Instrumental and chemometric methodologies to assess sensory quality of Mediterranean food

L'oli d'oliva, el vi o els fruits secs són productes típics de la regió Mediterrània que ofereixen un valor afegit gràcies als seus beneficis per a la salut i excel·lents característiques sensorials. Per aquest motiu és necessari un control de la qualitat i autenticitat d'aquests productes, que són altament susceptibles a fraus i adulteracions. Un aspecte important és l'avaluació de la qualitat sensorial, que descriu paràmetres percebuts pels sentits (gust, visió, olor i tacte) mitjançant panells validats i entrenats d'experts. Aquests panells tendeixen a ser subjectius i requereixen llargs temps d'anàlisi i alts costos. Com a conseqüència hi ha hagut un increment en el desenvolupament de tècniques d'anàlisi capaces de simular les respostes obtingudes amb el panell de tast humà. L'anomenat 'panell electrònic' ofereix respostes objectives mitjançant l'ús de tècniques multivariants que permeten establir correlacions entre els descriptors definits pels humans i els senyals obtingudes instrumentalment. Aquesta tesi pretén oferir tècniques instrumentals alternatives, ràpides i senzilles per determinar la qualitat sensorial d'aliments com l'oli d'oliva, el vi o les ametlles. Els estudis duts a terme inclouen el tractament de les respostes sensorials obtingudes mitjançant metodologies de referència (principalment panells de tast humans), l'optimització dels procediments analítics per treballar amb tècniques instrumentals i el desenvolupament d'eines quimiomètriques adequades per construir els models multivariants. També s'han desenvolupat estratègies de fusió de dades per combinar les diferents dades instrumentals que simulen els sentits humans (olor, gust i visió).El aceite de oliva, el vino o los frutos secos son productos típicos de la región Mediterránea que ofrecen un valor añadido gracias a sus beneficios para la salud y excelentes características sensoriales. Por este motivo es necesario un control de la calidad y autenticidad de estos productos, que son altamente susceptibles a fraudes y adulteraciones. Un aspecto importante es la evaluación de la calidad sensorial, que describe parámetros percibidos por los sentidos (gusto, visión, olor y tacto) mediante paneles validados y entrenados de expertos. Estos paneles tienden a ser subjetivos, requieren largos tiempos de análisis y altos costes. Como consecuencia ha habido un incremento en el desarrollo de técnicas de análisis capaces de simular las respuestas obtenidas con el panel de cata humano. El llamado 'panel electrónico' ofrece respuestas objetivas mediante el uso de técnicas multivariantes que permiten establecer correlaciones entre los descriptores definidos por los humanos y las señales obtenidas instrumentalmente. Esta tesis pretende ofrecer técnicas instrumentales alternativas, rápidas y sencillas para determinar la calidad sensorial de alimentos como el aceite de oliva, el vino o las almendras. Los estudios llevados a cabo incluyen el tratamiento de las respuestas sensoriales obtenidas mediante metodologías de referencia (principalmente paneles de cata humanos), la optimización de los procedimientos analíticos para trabajar con técnicas instrumentales y el desarrollo de herramientas quimiométricas adecuadas para construir los modelos multivariantes. También se han desarrollado estrategias de fusión de datos para combinar los diferentes datos instrumentales que simulan los sentidos humanos (olor, gusto y visión).Olive oil, wine or nuts are typical products of the Mediterranean region that offer added value thanks to its health benefits and excellent sensory characteristics. Therefore, the control the quality and authenticity of these products is necessary, mainly because they are highly susceptible to fraud and adulterations. An important aspect is the evaluation of sensory quality that describe parameters perceived by the senses (taste, sight, smell and touch) using validated and trained panels of experts. These panels tend to be subjective, requiring long-time analysis and high costs. As a result there has been an increase in the development of analytical techniques capable to simulate the responses obtained with the human taste panel. The so-called 'electronic panel' provides objective responses using multivariate techniques, which establish correlations between descriptors defined by humans and signals obtained instrumentally. This thesis aims to offer fast and simple alternative instrumental techniques to determine the sensory quality of foods such as olive oil, wine and almonds. Studies carried out include the treatment of sensory responses obtained by reference methodologies (mainly human taste panels), optimization of analytical procedures to work with instrumental techniques and the development of appropriate chemometric tools to build multivariate models. Data fusion strategies have also been studied by combining different instrumental data that simulate the human senses (smell, taste and sight)

