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1.
Food Chem ; 441: 138400, 2024 May 30.
Artigo em Inglês | MEDLINE | ID: mdl-38199107

RESUMO

The low bioavailability and poor gastrointestinal instability of curcumin hampers its application in pharmaceutical and food industries. Thus, it is essential to explore efficient carrier (e.g. a combination of polyphenols and proteins) for food systems. In this study, covalent ß-lactoglobulin (LG)-dicaffeoylquinic acids (DCQAs) complexes were prepared by combining ultrasound and free radical induction methods. Covalent interactions between LG and DCQAs were confirmed by analyzing reactive groups. Variations in secondary or tertiary structure and potential binding sites of covalent complexes were explored using Fourier transform infrared spectroscopy and circular dichroism. Results showed that the ß-sheet content decreased and the unordered content increased significantly (P < 0.05). The embedding rate of curcumin in prepared LG-DCQAs complexes using ultrasound could reach 49 % - 62 %, proving that complexes could embed curcumin effectively. This study highlights the benefit of ultrasound application in fabrication of protein-polyphenol complexes for delivering curcumin.


Assuntos
Curcumina , Lactoglobulinas , Ácido Quínico/análogos & derivados , Lactoglobulinas/química , Curcumina/química , Sítios de Ligação , Polifenóis/química , Dicroísmo Circular , Espectroscopia de Infravermelho com Transformada de Fourier
2.
Food Sci Biotechnol ; 29(7): 889-896, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32582451

RESUMO

When transporting yogurt, vibrations and sharp movements can damage its quality. This study developed a model to connect the changes in yogurt quality with the transportation distance as simulated by the total number of vibrations. Linear regression analysis showed that there was a significant negative correlation between the water holding capacity and hardness of the yogurt over the same transport distance (p < 0.05). The yogurt vibration model was established by combining principal component analysis with a Back-Propagation Artificial Neural Network model. The number of training iterations was 2669, with a correlation coefficient of 0.96611, indicating that the model was reliable. The optimal transportation distance was determined to be within the range from 20 rpm for 8 h to 100 rpm for 4 h.

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