Marta Bevilacqua

Tracing the origin of extra virgin olive oils by infrared spectroscopy and chemometrics: A case study

In this paper, the potential of coupling mid- and near-infrared spectroscopic fingerprinting techniques and chemometric classification methods for the traceability of extra virgin olive oil samples from the PDO Sabina was investigated. To this purpose, two different pattern recognition algorithm representative of the discriminant (PLS-DA) and modeling (SIMCA) approach to classification were employed. Results obtained after processing the spectroscopic data by PLS-DA evidenced a rather high classification accuracy, NIR providing better predictions than MIR (as evaluated both in cross-validation and on an external test set). SIMCA confirmed these results and showed how the category models for the class Sabina can be rather sensitive and highly specific. Lastly, as samples from two harvesting years (2009 and 2010) were investigated, it was possible to evidence that the different production year can have a relevant effect on the spectroscopic fingerprint. Notwithstanding this, it was still possible to build models that are transferable from one year to another with good accuracy.

Classification and Class-Modelling

Abstract This chapter describes the basic theory about classification, starting from a general description of the different approaches to classification and then illustrating in detail the principal methods which are used in the framework of assessment of food quality. Examples of application of the methods to different data sets are also provided.

Assessment of the applicability of a “toolbox” designed for microbially assisted phytoremediation: the case study at Ingurtosu mining site (Italy)

The paper describes the fieldwork at the Italian test site of the abandoned mine of sphalerite and galena in Ingurtosu (Sardinia), with the aim to assess the applicability of a “toolbox” to establish the optimized techniques for remediation of soils contaminated by mining activities. A preliminary characterization—including (hydro)geochemistry, heavy metal concentration and their mobility in soil, bioprospecting for microbiology and botany—provided a data set for the development of a toolbox to deliver a microbially assisted phytoremediation process. Euphorbia pithyusa was selected as an endemic pioneer plant to be associated with a bacterial consortium, established with ten selected native strains, including metal-tolerant bacteria and producers of plant growth factors. The toolbox was firstly assessed in a greenhouse pot experiment. A positive effect of bacterial inoculum on E. pithyusa germination and total plant survival was observed. E. pithyusa showed to be a well-performing metallophyte species, and only inoculated soil retained a microbial activity with a high functional diversity, expanding metabolic affinity also towards root exudates. These results supported the decision to proceed with a field trial, investigating different treatments used singly or in combination: bioaugmentation with bacterial consortia, mycorrhizal fungi and a commercial mineral amendment. Microbial activity in soil, plant physiological parameters and heavy metal content in plants and in soil were monitored. Five months after the beginning, an early assessment of the toolbox under field conditions was carried out. Despite the cold season (October–March), results suggested the following: (1) the field setup as well as the experimental design proved to be effective; (2) plant survival was satisfactory; (3) soil quality was increased and bioaugmentation improved microbial activity, expanding the metabolic competences towards plant interaction (root exudates); and (4) multivariate analysis supported the data provided that the proposed toolbox can be established and the field trial can be carried forward.

Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA)

The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks).

Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation.

It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method.

1H NMR-based urinary metabolic profiling reveals changes in nicotinamide pathway intermediates due to postnatal stress model in rat

The maternal separation protocol in rodents is a widely recognized model of early life stress allowing acute and chronic physiological consequences to be studied. An (1)H NMR-based metabolomic approach was applied to urines to evaluate the systemic metabolic consequences of maternal separation stress in female rats after the beginning of weaning and 4 weeks later when the rats were reaching adulthood. Furthermore, because maternal separation is considered as a model mimicking the inflammatory bowel syndrome, the lactulose/mannitol test was used to evaluate the influence of postnatal maternal separation on gut permeability and mucosal barrier function by (1)H NMR spectroscopy analysis of urine. The results showed no statistical differences in gut permeability due to maternal separation. The application of ANOVA simultaneous component analysis allowed the contributions of physiological adaptations to the animals development to be separated from the metabolic consequences due to postnatal stress. Systemic metabolic differences in the maternally separated pups were mainly due to the tryptophan/NAD pathway intermediate levels and to the methyladenosine level. Urinary NMR-based metabolic profiling allowed us to disentangle the metabolic adaptive response of the rats to postnatal stress during the animals growth, highlighting the metabolic changes induced by weaning, gut closure, and maturity.

