Mario Li Vigni

RP-HPLC and chemometrics for wheat flour protein characterisation in an industrial bread-making process monitoring context

In the baking industry, a difficult task is to keep the quality perceived by the consumer as constant as possible, given the inner variability of flour, e.g. due to different wheat mixtures, harvesting time, etc. Here, we evaluated the influence of flour batches properties on bread quality, considering an industrial bread making process. In particular, flour composition in terms of protein fractions (gliadins, glutenins) has been determined by means of RP-HPLC, to assess the inter- and intra-batch variability of flour mixtures deliveries at a baking plant. Multivariate data analysis allowed evaluation of correlation between flour protein composition and technological properties. A great variability within different deliveries of a same flour batch emerged, as well as a considerable seasonal variability. Correlation models among protein sub-fractions, technological properties and bread quality are difficult to establish; however, the role of the protein profile on flour behaviour in bread making could be highlighted.

The Impact of Chemometrics on Food Traceability

Abstract In the last decades, mankind has become totally aware about the importance of food quality: nowadays authentication and traceability are words of general use. Food authentication verifies how much a food is in accordance with its label description and law and it could be considered a further guarantee for the quality and safety of a foodstuff. The traceability of food could be considered an essential element in ensuring safety and high quality of food. The synergistic use of instrumental analytical techniques and chemometrics represents a promising way to obtain trustworthy results in the development of authenticity and traceability models. This chapter deals with the potentialities of chemometrics tools in resolving some real issues related to food traceability and authenticity. Particular attention will be paid to the use of some exploratory, classification, and discrimination techniques. In the first part of this chapter, a briefly description of European regulations ( Authenticity and Traceability: the European Union point of view ), and traceability and authenticity markers ( Authenticity and Traceability: a scientific point of view ) is reported. The second part is split into two sections: namely Food Authenticity and Food Traceability applications , where the main features and advantages of some chemometrics approaches are presented.

Chapter 3 – Exploratory Data Analysis

In the food research and production field, system complexity is increasing and several new challenges are emerging every day. This implies an urgent necessity to extract information and obtain models capable of inferring the underlying relationships that link all the variability sources which characterize food or its production process (e.g. compositional profile, processing conditions) to very general end properties of foodstuff, such as the healthiness, the consumer perception, the link to a territory and the effect of the production chain itself on food. This makes a ‘deductive’ theory-driven research approach inefficient, as it is often difficult to formulate hypotheses. Explorative multivariate data analysis methods, together with the most recent analytical instrumentation, offer the possibility to come back to an ‘inductive’ data-driven attitude with a minimum of a priori hypotheses, instead helping in formulating new ones from the direct observation of data. The aim of this chapter is to offer the reader an overview of the most significant tools that can be used in a preliminary, exploratory phase, ranging from the most classical descriptive statistics methods, to multivariate analysis methods, with particular attention to projection methods. For all techniques, examples are given so that the main advantage of these techniques, which is a direct, graphical representation of data and their characteristics, can be immediately experienced by the reader.

Near infrared spectroscopy and multivariate analysis to evaluate wheat flour doughs leavening and bread properties

A mixture design of experiment approach was followed to explore formulation effects on the technological properties of wheat flours optimized for industrial bread-making purposes. Ten different flour mixtures were investigated by means of near infrared spectroscopy (NIRS) to obtain information on flour performance in a critical phase such as dough leavening. For each mixture, a laboratory-scale bread making experiment was carried out according to a standardized recipe and the leavening phase of each dough sample was monitored by means of NIRS at different times. Parallel factor analysis (PARAFAC) was used to highlight the existence of differences among the mixtures on the basis of NIR spectrum variability with respect to the leavening time. Additionally, the relationship among the 3-way NIR dataset and some parameters measured on the baked bread loaves (dimensions, volume, weight) was investigated by means of the n-way extension of partial least squares regression (nPLS), in order to evaluate product properties from its leavening step and mixture formulation. The results give better insight on the relationships among wheat flour formulation and its performance in the leavening phase and as far as some properties of the final product are concerned, thus offering a way to monitor the leavening phase and give information on its influence on the final product properties.

At-Line Monitoring of the Leavening Process in Industrial Bread Making by near Infrared Spectroscopy

The potential of near infrared (NIR) spectroscopy to characterise doughs for industrial bread making, directly at the production plant, was investigated. Different stages of dough processing have been monitored at-line, employing a Fourier transform-NIR instrument equipped with an optical fibre. Parallel factors analysis has been used to study the spectral variation throughout the production line, with the aim of acquiring indications on the modifications the dough undergoes during the process. Moreover, wavelet interface for linear modelling analysis, which performs variable selection in the wavelet domain, has been employed to explore the possibility of monitoring the leavening step by identifying a relationship between the NIR signal and the leavening phase, considering the leavening time and the total titrable acidity of the dough. Results show that some aspects of the leavening process can be calibrated from the NIR spectra, thus corroborating the fact that the NIR signal is influenced by the modifications that occur along with the production process

Monitoring flour performance in bread making.

