Prediction of Inverted Cane Sugar Adulteration of Honey by Fourier Transform Infrared Spectroscopy

Comprehensive Elemental Profiling of Romanian Honey: Exploring Regional Variance, Honey Types, and Analyzed Metals for Sustainable Apicultural and Environmental Practices

We investigated the mineral concentrations of 61 honey samples from eight Romanian regions, employing advanced techniques to assess 30 chemical elements. Potassium emerged as the dominant element, showcasing significant variations across geographical locations. Essential minerals like calcium, magnesium, sodium, and manganese maintained consistent levels, while zinc, copper, and chromium were present in smaller proportions. Critically, lead and cadmium levels exceeded established safety limits in some samples, suggesting potential environmental contamination. Additionally, elevated levels of lithium, strontium, nickel, and aluminum were detected, hinting at possible atmospheric pollution. These findings highlight the importance of regional analysis, as mineral content varied significantly between locations. Furthermore, correlation analysis revealed interdependencies among elements, suggesting shared environmental influences. Advanced statistical techniques like hierarchical clustering and principal component analysis effectively captured the impact of geographical origin on honey composition. These insights contribute valuable information for future efforts in honey quality control, traceability systems, and regulatory measures. By providing valuable insights into environmental influences on honey composition, this study informs future research endeavors and paves the way for the development of robust regulatory measures to ensure honey safety for consumers.

A rapid spectroscopic method for the identification of the filamentous fungi isolated from Turkish traditional mold-ripened cheeses

Fourier transform infrared spectroscopy (FTIR) is an alternative microbial identification technique due to its faster analysis times and lower cost compared to molecular methods. In this study, forty-three fungal strains isolated from different Turkish traditional mold-ripened cheeses representing nine different Penicillium species (P. roqueforti, P. corylophilum, P. before, P. crustosum, P. spinulosum, P. rubens, P. brevicompactum, P. paneum, and P. solitum) were analyzed by using FTIR HTS-XT (High Throughput Screening Extension) method in the 4000-400 cm-1 wavenumber range. The spectra of the isolates were evaluated, and the chemical structures corresponding to the fungus-specific spectral regions were determined as fatty acids (3600-2800 cm-1), amide I and amide II of proteins and peptides (1740-1500 cm-1), polysaccharides (1200-900 cm-1) and carbohydrates (900-600 cm-1). The isolates were grouped according to the hierarchical clustering analysis (HCA) by applying chemometrics combined with FTIR spectroscopy. Results showed that FTIR spectroscopy has a high capability for rapid determination of cheese fungi based on their FTIR spectra.

Application of Fourier transform infrared (FT-IR) spectroscopy and multivariate analysis for detection of adulteration in honey markets in Ethiopia

Honey is a highly susceptible food item to adulteration in national and international trade. Spectrum screening by FTIR coupled with multivariate analysis was investigated as an alternate analytical technique for honey adulterations and authentication. This technique was evaluated using pure honey samples that were blended at a ratio of 0-50% with commonly known adulterant materials and honey samples that were readily available for purchase in the Addis Ababa markets channel. Holeta Bee Research's bee farm pure honey, which is authentic honey, is employed as the control in this experiment. In the region, 4000-400 cm-1, spectral data of honey samples and five adulterant materials were recorded. The combination of spectra measurement with multivariate analyses resulted in the visualization of honey grouping and classification based on their functional group. The bands at 1800-650 cm-1 spectral region were selected for successful discrimination of clusters. Based on spectral differences, cluster analysis (CA) is also capable of grouping and separating pure from contaminated honey. Principle component analysis was able to visualize the differentiation of deliberately adulterated honey and commercially available from authentic ones. According to the results of our investigation, using FTIR analysis methods along with multivariate statistical analysis of the data could be considered useful fingerprinting procedures for identifying samples of pure and adulterated honey.

