Details

Autor(en) / Beteiligte

• Wadood, Syed Abdul
• Nie, Jing
• Li, Chunlin
• Rogers, Karyne M.
• Zhang, Yongzhi
• Yuan, Yuwei

Titel

Geographical origin classification of peanuts and processed fractions using stable isotopes

Ist Teil von

• Food Chemistry: X, 2022-12, Vol.16, p.100456, Article 100456

Ort / Verlag

Netherlands: Elsevier Ltd

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Stable isotopes of peanuts and their different fractions are investigated.•Stable C, N, O and H isotopes of peanuts are used to assign production origin.•Peanuts are leguminous plants and fix nitrogen from the atmosphere.•Peanut δ15N is unaffected by processing and indicates soil nitrification processes.•LDA model achieved higher classification rates than k-NN and SVM models. This study investigates the use of stable isotopes (C, N, H, and O) to characterize the geographical origin of peanuts along with different peanut fractions including whole peanut kernel, peanut shell, delipidized peanuts and peanut oil. Peanut samples were procured in 2017 from three distinctive growing regions (Shandong, Jilin, and Jiangsu) in China. Peanut processing significantly influenced the δ13C, δ2H, and δ18O values of different peanut fractions, whereas δ15N values were consistent across all fractions and unaffected by peanut processing. Geographical differences of peanut kernels and associated peanut fractions showed a maximum variance for δ15N and δ18O values which indicated their strong potential to discriminate origin. Different geographical classification models (SVM, LDA, and k-NN) were tested for peanut kernels and associated peanut fractions. LDA achieved the highest classification percentage, both on the training and validation sets. Delipidized peanuts had the best classification rate compared to the other fractions.