The existing in vitro and clinical trial evidence supports the health and wellness benefits of collagen peptides sourced from various origins. Despite this, research on collagen peptides from tuna remains limited. Notably, tuna-derived peptides possess an inherent fishy odor, rendering them unsuitable for direct application in humans. This study explores the enhancement of tuna peptides’ applicability in cosmetics through odor mitigation. We developed a dual-phase ozone treatment, employing both dry and wet ozone, to deodorize tuna peptide powder, enabling its use in cosmetic formulations. The deodorized tuna peptide powder can be used in cosmetics. We optimized the ozone nitrification and deodorization conditions for tuna peptide powder by adjusting the treatment time, ozone concentration, and temperature. Sensory evaluation and GC-MS analysis confirmed the effectiveness of fishy odor removal, offering a comprehensive understanding of the deodorization process. The findings reveal that wet ozonation at 50 °C with an ozone concentration of 99.1 mg/L for 40 min significantly reduces the fishy odor of tuna peptide powder. Notably, n-Hexaldehyde, the primary odor-contributing volatile compound, decreased by 66.5%, confirming the efficacy of ozone treatment in odor mitigation. Moreover, the protein activity within the powder remained unaffected, ensuring the preservation of its functional properties. This study demonstrates the efficacy of ozone oxidation in adapting tuna peptide powder for cosmetic use.
A direct, high-throughput and accurate method, multi-molecular infrared spectroscopic (MMIR) data fusion, was established for the detection of trace of estrone, diethylstilbestrol, betamethasone and prednisone acetate in peptide powders based on near-infrared and mid-infrared fusion datasets. Tri step MMIR with progressively improved resolution captured key fingerprint features of hormones in the regions of 3430-3250, 2950-2850, 1750-1550 and 1335-1050 cm-1 which were exclusively selected as the datasets. The back propagation neural network (BPNN) model with the best performance used a mid-level fusion strategy and achieved excellent root mean square error of prediction (RMSEP, 1.101 mg kg-1) and residual prediction deviation (RPD, 9.890). Compared to those of single dataset models, the RMSEP was reduced by more than 82.03%. This method had remarkable practical significance for the direct and accurate quality evaluation of peptide powder products with favorable limit of detection (LOD) but without pretreatment and chemical agents, verifying the feasibility of multiple analytical instruments for rapid and comprehensive detection of powdered foods.
危险品化学品经营许可证（带存储）