Fatty acid compositional data for Greek virgin olive oils from 24 years of harvest and various regions and cultivars were evaluated using chemometric methods. Non-parametric discriminant analysis after proper transformation of the data seems to be a suitable approach to characterise the oils according to the geographical origin and may produce a scientific basis for the assignment of an ‘appellation d'origine’ trade mark.

Geographical classification of Greek virgin olive oil by non‐parametric multivariate evaluation of fatty acid composition

Multivariate characterization of wine vinegars from the south of Spain according to their metallic content.

To elucidate the weight and the influence of the origin area on the analytical and compositional variables of olive oil, an investigation was carried out. Five Italian geographical zones were chosen. They were in the neighbour of the: (i) Faculty of Agriculture of University of Perugia (UNI-PG); (ii) the Olive Growing Institute-National Research Council of Perugia (NRC-PG); (iii) the Experimental Olive Growing Institute of Spoleto (EOI-SPOL); (iv) the Agricultural Technical Institute of Pescia (ATI-PES); and (v) the Faculty of Agriculture of University of Florence (UNI-FL). From these areas, Frantoio, Leccino and Moraiolo olive variety samples were taken, which were processed by a mini oil-mill. The research results showed that the quality, typicality and shelf-life parameters and the flavour of the oils were largely influenced by the origin zone, i.e., by the climatic and pedologic factors of the production environment. The influence exerted on some oil constituent groups, as phenols, tocopherols, aromatic volatile compounds, and fatty acids, should be emphasized as these components are closely related to the quality and typicality of product. Some oil genuineness parameters were affected as well by the environmental variables.

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Quality of virgin olive oil as influenced by origin area.

Deficit schizophrenia is characterized by neurocognitive impairments and changes in the patterning of IgA/IgM responses to plasma tryptophan catabolites (TRYCATs). In the current study, supervised pattern recognition methods, including logistic regression analysis (LRA), Support Vector Machine (SVM), and Soft Independent Modeling of Class Analogy (SIMCA), were used to examine whether deficit schizophrenia is a discrete diagnostic class with respect to Consortium To Establish a Registry for Alzheimer’s disease (CERAD) and Cambridge Neuropsychological Test Automated Battery (CANTAB) tests and IgA/IgM responses to noxious (NOX) and generally more protective (PRO) TRYCATs. We recruited patients with (n = 40) and without (n = 40) deficit schizophrenia and healthy volunteers (n = 40). The combined use of TRYCAT and CERAD features strongly segregates deficit from nondeficit schizophrenia and healthy controls. Three out of the top five most important features in LRA, SVM and SIMCA agreed, namely two different NOX/PRO TRYCAT ratios and false memory recall. SIMCA shows that deficit schizophrenia is significantly separated from nondeficit schizophrenia and controls with as top 6 features IgA responses to picolinic acid, IgM responses to 3-OH-kynurenine and kynurenic acid, and impairments in Word List Memory and Verbal Fluency Tests and Mini-Mental State Examination. Nevertheless, nondeficit schizophrenia was not significantly separated from controls. The results show that schizophrenia is not a unitary disease with mere continuous differences in severity of illness between apparent subtypes. Deficit schizophrenia is a qualitatively distinct class defined by neuroimmune (autoimmune responses to TRYCATs) and neurocognitive (episodic and semantic memory) features coupled or not with clinical (negative) symptoms.

Deficit schizophrenia is a discrete diagnostic category defined by neuro-immune and neurocognitive features: results of supervised machine learning

Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person’s own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.

/pdf/precision-nomothetic-medicine-in-depression-research-a-new-3brv1pmj.pdf

Coomans Danny

Papers

SIMCA (Soft Independent Modeling of Class Analogy) Demonstrated with Characterization and Classification of Italian Olive Oil