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Assessing Seasonal Effects on Identification of Cultivation Methods of Short-Growth Cycle Brassica chinensis L. Using IRMS and NIRS

文献类型： 外文期刊

作者： Liu, Xing 1 ; Fan, Kai 1 ; Lu, Yangyang 1 ; Zhao, Hong 1 ; Rao, Qinxiong 1 ; Geng, Hao 1 ; Chen, Yijiao 1 ; Rogers, Karyne Maree 3 ; Song, Weiguo 1 ;

作者机构： 1.Shanghai Acad Agr Sci, Inst Agrofood Stand & Testing Technol, Shanghai 201403, Peoples R China

2.Shanghai Serv Platform Agroprod Qual & Safety Eval, Shanghai 201403, Peoples R China

3.GNS Sci, Natl Isotope Ctr, 30 Gracefield Rd, Lower Hutt 5040, New Zealand

4.Zhejiang Acad Agr Sci, Inst Agroprod Safety & Nutr, Hangzhou 310021, Peoples R China

关键词： Brassica chinensis L.; seasonal effect; stable isotope; NIRS; cultivation method; PLS-DA

期刊名称：FOODS （ 影响因子：5.2； 五年影响因子：5.5 ）

ISSN：

年卷期： 2024 年 13 卷 8 期

页码：

收录情况： SCI

摘要： Seasonal (temporal) variations can influence the delta C-13, delta H-2, delta O-18, and delta N-15 values and nutrient composition of organic (ORG), green (GRE), and conventional (CON) vegetables with a short growth cycle. Stable isotope ratio mass spectrometry (IRMS) and near-infrared spectroscopy (NIRS) combined with the partial least squares-discriminant analysis (PLS-DA) method were used to investigate seasonal effects on the identification of ORG, GRE, and CON Brassica chinensis L. samples (BCs). The results showed that delta N-15 values had significant differences among the three cultivation methods and that delta C-13, delta H-2, and delta O-18 values were significantly higher in winter and spring and lower in summer. The NIR spectra were relatively clustered across seasons. Neither IRMS-PLS-DA nor NIRS-PLS-DA could effectively identify all BC cultivation methods due to seasonal effects, while IRMS-NIRS-PLS-DA combined with Norris smoothing and derivative pretreatment had better predictive abilities, with an 89.80% accuracy for ORG and BCs, 88.89% for ORG and GRE BCs, and 75.00% for GRE and CON BCs. The IRMS-NIRS-PLS-DA provided an effective and robust method to identify BC cultivation methods, integrating multi-seasonal differences.

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