of process variables on mechanical strength, size and homogeneity of pharmaceutical granules produced by fluidised hot melt granulation. Powder Technol. 2015;272:173–80.CrossRefGoogle Scholar
  9. 9.
    Prado HJ, Bonelli PR, Cukierman AL. In situ fluidized hot melt granulation using a novel meltable binder: effect of formulation variables on granule characteristics and controlled release tablets. Powder Technol. 2014;264:498–506.CrossRefGoogle Scholar
  10. 10.
    Zhai H, Li S, Andrews G, Jones D, Bell S, Walker G. Nucleation and growth in fluidized hot melt granulation. Powder Technol. 2009;189:230–7.CrossRefGoogle Scholar
  11. 11.
    Mašić I, Ilić I, Dreu R, Ibrić S, Parojčić J, Đurić Z. An investigation into the effect of formulation variables and process parameters on characteristics of granules obtained by in situ fluidized hot melt granulation. Int J Pharm. 2012;423:202–12.CrossRefGoogle Scholar
  12. 12.
    Mašić I, Ilić I, Dreu R, Ibrić S, Parojčić J, Srčič S. Melt granulation in fluidized bed: a comparative study of spray-on versus in situ procedure. Drug Dev Ind Pharm. 2014;40:23–32.CrossRefGoogle Scholar
  13. 13.
    Walker GM, Holland CR, Ahmad MMN, Craig DQM. Influence of process parameters on fluidised hot-melt granulation and tablet pressing of pharmaceuticals powders. Chem Eng Sci. 2005;60:3867–77.CrossRefGoogle Scholar
  14. 14.
    Mu B, Thompson MR, Sheskey PJ, O’Donnell KP. Hot-melt granulation in a twin-screw extruder. Chem Eng Sci. 2012;81:46–56.CrossRefGoogle Scholar
  15. 15.
    Weatherley S, Mu B, Thompson MR, Sheskey PJ, O’Donnell KP. Hot-melt granulation in a twin screw extruder: effects of processing on formulations with caffeine and ibuprofen. J Pharm Sci. 2013;102:4330–6.CrossRefGoogle Scholar
  16. 16.
    Giron D, Goldbronn C. Use of DSC and TG for identification and quantification of the dosage form. J Therm Anal. 1997;48:473–83.CrossRefGoogle Scholar
  17. 17.
    Vasanthavada M, Wang Y, Haefele T, Lakshman JP, Mone M, Tong W, Joshi YM, Serajuddin ATM. Application of melt granulation technology using twin-screw extruder in development of high-dose modified-release tablet formulation. J Pharm Sci. 2011;100:1923–34.CrossRefGoogle Scholar
  18. 18.
    Giron D. Thermal analysis and calorimetric methods in the characterisation of polymorphs and solvates. Thermochim Acta. 1995;248:1.CrossRefGoogle Scholar
  19. 19.
    Ozawa T. Thermal analysis—review and prospect. Thermochim Acta. 2000;355:5–42.CrossRefGoogle Scholar
  20. 20.
    Reitz C, Kleinebudde P. Influence of thermal and thermo-mechanical treatment, Comparison of two lipids with respect to their suitability for solid lipid extrusion. J Therm Anal Calorim. 2007;89:669–73.CrossRefGoogle Scholar
  21. 21.
    Mojumdar SC, Sain M, Prasad RC, Sun L, Venart JES. Selected thermoanalytical methods and their applications from medicine to construction. J Therm Anal Calorim. 2007;90:653–62.CrossRefGoogle Scholar
  22. 22.
    Gombás Á, Szabó-Révész P, Kata M, Regdon G Jr, Erős I. Quantitative determination of crystallinity of α-lactose monohydrate by DSC. J Therm Anal Calorim. 2002;68:503–10.CrossRefGoogle Scholar
  23. 23.
    Jayaraman K, Kok MV, Gokalp I. Combustion properties and kinetics of different biomass samples using TG–MS technique. J Therm Anal Calorim. 2017;127:1361–70.CrossRefGoogle Scholar
  24. 24.
    Wang Z, Li H, Zheng J. TG–MS study on the effect of multi-walled carbon nanotubes and nano-Fe2O3 on thermo-oxidative stability of silicone rubber. J Therm Anal Calorim. 2016;126:733–42.CrossRefGoogle Scholar
  25. 25.
    Li S, Yang C, Li C, Yan S. Synthesis, characterization of new bisphenol-based benzoxazines and the thermal properties of their polymers. J Therm Anal Calorim. 2017;128(3):1711–7.CrossRefGoogle Scholar
  26. 26.
    Zohari N, Abrishami F, Sheibani N. A novel simple correlation for predicting glass transition temperature of energetic azido-ester plasticizers through molecular structures. J Therm Anal Calorim. 2017;127:2243–51.CrossRefGoogle Scholar
  27. 27.
    Hotová G, Slovác V. Quantitative TG–MS analysis of evolved gases during the thermal decomposition of carbon containing solids. Thermochim Acta. 2016;632:23–8.CrossRefGoogle Scholar
  28. 28.
    Esfe MH. Designing an artificial neural network using radial basis function (RBF-ANN) to model thermal conductivity of ethylene glycol–water-based TiO2 nanofluids. J Therm Anal Calorim. 2017;127:2125–31.CrossRefGoogle Scholar
  29. 29.
    Esfe MH, Rejvani M, Karimpour R, Arani AAA. Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT-AL2O3 nanoparticles by correlation and ANN methods using experimental data. J Therm Anal Calorim. 2017;128:1359–71.CrossRefGoogle Scholar
  30. 30.
    Kullyakool S, Siriwong K, Noisong P, Danvirutai C. Kinetic triplet evaluation of a complicated dehydration of (CO4)2·8H2O using the deconvolution and the simplified master plots combined with nonlinear regression. J Therm Anal Calorim. 2017;127:1963–74.CrossRefGoogle Scholar
  31. 31.
    Çepelioğullar Ö, Mutlu I, Yaman S, Haykiri-Acma H. A study to predict pyrolytic behaviors of refuse-derived fuel (RDF): artificial neural network application. J Anal Appl Pyrolysis. 2016;122:84–94.CrossRefGoogle Scholar
  32. 32.
    Raut DM, Allada R, Pavan KV, Deshpande G, Patil D, Patil A, Deshmukh A, Sakharkar DM, Bodke PS, Mahajan DT. Dehydration of lactose monohydrate: analytical and physical characterization. Der Pharma Lett. 2011;3:202–12.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  1. 1.Institute of Pharmaceutical Technology and Regulatory AffairsUniversity of SzegedSzegedHungary

Personalised recommendations