He Xi, Gbiorczyk Krystyna, Jeleń Henryk H
Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, Poland.
Wyborowa SA, Janikowska 23, 61-070 Poznań, Poland.
J Agric Food Chem. 2023 Feb 8;71(5):2637-2643. doi: 10.1021/acs.jafc.2c08141. Epub 2023 Jan 26.
Mass spectrometry based quasi-electronic nose using solid-phase microextraction to introduce volatiles directly to mass spectrometer without chromatographic separation (HS-SPME-MS) was used to discriminate 45 raw spirits produced from C3 (potato, rye, wheat) and C4 (corn, sorghum) plants. The samples were also subjected to isotope ratio mass spectrometry (IRMS), which unequivocally distinguished C3 from C4 samples; however, no clear differentiation was observed for C3 samples. On the contrary, HS-SPME-MS, which uses unresolved volatile compounds "fingerprints" in a form of ions of a given / range and various intensities provided excellent sample classification and prediction after OPLS-DA data processing verified also by the artificial neural network (ANN).
基于质谱的准电子鼻，采用固相微萃取技术将挥发性物质直接引入质谱仪而无需色谱分离（HS-SPME-MS），用于鉴别由C3（土豆、黑麦、小麦）和C4（玉米、高粱）植物生产的45种原酒。这些样品还进行了同位素比率质谱分析（IRMS），该分析明确区分了C3和C4样品；然而，对于C3样品未观察到明显的区分。相反，HS-SPME-MS以给定范围和不同强度的离子形式使用未解析的挥发性化合物“指纹”，在经人工神经网络（ANN）验证的OPLS-DA数据处理后提供了出色的样品分类和预测。
