Journal
TALANTA
Volume 217, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.talanta.2020.121115
Keywords
Data fusion; Decision rule; Chemometrics; Vibrational spectroscopy; Olive oil; Cultivars
Categories
Funding
- French National Agency for Research (ANR) as part of the European Union's Seventh Framework Program for research, technological development and demonstration [618127]
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Combining data from different analytical sources could be a way to improve the performances of chemometric models by extracting the relevant and complementary information for food authentication. In this study, several data fusion strategies including concatenation (low-level), multiblock and hierarchical models (mid-level), and majority vote (high-level) are applied to near- and mid-infrared (NIR and MIR) spectral data for the varietal discrimination of olive oils from six French cultivars by partial least square discriminant analysis (PLS1-DA). The performances of the data fusion models are compared to each other and to the results obtained with NIR or MIR data alone, with a choice of chemometric pre-treatments and either an arbitrarily fixed limit or a control chart decision rule. Concatenation and hierarchical PLS1-DA fail to improve the prediction results compared to individual models, whereas weighted multiblock PLS1-DA models with the control chart approach provide a more efficient differentiation for most, but not all, of the cultivars. The high-level models using a majority vote with the control chart decision rule benefit from the complementary results of the individual NIR and MIR models leading to more consistently improved results for all cultivars.
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