Multiblock chemometrics for the discrimination of three extra virgin olive oil varieties

Improving the Accuracy of Saffron Adulteration Classification and Quantification through Data Fusion of Thin-Layer Chromatography Imaging and Raman Spectral Analysis

Agricultural crops of high value are frequently targeted by economic adulteration across the world. Saffron powder, being one of the most expensive spices and colorants on the market, is particularly vulnerable to adulteration with extraneous plant materials or synthetic colorants. However, the current international standard method has several drawbacks, such as being vulnerable to yellow artificial colorant adulteration and requiring tedious laboratory measuring procedures. To address these challenges, we previously developed a portable and versatile method for determining saffron quality using a thin-layer chromatography technique coupled with Raman spectroscopy (TLC-Raman). In this study, our aim was to improve the accuracy of the classification and quantification of adulterants in saffron by utilizing mid-level data fusion of TLC imaging and Raman spectral data. In summary, the featured imaging data and featured Raman data were concatenated into one data matrix. The classification and quantification results of saffron adulterants were compared between the fused data and the analysis based on each individual dataset. The best classification result was obtained from the partial least squares-discriminant analysis (PLS-DA) model developed using the mid-level fusion dataset, which accurately determined saffron with artificial adulterants (red 40 or yellow 5 at 2-10%, w/w) and natural plant adulterants (safflower and turmeric at 20-100%, w/w) with an overall accuracy of 99.52% and 99.20% in the training and validation group, respectively. Regarding quantification analysis, the PLS models built with the fused data block demonstrated improved quantification performance in terms of R² and root-mean-square errors for most of the PLS models. In conclusion, the present study highlighted the significant potential of fusing TLC imaging data and Raman spectral data to improve saffron classification and quantification accuracy via the mid-level data fusion, which will facilitate rapid and accurate decision-making on site.

Chemometric Discrimination of the Varietal Origin of Extra Virgin Olive Oils: Usefulness of 13C Distortionless Enhancement by Polarization Transfer Pulse Sequence and 1H Nuclear Magnetic Resonance Data and Effectiveness of Fusion with Mid-Infrared Spectroscopy Data

The label authentication of monovarietal extra virgin olives is of great relevance from a socio-economical point of view. This work aims to gain insights into the prediction of the varietal origin of extra virgin olive oil (EVOO) samples obtained from single olive cultivars, French cultivars Olivière, Salonenque, and Tanche and Portuguese cultivars Blanqueta, Carrasquenha, and Galega Vulgar, collected in 2016-2017 and 2017-2018 harvest seasons. To pursue this study, spectroscopic approaches based on one-dimensional nuclear magnetic resonance (1D NMR) spectroscopy, namely, ¹H and ¹³C NMR distortionless enhancement by polarization transfer (DEPT) 45 pulse sequence, and Fourier transform mid-infrared spectroscopy (FT-MIR) are used in combination with partial least square discriminant analysis (PLS1-DA). The results obtained by PLS1-DA models using ¹H and ¹³C NMR DEPT 45 data are compared to those of PLS1-DA models using MIR data. The application of a control chart method allows for the optimization of the interpretation of the PLS1-DA results, and an efficient two-step strategy is proposed to improve the discrimination of the six studied cultivars. Then, NMR and MIR data are combined by either a mid- or high-level data fusion approach to further improve the discrimination. The models are also tested on samples from other cultivars to check their ability to reject varieties that were not considered in the calibration process.

Chemometric Strategies for Spectroscopy-Based Food Authentication

In the last decades, spectroscopic techniques have played an increasingly crucial role in analytical chemistry, due to the numerous advantages they offer. Several of these techniques (e.g., Near-InfraRed—NIR—or Fourier Transform InfraRed—FT-IR—spectroscopy) are considered particularly valuable because, by means of suitable equipment, they enable a fast and non-destructive sample characterization. This aspect, together with the possibility of easily developing devices for on- and in-line applications, has recently favored the diffusion of such approaches especially in the context of foodstuff quality control. Nevertheless, the complex nature of the signal yielded by spectroscopy instrumentation (regardless of the spectral range investigated) inevitably calls for the use of multivariate chemometric strategies for its accurate assessment and interpretation. This review aims at providing a comprehensive overview of some of the chemometric tools most commonly exploited for spectroscopy-based foodstuff analysis and authentication. More in detail, three different scenarios will be surveyed here: data exploration, calibration and classification. The main methodologies suited to addressing each one of these different tasks will be outlined and examples illustrating their use will be provided alongside their description.

Tracing the Geographical Origins of Dendrobe (Dendrobium spp.) by Near-Infrared Spectroscopy Sensor Combined with Porphyrin and Chemometrics

Dendrobe (Dendrobium spp.) is a traditional medicinal and edible food, which is rich in nutrients and contains biologically active metabolites. The quality and price of dendrobe are related to its geographical origins, and high quality dendrobe is often imitated by low quality dendrobe in the market. In this work, near-infrared (NIR) spectroscopy sensor combined with porphyrin and chemometrics was used to distinguish 360 dendrobe samples from twelve different geographical origins. Partial least squares discriminant analysis (PLSDA) was used to study the sensing performance of traditional NIR and tera-(4-methoxyphenyl)-porphyrin (TMPP)-NIR on the identification of dendrobe origin. In the PLSDA model, the recognition rate of the training and prediction set of the TMPP-NIR could reach 100%, which was higher than the 91.85% and 91.34% of traditional NIR. And the accuracy, sensitivity, and specificity of the TMPP-NIR sensor are all 1.00. The mechanism of TMPP improving the specificity of NIR spectroscopy should be related to the π-π conjugated system and the methoxy groups of TMPP interact with the chemical components of dendrobe. This study reflected that NIR spectrum with TMPP sensor was an effective approach for identifying the geographic origin of dendrobe.