A simple and practical control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry

Food Chemistry

Volumn 184, Issue , 2015, Pages 154-159

  Barbosa, Rommel M ^a   Batista, Bruno L ^b   Barião, Camila V ^b   Varrique, Renan M ^b   Coelho, Vinicius A ^b   Campiglia, Andres D ^c   Barbosa, Fernando ^b  

^a FEDERAL UNIVERSITY OF GOIÁS   (Brazil)

^b UNIVERSITY OF SÃO PAULO   (Brazil)

^c UNIVERSITY OF CENTRAL FLORIDA   (United States)

Author keywords

Chemometric; Food samples; ICP MS; Machine learning; Sugarcane; Trace elements

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CHEMICAL ELEMENTS; DECISION TREES; INDUCTIVELY COUPLED PLASMA; INDUCTIVELY COUPLED PLASMA MASS SPECTROMETRY; LEARNING SYSTEMS; MASS SPECTROMETRY; SPECTROMETRY; SUGAR CANE; TRACE ANALYSIS; TRACE ELEMENTS;

CHEMOMETRICES; ELEMENT LEVEL; FEATURE SELECTION ALGORITHM; FOOD SAMPLES; ICP-MS; PRACTICAL CONTROLS; RANDOM FORESTS; REFERENCE RANGE;

LEARNING ALGORITHMS;

DYSPROSIUM; MANGANESE; MOLYBDENUM; NIOBIUM; RUBIDIUM; SAMARIUM; SELENIUM; STRONTIUM; TANTALUM; THORIUM; TRACE ELEMENT; URANIUM; YTTERBIUM; YTTRIUM;

ACCURACY; ARTICLE; BAYESIAN LEARNING; CHEMOMETRIC ANALYSIS; CLASSIFICATION ALGORITHM; CLASSIFIER; MASS SPECTROMETRY; RANDOM FOREST; SAMPLE; SUGARCANE; ALGORITHM; ATOMIC ABSORPTION SPECTROMETRY; BAYES THEOREM; CHEMISTRY; PROCEDURES; REFERENCE VALUE;

ALGORITHMS; BAYES THEOREM; MASS SPECTROMETRY; REFERENCE VALUES; SACCHARUM; SPECTROPHOTOMETRY, ATOMIC; TRACE ELEMENTS;

EID: 84926188310     PISSN: 03088146     EISSN: 18737072     Source Type: Journal    
DOI: 10.1016/j.foodchem.2015.02.146     Document Type: Article

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