Determination of the Geographical Origin of Asparagus officinalis L. by ¹H NMR Spectroscopy

Abstract

Food authenticity concerning the geographical origin becomes increasingly important for consumers, food industries, and food authorities. In this study, nontargeted 1 H NMR metabolomics combined with machine learning methodologies was applied to successfully distinguish the geographical origin of 237 samples of white asparagus from Germany, Poland, The Netherlands, Spain, Greece, and Peru. Support vector classification of the geographical origin achieved an accuracy of 91.5% for the entire sample set and 87.8% after undersampling the majority class. Important regions of the spectra could be identified and assigned to potential chemical markers. A subset of samples was compared to isotope-ratio mass spectrometry (IRMS), an established method for the determination of origin of white asparagus in Germany. Here, SVM classification led to accuracies of 79.4% for NMR and 70.9% for IRMS. Finally, the classification of asparagus from different German regions was evaluated, and the influence of year and variety was analyzed.

Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth

“…Since Germany borders the Netherlands and Poland, less pronounced differences between metabolomes are to be expected here than, for example, in the distinction between Schleswig-Holstein and Bavaria (distance approximately 850 km). Overall, the classification performance regarding the determination of the geographical origin is similar to previous work using LC-MS [26] and other analytical techniques [23][24][25]. The results for botanical diversity classification of 56 Backlim, 23 Cumulus, 42 Gijnlim, and 29 Grolim samples are shown in Table 2.…”

“…Overall, the classification results are worse than for the determination of the geographical origin. However, previous food profiling techniques that analyze the metabolome of asparagus did not focus on the determination of the variety [23][24][25][26], for which usually the genome is evaluated [29,30]. Hence, with this novel approach, we established a new level for the classification of asparagus LC-MS data showing classification accuracies that are substantially higher than the random distribution of 25%.…”

“…In this study, we show that RF approaches and, in particular, SMD can be applied to LC-MS data sets for comprehensive multi-level classification and characterization. For this, we use data from asparagus authentication experiments [23][24][25][26][27][28].…”

Metabolomics as a marketing tool for geographical indication products: a literature review

“…Recently, there is no doubt that metabolomics has the potential to play major roles in many aspects of food authenticity and traceability [ 28 ]. In this sense, several studies using metabolomic methods and analyses have been carried out to investigate food authenticity [ 21 , 100 ] and [ 44 ] and characterization [ 54 ], to the determination of samples' geographical origin [ 8 , 57 ], and to the separation/distinction of samples produced in different geographical origins [ 45 , 98 ].…”

Effect of Different Processing Methods on the Chemical Constituents of Scrophulariae Radix as Revealed by 2D NMR-Based Metabolomics

“…Untargeted metabolomics is a technology allowing an unbiased and hypothesis-free assessment of the metabolome [ 20 ]. Nuclear magnetic resonance (NMR) is a powerful platform in untargeted metabolomics due to its ability to analyze essentially all types of known and unknown natural products [ 21 ].…”