Sugars' quantifications using a potentiometric electronic tongue with cross-selective sensors: Influence of an ionic background

Glucose, fructose and sucrose are sugars with known physiological e ects, and their consumption has impact on the human health, also having an important e ect on food sensory attributes. The analytical methods routinely used for identification and quantification of sugars in foods, like liquid chromatography and visible spectrophotometry have several disadvantages, like longer analysis times, high consumption of chemicals and the need for pretreatments of samples. To overcome these drawbacks, in this work, a potentiometric electronic tongue built with two identical multi-sensor systems of 20 cross-selectivity polymeric sensors, coupled with multivariate calibration with feature selection (a simulated annealing algorithm) was applied to quantify glucose, fructose and sucrose, and the total content of sugars as well. Standard solutions of ternary mixtures of the three sugars were used for multivariate calibration purposes, according to an orthogonal experimental design (multilevel fractional factorial design) with or without ionic background (KCl solution). The quantitative models’ predictive performance was evaluated by cross-validation with K-folds (internal validation) using selected data for training (selected with the K-means algorithm) and by external validation using test data. Overall, satisfactory predictive quantifications were achieved for all sugars and total sugar content based on subsets comprising 16 or 17 sensors. The test data allowed us to compare models’ predictions values and the respective sugar experimental values, showing slopes varying between 0.95 and 1.03, intercept values statistically equal to zero (p-value 0.05) and determination coe cients equal to or greater than 0.986. No significant di erences were found between the predictive performances for the quantification of sugars using synthetic solutions with or without KCl (1 mol L1), although the adjustment of the ionic background allowed a better homogenization of the solution’s matrix and probably contributed to an enhanced confidence in the analytical work across all of the calibration working range.This research work was funded by strategic project CIMO–PEst-OE/AGR/UI0690/2014 and Associate Laboratory LSRE-LCM–UID/EQU/50020/2019, financially supported by the FEDER—Fundo Europeu de Desenvolvimento Regional through COMPETE2020—Programa Operacional Competitividade e Internacionalização (POCI); and by national funds through FCT—Fundação para a Ciência e a Tecnologia, Portugalinfo:eu-repo/semantics/publishedVersio

Semi‐quantitative discrimination of honey adulterated with cane sugar solution by an ETongue

This study successfully applied a potentiometric E-tongue with 20 cross-selectivity lipidic polymeric membranes in the discrimination of three semi-quantitative groups, that represented the following intervals of honey adulteration percentage with cane sugar: 0 %; [0, 10]%; [10, 20]% of adulteration. We analysed five different types of Portuguese honey; five brands of cane sugar were added to the adulterated samples; a comparative analysis was then performed. Linear discriminant analysis coupled with a tabu search algorithm for feature selection was applied to the ETongue's analytical data to select the best model. A discriminant model with 12 sensors was obtained. This model classified correctly all samples in both in internal (train data, 15 samples) and external validation (test data,10 samples). Also, multiple linear regression with tabu search was applied to verify if ETongue's data would allow quantifying the honey's adulteration level. The results showed that it was possible to obtain a quantitative model but with unsatisfactory predictive performance in the test data group (external validation), giving, in general, values below the expected concentrations. E-tongue is a real-time green, flexible and low-cost analytical tool that requires minimum sample preparation and no special technical skills, being a promising tool for everyday application.This work was carried out in the course of ‘Electronic Tongue and Nose in Food Technology’ of the Master's degree in Food Technology of Universidade Tecnológica Federal do Paraná, Campo Mourão, Brasil. This study was financed in part by Conselho Nacional de Pesquisa e Desenvolvimento Tecnológico (CNPq, 308153/2018‐9) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior ‐ Brasil (CAPES) – Finance Code 001. The Portuguese authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for the financial support through national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and UIDP/00690/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

Can Near-Infrared Spectroscopy Replace a Panel of Tasters in Sensory Analysis of Dry-Cured Bísaro Loin?

Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).[EN] This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616–0.9955) and low RMSE values (0.0400–0.1031). The prediction set’s relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations.SIThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and UIDP/00690/2020) and SusTEC (LA/P/0007/2021) and to the Laboratory of Carcass and Meat Quality of the Agriculture School of the Instituto Politécnico de Bragança “Cantinho do Alfredo”. The grants of L.V., A.L. and I.F. are due to NORTE-01-0247-FEDER-072234. The authors (A.T., L.G.D. and S.R.) are members of the Healthy Meat network, funded by CYTED (ref. 119RT0568). This study is part of a project between a research center (LTQCC-IPB) and a meat manufacturing industry (Bísaro industry—Salsicharia Tradicional, Lda) to develop and add value for animals reared in an extensive system and to create new processed meat products.This research was funded by “BisOlive: Use of olive pomace in the feeding of Bísaro swine. Evaluation of the effect on meat quality” project. NORTE-01-0247-FEDER-072234. Financial support under the CIMO project (UIDB/00690/2020)

