Class modelling by soft independent modelling of class analogy: why, when, how? A tutorial

This article contains a comprehensive tutorial on classification by means of Soft Independent Modelling of Class Analogy (SIMCA). Such a tutorial was conceived in an attempt to offer pragmatic guidelines for a sensible and correct utilisation of this tool as well as answers to three basic questions: “why employing SIMCA?”, “when employing SIMCA?” and “how employing/not employing SIMCA?”. With this purpose in mind, the following points are here addressed: i) the mathematical and statistical fundamentals of the SIMCA approach are presented; ii) distinct variants of the original SIMCA algorithm are thoroughly described and compared in two different case-studies; iii) a flowchart outlining how to fine-tune the parameters of a SIMCA model for achieving an optimal performance is provided; iv) figures of merit and graphical tools for SIMCA model assessment are illustrated and v) computational details and rational suggestions about SIMCA model validation are given. Moreover, a novel Matlab toolbox, which encompasses routines and functions for running and contrasting all the aforementioned SIMCA versions is also made available

Authentication of Sorrento walnuts by NIR spectroscopy coupled with different chemometric classification strategie

Walnuts have been widely investigated because of their chemical composition, which is particularly rich in unsaturated fatty acids, responsible for different benefits in the human body. Some of these fruits, depending on the harvesting area, are considered a high value-added food, thus resulting in a higher selling price. In Italy, walnuts are harvested throughout the national territory, but the fruits produced in the Sorrento area (South Italy) are commercially valuable for their peculiar organoleptic characteristics. The aim of the present study is to develop a non-destructive and shelf-life compatible method, capable of discriminating common walnuts from those harvested in Sorrento (a town in Southern Italy), considered a high quality product. Two-hundred-and-twenty-seven walnuts (105 from Sorrento and 132 grown in other areas) were analyzed by near-infrared spectroscopy (both whole or shelled), and classified by Partial Least Squares-Discriminant Analysis (PLS-DA). Eventually, two multi-block approaches have been exploited in order to combine the spectral information collected on the shell and on the kernel. One of these latter strategies provided the best results (98.3% of correct classification rate in external validation, corresponding to 1 misclassified object over 60). The present study suggests the proposed strategy is a suitable solution for the discrimination of Sorrento walnuts. © 2020 by the authors

Deep multiblock predictive modelling using parallel input convolutional neural networks

In the domain of chemometrics, multiblock data analysis is widely performed for exploring or fusing data from multiple sources. Commonly used methods for multiblock predictive analysis are the extensions of latent space modelling approaches. However, recently, deep learning (DL) approaches such as convolutional neural networks (CNNs) have outperformed the single block traditional latent space modelling chemometric approaches such as partial least-square (PLS) regression. The CNNs based DL modelling can also be performed to simultaneously deal with the multiblock data but was never explored until this study. Hence, this study for the first time presents the concept of parallel input CNNs based DL modelling for multiblock predictive chemometric analysis. The parallel input CNNs based DL modelling utilizes individual convolutional layers for each data block to extract key features that are later combined and passed to a regression module composed of fully connected layers. The method was tested on a real visible and near-infrared (Vis-NIR) large data set related to dry matter prediction in mango fruit. To have the multiblock data, the visible (Vis) and near-infrared (NIR) parts were treated as two separate blocks. The performance of the parallel input CNN was compared with the traditional single block CNNs based DL modelling, as well as with a commonly used multiblock chemometric approach called sequentially orthogonalized partial least-square (SO-PLS) regression. The results showed that the proposed parallel input CNNs based deep multiblock analysis outperformed the single block CNNs based DL modelling and the SO-PLS regression analysis. The root means squared errors of prediction obtained with deep multiblock analysis was 0.818%, relatively lower by 4 and 20% than single block CNNs and SO-PLS regression, respectively. Furthermore, the deep multiblock approach attained ~3% lower RMSE compared to the best known on the mango data set used for this study. The deep multiblock analysis approach based on parallel input CNNs could be considered as a useful tool for fusing data from multiple sources.info:eu-repo/semantics/publishedVersio

Chemometric Methods for Spectroscopy-Based Pharmaceutical Analysis

Spectroscopy is widely used to characterize pharmaceutical products or processes, especially due to its desirable characteristics of being rapid, cheap, non-invasive/non-destructive and applicable both off-line and in-/at-/on-line. Spectroscopic techniques produce profiles containing a high amount of information, which can profitably be exploited through the use of multivariate mathematic and statistic (chemometric) techniques. The present paper aims at providing a brief overview of the different chemometric approaches applicable in the context of spectroscopy-based pharmaceutical analysis, discussing both the unsupervised exploration of the collected data and the possibility of building predictive models for both quantitative (calibration) and qualitative (classification) responses

