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

Influence of Chemical and Physical Variables on 87Sr/86Sr Isotope Ratios Determination for Geographical Traceability Studies in the Oenological Food Chain

This study summarizes the results obtained from a systematic and long-term project aimed at the development of tools to assess the provenance of food in the oenological sector. 87Sr/86Sr isotope ratios were measured on a representative set of soils, branches, and wines sampled from the Chianti Classico wine production area. In particular, owing to the high spatial resolution of the 87Sr/86Sr ratio in the topsoil, the effect of two mill techniques for soil pretreatment was investigated to verify the influence of the particle dimension on the measured isotopic ratios. Samples with particle sizes ranging from 250 to less than 50 m were investigated, and the extraction was performed by means of the DIN 19730 procedure. For each sample, the Sr isotope ratio was determined as well. The obtained results showed that the 87Sr/86Sr ratio is not influenced by soil particle size and may represent an effective tool as a geographic provenance indicator for the investigated product

Exploring the Impact of Various Wooden Barrels on the Aromatic Profile of Aceto Balsamico Tradizionale di Modena by Means of Principal Component Analysis

The study examines the unique production process of Aceto Balsamico Tradizionale di Modena PDO (ABTM), emphasizing its complex phases and the impact of raw materials and artisanal skill on its flavor characteristics. Analytical tests focused on the volatile composition of vinegars from different wood barrels at different aging stages, using solid-phase micro-extraction (SPME) coupled with gas chromatography, either with mass spectrometry (GC/MS) or flame ionization detector (FID). Multivariate analysis, including principal component analysis (PCA), was employed to investigate the presence of peculiarities among the volatile profiles of samples of different barrel origin. The research focuses on characterizing the volatile composition of vinegars sourced from individual wood barrels, such as Cherry, Chestnut, Mulberry, Juniper, and Oak. Although it was not possible to identify molecules directly connected to the woody essence, some similarities emerged between vinegar samples from mulberry and cherry barrels and between those of juniper and oak. The former group is characterized by analytes with high molecular weights, such as furfural and esters, while the latter group shows more intense peaks for ethyl benzoate. Moreover, ethyl benzoate appears to predominantly influence samples from chestnut barrels. Due to the highly complex production process of ABTM, where each battery is influenced by several factors, this study’s findings are specific to the current experimental conditions

Fast GC E-Nose and Chemometrics for the Rapid Assessment of Basil Aroma

The aim of this work is to assess the potentialities of the synergistic combination of an ultra-fast chromatography-based electronic nose as a fingerprinting technique and multivariate data analysis in the context of food quality control and to investigate the influence of some factors, i.e., basil variety, cut, and year of crop, in the final aroma of the samples. A low = level data fusion approach coupled with Principal Component Analysis (PCA) and ANOVA—Simultaneous Component Analysis (ASCA) was used in order to analyze the chromatographic signals acquired with two different columns (MXT-5 and MXT-1701). While the PCA analysis results highlighted the peculiarity of some basil varieties, differing either by a higher concentration of some of the detected chemical compounds or by the presence of different compounds, the ASCA analysis pointed out that variety and year are the most relevant effects, and also confirmed the results of previous investigations

Characterization of Basil Volatile Fraction and Study of its Agronomic Variation by ASCA

Basil is a plant known worldwide for its culinary and health attributes. It counts more than a hundred and fifty species and many more chemo-types due to its easy cross-breeds. Each species and each chemo-type have a typical aroma pattern and selecting the proper one is crucial for the food industry. Twelve basil varieties have been studied over three years (2018–2020), as have four different cuts. To characterize the aroma profile, nine typical basil flavour molecules have been selected using a gas chromatography–mass spectrometry coupled with an olfactometer (GC–MS/O). The concentrations of the nine selected molecules were measured by an ultra-fast CG e-nose and Principal Component Analysis (PCA) was applied to detect possible differences among the samples. The PCA results highlighted differences between harvesting years, mainly for 2018, whereas no observable clusters were found concerning varieties and cuts, probably due to the combined effects of the investigated factors. For this reason, the ANOVA Simultaneous Component Analysis (ASCA) methodology was applied on a balanced a posteriori designed dataset. All the considered factors and interactions were statistically significant (p < 0.05) in explaining differences between the basil aroma profiles, with more relevant effects of variety and year

Data fusion strategies for the integration of diverse non-destructive spectral sensors (NDSS) in food analysis

The evolving landscape of agri-food systems, driven by climate change and population growth, necessitates innovative approaches to ensure food integrity, safety, and sustainability. This review explores the role of data fusion strategies, particularly focusing on non-destructive spectroscopic sensors (NDSS) in three key application contexts: in-field monitoring, on/in-line food processing, and food quality authentication. Various data fusion scenarios, including fusing spectra from different spectroscopic platforms, integrating images and spectra, and combining non-spectroscopic sensor data with spectroscopic ones are reviewed. Focus is set on practical considerations, such as selecting the level of data fusion, defining blocks, variable selection, and validation methods, highlighting the importance of tailored approaches based on research aims and data characteristics. While combining information from diverse sensors generally enhances information extraction and modelling performance, their implementation in routine applications is still limited and especially studies focused on data fusion models’ performance over time and their maintenance are lacking.publishedVersio

