Dataset of fluorescence spectra and chemical parameters of olive oils

This dataset encompasses fluorescence spectra and chemical parameters of 24 olive oil samples from the 2019-2020 harvest provided by the producer Conde de Benalua, Granada, Spain. The oils are characterized by different qualities: 10 extra virgin olive oil (EVOO), 8 virgin olive oil (VOO), and 6 lampante olive oil (LOO) samples. For each sample, the dataset includes fluorescence spectra obtained with two excitation wavelengths, oil quality, and five chemical parameters necessary for the quality assessment of olive oil. The fluorescence spectra were obtained by exciting the samples at 365 nm and 395 nm under identical conditions. The dataset includes the values of the following chemical parameters for each olive oil sample: acidity, peroxide value, K270, K232, ethyl esters, and the quality of the samples (EVOO, VOO, or LOO). The dataset offers a unique possibility for researchers in food technology to develop machine learning models based on fluorescence data for the quality assessment of olive oil due to the availability of both spectroscopic and chemical data. The dataset can be used, for example, to predict one or multiple chemical parameters or to classify samples based on their quality from fluorescence spectra

Viral Encephalopathy and Retinopathy (VER) in Mediterranean wild and farmed fish species : the experience of the ‘Istituto Zooprofilattico Sperimentale’ in Sicily

Betanodavirus infection is widespread in a broad spectrum of fish species worldwide. In Italy, it is responsible for outbreaks of Viral Encephalo-Retinopathy (VER) that causes mortality and economic losses in sea fish farming. The infection is also widespread in the wild and not only in managed systems, where there are generally no observed clinical manifestations.peer-reviewe

One-dimensional convolutional neural networks design for fluorescence spectroscopy with prior knowledge : explainability techniques applied to olive oil fluorescence spectra

Optical spectra, and particularly fluorescence spectra, contain a large quantity of information about the substances and their interaction with the environment. It is of great interest, therefore, to try to extract as much of this information as possible, as optical measurements can be easy, non-invasive, and can happen in-situ making the data collection a very appealing method of gathering knowledge. Artificial neural networks are known for their feature extraction capabilities and are therefore well suited for this challenge. In this work, inspired by convolutional neural network (CNN) architectures in 2D and their success with images, a novel approach using one-dimensional convolutional neural networks (1D-CNN) is used to extract information on the measured spectra by using explainability techniques. The 1D-CNN architecture has as input the entire fluorescence spectrum and takes advantage in its design of prior knowledge about the instrumentation and sample characteristics as, for example, spectrometer resolution or the expected number of relevant features in the spectrum. Even if network performance is good, it remains an open question if the features used for the predictions make sense from a physical and chemical point of view and if they match what is known from existing studies. This work studies the output of the convolutional layers, known as feature maps, to understand which features the network has effectively used for the predictions, and thus which part of the measured spectra contains the relevant information about the phenomena at the basis of what has to be predicted. The proposed approach is demonstrated by applying it to the determination of the UV absorbance at 232 nm, K232, from fluorescence spectra using a dataset of 18 Spanish olive oils, which were chemically analyzed from certified laboratories. The 1D-CNN successfully predicts the parameter K232 and enables, by studying feature maps, the clear identification of the relevant spectral features. The main contributions of this work are two. Firstly, it describes how designing the neural network architecture with prior knowledge (spectrometer resolution, etc.) will help the network in learning features that have a clear connection to the chemical composition of the substances, and thus are clearly explainable. Secondly, it shows how, in the case of olive oil, the identified features match perfectly the relevant features known from existing previous studies, thus confirming that the network is learning from the underlying chemical process

Molecular characterization of human enteric viruses in food, water samples, and surface swabs in Sicily

Objectives: Enteric viruses are responsible for foodborne and waterborne infections affecting a large number of people. Data on food and water viral contamination in the south of Italy (Sicily) are scarce and fragmentary. The aim of this study was to evaluate the presence of viral contamination in food, water samples, and surface swabs collected in Sicily Methods: The survey was conducted on 108 shellfish, 23 water samples (seawater, pipe water, and torrent water), 52 vegetables, one peach and 17 berries, 11 gastronomic preparations containing fish products and/or raw vegetables, and 28 surface swabs. Hepatitis A virus (HAV), genogroup GI, GII, and GIV norovirus (NoV), enterovirus (EV), rotavirus (RoV), hepatitis E virus (HEV), adenovirus (AdV), and bocavirus (BoV) were detected by nested (RT) PCR, real-time PCR, and sequence analysis. Results: The most frequently detected viruses in shellfish were HAV (13%), NoV (18.5%), and EV (7.4%). Bocavirus was found in 3.7%, HEV in 0.9%, and AdV in 1.9% of the molluscs. Of the 23 water samples, 21.7% were positive for GII NoV and 4.3% for RoV and HEV genotype 3. Of the 70 vegetable samples, 2.9% were positive for NoV GI (GI.5 and GI.6), 2.9% for EV, and 1.4% for HEV. In the gastronomic preparations, only one EV (9%) was detected. No enteric viruses were detected in the berries, fruit, or swabs analyzed. Conclusions: Molecular surveillance of water and food samples clearly demonstrated that human pathogenic viruses are widely found in aquatic environments and on vegetables, and confirmed the role of vegetables and bivalve molluscs as the main reservoirs. Keywords: Enteric virus, PCR, Genotyping, Shellfish, Waters, Vegetable

Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil

One of the main challenges for olive oil producers is the ability to assess oil quality regularly during the production cycle. The quality of olive oil is evaluated through a series of parameters that can be determined, up to now, only through multiple chemical analysis techniques. This requires samples to be sent to approved laboratories, making the quality control an expensive, time-consuming process, that cannot be performed regularly and cannot guarantee the quality of oil up to the point it reaches the consumer. This work presents a new approach that is fast and based on low-cost instrumentation, and which can be easily performed in the field. The proposed method is based on fluorescence spectroscopy and one-dimensional convolutional neural networks and allows to predict five chemical quality indicators of olive oil (acidity, peroxide value, UV spectroscopic parameters K270 and K232, and ethyl esters) from one single fluorescence spectrum obtained with a very fast measurement from a low-cost portable fluorescence sensor. The results indicate that the proposed approach gives exceptional results for quality determination through the extraction of the relevant physicochemical parameters. This would make the continuous quality control of olive oil during and after the entire production cycle a reality

Hepatitis E Virus Occurrence in Pigs Slaughtered in Italy

In Europe, foodborne transmission has been clearly associated to sporadic cases and small clusters of hepatitis E in humans linked to the consumption of contaminated pig liver sausages, raw venison, or undercooked wild boar meat. In Europe, zoonotic HEV-genotype 3 strains are widespread in pig farms but little information is available on the prevalence of HEV positive pigs at slaughterhouse. In the present study, the prevalence of HEV-RNA positive pigs was assessed on 585 animals from 4 abattoirs located across Italy. Twenty-one pigs (3.6%) tested positive for HEV in either feces or liver by real-time RT-PCR. In these 21 pigs, eight diaphragm muscles resulted positive for HEV-RNA. Among animals collected in one abattoir, 4 out of 91 plasma tested positive for HEV-RNA. ELISA tests for the detection of total antibodies against HEV showed a high seroprevalence (76.8%), confirming the frequent exposure of pigs to the virus. The phylogenetic analyses conducted on sequences of both ORF1 and ORF2 fragments, shows the circulation of HEV-3c and of a novel unclassified subtype. This study provides information on HEV occurrence in pigs at the slaughterhouse, confirming that muscles are rarely contaminated by HEV-RNA compared to liver, which is the most frequently positive for HEV

Pilot Investigation of SARS-CoV-2 Variants in the Island of Sicily Prior to and in the Second Wave of the COVID-19 Pandemic

8 páginas, 2 tablas, 2 figuras. The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/genbank/, OM510944; https://www.ncbi.nlm.nih.gov/genbank/, OM510945; https://www.ncbi. nlm.nih.gov/genbank/, OM510946; https://www.ncbi.nlm.nih. gov/genbank/, OM510947; https://www.ncbi.nlm.nih.gov/genbank/, OM510948; https://www.ncbi.nlm.nih.gov/genbank/, OM510949; https://www.ncbi.nlm.nih.gov/genbank/, OM51 0950; https://www.ncbi.nlm.nih.gov/genbank/, OM510951; and https://www.ncbi.nlm.nih.gov/genbank/, OM510952.After 2 years of the COVID-19 pandemic, we continue to face vital challenges stemming from SARS-CoV-2 variation, causing changes in disease transmission and severity, viral adaptation to animal hosts, and antibody/vaccine evasion. Since the monitoring, characterization, and cataloging of viral variants are important and the existing information on this was scant for Sicily, this pilot study explored viral variants circulation on this island before and in the growth phase of the second wave of COVID-19 (September and October 2020), and in the downslope of that wave (early December 2020) through sequence analysis of 54 SARS-CoV-2-positive samples. The samples were nasopharyngeal swabs collected from Sicilian residents by a state-run one-health surveillance laboratory in Palermo. Variant characterization was based on RT-PCR amplification and sequencing of four regions of the viral genome. The B.1.177 variant was the most prevalent one, strongly predominating before the second wave and also as the wave downsized, although its relative prevalence decreased as other viral variants, particularly B.1.160, contributed to virus circulation. The occurrence of the B.1.160 variant may have been driven by the spread of that variant in continental Europe and by the relaxation of travel restrictions in the summer of 2020. No novel variants were identified. As sequencing of the entire viral genome in Sicily for the period covered here was restricted to seven deposited viral genome sequences, our results shed some light on SARS-CoV-2 variant circulation during that wave in this insular region of Italy which combines its partial insular isolation with being a major entry point for the African immigration.This research received external funding to CR-G and EM from “Agencia Valenciana de Innovación: COVID-19, Ayudas de Concesión Directa a Soluciones Científico-Innovadoras Directamente Relacionadas Con La Lucha Contra La COVID-19,” (Ref COVID-19-203) and from “Conselleria de Innovación Universidades, Ciencia y Sociedad Digital: Subvenciones a Grupos de Investigación Emergentes” (Ref, GV/2021/163); to VR from the Agencia Estatal de Investigación of the Spanish Government (Ref PID2020-120322RB-C21) and from the European Commission–NextGeneration EU CSIC Global Health Platform, Spanish Ministry of Science and Innovation (Ref MCIN/AEI/10.13039/501100011033); and to the Istituto Zooprofilattico Sperimentale della Sicilia “A. Mirri” by the Project “COVID-19: Traiettorie Evolutive di SARS-CoV-2 ed Indagine Sul Ruolo Degli Animali” (IZS SI 03/20 RC), funded by the Italian Ministry of Health.Peer reviewe