Publisher Summary An approach, based on multivariate control chart (MCC) methodology, was proposed by Li Vigni et al., that allows monitoring of flour quality and early identification of flour batches potentially leading to poor performance in production. Using this approach, all rheological properties of incoming flour batches are evaluated multivariately, and these values are projected on a model based on historical data, thus highlighting potential deviances from optimal flour batches employed in the past. This chapter extends this strategy to a more general framework that considers routine flour quality control at the miller and routine control of incoming flour batches at the bakery. This approach offers an interesting tool to detect anomalous flour batches; however, the relationship between technological parameters and bread properties is often poorly known. Multivariate evaluation of bread quality allows one to obtain a more compact and complete representation of production performance than considering univariate control charts for each property separately. Moreover, it allows a more realistic evaluation of product and departure from standards taking into account all different properties simultaneously. Evaluation of the rheological properties of incoming flour batches with an MCC approach helps in assessing the similarities and differences among new deliveries and historical data at a very preliminary step of the production chain so that rational planning of the best recipe to apply to exploit flour properties can be done at the beginning of production, instead of modifying it on the basis of the previous production outcome.

Coupling 2D-wavelet decomposition and multivariate image analysis (2D WT-MIA): Coupling 2D-WT to Multivariate Image Analysis (2D WT-MIA)

The use of 2D discrete wavelet transform in the feature enhancement phase of multivariate image analysis is discussed and implemented in a comparative way with respect to previous publications. In the proposed approach, all the resulting subimages obtained by discrete wavelet transform decomposition are unfolded pixel‐wise and midlevel data fused to a feature matrix that is used for the feature analysis phase. Congruent subimages can be obtained either by reconstruction of each decomposition block to the original pixel dimensions or by using the stationary wavelet transform decomposition scheme. The main advantage is that all possible relationships among blocks, decomposition levels, and channels are assessed in a single multivariate analysis step (feature analysis). This is particularly useful in a monitoring context where the aim is to build multivariate control charts based on images. Moreover, the approach can be versatile for contexts where several images are analyzed at a time as well as in the multispectral image analysis.

Aesthetic compatibility assessment of consolidants for wall paintings by means of multivariate analysis of colorimetric data

Background and methodsWall paintings and architectural surfaces in outdoor environments are exposed to several physical, chemical and biological agents, hence they are often treated with different products to prevent or slow down their deterioration. Among the factors that have to be taken into account in the selection of the most suitable treatment for decorated surfaces, the aesthetic compatibility with the substrate is of great importance in the cultural heritage field; minimizing colour variation after treatment application is a crucial issue in particular for painted surfaces. In the framework of the European Project Nanomatch the color variation induced on wall painting mock-ups by the two innovative consolidants (calcium alkoxides) developed was evaluated using colorimetry in comparison with two traditional products. In this work these innovative consolidants have been also tested in combination with two commercial biocides and the results of colorimetric measurements discussed. Moreover, as the univariate approach didn’t allow to draw clear conclusions on the relation between the different sources of data variability, multivariate analysis was performed on colorimetric data.ResultsPrincipal Component Analysis and multi-way Parallel Factor Analysis (PARAFAC) were successfully applied to colorimetric data to investigate the short-term effects of the application of different consolidants on wall painting surfaces, making it possible to study at the same time the different sources of data variability, i.e. treatments, painting techniques, pigments. Finally, a ranking list of the treatments under study in terms of colour variation induced on the surface was established, in function of the painting technique and pigment, taking also in consideration the combination consolidant/biocide. In particular, given the true multi-way nature of the data, PARAFAC model turned out to be extremely useful in the study of the dependence of colour variation on pigments, a critical issue for painted surfaces, that was not clear using univariate approach.ConclusionsMultivariate approach to colorimetric data and especially 3-way PARAFAC method resulted a powerful technique to evaluate in short-term the color compatibility of consolidants for wall paintings, improving data interpretation and visualization, and thus outperforming the univariate statistical analysis.

Seeds of Horse Chestnut (Aesculus hippocastanum L.) and Their Possible Utilization for Human Consumption

Publisher Summary This chapter appraises the seeds of horse chestnuts (Aesculus hippocastanum [AH]) and their derived products. Escin, the major bioactive principle in AH seeds, has shown satisfactory evidence of clinically significant activity in the treatment of chronic venous insufficiency, hemorrhoids, postoperative edema, and mammary induration. There is some evidence that various escin molecules, such as saponins and sapogenins, show beneficial effects when administered at the right concentration, exhibiting an ethanol absorption inhibitory effect and hypoglycemic activity in the oral glucose tolerance test in vivo. Horse chestnut extract has a higher antioxidant activity than vitamin E, showing one of the highest “active-oxygen” scavenging abilities compared to other natural products. β-Escin from AH extracts was also tested to evaluate the chemopreventive efficacy of its dietary intake on azoxymethane-induced colonic aberrant crypt foci. The main adverse effects of escins in humans are due to their hemolytic activity. Research efforts in this field are devoted to improving the selectivity for aberrant red corpuscles, promoting the β-escin fraction as a useful candidate agent for exploring new potential antileukemic drugs. Fresh or naturally desiccated seeds are usually treated by long leaching with water or wooden ashes to remove harshness and bitterness. These treatments cause a variation in the molecular structures of escin fractions, reducing the toxicity but maintaining their nutraceutical potential and anti-obesity effects. Alternatively, the slow roasting of nuts makes the escins harmless and the seeds edible. The claimed toxicity of these extracts makes them natural antibacterials, antimicrobials, antivirals, and antifungals, to some extent, that also act as environmentally biocompatible phytotherapeutics.