Multivariate image analysis for the rapid detection of residues from packaging remnants in former foodstuff products (FFPs)–a feasibility study

From a circular economy perspective, feeding livestock with food leftovers or former foodstuff products (FFPs) could be an effective option aimed at exploiting food leftover resources and reducing food losses. FFPs are valuable energy sources, characterised by a beneficial starch/sugar content, and also fats. However, besides these nutritional aspects, safety is a key concern given that FFPs are generally derived from packaged food. Packaging materials, such as plastics and paper, are not accepted as a feed ingredient which means that residues should be rigorously avoided. A sensitive and objective detection method is thus essential for an accurate risk evaluation throughout the former food production chain. To this end, former food samples were collected in processing plants of two different European countries and subjected to multivariate analysis of red, green, and blue (RGB) microscopic images, in order to evaluate the possible application of this non-destructive technique for the rapid detection of residual particles from packaging materials. Multivariate Image Analysis (MIA) was performed on single images at the pixel level, which essentially consisted in an exploratory analysis of the image data by means of Principal Component Analysis, which highlighted the differences between packaging and foodstuff particles, based on their colour. The whole dataset of images was then analysed by means of a multivariate data dimensionality reduction method known as the colourgrams approach, which identified clusters of images sharing similar features and also highlighted outlier images due to the presence of packaging particles. The results obtained in this feasibility study demonstrated that MIA is a promising tool for a rapid automated method for detecting particles of packaging materials in FFPs

Multivariate Analysis in Microbiome Description: Correlation of Human Gut Protein Degraders, Metabolites, and Predicted Metabolic Functions

Protein catabolism by intestinal bacteria is infamous for releasing many harmful compounds, negatively affecting the health status, both locally and systemically. In a previous study, we enriched in protein degraders the fecal microbiota of five subjects, utilizing a medium containing protein and peptides as sole fermentable substrates and we monitored their evolution by 16S rRNA gene profiling. In the present study, we fused the microbiome data and the data obtained by the analysis of the volatile organic compounds (VOCs) in the headspace of the cultures. Then, we utilized ANOVA simultaneous component analysis (ASCA) to establish a relationship between metabolites and bacteria. In particular, ASCA allowed to separately assess the effect of subject, time, inoculum concentration, and their binary interactions on both microbiome and volatilome data. All the ASCA submodels pointed out a consistent association between indole and Escherichia–Shigella, and the relationship of butyric, 3-methyl butanoic, and benzenepropanoic acids with some bacterial taxa that were major determinants of cultures at 6 h, such as Lachnoclostridiaceae (Lachnoclostridium), Clostridiaceae (Clostridium sensu stricto), and Sutterellaceae (Sutterella and Parasutterella). The metagenome reconstruction with PICRUSt2 and its functional annotation indicated that enrichment in a protein-based medium affected the richness and diversity of functional profiles, in the face of a decrease of richness and evenness of the microbial community. Linear discriminant analysis (LDA) effect size indicated a positive differential abundance (p < 0.05) for the modules of amino acid catabolism that may be at the basis of the changes of VOC profile. In particular, predicted genes encoding functions belonging to the superpathways of ornithine, arginine, and putrescine transformation to GABA and eventually to succinyl-CoA, of methionine degradation, and various routes of breakdown of aromatic compounds yielding succinyl-CoA or acetyl-CoA became significantly more abundant in the metagenome of the bacterial community

Former Foodstuff Products (FFPs) as Circular Feed: Types of Packaging Remnants and Methods for Their Detection

Alternative feed ingredients in farm animal diets are a sustainable option from several perspectives. Former food products (FFPs) provide an interesting case study, as they represent a way of converting food industry losses into ingredients for the feed industry. A key concern regarding FFPs is the possible packaging residues that can become part of the product, leading to potential contamination of the feed. Although the level of contamination has been reported as negligible, to ensure a good risk evaluation and assessment of the presence of packaging remnants in FFPs, several techniques have been proposed or are currently being studied, of which the main ones are summarized in this review. Accordingly visual inspections, computer vision (CV), multivariate image analysis (MIA), and electric nose (e-nose) are discussed. All the proposed methods work mainly by providing qualitative results, while further research is needed to quantify FFP-derived packaging remnants in feed and to evaluate feed safety as required by the food industries