Drug-Delivery Silver Nanoparticles: A New Perspective for Phenindione as an Anticoagulant

Anticoagulants prevent the blood from developing the coagulation process, which is the primary cause of death in thromboembolic illnesses. Phenindione (PID) is a well-known anticoagulant that is rarely employed because it totally prevents coagulation, which can be a life-threatening complication. The goal of the current study is to synthesize drug-loaded Ag NPs to slow down the coagulation process. Methods: A rapid synthesis and stabilization of silver nanoparticles as drug-delivery systems for phenindione (PID) were applied for the first time. Results: Several methods are used to determine the size of the resulting Ag NPs. Additionally, the drug-release capabilities of Ag NPs were established. Density functional theory (DFT) calculations were performed for the first time to indicate the nature of the interaction between PID and nanostructures. DFT findings supported that galactose-loaded nanostructure could be a proper delivery system for phenindione. The drug-loaded Ag NPs were characterized in vitro for their antimicrobial, cytotoxic, and anticoagulant activities, and ex vivo for spasmolytic activity. The obtained data confirmed the drug-release experiments. Drug-loaded Ag NPs showed that prothrombin time (PT, sec) and activated partial thromboplastin time (APTT, sec) are approximately 1.5 times longer than the normal values, while PID itself stopped coagulation at all. This can make the PID-loaded Ag NPs better therapeutic anticoagulants. PID was compared to PID-loaded Ag NPs in antimicrobial, spasmolytic activity, and cytotoxicity. All the experiments confirmed the drug-release results.

Drug-Loaded Silver Nanoparticles-A Tool for Delivery of a Mebeverine Precursor in Inflammatory Bowel Diseases Treatment

Chronic, multifactorial illnesses of the gastrointestinal tract include inflammatory bowel diseases. One of the greatest methods for regulated medicine administration in a particular region of inflammation is the nanoparticle system. Silver nanoparticles (Ag NPs) have been utilized as drug delivery systems in the pharmaceutical industry. The goal of the current study is to synthesize drug-loaded Ag NPs using a previously described 3-methyl-1-phenylbutan-2-amine, as a mebeverine precursor (MP). Methods: A green, galactose-assisted method for the rapid synthesis and stabilization of Ag NPs as a drug-delivery system is presented. Galactose was used as a reducing and capping agent forming a thin layer encasing the nanoparticles. Results: The structure, size distribution, zeta potential, surface charge, and the role of the capping agent of drug-loaded Ag NPs were discussed. The drug release of the MP-loaded Ag NPs was also investigated. The Ag NPs indicated a very good drug release between 80 and 85%. Based on the preliminary results, Ag NPs might be a promising medication delivery system for MP and a useful treatment option for inflammatory bowel disease. Therefore, future research into the potential medical applications of the produced Ag NPs is necessary.

Characterizing Spray-Dried Powders through NIR Spectroscopy: Effect of Two Preparation Strategies for Calibration Samples and Comparison of Two Types of NIR Spectrometers

Emerging portable near infrared (NIR) spectroscopic approaches coupled with data analysis and chemometric techniques provide opportunities for the rapid characterization of spray-dried products and process optimization. This study aimed to enhance the understanding of applying NIR spectroscopy in spray-dried samples by comparing two sample preparation strategies and two spectrometers. Two sets of whey protein-maltodextrin matrixes, one with a protein content gradient and one with a consistent protein content, were spray-dried, and the effect of the two preparation strategies on NIR calibration model development was studied. Secondly, a portable NIR spectrometer (PEAK) was compared with a benchtop NIR spectrometer (CARY) for the moisture analysis of prepared samples. When validating models with the samples with focused protein contents, the best PLS protein models established from the two sample sets had similar performances. When comparing two spectrometers, although CARY outperformed PEAK, PEAK still demonstrated reliable performance for moisture analysis, indicating that it is capable as an inline sensor.

The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools

The newly developed prediction models, having the aim to classify Romanian honey samples by associating ATR-FTIR spectral data and the statistical method, PLS-DA, led to reliable differentiations among the samples, in terms of botanical and geographical origin and harvesting year. Based on this approach, 105 out of 109 honey samples were correctly attributed, leading to true positive rates of 95% and 97% accuracy for the harvesting differentiation model. For the botanical origin classification, 83% of the investigated samples were correctly predicted, when four honey varieties were simultaneously discriminated. The geographical assessment was achieved in a percentage of 91% for the Transylvanian samples and 85% of those produced in other regions, with overall accuracy of 88% in the cross-validation procedure. The signals, based on which the best classification models were achieved, allowed the identification of the most significant compounds for each performed discrimination.

