The Effect of Hybrid Carrageenan on the Thermo-rheological Properties of Gluten-Free Flour Doughs Using a Modified Kneading Protocol

Thermo-rheological behaviour of chestnut flour doughs supplemented with kappa/iota-hybrid carrageenan (HC) (up to 2.0%, flour basis (f.b.)) and sodium chloride (1.8%, f.b.) was determined at both target (C1) and final (C5) mixing peaks. For this purpose, small amplitude oscillatory shear (0.01 to 100 Hz), creep–recovery (loading of 50 Pa for 60 s, 30 °C), temperature sweeps (from 30 up to 180 °C) and heating/cooling cycles (between 30 and 60 °C) were conducted on a controlled stress rheometer. Previously, the thermal-mixing behaviour at proposed mixing temperature (50 °C) was conducted on Mixolab® apparatus. Results showed that the dough stability (from 2.2 to 5.8 min) in mixing stage and starch heat resistance to dough processing were significantly improved at proposed mixing temperature, even in the absence of HC. No statistical differences in rheological properties were observed for doughs evaluated in C1; however, those analysed in C5 were significantly modified in the presence of HC, mainly in terms of viscous behaviour (from 52.1 × 10⁶ to 39.1 × 10⁶ Pa s). Creep–recovery data sets, successfully fitted using Burgers model, revealed that the elasticity (J _r/J _max from 73.3 to 87.6%) of doughs analysed in C5 improved with HC addition. Thermal tests showed that the starch transitions were significantly promoted and stabilized with HC addition.     

Optimization of MRI Acquisition and Texture Analysis to Predict Physico-chemical Parameters of Loins by Data Mining

The main objective of this study was to configure the acquisition and analysis of low-field magnetic resonance imaging (MRI) to predict physico-chemical characteristics of Iberian loin, evaluating the use of different MRI sequences (spin echo, SE; gradient echo, GE; turbo 3D, T3D), computational texture feature methods (GLCM, NGLDM, GLRLM, GLCM + NGLDM + GLRLM), and data mining techniques (multiple linear regression, MLR; isotonic regression, IR). Moderate to very good correlation coefficients and low mean absolute error were found when applying MLR or IR on any method of computational texture features from MRI acquired with SE or GE. For T3D sequence, accurate results are only obtained by applying IR on GLCM or GLCM + NGLDM + GLRLM methods. Considering not only the accuracy of the methodology but also consumed time and required resources, the use of SE sequences for MRI acquisition, GLCM method for MRI texture analysis, and MLR could be indicated for prediction physico-chemical characteristics of loin.     

Fast Fluorescence Spectroscopy Methodology to Monitor the Evolution of Extra Virgin Olive Oils Under Illumination

Detailed regions of excitation and emission wavelengths for extra virgin olive oil samples have been extracted from three dimensional front-face fluorescence spectra. Information was applied to establish a right-angle fluorescence procedure. A right-angle device was assembled and tested with simultaneous excitation from 200 to 400 nm and registration of the fluorescence signal emitted from 400 to 850 nm. A principal component analysis was performed on the signal ranging from 400 to 550 nm from spectra of olive oils officially categorized as extra virgin in order to model the expected variability of compounds related to oxidative processes. Such model was useful to monitor the spectral evolution of extra virgin olive oil samples acquired at retail markets, which were exposed to indirect light during 2 months, through the analysis of the effect on their scores. Three relevant peaks characterized such evolution, with local maxima at around 434 to 437, 464 to 469 and 510 to 518 nm. Polynomial relationship was found between the evolution of those peaks and that of the chlorophyll, at around 670 to 673 nm, with R ² values of 0.98 and 0.99.     

