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
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

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Auteurs

Ya-Qin Wang (YQ)

Institute of Agri-Food Processing and Nutrition, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China.

Guang-Min Liu (GM)

Institute of Agri-Food Processing and Nutrition, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China.

Li-Ping Hu (LP)

Institute of Agri-Food Processing and Nutrition, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China.

Xue-Zhi Zhao (XZ)

Institute of Agri-Food Processing and Nutrition, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China.

De-Shuang Zhang (DS)

Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.

Hong-Ju He (HJ)

Institute of Agri-Food Processing and Nutrition, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China.

Classifications MeSH