A new honey adulteration detection approach using hyperspectral imaging and machine learning

This paper develops a new approach to fraud detection in honey. Specifically, we examine adulterating honey with sugar and use hyperspectral imaging and machine learning techniques to detect adulteration. The main contributions of this paper are introducing a new feature smoothing technique to conform to the classification model used to detect the adulterated samples and the perpetration of an adulterated honey data set using hyperspectral imaging, which has been made available online for the first time. Above $$95\%$$ 95 % accuracy was achieved for binary adulteration detection and multi-class classification between different adulterant concentrations. The system developed in this paper can be used to prevent honey fraud as a reliable, low cost, data-driven solution.

Classification of Unifloral Honeys from SARDINIA (Italy) by ATR-FTIR Spectroscopy and Random Forest

Nowadays, the mislabeling of honey floral origin is a very common fraudulent practice. The scientific community is intensifying its efforts to provide the bodies responsible for controlling the authenticity of honey with fast and reliable analytical protocols. In this study, the classification of various monofloral honeys from Sardinia, Italy, was attempted by means of ATR-FTIR spectroscopy and random forest. Four different floral origins were considered: strawberry-tree (Arbutus Unedo L.), asphodel (Asphodelus microcarpus), thistle (Galactites tormentosa), and eucalyptus (Eucalyptus calmadulensis). Training a random forest on the infrared spectra allowed achieving an average accuracy of 87% in a cross-validation setting. The identification of the significant wavenumbers revealed the important role played by the region 1540-1175 cm-1 and, to a lesser extent, the region 1700-1600 cm-1. The contribution of the phenolic fraction was identified as the main responsible for this observation.

Detection of Orange Essential Oil, Isopropyl Myristate, and Benzyl Alcohol in Lemon Essential Oil by FTIR Spectroscopy Combined with Chemometrics

Essential oils are high-valued natural extracts that are involved in industries such as food, cosmetics, and pharmaceutics. The lemon essential oil (LEO) has high economic importance in the food and beverage industry because of its health-beneficial characteristics and desired flavor properties. LEO, similar to other natural extracts, is prone to being adulterated through economic motivations. Adulteration causes unfair competition between vendors, disruptions in national economies, and crucial risks for consumers worldwide. There is a need for cost-effective, rapid, reliable, robust, and eco-friendly analytical techniques to detect adulterants in essential oils. The current research developed chemometric models for the quantification of three adulterants (orange essential oil, benzyl alcohol, and isopropyl myristate) in cold-pressed LEOs by using hierarchical cluster analysis (HCA), principal component regression (PCR), and partial least squares regression (PLSR) based on FTIR spectra. The cold-pressed LEO was successfully distinguished from adulterants by robust HCA. PLSR and PCR showed high accuracy with high R² values (0.99-1) and low standard error of cross-validation (SECV) values (0.58 and 5.21) for cross-validation results of the raw, first derivative, and second derivative FTIR spectra. The findings showed that FTIR spectroscopy combined with multivariate analyses has a considerable capability to detect and quantify adulterants in lemon essential oil.

An indirect analytical approach based on ATR-FTIR spectroscopy for determining the FFA content in vegetable oils

In this study, we developed a novel approach for determining a free fatty acid (FFA) in vegetable oils using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. FFA was converted to carboxylate species by a reaction with phthalimide potassium salt, and the linear relationship between FFA content and ATR-FTIR peak areas at 1541–1616 cm⁻¹ (1595 cm⁻¹ as baseline) was established. Results showed that the R² values obtained during calibration and validation were more than 0.99. The calibration method concurred to within ±0.035% over the range of 0.4% to 4.0% (quantitative determination of the percentage of FFA in oils, expressed as the percentage of oleic acid). In the calibration model, the root mean square error of prediction was 0.0104, the relative error was less than 0.246% and the relative average deviation was 0.386%, respectively. These indexes demonstrated that the calibration model has great accuracy, high precision and good stability. The indirect method established using ATR-FTIR has the advantages of excellent reproducibility, high exactitude, independent of oil type, simple operation and easy cleaning of the instrument surface. The slope of the verification equation between FFA prediction values and American Oil Chemists' Society's (AOCS) titration method was close to 1, R² value was more than 0.99. These indicators suggested that the proposed method and the AOCS method have a good correlation through AOCS titration and ATR–FTIR spectroscopy to determine validation samples parallel. In addition, for comparison, when the AOCS titration and ATR-FTIR spectroscopy methods were used for sample validation, the results indicated that the latter method is more reproducible, highly sensitive and has strong anti-disturbance. Therefore, the ATR-FTIR technique can be applied as a simple, highly sensitive, convenient and timely method for the analysis of FFAs in oils.