Noise reduction in muon tomography for detecting high density objects

The muon tomography technique, based on multiple Coulomb scattering of cosmic ray muons, has been proposed as a tool to detect the presence of high density objects inside closed volumes. In this paper a new and innovative method is presented to handle the density fluctuations (noise) of reconstructed images, a well known problem of this technique. The effectiveness of our method is evaluated using experimental data obtained with a muon tomography prototype located at the Legnaro National Laboratories (LNL) of the Istituto Nazionale di Fisica Nucleare (INFN). The results reported in this paper, obtained with real cosmic ray data, show that with appropriate image filtering and muon momentum classification, the muon tomography technique can detect high density materials, such as lead, albeit surrounded by light or medium density material, in short times. A comparison with algorithms published in literature is also presented

Precision measurements of Linear Scattering Density using Muon Tomography

We demonstrate that muon tomography can be used to precisely measure the properties of various materials. The materials which have been considered have been extracted from an experimental blast furnace, including carbon (coke) and iron oxides, for which measurements of the linear scattering density relative to the mass density have been performed with an absolute precision of 10%. We report the procedures that are used in order to obtain such precision, and a discussion is presented to address the expected performance of the technique when applied to heavier materials. The results we obtain do not depend on the specific type of material considered and therefore they can be extended to any application.Comment: 16 pages, 4 figure

Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy

Near Infrared (NIR) spectroscopy is an analytical technology widely used for the non-destructive characterisation of organic samples, considering both qualitative and quantitative attributes. In the present study, the combination of Multi-target (MT) prediction approaches and Machine Learning algorithms has been evaluated as an effective strategy to improve prediction performances of NIR data from wheat flour samples. Three different Multi-target approaches have been tested: Multi-target Regressor Stacking (MTRS), Ensemble of Regressor Chains (ERC) and Deep Structure for Tracking Asynchronous Regressor Stack (DSTARS). Each one of these techniques has been tested with different regression methods: Support Vector Machine (SVM), Random Forest (RF) and Linear Regression (LR), on a dataset composed of NIR spectra of bread wheat flours for the prediction of quality-related parameters. By combining all MT techniques and predictors, we obtained an improvement up to 7% in predictive performance, compared with the corresponding Single-target (ST) approaches. The results support the potential advantage of MT techniques over ST techniques for analysing NIR spectra

Enhanced catecholamine transporter binding in the locus coeruleus of patients with early Parkinson disease

<p>Abstract</p> <p>Background</p> <p>Studies in animals suggest that the noradrenergic system arising from the locus coeruleus (LC) and dopaminergic pathways mutually influence each other. Little is known however, about the functional state of the LC in patients with Parkinson disease (PD).</p> <p>Methods</p> <p>We retrospectively reviewed clinical and imaging data of 94 subjects with PD at an early clinical stage (Hoehn and Yahr stage 1-2) who underwent single photon computed tomography imaging with FP-CIT ([¹²³I] N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl) tropane). FP-CIT binding values from the patients were compared with 15 healthy subjects: using both a voxel-based whole brain analysis and a volume of interest analysis of <it>a priori </it>defined brain regions.</p> <p>Results</p> <p>Average FP-CIT binding in the putamen and caudate nucleus was significantly reduced in PD subjects (43% and 57% on average, respectively; p < 0.001). In contrast, subjects with PD showed an increased binding in the LC (166% on average; p < 0.001) in both analyses. LC-binding correlated negatively with striatal FP-CIT binding values (caudate: contralateral, ρ = -0.28, p < 0.01 and ipsilateral ρ = -0.26, p < 0.01; putamen: contralateral, ρ = -0.29, p < 0.01 and ipsilateral ρ = -0.29, p < 0.01).</p> <p>Conclusions</p> <p>These findings are consistent with an up-regulation of noradrenaline reuptake in the LC area of patients with early stage PD, compatible with enhanced noradrenaline release, and a compensating activity for degeneration of dopaminergic nigrostriatal projections.</p