A combination of molecular and clinical parameters provides a new strategy for high-grade serous ovarian cancer patient management

High-grade serous carcinoma (HGSC) is the most common and deadly subtype of ovarian cancer. Although most patients will initially respond to first-line treatment with a combination of surgery and platinum-based chemotherapy, up to a quarter will be resistant to treatment. We aimed to identify a new strategy to improve HGSC patient management at the time of cancer diagnosis (HGSC-1LTR). A total of 109 ready-available formalin-fixed paraffin-embedded HGSC tissues obtained at the time of HGSC diagnosis were selected for proteomic analysis. Clinical data, treatment approach and outcomes were collected for all patients. An initial discovery cohort (n = 21) were divided into chemoresistant and chemosensitive groups and evaluated using discovery mass-spectrometry (MS)-based proteomics. Proteins showing differential abundance between groups were verified in a verification cohort (n = 88) using targeted MS-based proteomics. A logistic regression model was used to select those proteins able to correctly classify patients into chemoresistant and chemosensitive. The classification performance of the protein and clinical data combinations were assessed through the generation of receiver operating characteristic (ROC) curves. Using the HGSC-1LTR strategy we have identified a molecular signature (TKT, LAMC1 and FUCO) that combined with ready available clinical data (patients' age, menopausal status, serum CA125 levels, and treatment approach) is able to predict patient response to first-line treatment with an AUC: 0.82 (95% CI 0.72-0.92). We have established a new strategy that combines molecular and clinical parameters to predict the response to first-line treatment in HGSC patients (HGSC-1LTR). This strategy can allow the identification of chemoresistance at the time of diagnosis providing the optimization of therapeutic decision making and the evaluation of alternative treatment strategies. Thus, advancing towards the improvement of patient outcome and the individualization of HGSC patients' care. The online version contains supplementary material available at 10.1186/s12967-022-03816-7

A combination of molecular and clinical parameters provides a new strategy for high-grade serous ovarian cancer patient management

Biomarker; Prediction; ProteomicsBiomarcador; Predicción; ProteómicaBiomarcador; Predicció; ProteòmicaBackground High-grade serous carcinoma (HGSC) is the most common and deadly subtype of ovarian cancer. Although most patients will initially respond to first-line treatment with a combination of surgery and platinum-based chemotherapy, up to a quarter will be resistant to treatment. We aimed to identify a new strategy to improve HGSC patient management at the time of cancer diagnosis (HGSC-1LTR). Methods A total of 109 ready-available formalin-fixed paraffin-embedded HGSC tissues obtained at the time of HGSC diagnosis were selected for proteomic analysis. Clinical data, treatment approach and outcomes were collected for all patients. An initial discovery cohort (n = 21) were divided into chemoresistant and chemosensitive groups and evaluated using discovery mass-spectrometry (MS)-based proteomics. Proteins showing differential abundance between groups were verified in a verification cohort (n = 88) using targeted MS-based proteomics. A logistic regression model was used to select those proteins able to correctly classify patients into chemoresistant and chemosensitive. The classification performance of the protein and clinical data combinations were assessed through the generation of receiver operating characteristic (ROC) curves. Results Using the HGSC-1LTR strategy we have identified a molecular signature (TKT, LAMC1 and FUCO) that combined with ready available clinical data (patients’ age, menopausal status, serum CA125 levels, and treatment approach) is able to predict patient response to first-line treatment with an AUC: 0.82 (95% CI 0.72–0.92). Conclusions We have established a new strategy that combines molecular and clinical parameters to predict the response to first-line treatment in HGSC patients (HGSC-1LTR). This strategy can allow the identification of chemoresistance at the time of diagnosis providing the optimization of therapeutic decision making and the evaluation of alternative treatment strategies. Thus, advancing towards the improvement of patient outcome and the individualization of HGSC patients’ care.This work was supported by the PhD4MD collaborative research program between the Vall d’Hebron Research Institute (VHIR) and the Centre for Genomic Regulation (CRG). It has been supported by grants from the Instituto Carlos III (PI18/01017), the Miguel Servet Program (CPII18/00027) and the Ministerio de Economía y Competitividad y Fondos FEDER (RTC-2015-3821-1 to AS and CTQ2016-80364-P to ES). This project has also received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 823839 (EPIC-XS).The CRG/UPF Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech) and it is supported by “Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya” (2017SGR595 and 2017SGR1661). We also acknowledge support of the Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme / Generalitat de Catalunya

