1,720,990 research outputs found
Near infrared spectroscopy and multivariate analysis to evaluate wheat flour doughs leavening and bread properties
No full textA 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
No full textThe 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
RP-HPLC and Chemometrics for wheat flour protein characterization in an industrial bread-making process monitoring context
No full textIn 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
Assessing feature relevance in NPLS models by VIP
No full textMultilinear 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
CHEMOMETRICS IN FOOD AUTHENTICATION
No full textAccording 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
LF-1H-NMR AND MULTIVARIATE ANALYSIS TO STUDY OENOLOGICAL PRODUCTS UNDER AUTHENTICITY REGULATION
No full textThe 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
Exploratory Data Analysis
No full textIn the Food research and production field, system complexity is increasing and several new challenges are emerging every day. This implies a 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, since 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 which 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 this techniques, that is a direct, graphical representation of data and their characteristics, can be immediately experienced by the reader
Chemometrics, Bioinformatics
No full textThe need to consider variability due to raw materials, seasonality, agricultural practices, and food processing, that are aspects which all play a role in authenticity tasks, justifies the need for chemometrics methods. This chapter presents a few basic chemometrics methods, such as exploratory data analysis, multiway data analysis, and data fusion. New chemometrics methods are continuously being updated and improved upon, two main distinctive characteristics are required: data exploration and graphical representation; and deep model validation through all steps of data processing. This also explains why chemometrics tools based on latent variables, for example, principal component analysis (PCA), soft independent modeling of class analogies (SIMCA), partial least squares (PLS), and PLS discriminant analysis (PLSDA), are still so popular and powerful. Nowadays, there is an established set of chemometric multiway methods and algorithms. The chapter mentions those that can serve the purposes of exploratory data analysis and classification, the tasks most frequently encountered in food authentication
Determinazione di acidi e zuccheri presenti negli impasti per la produzione industriale di pane a diversi stadi della lavorazione
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