Classification of the botanical and geographical origins of Chinese honey based on 1H NMR profile with chemometrics

Does sourdough bread provide clinically relevant health benefits?

During the last decade, scientific interest in and consumer attention to sourdough fermentation in bread making has increased. On the one hand, this technology may favorably impact product quality, including flavor and shelf-life of bakery products; on the other hand, some cereal components, especially in wheat and rye, which are known to cause adverse reactions in a small subset of the population, can be partially modified or degraded. The latter potentially reduces their harmful effects, but depends strongly on the composition of sourdough microbiota, processing conditions and the resulting acidification. Tolerability, nutritional composition, potential health effects and consumer acceptance of sourdough bread are often suggested to be superior compared to yeast-leavened bread. However, the advantages of sourdough fermentation claimed in many publications rely mostly on data from chemical and in vitro analyzes, which raises questions about the actual impact on human nutrition. This review focuses on grain components, which may cause adverse effects in humans and the effect of sourdough microbiota on their structure, quantity and biological properties. Furthermore, presumed benefits of secondary metabolites and reduction of contaminants are discussed. The benefits claimed deriving from in vitro and in vivo experiments will be evaluated across a broader spectrum in terms of clinically relevant effects on human health. Accordingly, this critical review aims to contribute to a better understanding of the extent to which sourdough bread may result in measurable health benefits in humans.

Insight into the Recent Application of Chemometrics in Quality Analysis and Characterization of Bee Honey during Processing and Storage

The application of chemometrics, a widely used science in food studies (and not only food studies) has begun to increase in importance with chemometrics being a very powerful tool in analyzing large numbers of results. In the case of honey, chemometrics is usually used for assessing honey authenticity and quality control, combined with well-established analytical methods. Research related to investigation of the quality changes in honey due to modifications after processing and storage is rare, with a visibly increasing tendency in the last decade (and concentrated on investigating novel methods to preserve the honey quality, such as ultrasound or high-pressure treatment). This review presents the evolution in the last few years in using chemometrics in analyzing honey quality during processing and storage. The advantages of using chemometrics in assessing honey quality during storage and processing are presented, together with the main characteristics of some well-known chemometric methods. Chemometrics prove to be a successful tool to differentiate honey samples based on changes of characteristics during storage and processing.

Strategies for the content determination of capsaicin and the identification of adulterated pepper powder using a hand-held near-infrared spectrometer

To achieve the goals of rapid content determination of capsaicin and adulteration detection of pepper powder. The method based on the hand-held near-infrared spectrometer combined with ensemble preprocessing was proposed. DoE-based ensemble preprocessing technique was utilized to develop the partial least squares regression models of red pepper [Capsicum annuum L. var. conoides (Mill.) Irish] powders. The performance of final models was evaluated using coefficient of determination (R²), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Model development using selective ensemble preprocessing gave the best prediction of capsaicin in Yanjiao pepper powder (R² = 0.9800, RPD = 7.090, RMSEP = 0.00689) and Tianying pepper powder (R² = 0.8935, RPD = 3.017, RMSEP = 0.06154). Moreover, the potential of grey wolf optimizer-support vector machine (GWO-SVM) to detect adulterated pepper powder was investigated. The samples were composed of two authentic products and three different adulterants with different adulteration levels. The results showed that the classification accuracy of GWO-SVM model for Yanjiao peppers was over 90 %, which realized the adulteration detection of Yanjiao pepper. And GWO-SVM showed better performance in detecting adulterated Tianying pepper compared to hierarchical cluster analysis, orthogonal partial least squares discriminant analysis and random forest. In summary, the quality control strategy established in this paper can provide a solution for the adulteration detection and quality evaluation of pepper powder in a rapid and on-site way.

Multi-element Analysis of Honey from Amhara Region-Ethiopia for Quality, Bioindicator of Environmental Pollution, and Geographical Origin Discrimination

Honey is a widely utilized sweetener containing mainly sugars with many other minor ingredients such as metallic elements. The analysis aimed to develop a chemometric model for tracing the geographical origin, evaluating nutritional quality, assessing pollution effect, and searching for marker metals for the region's honey. Forty-seven honey samples were collected directly from the apiarists at seven administrative zones. The contents of 14 metals were analyzed using inductively coupled plasma optical emission spectrometry after standard sample digestion. The findings showed us the major elements ranged from 24.8 to 1996 mg/kg of the honey sample with K > Ca > Na > Mg. The minimum and maximum values for the trace metals were 2.35 mg/kg and 163 mg/kg, respectively, in the order of Fe > Cr > Zn > Ni > Mn > Cu > Co. From this data, the region's honey has its own contribution as a source of major and trace elements. Furthermore, mean values for the toxic heavy metals were 0.57 to 1.85 for Pb, 1.03 to 1.21 for Cd, and 2.85 to 6.21 for As in mg/kg. Thus, the pollution level in the environment seems to be at an alarming rate. Using principal components analysis (PCA), the first four principal components explained 80.16% of the total variation. The region's honey was best classified into five major clusters using linear discriminant analysis (LDA) with an average discrimination power of 89.91%. The LDA sorting model was verified by the cross-validation method. The verification revealed that the model has 92.11% recognition power and 93.33% prediction ability.