BRCA1 mutations in high-grade serous ovarian cancer are associated with proteomic changes in DNA repair, splicing, transcription regulation and signaling

Despite recent advances in the management of BRCA1 mutated high-grade serous ovarian cancer (HGSC), the physiology of these tumors remains poorly understood. Here we provide a comprehensive molecular understanding of the signaling processes that drive HGSC pathogenesis with the addition of valuable ubiquitination profiling, and their dependency on BRCA1 mutation-state directly in patient-derived tissues. Using a multilayered proteomic approach, we show the tight coordination between the ubiquitination and phosphorylation regulatory layers and their role in key cellular processes related to BRCA1-dependent HGSC pathogenesis. In addition, we identify key bridging proteins, kinase activity, and post-translational modifications responsible for molding distinct cancer phenotypes, thus providing new opportunities for therapeutic intervention, and ultimately advance towards a more personalized patient care

Experimental and genetic evidence for the impact of CD5 and CD6 expression and variation in inflammatory bowel disease

Crohn's disease (CD) and ulcerative colitis (UC) are inflammatory bowel diseases (IBD) resulting from the interaction of multiple environmental, genetic and immunological factors. CD5 and CD6 are paralogs encoding lymphocyte co-receptors involved in fine-tuning intracellular signals delivered upon antigen-specific recognition, microbial pattern recognition and cell adhesion. While CD5 and CD6 expression and variation is known to influence some immune-mediated inflammatory disorders, their role in IBD remains unclear. To this end, Cd5- and Cd6-deficient mice were subjected to dextran sulfate sodium (DSS)-induced colitis, the most widely used experimental animal model of IBD. The two mouse lines showed opposite results regarding body weight loss and disease activity index (DAI) changes following DSS-induced colitis, thus supporting Cd5 and Cd6 expression involvement in the pathophysiology of this experimental IBD model. Furthermore, DNA samples from IBD patients of the ENEIDA registry were used to test association of CD5 (rs2241002 and rs2229177) and CD6 (rs17824933, rs11230563, and rs12360861) single nucleotide polymorphisms with susceptibility and clinical parameters of CD (n=1352) and UC (n=1013). Generalized linear regression analyses showed association of CD5 variation with CD ileal location (rs2241002CC) and requirement of biological therapies (rs2241002C-rs2229177T haplotype), and with poor UC prognosis (rs2241002T-rs2229177T haplotype). Regarding CD6, association was observed with CD ileal location (rs17824933G) and poor prognosis (rs12360861G), and with left-sided or extensive UC, and absence of ankylosing spondylitis in IBD (rs17824933G). The present experimental and genetic evidence support a role for CD5 and CD6 expression and variation in IBD's clinical manifestations and therapeutic requirements, providing insight into its pathophysiology and broadening the relevance of both immunomodulatory receptors in immune-mediated disorders

Prediction of olive oil sensory descriptors using instrumental data fusion and partial least squares (PLS) regression

DOI: 10.1016/j.talanta.2016.04.040 URL: http://www.sciencedirect.com/science/article/pii/S0039914016302818 Filiació URV: SIHeadspace-Mass Spectrometry (HS-MS), Fourier Trans form Mid-Infrared spectroscopy (FT-MIR) and UV-Visible spectrophotometry (UV–vis) instrumental responses have been combined to predict virgin olive oil sensory descriptors. 343 olive oil samples analyzed during four consecutive harvests (2010–2014) were used to build multivariate calibration models using partial least squares (PLS) regression. There reference values of the sensory attributes were provided by expert assessors from an official taste panel. The instrumental data were modeled individually and also using data fusion approaches. The use of fused data with both low- and mid-level of abstraction improved PLS predictions for all the olive oil descriptors. The best PLS models were obtained for two positive attributes (fruity and bitter) and two defective descriptors (fusty and musty), all of them using data fusion of MS and MIR spectral fingerprints. Although good predictions were not obtained for some sensory descriptors, the results are encouraging, specially considering that the legal categorization of virgin olive oils only requires the determination of fruity and defective descriptors

