Exploring hydrodynamic cavitation for citrus waste valorisation in Malta: from beverage enhancement to potato sprouting suppression and water remediation.

Introduction: The endorsement of circular economy, zero-waste, and sustainable development by the EU and UN has promoted non-thermal technologies in agro-food and health industries. While northern European countries rapidly integrate these technologies, their implementation in Mediterranean food-supply chains remains uncertain. Aims: We evaluated the usefulness of hydrodynamic cavitation (HC) for valorizing orange peel waste in the fresh orange juice supply chain of the Maltese Islands. Method: We assessed: a) the effectiveness of HC in extracting bioactive compounds from orange peels (Citrus sinensis) in water (35°C) and 70% (v/v) ethanol (-10°C) over time, compared to conventional maceration, and b) the potato sprouting-suppression and biosorbent potential of the processed peel for copper, nitrate, and nitrite binding. Results: Prolonged HC-assisted extractions in water (high cavitation numbers), damaged and/or oxidized bioactive compounds, with flavonoids and ascorbic acid being more sensitive, whereas cold ethanolic extractions preserved the compounds involved in radical scavenging. HC-processing adequately modified the peel, enabling its use as a potato suppressant and biosorbent for copper, nitrate, and nitrite. Conclusion: Coupling HC-assisted bioactive compound extractions with using leftover peel for potato-sprouting prevention and as biosorbent for water pollutant removal offers a straightforward approach to promoting circular economic practices and sustainable agriculture in Malta.

Bioactive Potential of Olive Mill Waste Obtained from Cultivars Grown in the Island of Malta.

This study explores the bioactive potential of olive mill waste derived from cultivars grown in the Maltese Islands through various analytical approaches. Cell culture, cell staining, allelopathic assays, shrimp brine lethality assays, and HPLC analysis were conducted to assess the efficacy and bioactivity of the extracts using different treatments, including methanolic extraction, acid, and alkaline hydrolysis. Notably, the results from cell lines revealed that NB4r2 cells exhibited high susceptibility to the tested extracts, with the lowest IC50 recorded after 72 h of exposure. Notably, the 'Bajda' cultivar displayed the most effectiveness, particularly with acid hydrolysis. In allelopathic assays, higher concentrations of 'Malti', 'Bidni', and 'Bajda' extracts significantly inhibited lettuce seed germination. Similarly, in the brine shrimp lethality assay, higher concentrations led to increased mortality rates of Artemia salina, though rates decreased at lower concentrations. The identification of phenolic compounds found in olive mill waste was conducted using high-performance liquid chromatography (HPLC) with the use of internal standards. The identification revealed a variety of compounds, with 3-hydroxytyrosol and oleacein being present in high abundance in nearly all hydrolyzed and methanolic extracts, whereas gallic acid was found to be the least abundant. These findings highlight the rich bioactive potential of olive mill waste and provide insights into its applications in pharmaceuticals, nutraceuticals, and agriculture, emphasizing the importance of further research to fully exploit these valuable resources.

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.

Photocatalytic Activity of Titanium Dioxide Nanotubes Following Long-Term Aging.

Anodic titanium dioxide (TiO2) nanotubes were found to be active photocatalysts. These photocatalysts possess a high surface area, even when supported, rendering them potential candidates for water treatment. In this work, photocatalytic surfaces were produced by anodizing commercially pure Ti plates using two different electrolyte compositions and correspondingly diverse process parameters. Changes in the physical and chemical stability as well as photocatalytic activity were studied over a fifty-two-week aging process. During this period, the nanotubular surfaces were exposed to flowing synthetic greywater, solar irradiation, and the natural environment. The physical and phase stability of the materials anodized using the organic electrolyte were found to be outstanding and no degradation or change in crystalline structure was observed. On the other hand, materials anodized in the aqueous electrolyte proved to suffer from light-induced phase transition from anatase to rutile. Surfaces synthesized in the organic electrolyte were more resistant to fouling and showed a better tendency to recover photocatalytic activity upon cleaning. In conclusion, the nanotubes produced in the organic electrolyte proved to be stable, rendering them potentially suitable for real-life applications.

Suitability of Different Titanium Dioxide Nanotube Morphologies for Photocatalytic Water Treatment.

