Physicochemical characterization of Lavandula spp. honey with FT-Raman spectroscopy

First Chemical Profile Analysis of Acacia Pods

This study intended to evaluate the potential industrial applications of various Acacia species (Acacia melanoxylon, Acacia longifolia, Acacia cyclops, Acacia retinodes, Acacia pycnantha, Acacia mearnsii, and Acacia dealbata) by examining their chemical composition, antioxidant, and antimicrobial properties. Using high-resolution mass spectrometry, a comprehensive analysis successfully identified targeted compounds, including flavonoids (flavonols/flavones) and phenolic acids, such as 4-hydroxybenzoic acid, p-coumaric acid, and ellagic acid. Additionally, p-coumaric acid was specifically identified and quantified within the hydroxycinnamic aldehydes. This comprehensive characterization provides valuable insights into the chemical profiles of the studied species. Among the studied species, A. pycnantha exhibited a higher concentration of total phenolic compounds, including catechin, myricetin, quercetin, and coniferaldehyde. Furthermore, A. pycnantha displayed notable antibacterial activity against K. pneumoniae, E. coli, S. Typhimurium, and B. cereus. The identified compounds in Acacia pods and their shown antibacterial activities exhibit promising potential for future applications. Moreover, vibrational spectroscopy was a reliable method for distinguishing between species. These significant findings enhance our understanding of Acacia species and their potential for various industrial applications.

The role of 5-hydroxymethylfurfural in food and recent advances in analytical methods

The thermal processing, storage, and transportation of foodstuffs (e.g., fruit juices, coffee, honey, and vinegar) generate 5-hydroxymethylfurfural (HMF). The food industry uses this compound as a quality marker, thus increasing the demand for fast and reliable analytical methods for its determination. This review focuses on the formation of HMF in food, its desirable and toxic effects, and recent advances in analytical methods for its determination in foodstuffs. The advantages and limitations of these analytical approaches are discussed relative to the main analytical features.

Fertiliser Effect of Ammonia Recovered from Anaerobically Digested Orange Peel Using Gas-Permeable Membranes

The manufacture of mineral N fertilisers by the Haber–Bosch process is highly energy-consuming. The nutrient recovery technologies from wastes through low-cost processes will improve the sustainability of the agricultural systems. This work aimed to assess the suitability of the gas-permeable membrane (GPM) technology to recover N from an anaerobic digestate and test the agronomic behaviour of the ammonium sulphate solution (ASS) obtained. About 62% of the total ammonia nitrogen removed from digestate using GPM was recovered, producing an ASS with 14,889 ± 2324 mg N L−1, which was more than six-fold higher than in digestate. The ASS agronomic behaviour was evaluated by a pot experiment with triticale as a plant test for 34 days in a growth chamber. Compared with the triticale fertilised with the Hoagland solution (Hoag), the ASS provided significantly higher biomass production (+29% dry matter), N uptake (+22%), and higher N agronomic efficiency 3.80 compared with 1.81 mg DM mg−1N in Hoag, and a nitrogen fertiliser replacement value of 133%. These increases can be due to a biostimulant effect provided by the organic compounds of the ASS as assessed by the FT-Raman spectroscopy. The ASS can be considered a bio-based mineral N fertiliser with a biostimulant effect.

Identification and quantification of adulterated honey by Raman spectroscopy combined with convolutional neural network and chemometrics

In this study, Raman spectroscopy combined with convolutional neural network (CNN) and chemometrics was used to achieve the identification and quantification of honey samples adulterated with high fructose corn syrup, rice syrup, maltose syrup and blended syrup, respectively. The shallow CNNs utilized to analyze honey mixed with single-variety syrup classified samples into four categories by the adulteration concentration with more than 97% accuracy, and the general CNN model for simultaneously detecting honey adulterated with any type of syrup obtained an accuracy of 94.79%. The established CNNs had the best performance compared with several chemometric classification algorithms. In addition, partial least square regression (PLS) successfully predicted the purity of honey mixed with single syrup, while coefficients of determination and root mean square errors of prediction were greater than 0.98 and less than 3.50, respectively. Therefore, the proposed methods based on Raman spectra have important practical significance for food safety and quality control of honey products.