Tailoring swelling of alginate-gelatin hydrogel microspheres by crosslinking with calcium chloride combined with transglutaminase

lginate-based hydrogels can find uses in a wide range of applications, including in the encapsulation field. This type of hydrogels is usually ionically crosslinked using calcium sources giving rise to products with limited internal crosslinking. In this work, it is hypothesized that the combination of alginate crosslinked by calcium chloride (external crosslinking; ionic mechanism) with gelatin crosslinked by transglutaminase (internal crosslinking; enzymatic induced mechanism) can be used to tailor the swelling behavior of alginate-based hydrogel microspheres. A systematic study was conducted by covering process variables such as gelatin content, TGase concentration, and CaCl2 contact time, added by statistic tools as central composite rotatable design (CCRD), principal component analysis (PCA) and multiobjective optimization, to map their effect on the resulting water content after production (expressed as swelling ratio), and swelling properties at pH 3 and 7. Among the studied variables, particle's swelling was mostly affected by the gelatin content and transglutaminase concentration.This work was financially supported by Associate Laboratory LSRELCM (UID/EQU/50020/2019) funded by national funds through FCT/ MCTES (PIDDAC), and Foundation for Science and Technology (FCT, Portugal) and by CIMO (UID/AGR/00690/2019) trough FEDER under Program PT2020. The authors are grateful to CAPES, CNPq and Fundação Araucária for the support and also to Ajinomoto Foods Europe S.A.S. (France) for kindly provide the transglutaminase sample used in this work. I.P. Fernandes thanks the national funding by FCT, P.I., through the institutional scientific employment program-contract for her contract.info:eu-repo/semantics/publishedVersio

Heat and pH stable curcumin-based hydrophylic colorants obtained by the solid dispersion technology assisted by spray-drying

Natural food colorants are on demand due to food safety concerns related with some synthetic counterparts. Health-friendly alternatives can be available from plant sources, which include curcumin extracted from Curcuma longa L. However, its industrial use is difficult to achieve due to the low water affinity, pH and thermal instability, which is particularly challenging, e.g. for baked foods. In this work, the solid dispersion technique followed by spray-drying, an emergent approach in the context of colorants, was applied to curcumin using k-carrageenan, poly(vinyl alcohol) and polyvinylpyrrolidone, as the encapsulant materials. An orthogonal central composite design with dummy-variables was applied, and principal component analysis (PCA) and hierarchical cluster analysis (HCA) carried out to identify the experimental conditions leading to the most effective formulations. In general, particles with a wide range of pH and heat stability have been produced depending on the chosen encapsulant material, used formulation (curcumin, surfactant and polymer contents), and synthesis conditions (pH). Moreover, the used mathematical approach showed to be a valuable tool to support the development of tailor-made formulations directed to specific applications where pH and temperature are relevant processing parameters.This work was financially supported by Associate Laboratory LSRE-LCM (UID/EQU/50020/2019) funded by national funds through FCT/MCTES (PIDDAC), and Foundation for Science and Technology (FCT, Portugal). CIMO (UID/AGR/00690/2019) through FEDER under Program PT2020. Authors thank CAPES (Brazil), CNPq (Brazil) and Fundação Araucária (Brazil) for the support.info:eu-repo/semantics/publishedVersio

Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin?

This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616–0.9955) and low RMSE values (0.0400–0.1031). The prediction set’s relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations.This research was funded by “BisOlive: Use of olive pomace in the feeding of Bísaro swine. Evaluation of the effect on meat quality” project. NORTE-01-0247-FEDER-072234. Financial support under the CIMO project (UIDB/00690/2020).info:eu-repo/semantics/publishedVersio

Gradients methods for simultaneous optimizations: case studies for food systems

The objective of this work was to develop a software with efficient gradient methods for foods systems multiresponse optimization using the Derringer & Suich function, including a 4th order polynomial equation to remove non-differentiable points in that function. The software was tested in three food systems selected in specialized literature: 1) inactivated lipoxygenase and lipase and preserve phytase activity in barley during soaking; 2) simultaneous optimization of response in protein mixture formulation; 3)simultaneous optimization of parameters used in roasting process of corn germ to be used as an ingredient in foods. The program that was developed has show itself to be efficient and trustworthy for the optimization of multiresponse.Este trabalho teve como objetivo desenvolver um aplicativo com métodos de gradiente eficazes na otimização de sistemas alimentares com respostas múltiplas utilizando as funções de Derringer & Suich, incluindo um polinômio de 4º grau para a remoção das descontinuidades dessas funções. O aplicativo foi testado em três sistemas alimentares selecionados na literatura especializada: 1) inativação da lipoxigenase e lípase preservando-se a atividade da fitase durante o processamento hidrotérmico da cevada; 2) otimização simultânea das respostas na formulação de misturas de proteínas; 3) otimização simultânea dos parâmetros de torração de gérmen de milho visando aplicação como ingrediente em alimentos. O programa desenvolvido mostrou ser eficiente e confiável para a otimização em sistemas alimentares multirresposta

Aplicativo para otimização empregando o método simplex seqüencial

A computer program for process optimization influenced by continuous and qualitative variables was developed from the simplex method. Software was validated through case studies found in literature by predictive models with two distinct processes. The obtained results showed great concordance with values supplied by literature. The developed program is portable and friendly, and may be used in several optimization systems. Software complementation with other subroutines, as combined response optimization, may make its application more comprehensive.Um aplicativo para otimização de processos influenciados por variáveis contínuas e/ou qualitativas foi desenvolvido a partir do método simplex. O software foi validado através de casos da literatura especializada com modelos preditivos que representam dois processos distintos. Os resultados obtidos apresentaram ampla concordância com os valores fornecidos pela literatura consultada. O aplicativo desenvolvido é portável e amigável, podendo ser utilizado para a otimização de diversos sistemas. A complementação do software com outras sub-rotinas, como otimização de respostas combinadas, pode tornar a sua aplicação mais abrangente