In this study, we developed a novel approach for determining a free fatty acid (FFA) in vegetable oils using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy.

Detection of lard in butter using Raman spectroscopy combined with chemometrics

There is a contentious need for robust and rapid methodologies for maintaining the authenticity of foods and food additives. The current paper presented a new Raman spectroscopy-based methodology for detection and quantification of lard in butter. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were successfully performed for the classification and discrimination of butter and lard-adulterated samples. Strong discrimination pattern was observed in the HCA analysis. Also, partial least squares regression and principal component regression (R² = 0.99) were applied for quantification of lard in butter samples. Quite favorable prediction capabilities were observed in the cross-validation of PLS and PCR analysis for the adulteration levels between 0% and 100% lard fat (w/w). Raman spectroscopy coupled chemometrics was employed effectively for quantification of lard fat in butter fat samples with easy, robust, effective, low-cost and reliable application in the quality control of butter.

Indonesian wild honey authenticity analysis using attenuated total reflectance-fourier transform infrared (ATR-FTIR) spectroscopy combined with multivariate statistical techniques

Wild honeys in Indonesia are still widely believed to be good for health with high economic value. This honey is naturally produced by Apisdorsata bee. In this study, authentication analysis by classification and discrimination of attenuated total reflectance-fourier infrared spectroscopy (ATR-FTIR) spectra was conducted on several wild honeys from various places in Indonesia (n = 186) which then compared to adulterated honey contained commercial sugars of aren (Arenga pinnata), coconut, and cane sugar at 10-50% concentration (n = 57). Combination of spectra measurement at 4,000-650 cm-1 with Chemometric technique by several multivariate analyses resulted in visualization of honey grouping, classification, and regression model that differentiate these honeys, both partial and overall. Principle component analysis multivariate analysis was able to visualize the differentiation of adulterated honey from the authentic ones. Discriminant analysis, a supervised classification technique, was used to differentiate the fake from the authentic honey among those from various origins at wave number range of 4000-800 cm-1 with performance index of 91,8, 90.32-100% sensitivity, and 95. 70-100% specificity. Partial least-squares analysis was used to build a model provided quantitative results of commercial sugars content in honey allegedly added during adulteration. Authentic honeys had commercial sugars content less than 10% with R2 of aren, coconut, and cane sugar of 0.9995, 0.9980 and 0.9998, respectively, with their predictive R2 values of 0.9977, 0.9983 and 0.9946, respectively.

Development of an FTIR based chemometric model for the qualitative and quantitative evaluation of cane sugar as an added sugar adulterant in apple fruit juices

A Fourier Transform Infrared Spectroscopy based chemometric model was evaluated for the rapid identification and estimation of cane sugar as an added sugar adulterant in apple fruit juices. For all the ninety samples, spectra were acquired in the mid-infrared range (4000 cm^-1-400 cm^-1). The spectral analysis provided information regarding the distinctive variable region, which lies in the range of 1200cm^-1 to 900cm^-1, designated as fingerprint region for the carbohydrates. A specific peak in the fingerprint region was observed at 997cm^-1 in all the adulterated samples and was undetectable in pure samples. Based on different levels of cane sugar adulteration (5, 10, 15, and 20%), principal component analysis showed the clustering of samples and further helped us in compression of data by selecting wavenumbers with maximum variability based on the loading line plot. Supervised classification methods (SIMCA and LDA) were evaluated based on their classification efficiencies for a test set. Though SIMCA showed 100% classification efficiency (Raw data set), LDA was able to classify the test set with an accuracy of only 96.67% (Raw as well as Transformed data set) between pure and 5% adulterated samples. For the quantitative estimation, calibration models were developed using partial least square regression (PLS-R) and principal component regression method (PCR) methods. PLS-1^st derivative showed a maximum coefficient of determination (R²) with a value of 0.991 for calibration and 0.992 for prediction. The RMSECV, RMSEP, LOD and LOQ observed for PLS-1^st derivative model were 0.75% w/v, 0.61% w/v, 1.28%w/v and 3.88%w/v, respectively. The coefficient of variation as a measure of precision (repeatability) was also determined for all models, and it ranged from 0.23% to 1.83% (interday), and 0.25% to 1.43% (intraday).