Photocatalysis has long been touted as one of the most promising technologies for environmental remediation. The ability of photocatalysts to degrade a host of different pollutants, especially recalcitrant molecules, is certainly appealing. Titanium dioxide (TiO2) has been used extensively for this purpose. Anodic oxidation allows for the synthesis of a highly ordered nanotubular structure with a high degree of tunability. In this study, a series of TiO2 arrays were synthesised using different electrolytes and different potentials. Mixed anatase-rutile photocatalysts with excellent wettability were achieved with all the experimental iterations. Under UVA light, all the materials showed significant photoactivity towards different organic pollutants. The nanotubes synthesised in the ethylene glycol-based electrolyte exhibited the best performance, with near complete degradation of all the pollutants. The antibacterial activity of this same material was similarly high, with extremely low bacterial survival rates. Increasing the voltage resulted in wider and longer nanotubes, characteristics which increase the level of photocatalytic activity. The ease of synthesis coupled with the excellent activity makes this a viable material that can be used in flat-plate reactors and that is suitable for photocatalytic water treatment.

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.

Determination of the Geographical Origin of Maltese Honey Using 1H NMR Fingerprinting.

The price of honey, as a highly consumed natural product, depends on its botanical source and its production environment, causing honey to be vulnerable to adulteration through mislabeling and inappropriate, fraudulent production. In this study, a fast and simple approach is proposed to tackle this issue through non-target one dimensional zg30 and noesypr1d 1H NMR fingerprint analysis, in combination with multivariate data analysis. Results suggest that composition differences in sugars, amino acids, and carboxylic acid were sufficient to discriminate between the tested honey of Maltese origin and that of non-local origin. Indeed, all chemometric models based on noesypr1d analysis of the whole fraction honey showed better prediction in geographical discrimination. The possibility of discrimination was further investigated through analysis of the honey's phenolic extract composition. The partial least squares models were deemed unsuccessful to discriminate, however, some of the linear discriminant analysis models achieved a prediction accuracy of 100%. Lastly, the best performing models of both the whole fraction and the phenolic extracts were tested on five samples of unknown geographic for market surveillance, which attained a high agreement within the models. Thus, suggesting the use of non-target 1H NMR coupled with the multivariate-data analysis and machine learning as a potential alternative to the current time-consuming analytical methods.

Application of ATR-FT-MIR for Tracing the Geographical Origin of Honey Produced in the Maltese Islands.

Maltese honey has been produced, marketed, and sold as an exclusive local gourmet food product for countless years. Yet, thus far, no study has evaluated the individuality of this local food product. The evaluation of the parameters and properties which characterise the provenance and floral source of honey have been the subject of various studies worldwide, owing to the price and potential beneficial properties of this food product. Models analysing the potential of attenuated total reflection mid-infrared (ATR-FT-MIR) spectroscopy in discriminating and classifying local honey from that of foreign origin were investigated using 21 Maltese honey samples and 49 honey samples collected from abroad (Sicily, Greece, Sweden, Italy, France, Estonia and other samples of mixed geographical origin). Through a combination of spectroscopic techniques, spectral transformations, variable selection and partial least squares discriminant analysis (PLS-DA), chemometric models which successfully classified the provenance of local and non-local honey were developed. The results of these models were also corroborated with other classification and pattern recognition techniques, such as linear discriminate analysis (LDA), support vector machines (SVM) and feed-forward artificial neural networks (FF-ANN).

Application of 1H and 13C NMR Fingerprinting as a Tool for the Authentication of Maltese Extra Virgin Olive Oil.

The application of 1H and 13C nuclear magnetic resonance (NMR) in conjunction with chemometric methods was applied for the discrimination and authentication of Maltese extra virgin olive oils (EVOOs). A total of 65 extra virgin olive oil samples, consisting of 30 Maltese and 35 foreign samples, were collected and analysed over four harvest seasons between 2013 and 2016. A preliminary examination of 1H NMR spectra using unsupervised principle component analysis (PCA) models revealed no significant clustering reflecting the geographical origin. In comparison, PCA carried out on 13C NMR spectra revealed clustering approximating the geographical origin. The application of supervised methods, namely partial least squares discriminate analysis (PLS-DA) and artificial neural network (ANN), on 1H and 13C NMR spectra proved to be effective in discriminating Maltese and non-Maltese EVOO samples. The application of variable selection methods significantly increased the effectiveness of the different classification models. The application of 13C NMR was found to be more effective in the discrimination of Maltese EVOOs when compared to 1H NMR. Furthermore, results showed that different 1H NMR pulse methods can greatly affect the discrimination of EVOOs. In the case of 1H NMR, the Nuclear Overhauser Effect (NOESY) pulse sequence was more informative when compared to the zg30 pulse sequence, since the latter required extensive spectral manipulation for the models to reach a satisfactory level of discrimination.

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.