Food Authentication of Almonds (Prunus dulcis Mill.). Fast Origin Analysis with Laser Ablation Inductively Coupled Plasma Mass Spectrometry and Chemometrics.

Food fraud is a growing problem, especially misdeclaration due to regional price differences offering a wide field. Fast, powerful, and cost-effective analytical methods are therefore essential to counteract food fraud. The isotopolome is suitable for origin discrimination and was analyzed in this study using laser ablation inductively coupled plasma mass spectrometry (ICP-MS). A total of 250 almond samples from six countries and four crop years were analyzed and evaluated by chemometric methods. By using a ratio-based assessment, calibration problems were avoided and an origin predictive accuracy of 85.2 ± 1.2% was achieved. Compared to ICP-MS with solution nebulization, the analysis time could be reduced to about one-fifth.

Glass authentication: Laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS) for origin discrimination of glass bottles.

Counterfeiting is an omnipresent issue, among others in the cosmetics industry or on the art market. Particularly in the case of very expensive perfumes or very valuable art objects, counterfeits are strongly represented and are steadily increasing. Typically, the content of perfumes is analyzed, but the bottle offers another level of authentication, as it is an essential part of the product. For art objects made of glass, glass is an essential part of the artwork and thus provides an important contribution to the authenticity of the object. In the present pilot study, we developed a laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS) method to classify glass using perfume bottles manufactured at different production facilities, Germany, India, Peru and Poland as an example. Using minimally invasive laser ablation invisible to the eye, we were able to detect counterfeit flacons without having to open the vials. A total of 63 elements could be recorded during method development. After statistical evaluation (t-test, ANOVA, principal component analysis (PCA)), 15 (Li, Na, Al, Ti, V, Co, Rb, Sr, Mo, Ba, La, Ce, Pr, Er and Pb) significant marker elements were extracted from the data sets to differentiate the different glass origins. By using LDA, six different production sites from four different countries could be differentiated over a sample period of six months with a prediction accuracy of 100%.

Origin Determination of Walnuts (Juglans regia L.) on a Worldwide and Regional Level by Inductively Coupled Plasma Mass Spectrometry and Chemometrics.

To counteract food fraud, this study aimed at the differentiation of walnuts on a global and regional level using an isotopolomics approach. Thus, the multi-elemental profiles of 237 walnut samples from ten countries and three years of harvest were analyzed with inductively coupled plasma mass spectrometry (ICP-MS), and the resulting element profiles were evaluated with chemometrics. Using support vector machine (SVM) for classification, validated by stratified nested cross validation, a prediction accuracy of 73% could be achieved. Leave-one-out cross validation was also applied for comparison and led to less satisfactory results because of the higher variations in sensitivity for distinct classes. Prediction was still possible using only elemental ratios instead of the absolute element concentrations; consequently, a drying step is not mandatory. In addition, the isotopolomics approach provided the classification of walnut samples on a regional level in France, Germany, and Italy, with accuracies of 91%, 77%, and 94%, respectively. The ratio of the model's accuracy to a random sample distribution was calculated, providing a new parameter with which to evaluate and compare the performance of classification models. The walnut cultivar and harvest year had no observable influence on the origin differentiation. Our results show the high potential of element profiling for the origin authentication of walnuts.

Food Authentication: Species and Origin Determination of Truffles (Tuber spp.) by Inductively Coupled Plasma Mass Spectrometry and Chemometrics.

The aim of this study was to develop a protocol for the authentication of truffles using inductively coupled plasma mass spectrometry. The price of the different truffle species varies significantly, and because the visual differentiation is difficult within the white truffles and within the black truffles, food fraud is likely to occur. Thus, in the context of this work, the elemental profiles of 59 truffle samples of five commercially relevant species were analyzed and the resulting element profiles were evaluated with chemometrics. Classification models targeting the species and the origins were validated using nested cross validation and were able to differentiate the most expensive Tuber magnatum from any other examined truffle. For the black truffles, an overall classification accuracy of 90.4% was achieved, and, most importantly, a falsification of the expensive Tuber melanosporum by Tuber indicum could be ruled out. With regard to the geographical origin, for Italy and Spain, one-versus-all classification models were calculated each to differentiate truffle samples from any other origins by 75.0 and 86.7%, respectively. The prediction was still possible according to an internal mathematical normalization scheme using only the element ratios instead of the absolute element concentrations. The established authentication protocol was successfully tested with an external sample set of five fresh truffles. Our results show the high potential of the element profile for the parallel species and origin authentication of truffles.

Food Targeting: Determination of the Cocoa Shell Content (Theobroma cacao L.) in Cocoa Products by LC-QqQ-MS/MS.

A targeted metabolomics LC-ESI-QqQ-MS/MS application for the determination of cocoa shell based on 15 non-polar key metabolites was developed, validated according to recognized guidelines, and used to predict the cocoa shell content in various cocoa products. For the cocoa shell prediction, different PLSR models based on different cocoa shell calibration series were developed and their suitability and prediction quality were compared. By analysing samples from different origins and harvest years with known shell content, the prediction model could be confirmed. The predicted shell content could be verified with a deviation of about 1% cocoa shell. The presented method demonstrates the suitability of the targeted application of metabolomic profiling for the determination of cocoa shell and its applicability in routine analysis is discussed.

Food fingerprinting: Mass spectrometric determination of the cocoa shell content (Theobroma cacao L.) in cocoa products by HPLC-QTOF-MS.

The determination of cocoa shell content (Theobroma cacao L.) in cocoa products using a metabolomics approach was accomplished via high performance liquid chromatography quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS). The developed method was used to separately analyze the polar and non-polar metabolome of the cocoa testa (cocoa shell) and the cocoa cotyledons (cocoa nibs) of cocoa samples from 15 different geographic origins, harvest years, and varieties in positive and negative ion mode. Potential key metabolites were selected which are exclusively contained in the cocoa shell or with significant higher concentration in the cocoa shell than in the cocoa nibs. The pool of potential key metabolites was filtered by established selection criteria, such as temperature stability, fermentations stability, and independence from the geographic origin. Based on these key metabolites an inverse sparse partial least square regression (SPLS) was used for the prediction of the cocoa shell content.