Lucia Bagnasco

Characterisation of mucilages extracted from seven Italian cultivars of flax

The chemical composition, physicochemical, functional and sensory properties of mucilages, extracted from seven Italian flax cultivars, were evaluated. All samples were composed of neutral and acidic sugars, with a low protein content. From the NMR data, a rhamnogalacturonan backbone could be inferred as a common structural feature for all the mucilages, with some variations depending on the cultivar. All the suspensions showed a poor stability, which was consistent with a low zeta potential absolute value. The viscosity seemed to be positively correlated with the neutral sugars and negatively with the amount of proteins. Functional properties were dependent on the cultivar. The sensory analysis showed that most mucilages are tasteless. All these outcomes could support the use of flaxseed mucilages for industrial applications. In particular, Solal and Festival cultivars could be useful as thickeners, due to their high viscosity, while Natural, Valoal and Kaolin as emulsifiers for their good surface-active properties.

Application of a voltammetric electronic tongue and near infrared spectroscopy for a rapid umami taste assessment

The relationships between sensory attribute and analytical measurements, performed by electronic tongue (ET) and near-infrared spectroscopy (NIRS), were investigated in order to develop a rapid method for the assessment of umami taste. Commercially available umami products and some aminoacids were submitted to sensory analysis. Results were analysed in comparison with the outcomes of analytical measurements. Multivariate exploratory analysis was performed by principal component analysis (PCA). Calibration models for prediction of the umami taste on the basis of ET and NIR signals were obtained using partial least squares (PLS) regression. Different approaches for merging data from the two different analytical instruments were considered. Both of the techniques demonstrated to provide information related with umami taste. In particular, ET signals showed the higher correlation with umami attribute. Data fusion was found to be slightly beneficial - not so significantly as to justify the coupled use of the two analytical techniques.

Artificial nose, NIR and UV-visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil

An authentication study of the Italian PDO (Protected Designation of Origin) olive oil Chianti Classico, based on artificial nose, near-infrared and UV-visible spectroscopy, with a set of samples representative of the whole Chianti Classico production area and a considerable number of samples from other Italian PDO regions was performed. The signals provided by the three analytical techniques were used both individually and jointly, after fusion of the respective variables, in order to build a model for the Chianti Classico PDO olive oil. Different signal pre-treatments were performed in order to investigate their importance and their effects in enhancing and extracting information from experimental data, correcting backgrounds or removing baseline variations. Stepwise-Linear Discriminant Analysis (STEP-LDA) was used as a feature selection technique and, afterward, Linear Discriminant Analysis (LDA) and the class-modelling technique Quadratic Discriminant Analysis-UNEQual dispersed classes (QDA-UNEQ) were applied to sub-sets of selected variables, in order to obtain efficient models capable of characterising the extra virgin olive oils produced in the Chianti Classico PDO area.

NIR spectroscopy as a tool for discriminating between lichens exposed to air pollution

Lichens are used as biomonitors of air pollution because they are extremely sensitive to the presence of substances that alter atmospheric composition. Fifty-one thalli of two different varieties of Pseudevernia furfuracea (var. furfuracea and var. ceratea) were collected far from local sources of air pollution. Twenty-six of these thalli were then exposed to the air for one month in the industrial port of Genoa, which has high levels of environmental pollution. The possibility of using Near-infrared spectroscopy (NIRS) for generating a fingerprint of lichens was investigated. Chemometric methods were successfully applied to discriminate between samples from polluted and non-polluted areas. In particular, Principal Component Analysis (PCA) was applied as a multivariate display method on the NIR spectra to visualise the data structure. This showed that the difference between samples of different varieties was not significant in comparison to the difference between samples exposed to different levels of environmental pollution. Then Linear Discriminant Analysis (LDA) was carried out to discriminate between lichens based on their exposure to pollutants. The distinction between control samples (not exposed) and samples exposed to the air in the industrial port of Genoa was evaluated. On average, 95.2% of samples were correctly classified, 93.0% of total internal prediction (5 cross-validation groups) and 100.0% of external prediction (on the test set) was achieved.