Rheoreversible hydrogels in paper restoration processes: a versatile tool

BackgroundPaper based artworks are probably ones of the most difficult materials to restore, because of their complexity and fragile structure. Cleaning of paper artifacts, one of the process commonly carried out during restoration, usually involves the use of solvents (organic or not), that may cause several troubles, like swelling and dissolution of some components, and may also be harmful to the users.ResultsInnovative procedure for cleaning paper artworks is reported in this paper. It is based on the use of rheoreversible, biocompatible hydrogels containing poly(ethylene oxide) or poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide) and α-cyclodextrin. We have studied two types of polymer with different hydrophobic properties in order to obtain two different hydrogels with slightly different cleaning capabilities. Our overall strategy has been to develop innovative systems based on these hydrogels so as to better confront the problems that a restorer faces during the cleaning of paper samples. Rheoreversible hydrogels are intriguing materials because their application and removal is not invasive and does not require a liquid treatment that could induce damage to the paper.ConclusionsThese hydrogels have been applied in the cleaning of both new and aged paper samples and their cleaning efficiency has been established. Moreover, by comparison with traditional methods, the greater efficacy of the proposed procedure has been demonstrated.To assess the cleaning efficacy of these hydrogels, a multidisciplinary approach, combining non-invasive spectroscopic infrared techniques together with scanning electron microscopy, chromatographic (HPLC) analysis and pH investigations has been used. Near infrared spectroscopy spectra were coupled with a chemometric analysis to achieve a better interpretation of data.This work constitutes a preliminary step towards focused study in the development of α-cyclodextrin/polymer hydrogel family which will allow cleaning of paper artifacts with peculiar characteristics.

Classification Methods of Multiway Arrays as a Basic Tool for Food PDO Authentication

Abstract Food chain traceability, identification of adulteration and the control of labelling compliance are areas that require evaluation of foodstuff in its entirety. More and more researchers are investigating the possibility of using multidimensional or hyphenated techniques for fingerprinting of food products. However, these techniques produce data structures that are multidimensional as well and that require proper chemometric approaches for data processing (multiway data analysis). In this chapter, the state-of-the-art approaches for the classification of multiway data is discussed theoretically and compared with the case studies coming from the food authenticity context, such as the traceability of extra virgin olive oils of protected denomination of origin and table wines.

Data fusion for food authentication. Combining near and mid infrared to trace the origin of extra virgin olive oils

Introduction I n recent years, food authentication and food traceability have emerged as issues of primary and common concern for both consumers and producers. Because of this, the number of researchers who tackle these problems by combining chemometrics coupled with a fingerprinting technique continues to increase. This kind of approach, indeed, has the undeniable advantage of allowing, in many cases, the use of rapid and relatively inexpensive analytical techniques that can, nonetheless, lead to reliable and accurate results. Among these analytical methods, an important role is played by the spectroscopic techniques and in particular in the infrared wavelength ranges. These are often chosen as the ideal analytical methods for the development of food quality control methodologies because of their inherent characteristics of non-destructivity, speed and cheapness of analysis, as well as not needing any sample pre-treatment. However, food quality control, in particular when traceability and authentication are concerned, is a complex problem for the chemist, and sometimes the acquisition of a single fingerprinting signal and the subsequent construction of a chemometric classification model is not enough to develop a methodology reliable enough for the intended purpose. In such cases, it is often possible to rely on the possibility of acquiring more than one single signal for each sample, and then to use the combined information from the two (or more) techniques to develop the final chemometric model. In this way, it is possible, sometimes, to benefit from the specific advantages and characteristics of the different techniques to create a more reliable and stable final model, in particular whenever there is complementarity between the information provided by the techniques employed. In these cases, however, the problem of how to combine the different information coming from the various analytical methods, namely the data fusion issue, is of major importance for the accuracy and reliability of the final results of the proposed methodology. In this context, the use of near infrared (NIR) and mid infrared (MIR) spectroscopy to solve authentication or traceability issues has already been described by many researchers, showing the great potential that both these techniques have in the field of food quality control analysis. Moreover, the peculiar characteristics of NIR and MIR, being different from one another, make them perfectly adaptable to being used together. Due to its greater molecular selectivity, the use of MIR spectroscopy often provides easier spectral interpretation than NIR, and it can sometimes be more related to some compound that is typical of the investigated food. NIR spectroscopy is more useful for its peculiarity of giving an overall fingerprint of the entire sample and it is commonly applied for the analysis of foods with excellent results. In this work, the problem of the traceability of extra virgin olive oils coming from the PDO Sabina (Italy), already tackled by the same authors by means of MIR and NIR spectroscopy separately, is handled by means of different strategies of data fusion, in order to improve the results obtained from the two techniques and to demonstrate the synergistic effect of coupling information obtained from the two wavelength regions.

Chapter 4 – Regression

In this chapter, a survey of the theory behind the main chemometric methods used for multivariate calibration is presented. Ordinary least squares, multiple linear regression, principal component regression, partial least squares regression and principal covariate regression are discussed in detail. Tools for model diagnostics and model interpretation are presented, together with strategies for variable selection.