Phytopharmaceuticals - Группа авторов

Скачать книгу

from chia seeds Dropwise addition of citric acid into the emulsion (pH change) High encapsulation efficiency; oil shelf life increased significantly. [167] Stearidonic acid soybean oil Gelatine, gum Arabic maltodextrin pH was adjusted with citric acid Improved oil stability against oxidation reactions. [168] Flaxseed oil Flaxseed protein (FPI) and flaxseed gum (FG) pH adjusted with HCl Crosslinked FPI-FG complex coacervates showed high oil microencapsulation efficiency and high oxidation stability. [169] Sunflower Oil Fish gelatine and Arabic gum pH adjusted with lactic acid; glutaraldehyde as crosslinker Controlled microcapsules size with low energy at production scale consumption. [170] Olive Oil Gelatine and sodium alginate Dropwise addition of glacial acetic acid to pH 3.75, crosslinked with glutaraldehyde. Encapsulation efficiency increased with olive oil, glutaraldehyde and polymer concentration. [164]

       2.3.3.3 Management of Protein-Hydrocolloid Interactions for Designing Bioactive Delivery Systems

      The gums from seeds or extrudates can be employed in delivering systems and encapsulation matrices looking forward the modification of release kinetics and preservation of bioactive compounds.

      For example, by controlling the formation of mixed gels of proteins and polysaccharides, different microstructures can be obtained in order to serve as delivery materials.

      When aqueous solutions of two biopolymers of equal charge and/or neutral biopolymers are mixed above a given concentration, phase separation or thermodynamic incompatibility may occur [171]. When proteins are involved, this phenomenon is observed at pH higher than their isoelectric point [171]. Macroscopically separated phases are obtained, in the absence of gelation, each of one enriched in one of the two biopolymers. The obtained microstructure and the rheology behavior are defined by the relative rates of gelation and phase separation processes, representing a good alternative to control microstructure. For example, gels with different pore sizes or interstices can be obtained.

      Aqueous systems of mixtures with low ECG concentrations and milk proteins exhibit Newtonian flow behavior, and at higher ECG concentrations the mixtures show pseudoplastic behavior. If chymosin is added, the clots formed by enzymatic gelation of these mixtures showed a less continuous and interconnected protein phase while the non-protein phase, which constitutes the pore, occupies greater volume [172]. This is due to phase microseparation that would compete with the gelation process. During acid gelation of milk protein, induced by glucono-β-lactone (GDL), there is a decrease in the protein coagulation time and an increase in the pH at which coagulation occurs. The increase of ECG concentration may favour the electrostatic destabilization of milk protein, leading to the formation of gels with a lower degree of compactness [172].

      Thermodynamic incompatibility was studied in mixtures of soy protein isolates and ECG. When the ECG concentration increased in cold-set gels formed upon acidification after GDL addition, a less interconnected gel network with larger pores was formed. On the other hand, by adequate microstructure management though phase separation, ECG-protein cold-set gels could be useful to obtain products with reduced amounts of sugar and/or salt [173]. By increasing serum release from 2 to 12%, around 25% sugar reduction can be attained.

      The presence of vinal gum in maltodextrin encapsulating matrices increased the percentage of propolis antioxidants retention and the physical stability against humidification improving particles integrity and size homogeneity [174].

      The addition of small quantities of VG in ionotropic gelation matrix modified encapsulation matrix and wall material properties, improving lycopene preservation in calcium-alginate bead, by modifying beads microstructure, pores size and transport mechanism [175].

      The screening and detection of potential phytopharmaceutical sources from different plants or by-products from agroindustrial processing require the analysis of a great number of data and variables. Besides, most bioactive extracts are a complex mixture of compounds and a typical strategy is to determine few specific compounds, used as markers, to classify different types of samples. However, another possibility is to use a technique that provides a fingerprint related to a great number of compounds.

      Multivariate analysis (MVA) includes several mathematical tools applied to data provided by techniques and/or equipment in multiple dimensions or variables per sample. The main areas in which MVA is useful to nutraceutical production are: a) Quality control of raw materials: including bioactive extraction optimization, bioactive quantification and botanical/geographical discrimination, b) monitoring the changes in bioactive profiles during processing, c) Detailed metabolite analysis of samples, also known as metabolomics.

      MVA methods can be supervised or unsupervised and both are useful to classify or discriminate samples according their similarities. Supervised methods use information of the class of each sample analyzed to classify them into groups. On the other hand, in unsupervised methods (such as principal component analysis, PCA, and cluster analysis, CA) class information is not used and samples discrimination is performed according to similarities [176].

      Many nutraceutical components are a complex mixture of compounds and a typical strategy is to determine few specific compounds, used as markers, to classify different types of samples. However, another possibility is to use a technique that provides a fingerprint related to a great number of compounds. This approach was used by Chasset et al. [177] with propolis from different regions of France, applying PCA on the reverse phase high performance thin layer chromatography (RP-HPTLC) outcomes and direct analysis in real time mass spectroscopy (DART-MS). PCA shows that with both techniques together allowed to improve discrimination of the samples into three types of propolis (orange, blue and intermediate). Thus, the loadings from PCA were associated to three markers compounds (galangin, chrysin and pinocembrin) [177]. Additionally, Ciccoritti et al. [178] used bioactive profiles of different cultivars of wheat as MVA inputs. Total polyphenols content (TPC), antiradical capacity (DPPH), total alkylresorcinols (colorimetric determination) and quantification of alkylresorcinols by GC-FID in combination with PCA were useful to discriminate different wheat cultivars according to their botanical origin [178].

Скачать книгу