Application of data fusion techniques to direct geographical traceability indicators

A hierarchical data fusion approach has been developed proposing multivariate curve resolution (MCR) as a variable reduction tool. The case study presented concerns the characterization of soil samples of the Modena District. It was performed in order to understand, at a pilot study stage, the geographical variability of the zone prior to planning a representative soils sampling to derive geographical traceability models for Lambrusco Wines. Soils samples were collected from four producers of Lambrusco Wines, located in in-plane and hill areas. Depending on the extension of the sampled fields the number of points collected varies from three to five and, for each point, five depth levels were considered. The different data blocks consisted of X-ray powder diffraction (XRDP) spectra, metals concentrations relative to thirty-four elements and the 87Sr/86Sr isotopic abundance ratio, a very promising geographical traceability marker. A multi steps data fusion strategy has been adopted. Firstly, the metals concentrations dataset was weighted and concatenated with the values of strontium isotopic ratio and compressed. The resolved components described common patterns of variation of metals content and strontium isotopic ratio. The X-ray powder spectra profiles were resolved in three main components that can be referred to calcite, quartz and clays contributions. Then, a high-level data fusion approach was applied by combining the components arising from the previous data sets. The results show interesting links among the different components arising from XRDP, the metals pattern and to which of these 87Sr/86Sr Isotopic Ratio variation is closer. The combined information allowed capturing the variability of the analyzed soil samples

Comparative Analysis of VOCs from Winter Melon Pomace Fibers before and after Bleaching Treatment with H2O2

In this study, the trend of Volatile Organic Compounds (VOCs) in dietary fiber samples from the winter melon (Cucumis Melo var. Inodorus, Yellow Canary type) were investigated. This foodstuff, obtained as a by-product of agri-food production, has gained increasing attention and is characterized by many bioactive components and a high dietary-fiber content. As regards fiber, it is poorly colored, but it may be whitened by applying a bleaching treatment with H2O2. The result is a fibrous material for specific applications in food manufacturing, for example, as a corrector for some functional and technological properties. This treatment is healthy and safe for consumers and widely applied in industrial food processes. In this study, a method based on headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography\u2013mass spectrometry (GC-MS) was applied for the characterization of the aromatic profile of the dried raw materials. Furthermore, VOC variation was investigated as function of the bleaching treatment with H2O2. The bleached samples were also analyzed after a long storage period (24 months), to assess their stability over time. As a result, the VOC fraction of the fresh raw fiber showed nine classes of analytes; these were restricted to seven for the bleached fiber at t0 time, and further reduced to four classes at the age of 24 months. Alcohols were the main group detected in the fresh raw sample (33.8 % of the total chromatogram area), with 2,3-butanediol isomers as the main compounds. These analytes decreased with time. An opposite trend was observed for the acids (9.7% at t0), which increased with time and became the most important class in the 24-month aged and bleached sample (57.3%)

Implementing Multiblock Techniques in a full-scale plant scenario: On-line Prediction of Quality Parameters in a Continuous Process for Different Acrylonitrile Butadiene Styrene (ABS) Products

Background The study explores the challenges of handling multiblock data of different natures (process and NIR sensors) for on-line quality prediction in a full-scale plant scenario, namely a plant operating in continuous on an industrial scale and producing different grade Acrylonitrile Butadiene Styrene (ABS) products. This environment is an ideal scenario to evaluate the use of multiblock data analysis methods, which can enhance data interpretation, visualization, and predictive performances. In particular, a novel multiblock extension of Locally Weighted PLS has been proposed by the authors, namely Locally Weighted Multiblock Partial Least Squares (LW-MB-PLS). Response-Oriented Sequential Alternation (ROSA) has also been employed to evaluate the diverse block relevance for the prediction of two quality parameters associated with the polymer. Data are split in blocks both according to sensor type and different plant sections, and different models have been built by incremental addition of data blocks to evaluate if early estimation of product quality is feasible. Results ROSA method showed promising predictive performance for both quality parameters, highlighting the most influential plant sections through the selection of data blocks. The results suggested that both early and late-stage sensors play crucial roles in predicting product quality. A reasonable estimation of quality parameters before production completion has been achieved. On the other hand, the proposed LW-MB-PLS, while comparable in predictive performances, allowed reducing systematic prediction errors for specific products. Significance This study contributes valuable insights for continuous production processes, aiding plant operators and paving the way for advancements in online quality prediction and control. Furthermore, it is implemented as a locally weighted extension of MB-PLS