Intra-regional classification of grape seeds produced in Mendoza province (Argentina) by multi-elemental analysis and chemometrics tools

Abstract

The feasibility of the application of chemometric techniques associated with multi-element analysis for the classification of grape seeds according to their provenance vineyard soil was investigated. Grape seed samples from different localities of Mendoza province (Argentina) were evaluated. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of twenty-nine elements (Ag, As, Ce, Co, Cs, Cu, Eu, Fe, Ga, Gd, La, Lu, Mn, Mo, Nb, Nd, Ni, Pr, Rb, Sm, Te, Ti, Tl, Tm, U, V, Y, Zn and Zr). Once the analytical data were collected, supervised pattern recognition techniques such as linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), support vector machine (SVM) and Random Forest (RF) were applied to construct classification/discrimination rules. The results indicated that nonlinear methods, RF and SVM, perform best with up to 98% and 93% accuracy rate, respectively, and therefore are excellent tools for classification of grapes.

Intra-regional classification of Codonopsis Radix produced in Gansu province (China) by multi-elemental analysis and chemometric tools

“…Some counties in the southeast of Gansu Province are the most important production areas of Codonopsis Radix in China, and they account for more than 80% of the total output, such as Weiyuan, Lintao, Longxi, Zhang, Min, Tanchang and Wen 9 . The unique natural conditions of these placesare the guarantee for the production of high quality Codonopsis Radix, such as altitude, soil, precipitation and sunshine 10 . The composition of inorganic elements in plants is influenced by genetic and environmental factors.…”

Geographical origin discrimination of pepper (Capsicum annuum L.) based on multi-elemental concentrations combined with chemometrics

“…Simultaneously, although a successful classification was obtained, a supplementary study covering lots of pepper samples should be allowed to establish better discrimination models of pepper samples from different regions. Moreover, it was indicated that other chemometrics tools, e.g., support vector machine (SVM), random forest (RF), and k-nearest neighbors (k-NN), were applied to classify grapes seeds indicating the correct classification rates of k-NN, RF, and SVM were 85.7%, 98.3%, and 93.3%, respectively (Canizo et al, 2018). Furthermore, twenty-three elements, twelve metal isotope compositions, and four stable isotope ratios (d 13 C, d 15 N, d 2 H, and d 18 O) combined with PCA, LDA, PLS-DA, and Decision-making tree (DT) were used to distinguish the geographical origin of green tea from China and an accuracy rate of 90.0% (blind dataset, n = 107) was obtained by means of the DT tool (Ni et al, 2018).…”

Multielemental Analysis Associated with Chemometric Techniques for Geographical Origin Discrimination of Tea Leaves (Camelia sinensis) in Guizhou Province, SW China

“…Meanwhile, although the results presented in this study indicated that classification can be successful, a complementary study involving a larger number of tea samples would be necessary to establish better classification models for tea samples. Also, other chemometrics tools e.g., k-nearest neighbors (k-NN), support vector machine (SVM) and Random Forest (RF) were applied to classify grapes, which indicated that k-NN, RF and SVM perform best with up to 85.7%, 98.3% and 93.3% accuracy rate, respectively [ 50 ]. As a result, we can use these chemometrics tools to discriminate Guizhou tea in future studies.…”