FOOD SCIENCE ›› 2018, Vol. 39 ›› Issue (6): 291-297.doi: 10.7506/spkx1002-6630-201806045

• Safety Detection • Previous Articles     Next Articles

Rapid Identification of Maca Quality Based on Odor Fingerprint

DANG Yanting1, YUAN Peng1, XIA Kai1, HAN Xiaofeng1, LIU Shiwei1, ZHAO Kexin1, ZHOU Wenxuan1, WEN Lin2,*, LI Aimin3, DUAN Shenglin1,*   

  1. (1. China National Research Institute of Food & Fermentation Industries, Beijing 100015, China; 2. New Era Health Industry (Group) Co. Ltd., Beijing 102206, China; 3. Key Laboratory?of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China)
  • Online:2018-03-25 Published:2018-03-14

Abstract: This work focused on 36 maca samples collected from 24 producing regions. Headspace odors from maca samples were collected and analyzed by an electronic nose (E-nose) and total glucosinolate content was determined by high performance liquid chromatography (HPLC). The correlation between glucosinolate contents and E-nose responses was analyzed by SPSS 22.0 statistical analysis software in an effort to build a model to rapidly identify maca quality using a soft independent modeling of class analogy (SIMCA) algorithm. The results showed a significant correlation between three sensors, T30/1, P30/1 and P30/2, and glucosinolates level. According to the resulting SIMCA models, the samples could be divided into three grades: grade 1 (glucosinolates content ≥ 10 mg/g), grade 2 (5 mg/g ≤ glucosinolates content < 10 mg/g), and grade 3 (glucosinolates content < 5 mg/g). The SIMCA models based on electronic nose data could allow rapid grading of maca quality according to its glucosinolates content.

Key words: maca, glucosinolate, quality, electronic nose, odor fingerprint, soft independent modeling of class analogy (SIMCA)

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