Prediction of Anthocyanidins Content in Purple Chinese Cabbage Based on Visible/Near Infrared Spectroscopy.
anthocyanidins
fast determination
near infrared spectroscopy
vegetables
Journal
Foods (Basel, Switzerland)
ISSN: 2304-8158
Titre abrégé: Foods
Pays: Switzerland
ID NLM: 101670569
Informations de publication
Date de publication:
08 May 2023
08 May 2023
Historique:
received:
22
03
2023
revised:
04
05
2023
accepted:
05
05
2023
medline:
13
5
2023
pubmed:
13
5
2023
entrez:
13
5
2023
Statut:
epublish
Résumé
Purple Chinese cabbage (PCC) has become a new breeding trend due to its attractive color and high nutritional quality since it contains abundant anthocyanidins. With the aim of rapid evaluation of PCC anthocyanidins contents and screening of breeding materials, a fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR). The PCC samples were scanned by NIR, and the spectral data combined with the chemometric results of anthocyanidins contents obtained by high-performance liquid chromatography were processed to establish the prediction models. The content of cyanidin varied from 93.5 mg/kg to 12,802.4 mg/kg in PCC, while the other anthocyanidins were much lower. The developed NIR prediction models on the basis of partial least square regression with the preprocessing of no-scattering mode and the first-order derivative showed the best prediction performance: for cyanidin, the external correlation coefficient (RSQ) and standard error of cross-validation (SECV) of the calibration set were 0.965 and 693.004, respectively; for total anthocyanidins, the RSQ and SECV of the calibration set were 0.966 and 685.994, respectively. The established models were effective, and this NIR method, with the advantages of timesaving and convenience, could be applied in purple vegetable breeding practice.
Identifiants
pubmed: 37174459
pii: foods12091922
doi: 10.3390/foods12091922
pmc: PMC10178596
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : National Natural Science Foundation of China
ID : 31872094
Organisme : Innovation and Capacity Building Project of Beijing Academy of Agriculture and Forestry Sciences
ID : KJCX20200213, KJCX 20210437
Organisme : Beijing Innovation Consortium of Agriculture Research System
ID : BAIC01-2022
Organisme : National Key Research and Development Program of China
ID : 2022YFD1200805
Références
Food Chem. 2022 Aug 30;386:132750
pubmed: 35367800
Curr Pharm Des. 2017;23(41):6321-6346
pubmed: 28741457
Food Chem. 2014 Dec 1;164:536-43
pubmed: 24996367
Nutr Res. 2022 Nov;107:48-64
pubmed: 36179643
Molecules. 2020 Nov 26;25(23):
pubmed: 33256052
Appl Spectrosc. 2003 Feb;57(2):139-45
pubmed: 14610949
Talanta. 2005 Oct 15;67(4):736-40
pubmed: 18970233
Molecules. 2022 Jul 04;27(13):
pubmed: 35807546
Phytochemistry. 2000 Nov;55(6):481-504
pubmed: 11130659
Front Nutr. 2022 Jul 19;9:901342
pubmed: 35928834
Crit Rev Food Sci Nutr. 2022;62(8):2205-2220
pubmed: 33256437
Food Chem. 2022 Nov 1;393:133430
pubmed: 35696953
J Agric Food Chem. 2016 Jan 13;64(1):132-45
pubmed: 26709726
Pharmacol Res. 2020 Sep;159:104895
pubmed: 32422342
Food Chem X. 2022 Aug 08;15:100420
pubmed: 36211770
Front Chem. 2019 Feb 22;7:48
pubmed: 30854368