Qualitative and quantitative detection of honey adulterated with high-fructose corn syrup and maltose syrup by using near-infrared spectroscopy

Advances in infrared spectroscopy and chemometrics for honey analysis: a comprehensive review

Honey analysis plays a crucial role in ensuring its quality, authenticity, and compliance with regulatory standards. Traditional methods for honey analysis are often time-consuming, labor-intensive, and require complex sample preparation. Infrared spectroscopy is used in the food sector as a fast and reliable technique for the analysis of food. Multivariate analysis applied to infrared spectroscopy has proved to be effective in analyzing honey. In this paper, recently published studies using mid- and near- infrared spectroscopy for the analysis of honey will be reviewed. Honey analysis covers the following objectives: the determination of the physiochemical properties, the determination of the antioxidant activity, the detection of adulteration, the determination of 5-(hydroxymethyl) furfural (HMF) and diastase activity, and the determination of the botanical and geographical origins. A summary of the basic principles of infrared spectroscopy is presented. Different data preprocessing techniques are described. Moreover, this article emphasizes the wide application of chemometrics or multivariate analysis tools for data treatment.

Rapid Authentication of Intact Stingless Bee Honey (SBH) by Portable LED-Based Fluorescence Spectroscopy and Chemometrics

Indonesian stingless bee honey (SBH) of Geniotrigona thoracica is popular and traded at an expensive price. Brown rice syrup (RS) is frequently used as a cheap adulterant for an economically motivated adulteration (EMA) in SBH. In this study, authentic Indonesian Geniotrigona thoracica SBH of Acacia mangium (n = 100), adulterated SBH (n = 120), fake SBH (n = 100), and RS (n = 200) were prepared. In short, 2 mL of each sample was dropped directly into an innovative sample holder without any sample preparation including no dilution. Fluorescence intensity was acquired using a fluorescence spectrometer. This portable instrument is equipped with a 365 nm LED lamp as the fixed excitation source. Principal component analysis (PCA) was calculated for the smoothed spectral data. The results showed that the authentic SBH and non-SBH (adulterated SBH, fake SBH, and RS) samples could be well separated using the smoothed spectral data. The cumulative percentage variance of the first two PCs, 98.4749% and 98.4425%, was obtained for calibration and validation, respectively. The highest prediction accuracy was 99.5% and was obtained using principal component analysis-linear discriminant analysis (PCA-LDA). The best partial least square (PLS) calibration was obtained using the combined interval with R2cal = 0.898 and R2val = 0.874 for calibration and validation, respectively. In the prediction, the developed model could predict the adulteration level in the adulterated honey samples with an acceptable ratio of prediction to deviation (RPD) = 2.282, and range error ratio (RER) = 6.612.

Research advances in detection of food adulteration and application of MALDI-TOF MS: A review

Food adulteration and illegal supplementations have always been one of the major problems in the world. The threat of food adulteration to the health of consumers cannot be ignored. Food of questionable origin causes economic losses to consumers, but the potential health risks cannot be ignored. However, the traditional detection methods are time-consuming and complex. This review mainly discusses the types of adulteration and technologies used to detect adulteration. Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is also emphasized in the detection of adulteration and authenticity of origin analysis of various types of food (milk, meat, edible oil, etc.), and the future application direction and feasibility of this technology are analyzed. On this basis, MALDI-TOF MS was compared with other detection methods, highlighting the advantages of this technology in the detection of food adulteration. The future development prospect and direction of this technology are also emphasized.

Portable NIR Spectroscopy to Simultaneously Trace Honey Botanical and Geographical Origins and Detect Syrup Adulteration

Fraudulent practices concerning honey are growing fast and involve misrepresentation of origin and adulteration. Simple and feasible methods for honey authentication are needed to ascertain honey compliance and quality. Working on a robust dataset and simultaneously investigating honey traceability and adulterant detection, this study proposed a portable FTNIR fingerprinting approach combined with chemometrics. Multifloral and unifloral honey samples (n = 244) from Spain and Sardinia (Italy) were discriminated by botanical and geographical origin. Qualitative and quantitative methods were developed using linear discriminant analysis (LDA) and partial least squares (PLS) regression to detect adulterated honey with two syrups, consisting of glucose, fructose, and maltose. Botanical and geographical origins were predicted with 90% and 95% accuracy, respectively. LDA models discriminated pure and adulterated honey samples with an accuracy of over 92%, whereas PLS allows for the accurate quantification of over 10% of adulterants in unifloral and 20% in multifloral honey.

