Rapid evaluation of the quality of Smilax glabra Roxb. using QADS based on FT-NIR combined with multiple intelligent algorithms.
Food Chem
; 453: 139633, 2024 Sep 30.
Article
em En
| MEDLINE
| ID: mdl-38781896
ABSTRACT
Smilax glabra Roxb. (SGR) is known for its high nutritional and therapeutic value. However, the frequent appearance of counterfeit products causes confusion and inconsistent quality among SGR varieties. Herein, this study collected the proportion of SGR adulteration and used high-performance liquid chromatography (HPLC) to measure the astilbin content of SGR. Then Fourier-transform near-infrared (FT-NIR) technology, combined with multivariate intelligent algorithms, was used to establish partial least squares regression quantitative models for detecting SGR adulteration and measuring astilbin content, respectively. The method conducted a quantitative analysis of dual indicators through single-spectrum data acquisition (QADS) to comprehensively evaluate the authenticity and superiority of SGR. The coefficients of determination (R2) for both the calibration and prediction sets exceeded 0.96, which successfully leverages FT-NIR combined with multivariate intelligent algorithms to considerably enhance the accuracy and reliability of quantitative models. Overall, this research holds substantial value in the comprehensive quality evaluation in functional health foods.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Espectroscopia de Luz Próxima ao Infravermelho
/
Smilax
Idioma:
En
Revista:
Food Chem
/
Food chem
/
Food chemistry
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China