Classification of transformed anchovy products based on the use of element patterns and decision trees to assess traceability and country of origin labelling. (30th October 2021)

• Record Type: Journal Article
• Authors: Varrà, Maria Olga; Husáková, Lenka; Patočka, Jan; Ghidini, Sergio; Zanardi, Emanuela
• Is Part Of: Food chemistry. Volume 360(2021)
• Publication Date: 2021-10-30
• Subjects: CRMs Certified reference materials -- CART classification and regression trees -- CHAID chi-square automatic interaction detection -- HCA hierarchical cluster analysis -- HE He high energy Helium mode -- ICP-MS inductively coupled plasma mass spectrometry -- ICP-OES inductively coupled plasma optical emission spectrometry -- ISTD internal standard -- KED kinetic energy discrimination -- MDL method detection limit -- MLOQ method limit of quantification -- QUEST quick unbiased efficient statistical tree; Engraulis encrasicolus -- Fish products -- Decision trees -- Geographical origin -- Data mining -- ICP-MS; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2021.129790
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Elemental profiles of 180 anchovy samples are determined by ICP-MS. Influence of the production process on the element composition can be passed over. Pivotal elements for origin discrimination are identified. Element compositions provide highly specific fingerprint of anchovy products. CHAID and QUEST decision tree algorithms are recommended for origin prediction. Abstract: Quadrupole inductively coupled plasma mass spectrometry (Q-ICP-MS) and direct mercury analysis were used to determine the elemental composition of 180 transformed (salt-ripened) anchovies from three different fishing areas before and after packaging. To this purpose, four decision trees-based algorithms, corresponding to C5.0, classification and regression trees (CART), chi-square automatic interaction detection (CHAID), and quick unbiased efficient statistical tree (QUEST) were applied to the elemental datasets to find the most accurate data mining procedure to achieve the ultimate goal of fish origin prediction. Classification rules generated by the trained CHAID model optimally identified unlabelled testing bulk anchovies (93.9% F-score) by using just 6 out of 52 elements (As, K, P, Cd, Li, and Sr). The finished packaged product was better modelled by the QUEST algorithm which recognised the origin of anchovies with F-score of 97.7%, considering the information carried out by 5 elements (B, As, K. Cd, and Pd). Results obtained suggested that the traceability system in the fishery sector may be supported by simplified machine learning techniques applied to a limited but effective number of inorganic predictors of origin.