Simultaneous detection of glucose, triglycerides, and total cholesterol in whole blood by Fourier-Transform Raman spectroscopy

In this paper, a reagent-free simultaneous and direct detection method of three analytes in human blood based on Fourier-transform Raman (FT-Raman) spectroscopy with 1064 nm laser radiation was proposed for the first time. A total of 161 human blood samples were characterized by FT-Raman spectroscopy under the excitation laser source of 1064 nm. In order to achieve a robust regression model, the Nonlinear Iterative Partial Least Squares (NIPALS) with orthogonal signal correction (OSC) algorithm and sample set partition based on a joint x-y distance (SPXY) is used to establish multivariate calibration models. The root means square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), correlation coefficients (R²) and ratio of performance to deviation (RPD) were 0.34255 mg/dL, 0.3662 mg/dL, 0.99982 and 56.3524 for glucose, 0.33656 mg/dL, 0.75736 mg/dL, 0.99967 and 34.9169 for total cholesterol (TC), and 0.29956 mg/dL, 0.27469 mg/dL, 0.99998 and 173.5098 for triglycerides (TG), respectively. The analysis results showed that the proposed method could be able to accurately predict the concentration of glucose, TC and TG in blood. This method can instantaneous multi-component detection on whole blood.

The Use of SPME-GC-MS IR and Raman Techniques for Botanical and Geographical Authentication and Detection of Adulteration of Honey

The aim of this review is to describe the chromatographic, spectrometric, and spectroscopic techniques applied to honey for the determination of botanical and geographical origin and detection of adulteration. Based on the volatile profile of honey and using Solid Phase microextraction-Gas chromatography-Mass spectrometry (SPME-GC-MS) analytical technique, botanical and geographical characterization of honey can be successfully determined. In addition, the use of vibrational spectroscopic techniques, in particular, infrared (IR) and Raman spectroscopy, are discussed as a tool for the detection of honey adulteration and verification of its botanical and geographical origin. Manipulation of the obtained data regarding all the above-mentioned techniques was performed using chemometric analysis. This article reviews the literature between 2007 and 2020.

Botanical origin discrimination of Greek honeys: physicochemical parameters versus Raman spectroscopy

BACKGROUND

The authenticity of honey is of high importance since it affects its commercial value. The discrimination of the origin of honey is of prime importance to reinforce consumer trust. In this study, four chemometric models were developed based on the physicochemical parameters according to European and Greek legislation and one using Raman spectroscopy to discriminate Greek honey samples from three commercial monofloral botanical sources.

RESULTS

The results of physicochemical (glucose, fructose, electrical activity) parameters chemometric models showed that the percentage of correct recognition fluctuated from 92.2% to 93.8% with cross-validation 90.6-92.2%, and the placement of test set was 79.0-84.3% successful. The addition of maltose content in the previous discrimination models did not significantly improve the discrimination. The corresponding percentages of the Raman chemometric model were 95.3%, 90.6%, and 84.3%.

CONCLUSION

The five chemometric models developed presented similar and very satisfactory results. Given that the recording of Raman spectra is simple, fast, a minimal amount of sample is needed for the analysis, no solvent (environmentally friendly) is used, and no specialized personnel are required, we conclude that the chemometric model based on Raman spectroscopy is an efficient tool to discriminate the botanical origin of fir, pine, and thyme honey varieties. © 2020 Society of Chemical Industry.

Carbohydrate determination in honey samples by ion chromatography-mass spectrometry (HPAEC-MS)

Honey is a complex mixture of carbohydrates, in which the monosaccharides glucose and fructose are the most abundant compounds. Currently, more than 20 oligosaccharides have been identified in different varieties of honey normally at quite low concentration. A method was developed and validated using high-performance anion-exchange chromatography coupled to a mass spectrometry detector to investigate the composition of carbohydrates in honey samples. The method was tested for linearity range, trueness, instrumental and method detection and quantification limits, repeatability, and reproducibility. It was applied to determine seven monosaccharides, eight disaccharides, four trisaccharides, and one tetrasaccharide in various honey samples. The present work describes the composition of sugars in unifloral, multifloral, and some honeydew honey, which were produced and collected by beekeepers in the Trentino Alto-Adige region. Statistical techniques have been used to establish a relationship based on levels of carbohydrates among different Italian honey. The results emphasize that mono- and oligosaccharide profiles can be useful to discriminate different honeys according to their floral characteristics and inter-annual variability.

