Evaluation of the Importance of Multi-objective Particle Swarm Algorithm Parameters in Optimizing the Solutes Rejection of Camel Milk Ultrafiltration Using Partial Least Squares Regression

Document Type : Research Article

Authors

• Seyed Mohammad Ali Razavi
• Morteza Kashaninejad

Department of Food Science and Technology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

https://doi.org/10.22067/ifstrj.2021.69556.1028

Abstract

Introduction
 Ultrafiltration is one of the most common membrane processes in the dairy industry, especially for condensing and separating milk components. Using this process, several products can be produced, including milk concentrate used for cheese production, low-lactose dairy products, milk protein concentrate, and serum proteins for dietary supplements. The efficiency and cost of a membrane process depend on the percentage of rejection of the soluble components. Therefore, the use of concentrated milk made by ultrafiltration in the production of various dairy products depends on the efficiency of the membrane process and the changes in milk components during this process. On the one hand, the physicochemical properties of camel milk are different from those of cow milk, especially in terms of type and amount of protein. Because significant differences exist between the physicochemical properties of camel and cow milk, likely, the membrane processing conditions and the physicochemical properties of their products will be different completely. Although many studies have been conducted on the efficacy of the ultrafiltration processing of cow milk, there is no information about the efficacy of camel milk ultrafiltration, and most of the research done regarding optimizing is based on classical algorithms, Therefore, in this study, the effects of transmembrane pressure and temperature on the solutes rejection (protein, lactose, ash, and total solids) during camel milk ultrafiltration process were investigated, Then, these properties were optimized using particle swarm algorithm. Also, because the performance of the particle swarm algorithm is highly dependent on related parameters such as the number of iterations, the number of particles, accelerate constant, inertia weight, and velocity of the particles, so before optimization, the effect of these parameters on optimal responses were examined by partial least squares regression (PLS).
 
Materials and Methods
 In this study, a pilot crossflow ultrafiltration system was used. A UF membrane (Model 3838 HFK-131, Koch membrane systems, Inc., USA) made of polysulfone amid (PSA) with MWCO of 20 kDa was applied. Camel milk was purchased from a local market in Mashhad and for camel skim milk production, its fat was separated by a pilot plant milk fat separator in the Food Research Complex, Ferdowsi University of Mashhad. The weight percentages of protein, fat, lactose, ash, and total solids of UF permeate samples were measured by ISO 8968-1:2014, ISO 1211: 2010, ISO 26462/IDF 214:2010, ISO 5544:2008, and ISO 6731:2010 at two replications, respectively. the process treatments were performed in the form of a central composite design (CCD) (5 replications at the central point) for two independent variables at three levels so that the total number of 13 treatments was obtained. The data were modeled using the statistical software of Design Expert (version 11) based on the response surface methodology and each of the response variables in the form of a regression model was presented as a function of independent variables.
 
Results and Discussion
 The rejection of total solids and protein of the tested samples varied in the range of 45.4-51.03% and 94.09-97.51%, respectively. It means that in each TMP and T, more than 45% of the total solids and 94% of the protein of camel milk were kept by the membrane. The results also showed that none of the linear, quadratic and interactive effects of TMP and T on the total solids and protein rejections were not significant. According to the results, the R_L reduced with increasing T. Increasing the TMP also led to a reduction at high T and an increase in R_L rate of the samples at lover T. Also, the effect of TMP on R_A showed a non-linear trend, so that TMP at high T led to an increase, and at low T, it led to a reduction in the R_A of the samples.
 
Conclusion
 The optimization results with the particle swarm algorithm showed that this algorithm has a high convergence speed and by recognizing and analyzing its parameters, the optimal conditions can be easily found. The optimum ultrafiltration conditions in this study with the lowest R_L and R_A were determined as 80 kPa TMP and 29.85 ͦ C T.

Keywords

• Camel milk
• Partial least squares regression
• Particle swarm algorithm
• Solute rejection
• Ultrafiltration

Main Subjects

• Food Engineering

©2023 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

1.       Benmechernene, Z., Fernández-No, I., Quintela-Baluja, M., Böhme, K., Kihal, M., Calo-Mata, P., & Barros-Velázquez, J. (2014). Genomic and proteomic characterization of bacteriocin-producing leuconostoc mesenteroides strains isolated from raw camel milk in two southwest algerian arid zones. BioMed Research International, 2014, 853238. https://doi.org/10.1155/2014/853238

2.       Bergh, F.V.D., & Engelbrecht, A.P. (2001). Effects of swarm size on cooperative particle swarm optimisers. Paper presented at the Proceedings of the 3rd annual conference on genetic and evolutionary computation.

3.       Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE transactions on Evolutionary Computation, 6(1), 58-73.

