Multiobjective decision making strategy for selective albumin extraction from a rapeseed cold-pressed meal based on Rough Set approach
Introduction
Global production of rapeseed increased from 39.6 million tons in 2000 to over 70 million tons in 2019 (FAO, 2019) taking the third place in worldwide production of oilseed. The solid residue remaining after the oil extraction process, called rapeseed meal, contains a high amount of protein (from 30 to 50% on dry matter basis) (Aider and Barbana, 2011; Perera et al., 2016; von der Haar et al., 2014; Yoshie-Stark et al., 2008). It is therefore currently used as a feed supplement rich in protein and energy for livestock.
The two main classes of rapeseed proteins are 12S globulins (cruciferins) and 2S albumins (napins) (von der Haar et al., 2014; Wanasundara, 2011). Rapeseed globulins have a high-molecular weight (230−350 kDa) and an isoelectric point (pI) at pH 7.2, whereas rapeseed albumins are low-molecular weight proteins (12–15 kDa) with an average pI around 11 (Akbari and Wu, 2015; Cheung et al., 2014; Perera et al., 2016; Tan et al., 2011). Overall, rapeseed proteins have a well-balanced composition in amino acids (Gueguen et al., 2016) and good functional properties such as foaming and emulsifying (Aider and Barbana, 2011; Yoshie-Stark et al., 2008). For these reasons, rapeseed meal recently gains a great interest as a promising source of plant proteins for human nutrition.
For food applications such as the production of the nutritional, functional, or stabilization ingredients, isolate grade products (>90% of protein on dry matter basis) are typically used, produced in a two-step process. Many reports describe production process for total protein isolates from rapeseed meal. The common strategy consists of alkali extraction (pH 9.0–12.0) followed by protein purification by acidic precipitation (pH 2.5–4.0) (Aider and Barbana, 2011; Aluko and McIntosh, 2001; Fetzer et al., 2019; Tan et al., 2011). Nevertheless, this strategy presents several drawbacks: (i) a poor purification yield (about 30%) due to an important loss of albumins that remain soluble in the liquid phase of acidic precipitation (Akbari and Wu, 2015; Blaicher et al., 1983; Chen and Rohani, 1992); (ii) the production of protein isolates characterized by limited functional and nutritional properties due to the irreversible denaturation of proteins during alkaline extraction and again the loss of albumins which are well-soluble and have a high sulfur amino acid content (Pedroche et al., 2004; Tan et al., 2011); and (iii) the by-production of a large amount of poor-quality solid residue remaining after the protein extraction step. This remaining solid is indeed considerably reduced in proteins (about 50% of initial content), and enriched in antinutritional compounds, particularly in phytic acid (Hudson, 1994; Tan et al., 2011). Therefore, an alternative strategy that considers protein quality, recovery and the remaining residue value must be addressed.
Among the two main protein fractions, rapeseed albumins (RA) present a better amino acid composition (according to the recommendations of FAO/WHO/UNU 2007; World Health Organization, and United Nations University, 2007) and are rich in sulfur compounds which is noteworthy for plant proteins (Gueguen et al., 2016; Youle and Huang, 1981). Besides, some studies reported their promising functional properties (Aider and Barbana, 2011; Aluko and McIntosh, 2001; Krause and Schwenke, 2001; Yoshie-Stark et al., 2008) and biological activities, such as antifungal or antimicrobial activities (Nioi et al., 2012). In rapeseed meal, RA represent from 25 to 40% of the total proteins (Wanasundara, 2011), the others being mainly the rapeseed globulins, which are less interesting for food applications in comparison.
Interestingly, some authors reported selective extraction of RA upon acidic conditions (Nioi et al., 2012; Perera et al., 2016; Wanasundara et al., 2012; Wanasundara and McIntosh, 2013). This strategy of acidic extraction also allowed the production of solid residue with a high amount of nitrogen (mainly globulins). Besides, the phytic acid can be removed efficiently at acidic pH extraction (Akbari and Wu, 2015; Blaicher et al., 1983; Serraino and Thompson, 1984; Zhou et al., 1990). Hence, it will result in a high-quality residue with interest for feed application.