Investigation of Lactation Period and Technological Treatments on Mineral Composition and IR-Profiles of Donkey Milk by Chemometrics

Featured Application: Multi-platform analysis of donkey milk. Donkey milk represents an efficient substitute for human milk in infants’ diets being unlikely to cause allergic reactions. In this study, different donkey milks were collected at two lactation times (T0 and T1), subjected to freezing–thawing and freeze-drying, and analyzed by Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES) and ATR-FT-IR. The data collected on freeze–thaw (FT-) and reconstituted (R-)milks were investigated by ANOVA–Simultaneous Component Analysis (ASCA) and Principal Component Analysis (PCA). The following concentrations (μg/mL) for FT and R-milks, respectively, at T0, were found: Ca: 712 ± 71, 600 ± 72; Fe: 0.7 ± 0.3, 0.1 ± 0.1; K: 595 ± 49, 551 ± 59; Mg: 75 ± 5, 67 ± 4; Na: 117 ± 16, 114 ± 16; P: 403 ± 30, 404 ± 38; Zn: 1.6 ± 0.2, 1.6 ± 0.3. At T1, the concentrations (μg/mL for FT and R-milks, respectively) were: Ca: 692 ± 60, 583 ± 43; Fe: 0.13 ± 0.02, 0.13 ± 0.03; K: 641 ± 71, 574 ± 61; Mg: 72 ± 4, 63 ± 1; Na: 116 ± 9, 109 ± 8; P: 412 ± 30, 405 ± 24; Zn: 1.6 ± 0.3, 1.6 ± 0.3. ASCA demonstrated the treatment has a substantial effect, and PCA revealed that the largest quantities of metals, specifically Fe, Mg, and Ca for T0 and K, P, and Na for T1, are present in the FT-milk samples. The IR spectra of FT- and R-milks revealed no macroscopic changes among them or between lactation periods, indicating this technique may not suitably capture variability in lactation or conservation processes in donkey milk. Despite the relatively small sample size, this study offers insight on the mineral composition changes in donkey milk and emphasizes the significance of milk preprocessing and the lactation period on it

Monitoring Thermal and Non-Thermal Treatments during Processing of Muscle Foods : A Comprehensive Review of Recent Technological Advances

Muscle food products play a vital role in human nutrition due to their sensory quality and high nutritional value. One well-known challenge of such products is the high perishability and limited shelf life unless suitable preservation or processing techniques are applied. Thermal processing is one of the well-established treatments that has been most commonly used in order to prepare food and ensure its safety. However, the application of inappropriate or severe thermal treatments may lead to undesirable changes in the sensory and nutritional quality of heat-processed products, and especially so for foods that are sensitive to thermal treatments, such as fish and meat and their products. In recent years, novel thermal treatments (e.g., ohmic heating, microwave) and non-thermal processing (e.g., high pressure, cold plasma) have emerged and proved to cause less damage to the quality of treated products than do conventional techniques. Several traditional assessment approaches have been extensively applied in order to evaluate and monitor changes in quality resulting from the use of thermal and non-thermal processing methods. Recent advances, nonetheless, have shown tremendous potential of various emerging analytical methods. Among these, spectroscopic techniques have received considerable attention due to many favorable features compared to conventional analysis methods. This review paper will provide an updated overview of both processing (thermal and non-thermal) and analytical techniques (traditional methods and spectroscopic ones). The opportunities and limitations will be discussed and possible directions for future research studies and applications will be suggested

Untargeted cannabinomics reveals the chemical differentiation of industrial hemp based on the cultivar and the geographical field location

Cannabis sativa has long been harvested for industrial applications related to its fibers. Industrial hemp cultivars, a botanical class of Cannabis sativa with a low expression of intoxicating Δ9-tetrahydrocannabinol (Δ9-THC) have been selected for these purposes and scarcely investigated in terms of their content in bioactive compounds. Following the global relaxation in the market of industrial hemp-derived products, research in industrial hemp for pharmaceutical and nutraceutical purposes has surged. In this context, metabolomics-based approaches have proven to fulfill the aim of obtaining comprehensive information on the phytocompound profile of cannabis samples, going beyond the targeted evaluation of the major phytocannabinoids. In the present paper, an HRMS-based metabolomics study was addressed to seven distinct industrial hemp cultivars grown in four experimental fields in Northern, Southern, and Insular Italy. Since the role of minor phytocannabinoids as well as other phytocompounds was found to be critical in discriminating cannabis chemovars and in determining its biological activities, a comprehensive characterization of phytocannabinoids, flavonoids, and phenolic acids was carried out by LC-HRMS and a dedicated data processing workflow following the guidelines of the metabolomics Quality Assurance and Quality Control Consortium. A total of 54 phytocannabinoids, 134 flavonoids, and 77 phenolic acids were annotated, and their role in distinguishing hemp samples based on the geographical field location and cultivar was evaluated by ANOVA-simultaneous component analysis. Finally, a low-level fused model demonstrated the key role of untargeted cannabinomics extended to lesser-studied phytocompound classes for the discrimination of hemp samples

