de Rijke E, Schoorl J C, Cerli C, Vonhof H B, Verdegaal S J A, Vivó-Truyols G, Lopatka M, Dekter R, Bakker D, Sjerps M J, Ebskamp M, de Koster C G
Mass Spectrometry of Biomacromolecules, Swammerdam Institute of Life Sciences, University of Amsterdam, Sciencepark 904, 1090 GE Amsterdam, The Netherlands.
Earth Surface Science, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Sciencepark 904, 1090 GE Amsterdam, The Netherlands.
Food Chem. 2016 Aug 1;204:122-128. doi: 10.1016/j.foodchem.2016.01.134. Epub 2016 Feb 4.
Two approaches were investigated to discriminate between bell peppers of different geographic origins. Firstly, δ(18)O fruit water and corresponding source water were analyzed and correlated to the regional GNIP (Global Network of Isotopes in Precipitation) values. The water and GNIP data showed good correlation with the pepper data, with constant isotope fractionation of about -4. Secondly, compound-specific stable hydrogen isotope data was used for classification. Using n-alkane fingerprinting data, both linear discriminant analysis (LDA) and a likelihood-based classification, using the kernel-density smoothed data, were developed to discriminate between peppers from different origins. Both methods were evaluated using the δ(2)H values and n-alkanes relative composition as variables. Misclassification rates were calculated using a Monte-Carlo 5-fold cross-validation procedure. Comparable overall classification performance was achieved, however, the two methods showed sensitivity to different samples. The combined values of δ(2)H IRMS, and complimentary information regarding the relative abundance of four main alkanes in bell pepper fruit water, has proven effective for geographic origin discrimination. Evaluation of the rarity of observing particular ranges for these characteristics could be used to make quantitative assertions regarding geographic origin of bell peppers and, therefore, have a role in verifying compliance with labeling of geographical origin.
研究了两种区分不同地理来源甜椒的方法。首先，分析了甜椒果实水的δ(18)O以及相应的源水，并将其与区域GNIP（全球降水同位素网络）值进行关联。水和GNIP数据与甜椒数据显示出良好的相关性，同位素分馏常数约为-4。其次，使用化合物特异性稳定氢同位素数据进行分类。利用正构烷烃指纹数据，开发了线性判别分析（LDA）和基于似然的分类方法（使用核密度平滑数据）来区分不同产地的甜椒。两种方法均以δ(2)H值和正构烷烃相对组成作为变量进行评估。使用蒙特卡洛5折交叉验证程序计算误分类率。两种方法的总体分类性能相当，然而，它们对不同样本表现出不同的敏感性。δ(2)H IRMS的组合值以及甜椒果实水中四种主要烷烃相对丰度的补充信息，已被证明对地理来源判别有效。评估观察这些特征特定范围的稀有性，可用于对甜椒的地理来源进行定量断言，因此在验证地理来源标签的合规性方面具有作用。
