Development of Analytical Methods for Food Characterization and Security Evaluation
Bibliographic record
Abstract
Chapter 1 deals with the development of a rapid analytical method for evaluating the distribution of molecular weights of polysaccharides present in commercially available formulations. The analyses were performed by size exclusion chromatography with refractive index detector (SEC / RI).\nThe development of the chromatographic method has been carried out on the basis of the possible structural characteristics of investigated polysaccharides that may affect the retentive process in SEC. After sample treatment methods optimization (final products and vegetable raw materials), the best typology of standards calibration of the chromatographic column was selected to estimate the molecular weight of analytes in real samples. Chromatographic evaluation of standards (PEO and dextran) and of the polysaccharide extracts was carried out using not only water as the eluent phase, but also buffer solutions at different pH and ionic strength. The proposed methods have been applied for a rapid analytical control of medical devices.\nIn Chapter 2, a study on the determination of acrylamide in potatoes is presented. Acrylamide was considered as a process marker to test new ovens with both "traditional" and "steam" cooking options. Cooking was carried out with "dry air circulation" function and replicated with "25% moisture" function, using different time and temperature conditions. Portions of cooked product characterized by different colors were sampled and analyzed to measure acrylamide levels, in order to create a colour scale indicating the different content of acrylamide. The purpose was to verify whether similar colours, even obtained under different cooking conditions, were characterized by similar acrylamide content. Analyses were performed by a liquid chromatograph coupled to a mass spectrometer, equipped with an electrospray (ESI) interface and a linear ion trap analyzer (LC-ESI-MS / MS). The content of acrylamide in crisp potatoes on the market has been also explored. In this case, the development of a new sample treatment to eliminate the fat component due to frying oil was necessary.\nIn Chapter 3 a research about the development of appropriate preliminary sample preparation methods for characterization of nanomaterials contained into food is presented. Silver nanoparticles (AgNPs) are an example of nanomaterials that could be find in food due to their widespread use as antimicrobial, even if they are not allowed as food additive in European contest.\nInitially, a literature study was conducted to evaluate methods to extract AgNPs from the media in which they were dispersed. From this evaluation it has emerged that one of the methods proposed is the Cloud Point Extraction (CPE). Since pH is one of the parameters influencing CPE, the stability and behavior of two types of AgNPs in media characterized by different pH values has been evaluated. Comparison was made using Plasmon resonance (UV-VIS) and light scattering (DLS). In addition, to obtain information on shape and size distribution, suspensions of these nanoparticles in ultrapure water were analyzed by scanning electron microscopy in SEM mode (high vacuum conditions). The collected images were selected and analyzed using the ImageJ software.\nIn literature CPE is described for AgNPs, but not for these nanoparticles in complex matrices such as foodstuffs. For these reason a recent scientific article describing CPE as technique to separate AgNPs from environmental water was tested for its applicability to food matrices. AgNPs were evaluated in ultrapure water and in food matrices (juices and milk). Distribution of silver in the pellet and in the surfactant phases has been determined by ICP-MS after microwave digestion. In addition, different steps of CPE tested with AgNPs in ultrapure water have been examined by scanning electron microscope in different operation modes (Cryo-SEM, SEM and STEM), to confirm whether this extraction technique effectively preserve the shape and size of the nanoparticles.\nChapter 4 is based on the topic of Food Contact Materials. In particular, attention has been focused on a re-usable objects made of polycarbonate (PC) intended to come into contact with foodstuff. Migration tests on tableware of different age and degree of surface damage were performed, using ethanol and isooctane as food simulants (according to EU plastic regulation). Characterization and quantitative analysis of bisphenol A and some common additives used in plastic manufacturing were carried out by a new UHPLC-ESI-Orbitrap method, later validated.\nData were analyzed with the purpose of evaluating a possible correlation between bisphenol A and additives release, ageing, and surface integrity of plastic material.\nDue to prolonged use over time, plastic objects inevitably undergo to degradation and may release unknown substances, called non-intentionally added substances (NIAS). For this reason, it was important to evaluate in simulants the presence of oligomers derived from a possible degradation of the material during ageing. Results obtained by UHPLC coupled to HR-MS were analyzed to explore a possible correlation between type and amount of different oligomers and age of the polycarbonate samples.\nMigration experiments were also performed directly on food, to investigate in a complex matrix the possible presence of plastic additives, degradation products and colorants. Chocolate has been chosen as a model for a food with a high fat content and a new sample preparation methods was developed and validated. For the first time, HR-MS was applied for the identification and quantification of possible contaminants from food contact materials into chocolate.\nFinally, in the section called Collateral Activity, the use of an Oxitest reactor to evaluate the oxidative stability of fat food over time, is presented. In particular, the stability of olive and extra-virgin olive oils enriched with natural extracts (essential oils and red chilly powders), olive oils stored in different packaging materials, tuna fish fillet in olive oil and biscuits with different flour mixtures was evaluated. Measures were performed by using a reactor that allows to perform in a fast (in terms of hours) and simple way (without fat removal from the food) accelerated oxidation tests, based on two parameters: temperature and oxygen pressure. At the end of each analysis, the induction period value (IP) is automatically generated: higher values correspond to longer oxidative stability of the product tested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".