Application of supervised chemometric techniques and synchronized excitation-emission spectrofluorometric analysis for the verification of Maltese extra virgin olive oils

The authentication of virgin olive oil samples usually requires the use of sophisticated and very expensive analytical techniques. In this study, the potential of fluorescence spectroscopy for the authentication and discrimination of Maltese extra virgin olive oils was carried out using synchronized excitation-emission spectroscopy. Samples were collected from various producers around the Maltese islands. Synchronous excitation emission spectra were collected in the region of 240-750 nm with wavelength intervals of 10, 30, 60, 80 120 and 185 nm and subjected to several supervised chemometric procedures. Partial least square regression, linear discriminate analysis, and artificial neural network were used to define the origin of the Maltese olive oil against olive oils derived from other neighboring countries in the Mediterranean region. After subjecting the spectroscopic data to different pre-treatments and variable selection procedures results obtained evidenced a higher classification accuracy. This accuracy and predictability were highly dependent on the wave interval used and on the chemometric method used, however it was found that in general spectra obtained using δ 10 nm were deemed the most appropriate, with PLS, ANN and LDA reaching 100% accuracy and predictability in discriminating Maltese extra virgin olive oils when using derivatized spectral transformations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-022-05371-x.

Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs

BACKGROUND

With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and F_ST) to select the most informative SNPs for ancestry inference.

RESULTS

Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof.

CONCLUSIONS

The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.

First Profile of Phenolic Compounds from Maltese Extra Virgin Olive Oils Using Liquid-Liquid Extraction and Liquid Chromatography-Mass Spectrometry

This study presents the profile of phenolic extracts from different Extra Virgin Olive Oils (EVOOs) from Malta and is the first study that characterizes the phenolic profile of the Maltese EVOOs Bidni (B) and Malti (M) using liquid-liquid extraction (LLE) and Liquid Chromatography-Mass Spectrometry (LC-MS). The total phenolic content (TPC), ortho diphenolic content (TdPC) and flavonoid content (TFC) were determined using the Folin-Ciocalteau assay, the Arnow's assay and the Aluminium Chloride method respectively. Results show that the B variety had the highest TPC, TdPC and TFC. Using LC-MS analysis, over 30 phenolic compounds were identified belonging to different classes of phenolic compounds.

The First Identification of the Uniqueness and Authentication of Maltese Extra Virgin Olive Oil Using 3D-Fluorescence Spectroscopy Coupled with Multi-Way Data Analysis

The potential application of multivariate three-way data analysis techniques, namely parallel factor analysis (PARAFAC) and discriminant multi-way partial least squares regression (DN-PLSR), on three-dimensional excitation emission matrix (3D-EEM) fluorescent data were used to identify the uniqueness and authenticity of Maltese extra virgin olive oil (EVOO). A non-negativity constrained PARAFAC model revealed that a four-component model provided the most appropriate solution. Examination of the extracted components in mode 2 and 3 showed that these belonged to different fluorophores present in extra virgin olive oil. Application of linear discriminate analysis (LDA) and binary logistic regression analysis on the concentration of the four extracted fluorophores, showed that it is possible to discriminate Maltese EVOOs from non-Maltese EVOOs. The application of DN-PLSR provided superior means for discrimination of Maltese EVOOs. Further inspection of the extracted latent variables and their variable importance plots (VIPs) provided strong proof of the existence of four types of fluorophores present in EVOOs and their potential application for the discrimination of Maltese EVOOs.

Genetic flow among olive populations within the Mediterranean basin

Background

The olive tree is a typical crop of the Mediterranean basin where it shows a wide diversity, accounting for more than 2,600 cultivars. The ability to discriminate olive cultivars and determine their genetic variability is pivotal for an optimal exploitation of olive genetic resources.

Methods

We investigated the genetic diversity within 128 olive accessions belonging to four countries in the Mediterranean Basin (Italy, Algeria, Syria, and Malta), with the purpose of better understanding the origin and spread of the olive genotypes across Mediterranean Basin countries. Eleven highly polymorphic simple sequence repeat (SSR) markers were used and proved to be very informative, producing a total of 179 alleles.

Results

Cluster analysis distinguished three main groups according to their geographical origin, with the current sample of Maltese accessions included in the Italian group. Phylogenetic analysis further differentiated Italian and Maltese olive accessions, clarifying the intermediate position of Maltese accessions along the x/y-axes of principal coordinate analysis (PCoA). Model-based and neighbor clustering, PCoA, and migration analysis suggested the existence of two different gene pools (Algerian and Syrian) and that the genetic exchange occurred between the Syrian, Italian and Maltese populations.

Discussion

The close relationship between Syrian and Italian and Maltese olives was consistent with the historical domestication and migration of olive tree from the North Levant to eastern Mediterranean basin. This study lays the foundations for a better understanding of olive genetic diversity in the Mediterranean basin and represents a step toward an optimal conservation and exploitation of olive genetic resources.

Application of fluorescence spectroscopy and chemometric models for the detection of vegetable oil adulterants in Maltese virgin olive oils

Fluorescence spectrometry, combined with principle component analysis, partial least-squares regression (PLSR) and artificial neural network (ANN), was applied for the analysis of Maltese extra virgin olive oil (EVOO) adulterated by blending with vegetable oil (corn oil, soybean oil, linseed oil, or sunflower oil). The novel results showed that adjusted PLSR models based on synchronised spectra for detecting the % amount of EVOO in vegetable oil blends had a lower root mean square error (0.02-6.27%) and higher R² (0.983-1.000) value than those observed when using PLSR on the whole spectrum. This study also highlights the use of ANN as an alternative chemometric tool for the detection of olive oil adulteration. The performance of the model generated by the ANN is highly dependent both on the type of data input and the mode of cross validation; for spectral data which had a variable importance plot value > 0.8 the excluded row cross validation was more appropriate while for complete spectral analysis k-fold or CV-10 was more appropriate.