A PCA-based hyperspectral approach to detect infections by mycophilic fungi on dried porcini mushrooms (Boletus edulis and allied species)

Mycophilic fungi of anamorphic genus Sepedonium (telomorphs in Hypomyces, Hypocreales, Ascomycota) infect and parasitize sporomata of boletes. The obligated hosts such as Boletus edulis and allied species (known as porcini mushrooms) are among the most valued and prized edible wild mushrooms in the world. Sepedonium infections have a great morphological variability: at the initial state, contaminated mushrooms present a white coating covering tubes and pores; at the final state, Sepedonium forms a deep and thick hyphal layer that eventually leads to the total necrosis of the host. Up to date, Sepedonium infections in porcini mushrooms have been evaluated only through macroscopic and microscopic visual analysis. In this study, in order to implement the infection evaluation as a routine methodology for industrial purposes, the potential application of Hyperspectral Imaging (HSI) and Principal Component Analysis (PCA) for detection of Sepedonium presence on sliced and dried B. edulis and allied species was investigated. Hyperspectral images were obtained using a pushbroom line-scanning HSI instrument, operating in the wavelength range between 400 and 1000 nm with 5 nm resolution. PCA was applied on normal and contaminated samples. To reduce the spectral variability caused by factors unrelated to Sepedonium infection, such as scattering effects and differences in sample height, different spectral pre-treatments were applied. A supervised rule was then developed to assign spectra recorded on new test samples to each of the two classes, based on the PC scores. This allowed to visualize directly - within false-color images of test samples - which points of the samples were contaminated. The results achieved may lead to the development of a non-destructive monitoring system for a rapid on-line screening of contaminated mushrooms.

A NIR spectroscopy-based efficient approach to detect fraudulent additions within mixtures of dried porcini mushrooms.

Boletus edulis and allied species (BEAS), known as porcini mushrooms, represent almost the totality of wild mushrooms placed on the Italian market, both fresh and dehydrated. Furthermore, considerable amounts of these dried fungi are imported from China. The presence of Tylopilus spp. and other extraneous species (i.e., species edible but not belonging to BEAS) within dried porcini mushrooms - mainly from those imported from China and sold in Italy - may represent an evaluable problem from a commercial point of view. The purpose of the present study is to evaluate near-infrared spectroscopy (NIRS) as a rapid and effective alternative to classical methods for identifying extraneous species within dried porcini batches and detecting related commercial frauds. To this goal, 80 dried fungi including BEAS, Tylopilus spp., and Boletus violaceofuscus were analysed by NIRS. For each sample, 3 different parts of the pileus (pileipellis, flesh and hymenium) were analysed and a low-level strategy for data fusion, consisting of combining the signals obtained by the different parts before data processing, was applied. Then, NIR spectra were used to develop reliable and efficient class-models using a novel method, partial least squares density modelling (PLS-DM), and the two most commonly used class-modelling techniques, UNEQ and SIMCA. The results showed that NIR spectroscopy coupled with chemometric class-modelling technique can be suggested as an effective analytical strategy to check the authenticity of dried BEAS mushrooms.

UV-VIS spectroscopy for monitoring yogurt stability during storage time

Color, texture and taste are key elements of a consumers buying decision; thus, monitoring the stability of these features throughout the entire period of yogurt validity is fundamental for dairy product producers. Color, texture and taste deteriorations are due to changes in the physical, chemical and microbiological compositions of yogurt and they can be monitored using lab analyses (especially the microbiological ones) which are expensive and time consuming. In this study, ultraviolet-visible (UV-VIS) spectroscopy was applied as a rapid and alternative technique to traditional analytical methods, to monitor the stability of different commercial yogurt samples up to 49 days of storage at 4 °C. UV-VIS spectroscopy was employed with an integrating sphere for diffuse reflectance measurements and, for each yogurt, color stability during storage time was evaluated in terms of CIEL*a*b* color space values. Moreover, Partial Least Squares regression combined with Genetic Algorithms (GA-PLS) was performed for predicting the age of the yogurt samples from their UV-VIS spectra. In order to evaluate the texture and taste changes, flow curves and pH values of yogurt during storage were monitored once a week for the entire considered storage period. The UV-VIS and rheological datasets were elaborated by means of univariate and multivariate methods. It was interesting to notice that, in both datasets, the time-related information was not visible by simply comparing the profiles of signals, poorly visible in the principal component space, and clearly explained by three-way Principal Component Analysis (3-way PCA).