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Volumn 29, Issue 12, 2009, Pages 3283-3287
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Discriminating and quantifying potential adulteration in virgin olive oil by near infrared spectroscopy with BP-ANN and pls
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Author keywords
BP artificial neural network (BP ANN); Discrimination and quantification; Near infrared spectroscopy (NIR); Partial least square (PLS); Virgin olive oil
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Indexed keywords
ANALYSIS MODELS;
BP ARTIFICIAL NEURAL NETWORK;
CALIBRATION MODEL;
CROSS VALIDATION;
LEAST SQUARES;
NEAR INFRARED;
NEW APPROACHES;
NIR SPECTRUM;
ORIGINAL MODEL;
PARTIAL LEAST SQUARES;
PLS MODELS;
PRE-TREATMENT;
QUANTITATIVE ANALYSIS;
R VALUE;
RESEARCH METHODS;
SESAME OIL;
SEVERAL VARIABLES;
SOYBEAN OIL;
SPECTRAL DATA;
SPECTRAL RANGE;
STANDARD ERRORS;
SUNFLOWER OIL;
TEST SAMPLES;
VIRGIN OLIVE OIL;
BACKPROPAGATION;
INFRARED DEVICES;
LAW ENFORCEMENT;
NEAR INFRARED SPECTROSCOPY;
NEURAL NETWORKS;
PRINCIPAL COMPONENT ANALYSIS;
SPECTROSCOPIC ANALYSIS;
SPECTRUM ANALYSIS;
VEGETABLE OILS;
INFRARED SPECTROSCOPY;
OLIVE OIL;
SESAME SEED OIL;
SOYBEAN OIL;
SUNFLOWER OIL;
VEGETABLE OIL;
ARTICLE;
CALIBRATION;
FOOD CONTAMINATION;
NEAR INFRARED SPECTROSCOPY;
REGRESSION ANALYSIS;
CALIBRATION;
FOOD CONTAMINATION;
LEAST-SQUARES ANALYSIS;
PLANT OILS;
SESAME OIL;
SOYBEAN OIL;
SPECTROSCOPY, NEAR-INFRARED;
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EID: 72449208019
PISSN: 10000593
EISSN: None
Source Type: Journal
DOI: 10.3964/j.issn.1000-0593(2009)12-3283-05 Document Type: Article |
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Times cited : (16)
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References (17)
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