Data fusion methodologies for food and beverage authentication and quality assessment - A review

The ever increasing interest of consumers for safety, authenticity and quality of food commodities has driven the attention towards the analytical techniques used for analyzing these commodities. In recent years, rapid and reliable sensor, spectroscopic and chromatographic techniques have emerged that, together with multivariate and multiway chemometrics, have improved the whole control process by reducing the time of analysis and providing more informative results. In this progression of more and better information, the combination (fusion) of outputs of different instrumental techniques has emerged as a means for increasing the reliability of classification or prediction of foodstuff specifications as compared to using a single analytical technique. Although promising results have been obtained in food and beverage authentication and quality assessment, the combination of data from several techniques is not straightforward and represents an important challenge for chemometricians. This review provides a general overview of data fusion strategies that have been used in the field of food and beverage authentication and quality assessment

Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA)

Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV–visible spectrophotometry (UV–vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually. A general model was applied to discriminate high-quality non-defective olive oils (extra-virgin) and the lowest-quality olive oils considered non-edible (lampante). A specific identification of key off-flavours, such as musty, winey, fusty and rancid, was also studied. The data fusion of the three techniques improved the classification results in most of the cases. Low-level data fusion was the best strategy to discriminate musty, winey and fusty defects, using HS-MS, MIR and UV–vis, and the rancid defect using only HS-MS and MIR. The mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be the best strategy for defective vs non-defective and edible vs non-edible oil discrimination. However, the data fusion did not sufficiently improve the results obtained by a single technique (HS-MS) to classify non-defective classes. These results indicate that instrumental data fusion can be useful for the identification of sensory defects in virgin olive oils

Identification of olive oil sensory defects by multivariate analysis of mid infrared spectra

Mid-infrared (MIR) spectra (4000¿600 cm -1) of olive oils were analyzed using chemometric methods to identify the four main sensorial defects, musty, winey, fusty and rancid, previously evaluated by an expert sensory panel. Classification models were developed using partial least squares discriminant analysis (PLS-DA) to distinguish between extra-virgin olive oils (defect absent) and lower quality olive oils (defect present). The most important spectral ranges responsible for the discrimination were identified. PLS-DA models were able to discriminate between defective and high quality oils with predictive abilities around 87% for the musty defect and around 77% for winey, fusty and rancid defects. This methodology advances instrumental determination of results previously only achievable with a human test panel

BRCA1 mutations in high-grade serous ovarian cancer are associated with proteomic changes in DNA repair, splicing, transcription regulation and signaling

Despite recent advances in the management of BRCA1 mutated high-grade serous ovarian cancer (HGSC), the physiology of these tumors remains poorly understood. Here we provide a comprehensive molecular understanding of the signaling processes that drive HGSC pathogenesis with the addition of valuable ubiquitination profiling, and their dependency on BRCA1 mutation-state directly in patient-derived tissues. Using a multilayered proteomic approach, we show the tight coordination between the ubiquitination and phosphorylation regulatory layers and their role in key cellular processes related to BRCA1-dependent HGSC pathogenesis. In addition, we identify key bridging proteins, kinase activity, and post-translational modifications responsible for molding distinct cancer phenotypes, thus providing new opportunities for therapeutic intervention, and ultimately advance towards a more personalized patient care.This work was supported by the PhD4MD collaborative research program between the Vall d’Hebron Research Institute (VHIR) and the Centre for Genomic Regulation (CRG). The CRG/UPF Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech) and it is a member of the ProteoRed PRB3 consortium which is supported by grant PT17/0019 of the PE I+D+i 2013-2016 from the Instituto de Salud Carlos III (ISCIII) and ERDF. We acknowledge support from the Spanish Ministry of Science, Innovation and Universities, (CTQ2016-80364-P and “Centro de Excelencia Severo Ochoa 2013-2017”, SEV-2012-0208), and “Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya” (2017SGR595 and 2017SGR1661). This project has also received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 823839 (EPIC-XS). It has also been supported by grants from the Instituto Carlos III (PI15/00238, PI18/01017, PI21/00977), the Miguel Servet Program (CP13/00158 and CPII18/00027) and the Ministerio de Economía y Competitividad y Fondos FEDER (RTC-2015-3821-1