Detection of human enteric viruses in water and shellfish samples collected in Sicily

Historical flora and/or vegetation surveys highlight how the small islands of the Ionian coast of Sicily (Bella, Ciclopi islands, Vendicari, Capo Passero), currently protected as Nature Reserves and/or part of the Natura 2000 network have undergone major landscape changes following alterations in anthropogenic pressure. Landscape changes have been more prominent for the islands located in the northern part of the Ionian coast, with the consequence of increasing populations of invasive alien species. In contrast, landscape changes have decreased in the southernmost islands as indicated by the recovery of natural wood vegetation.peer-reviewe

A SARS-CoV-2 full genome sequence of the B.1.1 lineage sheds light on viral evolution in Sicily in late 2020

9 páginas, 2 figuras, 2 tablasTo investigate the influence of geographic constrains to mobility on SARS-CoV-2 circulation before the advent of vaccination, we recently characterized the occurrence in Sicily of viral lineages in the second pandemic wave (September to December 2020). Our data revealed wide prevalence of the then widespread through Europe B.1.177 variant, although some viral samples could not be classified with the limited Sanger sequencing tools used. A particularly interesting sample could not be fitted to a major variant then circulating in Europe and has been subjected here to full genome sequencing in an attempt to clarify its origin, lineage and relations with the seven full genome sequences deposited for that period in Sicily, hoping to provide clues on viral evolution. The obtained genome is unique (not present in databases). It hosts 20 single-base substitutions relative to the original Wuhan-Hu-1 sequence, 8 of them synonymous and the other 12 encoding 11 amino acid substitutions, all of them already reported one by one. They include four highly prevalent substitutions, NSP12:P323L, S:D614G, and N:R203K/G204R; the much less prevalent S:G181V, ORF3a:G49V and N:R209I changes; and the very rare mutations NSP3:L761I, NSP6:S106F, NSP8:S41F and NSP14:Y447H. GISAID labeled this genome as B.1.1 lineage, a lineage that appeared early on in the pandemic. Phylogenetic analysis also confirmed this lineage diagnosis. Comparison with the seven genome sequences deposited in late 2020 from Sicily revealed branching leading to B.1.177 in one branch and to Alpha in the other branch, and suggested a local origin for the S:G118V mutation.This research received external funding to CR-G from Conselleria de Innovación Universidades, Ciencia y Sociedad Digital: Subvenciones a Grupos de Investigación Emergentes (Ref, GV/2021/163); to VR from the Agencia Estatal de Investigación of the Spanish Government (Ref PID2020-120322RB-C21) and the European Commission-NextGeneration EU CSIC Global Health Platform, Spanish Ministry of Science and Innovation (Ref MCIN/AEI/10.13039/501100011033); and to the Istituto Zooprofilattico Sperimentale della Sicilia A. Mirri by the Project COVID-19: Traiettorie Evolutive di SARS-CoV-2 ed Indagine Sul Ruolo Degli Animali (IZS SI 03/20 RC), funded by the Italian Ministry of HealthPeer reviewe

Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks

Optical Sensing and Detection VII: 12139-81The chemical analysis of food is essential to monitor and guarantee its quality. The determination of the chemical parameters, like the concentration of particular substances, is performed by specialized laboratories and is a time-consuming and costly process. Therefore, alternative methods with easier handling are of great interest. Among these fluorescence spectroscopy offers great opportunities. Fluorescence spectra are one-dimensional arrays of values already successfully employed together with artificial neural networks for classification problems in chemistry, physics, and other fields. However, the extraction of specific quantities from the spectra poses a much harder challenge. This work analyzes and compares the ability of feed-forward neural networks (FFNN) and one-dimensional convolutional neural networks (1D-CNN) to extract relevant features from fluorescence spectra of olive oils. The results indicate that 1D-CNN, contrary to FFNN, successfully predicts the chemical parameters with high accuracy. The great advantages of the proposed method are: 1) the possibility of using optical methods instead of time-consuming chemical ones, like chromatography, 2) the lack of any special sample handling, like dilution and 3) the lack of any pre-processing of the data. The problem of small datasets, which may arise for novel techniques like the proposed one, is also addressed statistically by using the leave-one-out resampling technique