Recent trends in multi-block data analysis in chemometrics for multi-source data integration

In recent years, multi-modal measurements of process and product properties have become widely popular. Sometimes classical chemometric methods such as principal component analysis (PCA) and partial least squares regression (PLS) are not adequate to analyze this kind of data. In recent years, several multi-block methods have emerged for this purpose; however, their use is largely limited to chemometricians, and non-experts have little experience with such methods. In order to deal with this, the present review provides a brief overview of the multi-block data analysis concept, the various tasks that can be performed with it and the advantages and disadvantages of different techniques. Moreover, basic tasks ranging from multi-block data visualization to advanced innovative applications such as calibration transfer will be briefly highlighted. Finally, a summary of software resources available for multi-block data analysis is provided

Circulating Mitochondrial-Derived Vesicles, Inflammatory Biomarkers and Amino Acids in Older Adults With Physical Frailty and Sarcopenia: A Preliminary BIOSPHERE Multi-Marker Study Using Sequential and Orthogonalized Covariance Selection \u2013 Linear Discriminant Analysis

Physical frailty and sarcopenia (PF&S) is a prototypical geriatric condition characterized by reduced physical function and low muscle mass. The multifaceted pathophysiology of this condition recapitulates all hallmarks of aging making the identification of specific biomarkers challenging. In the present study, we explored the relationship among three processes that are thought to be involved in PF&S (i.e., systemic inflammation, amino acid dysmetabolism, and mitochondrial dysfunction). We took advantage of the well-characterized cohort of older adults recruited in the \u201cBIOmarkers associated with Sarcopenia and Physical frailty in EldeRly pErsons\u201d (BIOSPHERE) study to preliminarily combine in a multi-platform analytical approach inflammatory biomolecules, amino acids and derivatives, and mitochondrial-derived vesicle (MDV) cargo molecules to evaluate their performance as possible biomarkers for PF&S. Eleven older adults aged 70 years and older with PF&S and 10 non-sarcopenic non-frail controls were included in the analysis based on the availability of the three categories of biomolecules. A sequential and orthogonalized covariance selection\u2014linear discriminant analysis (SO-CovSel\u2013LDA) approach was used for biomarkers selection. Of the 75 analytes assayed, 16 had concentrations below the detection limit. Within the remaining 59 biomolecules, So-CovSel\u2013LDA selected a set comprising two amino acids (phosphoethanolamine and tryptophan), two cytokines (interleukin 1 receptor antagonist and macrophage inflammatory protein 1\u3b2), and MDV-derived nicotinamide adenine dinucleotide reduced form:ubiquinone oxidoreductase subunit S3 as the best predictors for discriminating older people with and without PF&S. The evaluation of these biomarkers in larger cohorts and their changes over time or in response to interventions may unveil specific pathogenetic pathways of PF&S and identify new biological targets for drug development

Amino acid profiles in older adults with frailty. Secondary analysis from MetaboFrail and BIOSPHERE studies

An altered amino acid metabolism has been described in frail older adults which may contribute to muscle loss and functional decline associated with frailty. In the present investigation, we compared circulating amino acid profiles of older adults with physical frailty and sarcopenia (PF&S, n = 94), frail/pre-frail older adults with type 2 diabetes mellitus (F-T2DM, n = 66), and robust non-diabetic controls (n = 40). Partial least squares discriminant analysis (PLS–DA) models were built to define the amino acid signatures associated with the different frailty phenotypes. PLS–DA allowed correct classification of participants with 78.2 ± 1.9% accuracy. Older adults with F-T2DM showed an amino acid profile characterized by higher levels of 3-methylhistidine, alanine, arginine, ethanolamine, and glutamic acid. PF&S and control participants were discriminated based on serum concentrations of aminoadipic acid, aspartate, citrulline, cystine, taurine, and tryptophan. These findings suggest that different types of frailty may be characterized by distinct metabolic perturbations. Amino acid profiling may therefore serve as a valuable tool for frailty biomarker discovery