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.

Hidalgo, M., Fechner, D., Ballabio, D., Marchevsky, E., Pellerano, R. (2020). Traceability of soybeans produced in Argentina based on their trace element profiles. JOURNAL OF CHEMOMETRICS, 34(12) [10.1002/cem.3252].

Traceability of soybeans produced in Argentina based on their trace element profiles

Ballabio D.;
2020

Abstract

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.
Articolo in rivista - Articolo scientifico
class-modeling techniques; geographical origin; soybean grains;
English
18-giu-2020
2020
34
12
e3252
reserved
Hidalgo, M., Fechner, D., Ballabio, D., Marchevsky, E., Pellerano, R. (2020). Traceability of soybeans produced in Argentina based on their trace element profiles. JOURNAL OF CHEMOMETRICS, 34(12) [10.1002/cem.3252].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/297397
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