4.       Eberhart, R.C., Shi, Y., & Kennedy, J. (2001). Swarm intelligence: Elsevier.

5.       Eckner, K., & Zottola, E. (1992). Partitioning of skim milk components as a function of ph, acidulant, and temperature during membrane processing. Journal of Dairy Science, 75(8), 2092-2097. https://doi.org/10.3168/jds.S0022-0302(92)77967-3

6.       Grandison, A.S., Youravong, W., & Lewis, M.J. (2000). Hydrodynamic factors affecting flux and fouling during ultrafiltration of skimmed milk. Le Lait, 80(1), 165-174. https://doi.org/10.1051/lait:2000116

7.       Hassl, M., JØrgensen, B., & Janhøj, T. (2011). Rennet gelation properties of ultrafiltration retentates from camel milk. Milchwissenschaft, 66(1), 80-84.

8.       Juneja, M., & Nagar, S. (2016). Particle swarm optimization algorithm and its parameters: A review. Paper presented at the 2016 International Conference on Control, Computing, Communication and Materials (ICCCCM).

9.       Kautake, M., Nabetani, H., & Matsuno, I. (1986). Influence of operation parameters on permeate flux in ultrafiltration of milks, technical research institute, snow brand milk products co. Ltd. Retrieved from

10.   Kennedy, J. (1999). Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance. Paper presented at the Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406).

11.   Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Paper presented at the Proceedings of ICNN'95-international conference on neural networks.

12.   Krstić, D.M., Tekić, M.N., Carić, M.D., & Milanov, S. (2002). The effect of turbulence promoter on cross-flow microfiltration of skim milk. Journal of Membrane Science, 208(1-2), 303-314. https://doi.org/10.1016/S0376-7388(02)00308-3

13.   Limsawat, P., & Pruksasri, S. (2010). Separation of lactose from milk by ultrafiltration. Asian Journal of Food and Agro-Industry, 3(2), 236-243. https://doi.org/ 10.1007/s12393-022-09330-2

14.   Luo, X., Ramchandran, L., & Vasiljevic, T. (2015). Lower ultrafiltration temperature improves membrane performance and emulsifying properties of milk protein concentrates. Dairy science & technology, 95(1), 15-31. https://doi.org/ 10.1007/s13594-014-0192-3

15.   Mehaia, M.A. (1996). Chemical composition of camel skim milk concentrated by ultrafiltration. International Dairy Journal, 6(7), 741-752. https://doi.org/10.1016/0958-6946(95)00063-1

16.   Montgomery, D.C. (2017). Design and analysis of experiments: John wiley & sons.

17.   Ng, K.S., et al. (2017). Mechanisms of flux decline in skim milk ultrafiltration: A review. Journal of Membrane Science, 523, 144-162. https://doi.org/10.1016/j.memsci.2016.09.036

18.   Olariu, S., & Zomaya, A.Y. (2005). Handbook of bioinspired algorithms and applications: CRC Press.

19.   Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence, 1(1), 33-57.

20.   Pompei, C., Resmini, P., & Peri, C. (1973). Skim milk protein recovery and purification by ultrafiltration influence of temperature on permeation rate and retention. Journal of Food Science, 38(5), 867-870.

21.   Rao, H.R. (2002). Mechanisms of flux decline during ultrafiltration of dairy products and influence of ph on flux rates of whey and buttermilk. Desalination, 144(1-3), 319-324. https://doi.org/10.1016/S0011-9164(02)00336-3

22.   Razavi, S.M., Alghooneh, A., & Behrouzian, F. (2017). Kinetic modelling of hydraulic resistance in colloidal system ultrafltration: Effect of physiochemical and hydrodynamic parameters. Journal of Membrane Science and Research, 3(4), 296-302. https://doi.org/10.22079/JMSR.2017.47339.1097

23.   Razavi, S.M.A., Mousavi, S.M., & Mortazavi, S.A. (2003). Dynamic prediction of milk ultrafiltration performance: A neural network approach. Chemical Engineering Science, 58(18), 4185-4195. https://doi.org/10.1016/S0009-2509(03)00301-4

24.   Saltelli, A. (2002). Sensitivity analysis for importance assessment. Risk analysis, 22(3), 579-590. https://doi.org/10.1111/0272-4332.00040

25.   Shi, Y,. & Eberhart, R.C. (1998). Parameter selection in particle swarm optimization. Paper presented at the International conference on evolutionary programming.

26.   Tenenhaus, M., et al. (2005). Pls methodology to study relationships between hedonic judgements and product characteristics. Food Quality and Preference, 16(4), 315-325. https://doi.org/10.1016/j.foodqual.2004.05.013

27.   Wang, K.Y., & Chung, T.-S. (2005). The characterization of flat composite nanofiltration membranes and their applications in the separation of cephalexin. Journal of Membrane Science, 247(1-2), 37-50. https://doi.org/10.1016/j.memsci.2004.09.007

28.   Yang, X.-S. (2010). Engineering optimization: An introduction with metaheuristic applications: John Wiley & Sons.