Assessing feature relevance in NPLS models by VIP

Multilinear PLS (NPLS) and its discriminant version (NPLS-DA) are very diffuse tools to model multi-way data arrays. Analysis of NPLS weights and NPLS regression coefficients allows data patterns, feature correlation and covariance structure to be depicted. In this study we propose an extension of the Variable Importance in Projection (VIP) parameter to multi-way arrays in order to highlight the most relevant features to predict the studied dependent properties either for interpretative purposes or to operate feature selection. The VIPs are implemented for each mode of the data array and in the case of multivariate dependent responses considering both the cases of expressing VIP with respect to each single y-variable and of taking into account all y-variables altogether. Three different applications to real data are presented: i) NPLS has been used to model the properties of bread loaves from near infrared spectra of dough, acquired at different leavening times, and corresponding to different flour formulations. VIP values were used to assess the spectral regions mainly involved in determining flour performance; ii) assessing the authenticity of extra virgin olive oils by NPLS-DA elaboration of gas chromatography/mass spectrometry data (GC–MS). VIP values were used to assess both GC and MS discriminant features; iii) NPLS analysis of a fMRI-BOLD experiment based on a pain paradigm of acute prolonged pain in healthy volunteers, in order to reproduce efficiently the corresponding psychophysical pain profiles. VIP values were used to identify the brain regions mainly involved in determining the pain intensity profile

A mid level data fusion strategy for the Varietal Classification of Lambrusco PDO wines

Nowadays the necessity to reveal the hidden information from complex data sets is increasing due to the development of high-throughput instrumentation. The possibility to jointly analyze data sets arising from different sources (e.g. different analytical determinations/platforms) allows capturing the latent information that would not be extracted by the individual analysis of each block of data. Several approaches are proposed in the literature and are generally referred to as data fusion approaches. In this work a mid level data fusion is proposed for the characterization of three varieties (Salamino di Santa Croce, Grasparossa di Castelvetro, Sorbara) of Lambrusco wine, a typical PDO wine of the district of Modena (Italy). Wine samples of the three different varieties were analyzed by means of 1H-NMR spectroscopy, Emission-Excitation Fluorescence Spectroscopy and HPLC-DAD of the phenolic compounds. Since the analytical outputs are characterized by different dimensionalities (matrix and tensor), several multivariate analyses were applied (PCA, PARAFAC, MCR-ALS) in order to extract and merge, in a hierarchical way, the information present in each data set. The results showed that this approach was able to well characterize Lambrusco samples giving also the possibility to understand the correlation between the sources of information arising from the three analytical techniques

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

Multiresolution analysis and chemometrics for pattern enhancement and resolution in spectral signals and images

The chapter illustrates the benefits and improvements of the integration of the wavelet transform with multivariate data analysis upon multiresolution analysis. This approach can be used for feature extraction both in signals and images in a broad sense, focusing on the capability to simultaneously accomplish de-noising and feature enhancement / selection. Different contexts are presented, ranging from feature selection applied to spectroscopic signals in classification and regression tasks, to multiresolution multivariate image analysis with special attention to quality monitoring, fault detection and classification. The proposed cases of study cover applications in the food and materials sciences

CHEMOMETRICS IN FOOD AUTHENTICATION

According to ISO definition “an entity is authentic if it is what it claims tobe.” and “Authentication is a process that is used to confirm that a claimed characteristic of an entity is actually correct”. When applied to food context this is actually a very broad concept covering several aspects including both the characterization and recognition of the identifiable features of a product as well as its accountability, in other words the degree of trustiness consumers associate to it. In practice, to assess authenticity it has to be taken into consideration in an integrated manner the several characteristics deliberately produced (sophistication, adulteration, counterfeit, appearance, etc.) or not (purity, contamination, degradation, etc.), but also the subjective consumer's perception of the worth of the product and issues such as terroir, authenticity, origin, production practice, etc.In this framework, quality control and authentication assessment need to go further traditional chemical analysis, which focuses on single classes of constituents/properties, and adopting a fingerprinting approach, trough fast,non-destructive techniques. In this perspective, the role of chemometrics is becoming basilar to efficiently extract the relevant information and to derive authenticity models. Nowadays, chemometrics offers tailored tools that allow exploratory data analysis, graphical representation and validation of the models during all steps of data processing as well as efficient storage, retrieval and sharing of information. In the present contribution, the focus will be on illustrating the chemometrics pipeline highlighting the main issues, critical points and the successfully strategies with application to geographical traceability and typicality assessment

RP-HPLC and Chemometrics for wheat flour protein characterization 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

LF-1H-NMR AND MULTIVARIATE ANALYSIS TO STUDY OENOLOGICAL PRODUCTS UNDER AUTHENTICITY REGULATION

The safeguard of food products which undergo European Union authenticity regulations, such as "Protected Designation of Origin" (PDO) or “Protected Geographical Indication” (PGI), is nowadays of increasing relevance, since several EU resolutions have shown an unequivocal intention towards the valorisation of food and oenological products which are typical of a given region, with the twofold aim of defending the producers, who have to undergo stricter regulations so that their product can bear a mark which grants a higher added value, and the consumers, for which the same mark carries with it a concept of higher quality of the product which must correspond to the truth. In an analytical chemistry context, much attention is given to the development of methods of analysis which can characterize in a fast and cheap way the products, giving information on their quality and authenticity. In the present study, Low-Field 1H Nuclear Magnetic Resonance Relaxometry (LF-1H-NMR) has been used to investigate a product which is typical of Modena (Italy) district, that is Aceto Balsamico Tradizionale di Modena (ABTM, Balsamic vinegar), which is appointed with the “Protected Designation of Origin” (PDO) mark. One of the main advantages of this technique, as reported in literature for food and beverage contexts, is the possibility of analysing the sample “as is” in few seconds and of acquiring insight on the relaxation behaviour. The results, interpreted with a Multivariate Analysis approach, allow a clear classification of products of different commercial classes and differentiation of succedaneum products, which can be of help in verifying the authenticity. In particular, both CPMG relaxation profile curves analysed as raw “fingerprint” signals and the transversal relaxation time T2 of the different components present in the samples obtained by means of the multi-way PowerSlicing approach can be used to elaborate SIMCA classification models which consistently distinguish the two main commercial classes and aging times of ABTM samples and correctly reject succedaneum products such as not PDO balsamic creams