Comparison of different classification algorithms to identify geographic origins of olive oils

A global systematic review and meta-analysis on prevalence of the aflatoxin B1 contamination in olive oil

Olive oil can be contaminated by fungal toxins; therefore, it is necessary to monitor the incidence of mycotoxins in this oil. In the present study, the pooled prevalence of detectable aflatoxin B₁ (AFB₁) in olive oil was evaluated using systematic review and meta-analysis approach from 1 January 1991 to 31 December 2020 (30 years study). The search was conducted via electronic databases involving Scopus, Web of Science, PubMed, Agris and Agricola. Synonyms were collected from combination of the MESH, Agrovoc and free text method. After screening and selection process of primary researches, full texts of eligible researches (46 studies) were evaluated and data of the nine studies as included researches were extracted. Random effect model was used to estimate the pooled prevalence of AFB₁ in olive oil and weighing model of Dersimonian-Laired was applied. Summary measure of mycotoxin prevalence was estimated using Metaprop module of STATA and 95% confidence interval (CI) were calculated using the Binomial Exact Method. Pooled prevalence of AFB₁ in olive oils were 32% (95% CI 8-56%) which means that 68% of olive oil were free of detectable contaminants of AFB₁. Due to controversy over the results of primary studies, future researches and consequent subgroup analysis based on the main variables affecting the aflatoxins contamination in olive oil are recommended to achieve the conclusive results.

Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea

The geographic origin of agri-food products contributes greatly to their quality and market value. Here, we developed a robust method combining metabolomics and machine learning (ML) to authenticate the geographic origin of Wuyi rock tea, a premium oolong tea. The volatiles of 333 tea samples (174 from the core region and 159 from the non-core region) were profiled using gas chromatography time-of-flight mass spectrometry and a series of ML algorithms were tested. Wuyi rock tea from the two regions featured distinct aroma profiles. Multilayer Perceptron achieved the best performance with an average accuracy of 92.7% on the training data using 176 volatile features. The model was benchmarked with two independent test sets, showing over 90% accuracy. Gradient Boosting algorithm yielded the best accuracy (89.6%) when using only 30 volatile features. The proposed methodology holds great promise for its broader applications in identifying the geographic origins of other valuable agri-food products.

Geographical Origin Assessment of Extra Virgin Olive Oil via NMR and MS Combined with Chemometrics as Analytical Approaches

Geographical origin assessment of extra virgin olive oil (EVOO) is recognised worldwide as raising consumers’ awareness of product authenticity and the need to protect top-quality products. The need for geographical origin assessment is also related to mandatory legislation and/or the obligations of true labelling in some countries. Nevertheless, official methods for such specific authentication of EVOOs are still missing. Among the analytical techniques useful for certification of geographical origin, nuclear magnetic resonance (NMR) and mass spectroscopy (MS), combined with chemometrics, have been widely used. This review considers published works describing the use of these analytical methods, supported by statistical protocols such as multivariate analysis (MVA), for EVOO origin assessment. The research has shown that some specific countries, generally corresponding to the main worldwide producers, are more interested than others in origin assessment and certification. Some specific producers such as Italian EVOO producers may have been focused on this area because of consumers’ interest and/or intrinsic economical value, as testified also by the national concern on the topic. Both NMR- and MS-based approaches represent a mature field where a general validation method for EVOOs geographic origin assessment could be established as a reference recognised procedure.

PLS-DA vs sparse PLS-DA in food traceability. A case study: Authentication of avocado samples

Conventional and sparse partial least squares-discriminant analysis (PLS-DA and sPLS-DA) have been successfully tested in order to authenticate avocado samples in terms of three different geographical origins and six kinds of cultivar. For this, lipid chromatographic fingerprints of different avocado fruits have been acquired using gas chromatography coupled with flame ionization detector (GC-FID) and employed for building classification models. In addition, classification models concatenating strategy has been applied, which has proved to be successful to resolve multiclass problems in food authentication. Finally, fine performance metrics around of 0.95 were obtained for both multivariate classification methods.