Classification of industrial tapioca starch hydrolysis products based on their Brix and dextrose equivalent values using near-infrared spectroscopy

BACKGROUND

Industrial starch hydrolysis allows the production of syrups with varying functionality depending on their Brix value and dextrose equivalent (DE). As the current methods for evaluating these products are labor-intensive and time-consuming, the objective of this study was to investigate the potential of near-infrared (NIR) spectroscopy for classifying the different tapioca starch hydrolysis products.

RESULTS

NIR spectra of samples of seven products (n = 410) were recorded in transflectance mode in the 12 000-4000 cm-1 range. Next, orthogonal partial least squares (OPLS) regression models were built to predict the Brix and DE values of the different samples. To classify the different starch hydrolysis products, support vector machines (SVM) were trained using either the raw spectra or latent variables (LVs) obtained from the OPLS models. The best classification accuracy was obtained by the SVM classifier based on the LVs from the OPLS model for DE prediction, resulting in 95% correct classification over all classes.

CONCLUSION

These results show the potential of NIR spectroscopy for classifying tapioca starch hydrolysis products with respect to their functional properties related to the Brix and DE values. © 2024 Society of Chemical Industry.

Establishment of rapid extraction and sensitive detection system of trace corn syrup DNA in honey

Honey adulteration with exogenous syrup has become a common phenomenon, and current detection techniques that require large instruments are cumbersome and time-consuming. In this study, a simple and efficient method was developed by integrating the rapid extraction of nucleic acids (REMD) and recombinase polymerase amplification (RPA), known as REMD-RPA, for the rapid screening of syrup adulteration in honey. First, a rapid extraction method was developed to rapidly extract corn syrup DNA in five minutes to meet the requirements of PCR and RPA assays. Then, the RPA method for detecting endogenous maize genes (ZssIIb) was established, which could detect 12 copies/μL of the endogenous maize gene within 30 min without cross-reacting with other plant-derived genes. This indicated that the RPA technique exhibited high sensitivity and specificity. Finally, the REMD-RPA detection platform was used to detect different concentrations of corn syrup adulteration, and 1 % adulteration could be detected within 30 min. The 22 commercially available samples were tested to validate the efficacy of this method, and the established RPA was able to detect seven adulterated samples in less than 30 min. Overall, the developed method is rapid, sensitive, and specific, providing technical support for the rapid field detection of honey adulteration and can serve as a reference for developing other field test methods.

Exploring the role of NIR spectroscopy in quantifying and verifying honey authenticity: A review

Honey, recognized for its diverse flavors and nutritional benefits, confronts challenges in maintaining authenticity and quality due to factors like adulteration and mislabelling. This review undertakes a comprehensive exploration of the utility of Near-Infrared (NIR) spectroscopy as a non-destructive analytical method for concurrently evaluating both honey quantity and authenticity. The primary purpose of this investigation is to delve into the various applications of NIR spectroscopy in honey analysis, with a specific focus on its capability to identify and quantify significant quality parameters such as sugar content, moisture levels, 5-HMF, and proline content. Results from the study underscore the effectiveness of NIR spectroscopy, especially when integrated with advanced chemometrics models. This combination not only facilitates quantification of diverse quality parameters but also enhances the classification of honey based on geographical and botanical origin. The technology emerges as a potent tool for detecting adulteration, addressing critical challenges in preserving the authenticity and quality of honey products. The impact of this critical analysis extends to shedding light on the current state, challenges, and future prospects of applying NIR spectroscopy in the honey industry. This analysis outlines the current challenges and future prospects of NIR spectroscopy in the honey industry. Emphasizing its potential to improve consumer confidence and food safety, the research has broader implications for authenticity and quality assurance in honey. Integrating NIR spectroscopy into industry practices could establish stronger quality control measures, benefiting both producers and consumers globally.

Rapid evaluation of Ziziphi Spinosae Semen and its adulterants based on the combination of FT-NIR and multivariate algorithms

Ziziphi Spinosae Semen (ZSS) is a valued seed renowned for its sedative and sleep-enhancing properties. However, the price increase has been accompanied by adulteration. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) combined with multivariate algorithms were employed to identify the adulteration and quantitatively predict the adulteration ratio. The findings suggested that the utilization of chromaticity extractor was insufficient for identification of adulteration ratio. The raw spectrum of ZMS and HAS adulterants extracted by FT-NIR was processed by SNV + CARS and 1d + SG + ICO respectively, the average accuracy of machine learning classification model was improved from 77.06 % to 97.58 %. Furthermore, the R2 values of the calibration and prediction set of the two quantitative prediction regression models of adulteration ratio are greater than 0.99, demonstrating excellent linearity and predictive accuracy. Overall, this study demonstrated that FT-NIR combined with multivariate algorithms provided a significant approach to addressing the growing issue of ZSS adulteration.