Screening of Over 600 Pesticides,Veterinary Drugs,Food-Packaging Contaminants,Mycotoxins, and Other Chemicals in Food by Ultra-High Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry (UHPLC-QTOFMS)

In this article, an accurate mass multiresidue screening method has been developed for the determination of over 630 multiclass food contaminants in different matrices using ultra-high performance liquid chromatography/(quadrupole)-time-of-flight mass spectrometry. The compounds included in the study were 426 pesticides, 117 veterinary drugs, 42 food-packaging contaminants, 21 mycotoxins, 10 perfluorinated compounds, 9 nitrosamines, and 5 sweeteners. The separation was carried out by liquid chromatography using a C₁₈ column (50 mm × 2.1 mm, 1.8 μm particle size). The identification of the targeted species was accomplished using accurate masses of the targeted ions (protonated or deprotonated molecule) along with retention time data and characteristic fragment ion for reliable identification, using specific software for automated data mining and exploitation. The performance of the screening method was validated in terms of linearity, matrix effect, and limits of quantification for three representative food matrices (tomato, orange, and baby food) using a generic sample treatment based on liquid partitioning with acetonitrile (QuEChERS). The overall method performance was satisfactory with limits of quantification lower than 10 μg kg ^?1 for the 44 % of studied compounds. In some cases (ca. 10–15 % of the pesticides depending on the matrix tested, maximum residue levels were not fulfilled). In orange, 15 % of the compounds displayed LOQs above the maximum residue levels (MRLs) set for the studied pesticides, which can be partially attributed to matrix effects. Moderate signal suppression was observed in the three matrices tested in most cases, being orange the matrix which produced the highest matrix effect and baby food the lowest one.     

Design of Sonotrode Ultrasound-Assisted Extraction of Phenolic Compounds from <Emphasis Type="Italic">Psidium guajava</Emphasis> L. Leaves

Psidium guajava L. has gained a special attention as health plant due to the presence of phenolic compounds. Box-Behnken design (BBD) has been applied for the extraction of target compounds from guava leaves via sonotrode ultrasound-assisted extraction (UAE). Different extraction times (5, 30, and 55 min), ratios of ethanol/water (50, 75, and 100% (v/v)), and ultrasound (US) power (80, 240, and 400 W) were tested to find their effect on the sum of phenolic compound (SPC), flavonols and flavan-3-ols via HPLC-ESI-QqQ-MS, and antioxidant activity (DPPH and TEAC assays). The best process conditions were as follows: 40 min, 60% ethanol/water (v/v), and 200 W. Established method has been used to extract phenolic compounds in two guava leaves varieties (pyrifera and pomifera). Pyrifera var. showed greater values of the SPC via HPLC-ESI-QqQ-MS (49.7 mg/g leaf dry weight (d.w.)), flavonols (12.51 mg/g d.w.), flavan-3-ols (7.20 mg/g d.w.), individual phenolic compounds, and antioxidant activity (8970 ± 5 and 465 ± 6 μmol Trolox/g leaf d.w, respectively) than pomifera var. Conventional extraction showed lower amounts of phenolic compounds (7.81 ± 0.03 and 4.64 ± 0.01 mg/g leaf d.w. for flavonols and flavan-3ols, respectively) in comparison to the ultrasound-assisted ones.     

Fungal Honeydew Elements as Potential Indicators of the Botanical and Geographical Origin of Honeys

The present study applied indicator value analysis as a novel approach to estimate honey authenticity: microscopic indicators of honey origin were identified based on the distinct fungal content of honeys from different sources. The abundance and the IndVal index of 34 selected fungal spore types were quantified in 82 melissopalynological honey samples originating from various honeydew (Pinus, Abies) or nectar sources using multivariate statistical approaches. A dissimilarity matrix of honey samples was obtained by computing Bray-Curtis coefficients, and the distances were visualized using non-metric multidimensional scaling. K-means clustering was applied to sample coordinates to create a classification based on the frequency of selected fungal taxa. Strikingly, the resulting clusters were on a high level of agreement with the melissopalynological or geographical classification of samples. Various fungal taxa were shown to characterize groups of honey samples with a significant indicator value: floral honeys (Metschnikowia reukaufii), Pinus honeydew honeys (Capnobotrys sp., Antennatula sp.), Abies honeydew honeys from Greece (staurospore and scolecospore types) and honeydew honeys from Italy (Tripospermum spp. and Excipularia fusispora). Having revealed that the mere presence of distinct fungal taxa can indicate differences in the botanical and geographical source of honeys, the present findings encourage the confirmation of honey origin also by recording the occurrence of given honeydew elements during routine melissopalynological analysis.