Chemical Fingerprinting of Seeds of Some Salvia Species in Turkey by Using GC-MS and FTIR

Six species of Salvia seeds cultivated and grown in Cumra/Konya (Turkey) were evaluated using headspace gas chromatography mass spectroscopy (GC-MS) and Fourier transform infrared spectroscopy-attenuated total reflectance (FTIR-ATR) combined chemometrics of hierarchical cluster analysis (HCA) and principal component analysis (PCA). The major volatile compounds in the Salvia species are determined as n-hexanal (present in seven samples), sabinene (present in three samples), α-pinene (present in 13 samples), α-thujone (present in four samples), borneol (present in 11 samples), linalyl acetate (present in 10 samples), β-pinene (present in 13 samples), camphene (present in 13 samples), α-thujene (present in four samples), 2,4(10)-thujadien (present in two samples), β-myrcene (present in seven samples), limonen (present in 12 samples), 1,8-cineole (eucalyptol) (present in 13 samples) and camphor (present in nine samples). The most abundant (%) volatile compounds among all were detected as α-pinene, camphene, β-pinene and eucalyptol. For the first time, chemometrics of HCA and PCA is applied to FTIR and GC-MS data. The classification of all samples is performed on the basis of their chemical similarities and differences.

Determination of honey adulteration with beet sugar and corn syrup using infrared spectroscopy and genetic-algorithm-based multivariate calibration

BACKGROUND

Fourier transform infrared spectroscopy (FTIR) equipped with attenuated total reflectance accessory was used to determine honey adulteration. Adulterated honey samples were prepared by adding corn syrup, beet sugar and water as adulterants to the pure honey samples in various amounts. The spectra of adulterated and pure honey samples (n = 209) were recorded between 4000 and 600 cm^-1 wavenumber range.

RESULTS

Genetic-algorithm-based inverse least squares (GILS) and partial least squares (PLS) methods were used to determine honey content and amount of adulterants. Results indicated that the multivariate calibration generated with GILS could produce successful models with standard error of cross-validation in the range 0.97-2.52%, and standard error of prediction between 0.90 and 2.19% (% w/w) for all the components contained in the adulterated samples. Similar results were obtained with PLS, generating slightly larger standard error of cross-validation and standard error of prediction values.

CONCLUSION

The fact that the models were generated with several honey samples coming from various different botanical and geographical origins, quite successful results were obtained for the detection of adulterated honey samples with a simple Fourier transform infrared spectroscopy technique. Having a genetic algorithm for variable selection helped to build somewhat better models with GILS compared with PLS. © 2018 Society of Chemical Industry.

A rapid ATR-FTIR spectroscopic method for detection of sibutramine adulteration in tea and coffee based on hierarchical cluster and principal component analyses

Sibutramine may be illicitly included in herbal slimming foods and supplements marketed as "100% natural" to enhance weight loss. Considering public health and legal regulations, there is an urgent need for effective, rapid and reliable techniques to detect sibutramine in dietetic herbal foods, teas and dietary supplements. This research comprehensively explored, for the first time, detection of sibutramine in green tea, green coffee and mixed herbal tea using ATR-FTIR spectroscopic technique combined with chemometrics. Hierarchical cluster analysis and PCA principle component analysis techniques were employed in spectral range (2746-2656cm^-1) for classification and discrimination through Euclidian distance and Ward's algorithm. Unadulterated and adulterated samples were classified and discriminated with respect to their sibutramine contents with perfect accuracy without any false prediction. The results suggest that existence of the active substance could be successfully determined at the levels in the range of 0.375-12mg in totally 1.75g of green tea, green coffee and mixed herbal tea by using FTIR-ATR technique combined with chemometrics.