A comprehensive review of the current trends and recent advancements on the authenticity of honey

The authenticity of honey currently poses challenges to food quality control, thus requiring continuous modernization and improvement of related analytical methodologies. This review provides a comprehensively overview of honey authenticity challenges and related analytical methods. Firstly, direct and indirect methods of honey adulteration were described in detail, commenting the existing challenges in current detection methods and market supervision approaches. As an important part, the integrated metabolomic workflow involving sample processing procedures, instrumental analysis techniques, and chemometric tools in honey authenticity studies were discussed, with a focus on their advantages, disadvantages, and scopes. Among them, various improved microscale extraction methods, combined with hyphenated instrumental analysis techniques and chemometric data processing tools, have broad application potential in honey authenticity research. The future of honey authenticity determination will involve the use of simplified and portable methods, which will enable on-site rapid detection and transfer detection technologies from the laboratory to the industry.

Review of scientific instruments: Evaluation of adulteration in honey using a microwave planar resonator sensor

A microwave microstrip line resonator sensor is developed as an alternative tool for detecting adulteration in honey. A honey-filled tube is placed at the position with the maximum electric field intensity. When the honey is adulterated, its permittivity is changed, leading to a distinct resonance frequency shift and enabling detection. Compared with the existing microwave sensors, this sensor offers the advantages of low cost, compact size, and easy fabrication. Moreover, quantitative analysis of the adulteration could be achieved. Electromagnetic simulation is performed using a co-simulation with CST and MATLAB. The simulation results reveal that the resonance frequency of the resonator decreases as the added water content increases, following a quadratic polynomial relationship. In the experiments, the results demonstrate a successive decrease in the resonance frequency from the empty tube, honey-filled tube to water-filled tube cases. Furthermore, honey samples with varying water contents (up to 70%) are tested, and the resonance frequency decreases with increasing added water content, which agrees well with the simulation results. In addition, there is a quadratic relationship between the two parameters. Principal component analysis is conducted on the transmission coefficients, and the first principal component decreases with increasing water content. With the addition of the second principal component, the cases of different water contents in honey can be well classified.

Ultraviolet-visible spectroscopy combined with machine learning as a rapid detection method to the predict adulteration of honey

Honey is often adulterated with inexpensive and artificial sweeteners. To overcome the time-consuming honey adulteration tests, which require precision, chemicals, and sample preparation, it is needful to develop trustworthy analytical methods to assure its authenticity. In the present study, the potential of ultraviolet-visible spectroscopy (UV-Vis) in predicting the sucrose content was evaluated by using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR). To predict the sucrose content based on diagnostic wavelengths, a Point Spectro Transfer Function (PSTF) was evaluated using Multiple Linear Regression (MLR). For this purpose, the spectra of authentic (n = 12), commercial (n = 12), and adulterated (n = 16) honey samples were recorded. Four distinguished wavelengths from correlation analysis between sucrose content and spectra absorption were 216, 280, 316, and 603 nm. The SVR performed better calibration model than the PLSR estimations (RMSE = 0.97, and R2 = 0.98). The predictive models result revealed that both models had high accuracy for the sucrose content estimation. This study proved that UV-Vis spectroscopy provides an economical alternative for the rapid quantification of adulterated honey samples with sucrose.

Rapid detection of micronutrient components in infant formula milk powder using near-infrared spectroscopy

In order to achieve rapid detection of galactooligosaccharides (GOS), fructooligosaccharides (FOS), calcium (Ca), and vitamin C (Vc), four micronutrient components in infant formula milk powder, this study employed four methods, namely Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Normalization (Nor), and Savitzky-Golay Smoothing (SG), to preprocess the acquired original spectra of the milk powder. Then, the Competitive Adaptive Reweighted Sampling (CARS) algorithm and Random Frog (RF) algorithm were used to extract representative characteristic wavelengths. Furthermore, Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) models were established to predict the contents of GOS, FOS, Ca, and Vc in infant formula milk powder. The results indicated that after SNV preprocessing, the original spectra of GOS and FOS could effectively extract feature wavelengths using the CARS algorithm, leading to favorable predictive results through the CARS-SVR model. Similarly, after MSC preprocessing, the original spectra of Ca and Vc could efficiently extract feature wavelengths using the CARS algorithm, resulting in optimal predictive outcomes via the CARS-SVR model. This study provides insights for the realization of online nutritional component detection and optimization control in the production process of infant formula.

