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Traceability of soybeans produced in Argentina based on their trace element profiles
Author(s) -
Hidalgo Melisa J.,
Fechner Diana C.,
Ballabio Davide,
Marchevsky Eduardo J.,
Pellerano Roberto G.
Publication year - 2020
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3252
Subject(s) - support vector machine , principal component analysis , traceability , pattern recognition (psychology) , artificial intelligence , chemometrics , linear discriminant analysis , mathematics , random forest , computer science , machine learning , statistics
Soybean ( Glycine max (L.) Merril) is a popular foodstuff and crop plant, used in human and animal food. In this work, multielement analysis of soybean grains samples in combination with chemometric tools was used to classify the geographical origins. For this purpose, 120 samples from three provinces of Argentina were analyzed for a panel of 20 trace elements by inductively coupled plasma mass spectrometry. First, we used principal component analysis for exploratory analysis. Then, supervised classification techniques such as support vector machine (SMV) discriminant analysis (SVM‐DA), random forest, k ‐nearest neighbors, and class‐modeling techniques such as soft independent modeling of class analogy (SIMCA), potential functions, and one‐class SVM were applied as tools to establish a model of origin of samples. The performance of the techniques was compared using global indexes. Among all the models tested, SVM and SIMCA showed the highest percentages in terms of prediction ability in cross‐validation with average values of 99.3% for SVM‐DA and a median value of balanced accuracy of 96.0%, 91.7%, and 88.3% for the three origins using SIMCA. Results suggested that the developed methodology by chemometric techniques is robust and reliable for the geographical classification of soybean samples from Argentina.