Chemometric analysis of the amino acid requirements of antioxidant food protein hydrolysates

Abstract

The contributions of individual amino acid residues or groups of amino acids to antioxidant activities of some food protein hydrolysates were investigated using partial least squares (PLS) regression method. PLS models were computed with amino acid composition and 3-z scale descriptors in the X-matrix and antioxidant activities of the samples in the Y-matrix; models were validated by cross-validation and permutation tests. Based on coefficients of the resulting models, it was observed that sulfur-containing (SCAA), acidic and hydrophobic amino acids had strong positive effects on scavenging of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and H(2)O(2) radicals in addition to ferric reducing antioxidant power. For superoxide radicals, only lysine and leucine showed strong positive contributions while SCAA had strong negative contributions to scavenging by the protein hydrolysates. In contrast, positively-charged amino acids strongly contributed negatively to ferric reducing antioxidant power and scavenging of DPPH and H(2)O(2) radicals. Therefore, food protein hydrolysates containing appropriate amounts of amino acids with strong contribution properties could be potential candidates for use as potent antioxidant agents. We conclude that information presented in this work could support the development of low cost methods that will efficiently generate potent antioxidant peptide mixtures from food proteins without the need for costly peptide purification.

Keywords: amino acids; antioxidant prop1erties; chemometrics; free radicals; partial least square regression; reactive oxygen species.

Figure 1.

The t/u score plots of the partial least squares (PLS) models showing relationships between the antioxidant activity and amino acid descriptors (AA + gAA + Σz_i) of the food protein hydrolysates and peptide fractions; (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H₂O₂-scavenging, and (D) superoxide radical-scavenging.

Figure 1.

The t/u score plots of the partial least squares (PLS) models showing relationships between the antioxidant activity and amino acid descriptors (AA + gAA + Σz_i) of the food protein hydrolysates and peptide fractions; (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H₂O₂-scavenging, and (D) superoxide radical-scavenging.

Figure 2.

Variable Importance for the Projection (VIP) of the 3-z scale models: (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H₂O₂-scavenging, and (D) superoxide radical-scavenging.

Figure 3.

Coefficient plots of scaled and centered data of the partial least square regression models: (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H₂O₂-scavenging; and (D) superoxide radical-scavenging. The importance of a given X-variable is proportional to its distance (coefficient value) from the origin (zero). Above zero values indicate positive contributions while values less than zero indicate negative contributions.

Figure 3.

Coefficient plots of scaled and centered data of the partial least square regression models: (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H₂O₂-scavenging; and (D) superoxide radical-scavenging. The importance of a given X-variable is proportional to its distance (coefficient value) from the origin (zero). Above zero values indicate positive contributions while values less than zero indicate negative contributions.