Elemental Profiling for the Detection of Food Mixtures: A Proof of Principle Study on the Detection of Mixed Walnut Origins Using Measured and Calculated Data.

Element profiling is a powerful tool for detecting fraud related to claims of geographical origin. However, these methods must be continuously developed, as mixtures of different origins in particular offer great potential for adulteration. This study is a proof of principle to determine whether elemental profiling is suitable for detecting mixtures of the same food but from different origins and whether calculated data from walnut mixtures could help to reduce the measurement burden. The calculated data used in this study were generated based on measurements of authentic, unadulterated samples. Five different classification models and three regression models were applied in five different evaluation approaches to detect adulteration or even distinguish between adulteration levels (10% to 90%). To validate the method, 270 mixtures of walnuts from different origins were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Depending on the evaluation approach, different characteristics were observed in mixtures when comparing the calculated and measured data. Based on the measured data, it was possible to detect admixtures with an accuracy of 100%, even at low levels of adulteration (20%), depending on the country. However, calculated data can only contribute to the detection of adulterated walnut samples in exceptional cases.

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.

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.