Analytical Methods Used in the Quality Control of Honey

Honey is a natural sweet substance produced by bees (Apis mellifera). In this work, the main parameters used in routine quality control of honey and the most commonly used analytical methods for their determination are reviewed. Honey can be adulterated with cheaper sweeteners or, indirectly, by feeding the bees with sugars. Therefore, methods for detecting and quantifying adulteration are necessary. Chromatographic techniques are widely used in honey analysis. More recently, techniques such as Raman, near-infrared, mid-infrared, and nuclear magnetic resonance spectroscopy in combination with chemometric data processing have been proposed. However, spectroscopy does not allow the determination of enzyme activities, one criteria of great importance for the honey trade. Methylglyoxal is an interesting compound for its antibacterial properties. Methods for its determination are also reviewed.

Chemical composition, characterization, and differentiation of honey botanical and geographical origins

Botanical and biographical origins of honey are an important issue in food quality and safety. This chapter focuses on use of chemical components to determine botanical and geographical origins of honey. The botanical and geographical origins of the nectar are related with the chemical composition of honey. Honey can originate from single and multiplant species. In general, the prices of honey from single plant species are much higher than those of common polyfloral honey because of consumer preferences. Single and multiple chemicals and components can well indicate the botanical and geographical origins of the honey. Marker chemicals and components include flavonoids, pollen, aroma compounds, oligosaccharides, trace elements, amino acids, and proteins. If multiple chemicals are used as markers, patterns of the chemicals are often used to detect the botanical and geographical origins of honey. Modern statistical software in combination with advanced analytical instrumentation provides high potential for the differentiation of the botanical and geographical origins of the honey.

Floral classification of honey using mid-infrared spectroscopy and surface acoustic wave based z-Nose Sensor

Fourier transform infrared spectroscopy (FTIR) and z-Nose were used as screening tools for the identification and classification of honey from different floral sources. Honey samples were scanned using microattenuated total reflectance spectroscopy in the region of 600-4000 cm(-1). Spectral data were analyzed by principal component analysis, canonical variate analysis, and artificial neural network for classification of the different honey samples from a range of floral sources. Classification accuracy near 100% was achieved for clover (South Dakota), buckwheat (Missouri), basswood (New York), wildflower (Pennsylvania), orange blossom (California), carrot (Louisiana), and alfalfa (California) honey. The same honey samples were also analyzed using a surface acoustic wave based z-Nose technology via a chromatogram and a spectral approach, corrected for time shift and baseline shifts. On the basis of the volatile components of honey, the seven different floral honeys previously mentioned were successfully discriminated using the z-Nose approach. Classification models for FTIR and z-Nose were successfully validated (near 100% correct classification) using 20 samples of unknown honey from various floral sources. The developed FTIR and z-Nose methods were able to detect the floral origin of the seven different honey samples within 2-3 min based on the developed calibrations.

Spectroscopic evaluation of the surface quality of apple

Different spectroscopic techniques based on infrared and Raman were used to evaluate the natural wax and related surface quality of apple fruit. Transmission near-infrared (NIR) spectroscopy was applied to solutions of single wax components and extracted apple wax. Fourier transform infrared (FTIR) spectroscopy was used for transmission measurements of wax films on NaCl crystals, diffuse reflectance spectroscopy (DRIFTS) was used to analyze wax powders, and FT-Raman spectroscopy was explored to examine intact wax layers on whole fruit. The natural wax layers of apple fruit from a maximum of three different cultivars (Jonagold, Jonagored, and Elshof) from three picking dates (early, commercial, and late), three controlled atmosphere storage durations (0, 4, and 8 months), and three shelf life periods (0, 1, and 2 weeks) within each storage duration were examined. Canonical discriminant analysis was carried out on the first derivative NIR and FTIR spectra to describe the information contained in the spectra. Discrimination between cultivars and between storage duration based on wax layer properties was achieved with reasonable accuracy from both of the techniques. Information contained in the spectra of apples from different picking dates and shelf life periods was not significant. Differences between cultivars and storage periods in this analysis mostly related to differences in the number of aliphatic chains (e.g., alkanes and esters) and the presence of alpha-farnesene. No satisfactory results were obtained by means of Raman spectroscopy and DRIFTS.