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

Abstract

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.

Determination of the Geographical Origin of Walnuts (Juglans regia L.) Using Near-Infrared Spectroscopy and Chemometrics

“…The aforementioned performance represents a relatively high accuracy considering the model’s complexity. According to Segelke et al, the evaluation of the performance of a classification model also has to include the number of different classes c [ 57 ]. First, the random distribution r is calculated according to Equation (1).…”

“…Considering the random distribution (see Equation (2)) of our seven-class model, the accuracy-to-random ratio a/r would be 5.35 (see Equation (3)). An exemplary two-class model ( r = 50%) with a classification accuracy of 90% would correspond to an accuracy-to-random ratio of 1.8 and hence to a less powerful model [ 57 ]. …”

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

“…A randomly selected almond sample was used as a reference for optimization. The reference sample for optimization was spiked with ∼5 mg/kg (Li, Na, Mg, Al, K, V, Cr, Mn, Co, Ni, Cu, Ga, Rb, Sr, Mo, Ag, Cd, Te, Ba, Tl, Pb, Bi, U, Sc, Y, La, Ce, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, and Th), ∼50 mg/kg (Be, B, Fe, Zn, As, Li, 11 B, 23 Na, 24 Mg, 27 Al, 31 P, 34 S, 39 K, 43 Ca, 47 and Se), and ∼500 mg/kg (Ca) to better estimate parameter changes.…”

“…40,44,45 The stratified approach helps to positively influence the sampling distribution for computing the test and training samples of nested cross-validation and leads to more consistent accuracy. 43 To counteract the curse of dimensionality, preselected elements were used for the study. According to Konstantinos Koutroumbas, at least 10 training examples are required for each dimension in the designwhich we fulfill.…”

Population identification strategies for counterfeit coin detection

“…watches has been demonstrated from composition analysis and correlation of the data using hierarchical cluster analysis [2]. Growing interest in food fraud has led to many studies of origin for select food items such as whiskies and wines [3,4], as well as more common items such as olive oil and walnuts [5,6]. An even larger global problem is found in fraudulent honey, which is not only a source problem but also tied to a global reduction in pollination with wide-spread effects [7].…”