The selective extraction can be impacted by various conditions such as the pH, the temperature, the meal/liquid ratio, and the ionic strength (NaCl concentration) (Du et al. 2018; Fetzer et al., 2018; Oomah et al., 1994). The four main performance indicators of the selective extraction process are the albumin extraction yield, the albumin content in the extract, and the phytic acid and the protein content in the solid residue. To date, plant protein extraction optimization mainly consists in identifying the extraction conditions that maximize the extraction yield and/or the protein content. To do so, design of experiments (DoE) is classically applied (Du et al., 2018; Jarpa-Parra et al., 2014; Mizubuti et al., 2000). However, to our knowledge, no optimization study has been reported considering simultaneously process performance indicators of RA extraction and quality of the solid residue. This can be explained by conflicting extraction performance indicators. Indeed, the maximization of protein extraction leads to the by-production of low-value residual meal that is poor in proteins and enriched in antinutritional phytic acid. This considerably limits its re-valorization in animal feeding.
Finding optimal solutions with antagonist process performance indicators usually requires multicriteria decision-making (MCDM) optimization. This method increases the reliability of the choice of extraction conditions by giving clear and straightforward decision rules. Many MCDM methods were reported (Wątróbski et al., 2019). Muniglia et al. (2004) proposed an approach involving Pareto’s domain determination and ranking by Pareto's domain determination and then ranking, a method applicable to several bioprocesses. The approach includes a DoE step to get information about the process system and polynomial models to reliably describe the variation of each process performance indicators as a function of the process conditions. Then, the simulation models are implemented in a real encoding diploid genetic algorithm exploiting the Pareto’s domination concept to generate the Pareto’s front and domain. In a final step of the method, the solutions belonging to the Pareto’s domain are ranked by Decision Engineering. The decision maker can eventually choose the best trade-off from its preferences. To date, such optimization approach has never been applied to valorize both RA and the remaining solid residue. In this paper, we therefore implemented this MCDM approach and analyzed its effectiveness to rationally identify the optimal conditions of selective RA extraction coupled with the production of a high-quality solid residue.
Section snippets
Methodology
The approach used for the multicriteria decision-making (MCDM) optimization is depicted on Fig. 1. The methodology includes three steps: (i) the process modelling (calculation of target process performance indicators as a function of process conditions; (ii) the determination of the Pareto’s domain (identification of all acceptable trade-offs of process performance indicators); and (iii) the application of the decision-making tool (selection of the most appropriate trade-off and the
Chemicals
Hydrochloric acid (HCl, CAS 7647-01-0) was purchased from Carlo Erba (Val-de-Reuil, France). Sodium chloride (NaCl, CAS 7647-14-201), sodium hydroxide pellets (NaOH, CAS 1310-73-2) ethylenediaminetetraacetic acid (EDTA, CAS 6381-92-6) were obtained from Sigma-Aldrich (St. Louis, MO, USA). Tris(hydroxymethyl)aminomethane (Tris, CAS 77-86-1), iron (III) chloride (FeCl3, CAS 7705-08-0), sodium sulfate (Na2SO4, CAS 7757-82-6), 5-sulfosalicylic acid hydrate (CAS 304851-84-1) was from Fisher
Extraction process modelling
Table 2 shows regression coefficients of the obtained models for the RA extraction yield (Y1), the RA content in the extract (Y2) and the phytic acid in the remaining solid residue (Y3). The significance of terms was assessed with an analysis of variance (ANOVA), and non-significant terms were removed to improve the reliability of the models. Transformation was applied when necessary. The intercept, linear, quadratic and interaction coefficients are presented for the three models.
Table 2 also
Conclusion
This study has considered the multicriteria optimization of selective albumin extraction from a rapeseed cold-pressed meal based on the Rough Set approach. For the first time, the optimization strategy of this extraction process considered both competing process performance indicators of RA extraction and quality of the solid residue remaining from this process. The results of the Pareto’s domain determination brought to light the competing objectives of the extraction process. Indeed, it was
Conflicts of interest
Authors disclose no potential conflict of interest.
Data availability
No data was used for the research described in the article.
Acknowledgements
Authors wish to thank the company Olead, Pessac, France, for providing the meal used in this study. We are also grateful to Mélody Basselin for her valuable help in collecting the data.
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