An optimized green preparation method for the successful application of Raman spectroscopy in honey studies

Raman spectroscopy represents an emerging technique for food authentication being a fast, reliable analytical method, requiring a minimum sample preparation step. Anyway, as in the case of any analytical techniques, there are some limitations which need to be properly assessed before applying this method in honey authentication. In this regard, the aim of this study consisted in the development of a simple working protocol, for honey sample preparation, which can simultaneously overcome the main limitations of Raman spectroscopy in honey studies, such as crystallization and fluorescence. Thus, in this work, a new green sample preparation method is proposed, discussed and its robustness is tested. It has been demonstrated that through honey dilution, in distilled water, reliable and reproductible spectra could be obtained, allowing the investigation of different types of honey. The main advantage of the method consists in the simultaneously overcoming of the most significant limitations of Raman spectroscopy employment in honey studies, such as crystallization and fluorescence, by a simple 1:1 w/v dilution of honey in distilled water.

FT-RAMAN methodology for the monitoring of honeys' spirit distillation process

Honey spirit is an alcoholic beverage produced by fermentation followed by distillation of the honey must, which has distinct organoleptic characteristics derived mostly from the raw material used. In order to accurately monitor the quality of the product throughout the distillation process (head, heart and tail stages), FT-RAMAN spectroscopy was applied. Dark honey, light honey and honey obtained following waxes' wash was used to produce honey spirit. The pH, alcoholic strength, methanol content, acetaldehyde content, ethyl acetate content and higher alcohols content were evaluated during the distillation process. The FT-RAMAN technique was used to obtain spectral information for all fractions collected during beverage production. The results suggest that the honey spirit had good quality concerning the volatile composition and methanol was not detected in any sample. FT-RAMAN is promising for the online monitoring of the distillation process in order to improve the final quality of this beverage.

Raman Spectroscopy and Chemometric Modeling to Predict Physical-Chemical Honey Properties from Campeche, Mexico

In this work, 10 chemometric models based on Raman spectroscopy were constructed to predict the physicochemical properties of honey produced in the state of Campeche, Mexico. The properties of honey studied were pH, moisture, total soluble solids (TSS), free acidity, lactonic acidity, total acidity, electrical conductivity, Redox potential, hydroxymethylfurfural (HMF), and ash content. These proprieties were obtained according to the methods described by the Association of Official Analytical Chemists, Codex Alimentarius, and the International Honey Commission. For the construction of the chemometric models, 189 honey samples were collected and analyzed in triplicate using Raman spectroscopy to generate the matrix data [X], which were correlated with each of the physicochemical properties [Y]. The predictive capacity of each model was determined by cross validation and external validation, using the statistical parameters: standard error of calibration (SEC), standard error of prediction (SEP), coefficient of determination of cross-validation (R2cal), coefficient of determination for external validation (R2val), and Student's t-test. The statistical results indicated that the chemometric models satisfactorily predict the humidity, TSS, free acidity, lactonic acidity, total acidity, and Redox potential. However, the models for electric conductivity and pH presented an acceptable prediction capacity but not adequate to supply the conventional processes, while the models for predicting ash content and HMF were not satisfactory. The developed models represent a low-cost tool to analyze the quality of honey, and contribute significantly to increasing the honey distribution and subsequently the economy of the region.

Chemical profiling and multivariate data fusion methods for the identification of the botanical origin of honey

The characterization of 72 Italian honey samples from 8 botanical varieties was carried out by a comprehensive approach exploiting data fusion of IR, NIR and Raman spectroscopies, Proton Transfer Reaction - Time of Flight - Mass Spectrometry (PTR-MS) and electronic nose. High-, mid- and low-level data fusion approaches were tested to verify if the combination of several analytical sources can improve the classification ability of honeys from different botanical origins. Classification was performed on the fused data by Partial Least Squares - Discriminant Analysis; a strict validation protocol was used to estimate the predictive performances of the models. The best results were obtained with high-level data fusion combining Raman and NIR spectroscopy and PTR-MS, with classification performances better than those obtained on single analytical sources (accuracy of 99% and 100% on test and training samples respectively). The combination of just three analytical sources assures a limited time of analysis.