Characterization, Classification and Authentication of Spanish Blossom and Honeydew Honeys by Non-Targeted HPLC-UV and Off-Line SPE HPLC-UV Polyphenolic Fingerprinting Strategies

Honey is a highly consumed natural product produced by bees which is susceptible to fraudulent practices, some of them regarding its botanical origin. Two HPLC-UV non-targeted fingerprinting approaches were evaluated in this work to address honey characterization, classification, and authentication based on honey botanical variety. The first method used no sample treatment and a universal reversed-phase chromatographic separation. On the contrary, the second method was based on an off-line SPE preconcentration method, optimized for the isolation and extraction of polyphenolic compounds, and a reversed-phase chromatographic separation optimized for polyphenols as well. For the off-line SPE method, the use of HLB (3 mL, 60 mg) cartridges, and 6 mL of methanol as eluent, allowed to achieve acceptable recoveries for the selected polyphenols. The obtained HPLC-UV fingerprints were subjected to an exploratory principal component analysis (PCA) and a classificatory partial least squares-discriminant analysis (PLS-DA) to evaluate their viability as sample chemical descriptors for authentication purposes. Both HPLC-UV fingerprints resulted to be appropriate to discriminate between blossom honeys and honeydew honeys. However, a superior performance was accomplished with off-line SPE HPLC-UV polyphenolic fingerprints, being able to differentiate among the different blossom honey samples under the study (orange/lemon blossom, rosemary, thyme, eucalyptus, and heather). In general, this work demonstrated the feasibility of HPLC-UV fingerprints, especially those obtained after off-line SPE polyphenolic isolation and extraction, to be employed as honey chemical descriptors to address the characterization and classification of honey samples according to their botanical origin.

Bioactive Compounds in Garlic (Allium sativum) and Black Garlic as Antigout Agents, Using Computer Simulation

Uric acid, which causes gout, is the end product of purine catabolism, synthesized by xanthine oxidase, guanine deaminase, adenine deaminase, purine nucleoside phosphorylase, and 5-nucleotidase II. Garlic contains bioactive compounds that have potential as antigout agents. Garlic fermentation to black garlic changes its components, which may affect its beneficial potential. This study aimed to select types of garlic (Indonesian garlic) and imported garlic, and to predict the interaction between their compounds and five target proteins through an in silico approach and a multivariate analysis, namely partial least squares-discriminant analysis (PLS-DA), to determine their different constituents. The target proteins were collected from open-access databases, and the compounds were identified using mass spectrometry data. The PLS-DA score plot succeeded in classifying the samples into three classes, with each class having a discriminatory compound. Based on the in silico studies, we predicted the best binding score of the five target proteins with seven important compounds: alliin, N-acetyl-S-allyl-L-cysteine, ajoene, pyridoxal, pyridoxamine, 4-guanidinobutyric acid, and D-glucosamine. These were mostly found in black garlic, with no different concentrations in the local and imported samples. Through this approach, we concluded that black garlic is a better candidate for antigout treatments, as several compounds were found to have good binding to the target proteins.

Physico-Chemical Properties and Principal Component Analysis of Biobased Thermosets Developed with Different Batches of Industrial Humins

Humins have already shown their potential as thermosetting resins to produce crosslinked networks and composites, with a large variety of properties depending on the used macromolecular approach. Our group has shown that a very interesting class of materials with tunable flexibility can be made by humins co-polymerization with glycerol diglycidyl ether (GDE). To create a clearer picture on structure-reactivity-properties-application interdependent relationship, a principal component analysis (PCA) was applied on several humins batches. The PCA allowed to obtain a clear discrimination between the humins/GDE resins samples in 3 groups which correlate very well with the results of copolymerization reactivity (DSC) and thermosets properties: crosslink density, thermal stability, tan δ, Shore D hardness values, etc.

NMR-based foodomics of common tubers and roots

Common roots and tubers such as arracacha, Asterix potato, cassava, potato, sweet potato, taro, and yam are consumed by millions of people. These foods are an integral part of the diet in developing countries and are nutritionally important as energy reserves due to their carbohydrate content. Although many studies have been performed on these foods, comparative chemical profiles have been still poorly evaluated. In this work, we applied nuclear magnetic resonance (NMR) analysis associated with chemometrics to evaluate the chemical composition of extracts obtained in deuterated water from roots and tubers that are commercially consumed in Brazil and the rest of the world. From the 31 metabolites characterized in the extracts, 22 were quantified. Multivariate analyses showed 8 metabolites which were primary responsible for the distinction between samples, including choline, γ-aminobutyrate (GABA), glutamine, asparagine, isoleucine, fructose, glucose, and sucrose. Thus, our work shows important information on the chemical composition in addition to the mere carbohydrate content of these food matrices. This knowledge can provide information about food safety and beneficial nutritional values of the studied tubers and roots, which can be useful to consumers and the food industry.

ICP–MS Analysis of Multi-Elemental Profile of Greek Wines and Their Classification According to Variety, Area and Year of Production

Major, minor and trace elements in wines from Greece were determined by inductively coupled plasma–mass spectrometry (ICP–MS). The concentrations of 44 elements (Na, Mg, P, K, Ca, Cu, Co, Cr, Zn, Sn, Fe, Mn, Li, Be, B, V, Sr, Ba, Al, Ag, Ni, As, Sn, Hg, Pb, Sb, Cd, Ti, Ga, Zr, Nb, Pd, Te, La, Sm, Ho, Tm, Yb, W, Os, Au, Tl, Th, U) in 90 white and red wines from six different regions in Greece for two consecutive vinification years, 2017 and 2018, were determined. Results for the elements aforementioned were evaluated by multivariate statistical methods, such as discriminant analysis and cluster analysis, and the wines were discriminated according to wine variety and geographical origin. Due to the specific choice of the analytes for multivariate statistical investigation, a prediction rate by cross-validation of 98% could be achieved. The aim of this study was not only to reveal specific relationships between the wine samples or between the chemical variables in order to classify the wines from different regions and varieties according to their elemental profile (wine authentication), but also to observe the annual fluctuation in the mineral content of the studied wine samples.