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.

Application of Handheld NIRS to proximate analysis of animal feed and detection of melamine adulteration of soya.

The authenticity and traceability of olive oils have been a growing concern over the past decades, generating numerous scientific studies. This article applies the tools of bibliometric analyses to explore the evolution and strategic orientation of the research focused on olive oil geographical and varietal origins. A corpus of 732 papers published in 178 different journals between 1991 and 2018 was considered. The most productive journals, authors and countries are highlighted, as well as the most cited articles associated with specific analytical techniques. A cluster analysis on the keywords generates 8 main themes of research, each focused on different analytical techniques or compounds of interest. A network between these thematic clusters and the main authors indicates their area of expertise. The metabolomics methods are drawing increasing interest and studies focused on the relationships between the origin and the sensory or nutritional properties provided by minor compounds of olive oils appear to be future lines of research.

Cables, especially their insulation and jacket materials made of polymers, are vulnerable to ageing degradation during normal operation. However, they must remain functional for the entire life of a nuclear power plant, or even in the event of an accident for cables with a safety requirement. This study focuses on models of crosslinked polyethylene (XLPE)-based insulation of cables and deals with the structure modification and the behavior of XLPE for nuclear applications due to the effect of additives. Various additives are added to the polymer formulation to evaluate their impact on ageing. The samples are irradiated at room temperature by several gamma doses, up to 374 kGy, with two dose rates (40 Gy/h and 300 Gy/h) and compared with a non-irradiated sample used as reference. To understand the impact of gamma irradiation on the materials, the principal component analysis (PCA) method is applied on spectra recorded through attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. The results highlight the effects of ageing depending on the dose rate and on the formulation of the materials, with the identification of different degradation products. A curve resolution study compares the effects of different additives on polymer oxidation and shows that the low dose rate leads to a higher degradation than the high dose rate.

To discriminate samples from three varieties of Tunisian extra virgin olive oils, weighted and non-weighted multiblock partial least squares-discriminant analysis (MB-PLS1-DA) models were compared to PLS1-DA models using data obtained by gas chromatography (GC), or global composition through mid-infrared spectra (MIR). Models performances were determined using percentages of sensitivity, specificity and total correct classification. The choice of threshold level for the interpretation of PLS1-DA results was considered. PLS1-DA models using GC data gave better results than those using MIR data. Even with the most conservative threshold, PLS1-DA on GC data allowed very good predictions for Chemlali variety (99% correct classification), but had more difficulty to discriminate Chetoui and Oueslati samples (95% and 84% correct classification respectively). Non-weighted MB-PLS1-DA models benefiting from the synergy between the two sources of data were more discriminative than simple PLS1-DA, yielding better prediction for Chetoui and Oueslati varieties (98% and 90% correct classification respectively).

The control of varietal origin is an important issue to insure the authenticity of olive oils. In this study, extra virgin olive oils from five French cultivars were discriminated by applying partial least square discriminant analysis (PLS1-DA) to their fatty acid and squalene compositions obtained by gas chromatography. Two decision rules were compared to determine the varietal origin of predicted samples: either a classical PLS-DA approach with uncertainty zones, or a control chart approach with warning and control limits. The control chart approach, being focused on characteristic samples from each modelled cultivar, is able to deal with classes having unbalanced number of samples and to identify atypical samples.

This paper describes a comparison between handheld near infrared (NIR) and benchtop instruments for determination of proximate analysis of poultry feed using off-the-shelf calibration models and including statistical analysis. In an additional application, melamine-adulterated soya bean products were used to develop qualitative and quantitative calibration models from the NIR spectral data collected. Excellent calibration models and prediction statistics were obtained.

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