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning

Honey is one of the most adulterated foods, usually through the addition of sweeteners or low-cost honeys. This study presents a method based on visible near infrared spectroscopy (Vis-NIRs), in combination with machine learning (ML) algorithms, for the correct identification and quantification of adulterants in honey. Honey samples from two botanical origins (orange blossom and sunflower) were evaluated and adulterated with low-cost honey in different percentages (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%). The results of the exploratory analysis showed a tendency to group the samples according to botanical origin, as well as the presence of adulteration. A supervised analysis was performed to detect the presence of adulterations. The best performance with 100% accuracy was achieved by support vector machines (SVM) and random forests (RF). A regression study was also carried out to quantify the percentage of adulteration. The best result was obtained by support vector regression (SVR) with a coefficient of determination (R²) of 0.991 and a root mean squared error (RMSE) of 1.894. These results demonstrate the potential of combining ML with spectroscopic data as a method for the automated quality control of honey.

Spectral separation degree method for Vis-NIR spectroscopic discriminant analysis of milk powder adulteration

Adulteration detection of adding ordinary milk powder to high-end dedicated milk powder is challenging due to the high similarity. Using visible and near-infrared (Vis-NIR) spectroscopy combined with k-nearest neighbor (kNN), the discriminant analysis models of pure brand milk powder and its adulterated milk powder (including unary and binary adulteration) were established. Standard normal variate transformation and Norris derivative filter (D = 2, S = 11, G = 5) were jointly used for spectral preprocessing. The separation degree and separation degree spectrum between two spectral populations were proposed and used to describe the differences between the two spectral populations, based on which, a novel wavelength selection method, named separation degree priority combination-kNN (SDPC-kNN), was proposed for wavelength optimization. SDPC-wavelength step-by-step phase-out-kNN (SDPC-WSP-kNN) models were established to further eliminate interference wavelengths and improve the model effect. The nineteen wavelengths in long-NIR region (1100-2498 nm) with a separation degree greater than 0 were used to establish single-wavelength kNN models, the total recognition-accuracy rates in prediction (RAR_P) all reached 100%, and the total recognition-accuracy rate in validation (RAR_V) of the optimal model (1174 nm) reached 97.4%. In the visible (400-780 nm) and short-NIR (780-1100 nm) regions with the separation degree all less than 0, the SDPC-WSP-kNN models were established. The two optimal models (N = 7, 22) were determined, the RAR_P values reached 100% and 97.4% respectively, and the RAR_V values reached 96.1% and 94.3% respectively. The results indicated that Vis-NIR spectroscopy combined with few-wavelength kNN has feasibility of high-precision milk powder adulteration discriminant. The few-wavelength schemes provided a valuable reference for designing dedicated miniaturized spectrometer of different spectral regions. The separation degree spectrum and SDPC can be used to improve the performance of spectral discriminant analysis. The SDPC method based on the separation degree priority proposed is a novel and effective wavelength selection method. It only needs to calculate the distance between two types of spectral sets at each wavelength with low computational complexity and good performance. In addition to combining with kNN, SDPC can also be combined with other classifier algorithms (e.g. PLS-DA, PCA-LDA) to expand the application scope of the method.

Response mechanism and rapid detection of phenotypic information in rice root under heavy metal stress

The root is an important organ affecting cadmium accumulation in grains, but there is no comprehensive research involving rice root phenotype under cadmium stress yet. To assess the effect of cadmium on root phenotypes, this paper investigated the response mechanism of phenotypic information including cadmium accumulation, adversity physiology, morphological parameters, and microstructure characteristics, and explored rapid detection methods of cadmium accumulation and adversity physiology. We found that cadmium had the effect of "low-promotion and high-inhibition" on root phenotypes. In addition, the rapid detection of cadmium (Cd), soluble protein (SP), and malondialdehyde (MDA) were achieved based on spectroscopic technology and chemometrics, where the optimal prediction model was least squares support vector machine (LS-SVM) based on the full spectrum (R_p=0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (R_p=0.9161) for SP and CARS-ELM (R_p=0.9021) for MDA, all with R_p higher than 0.9. Surprisingly, it took only about 3 min, which was more than 90% reduction in detection time compared with laboratory analysis, demonstrating the excellent ability of spectroscopy for root phenotype detection. These results reveal response mechanism to heavy metal and provide rapid detection method for phenotypic information, which can substantially contribute to crop heavy metal control and food safety supervision.

Rapid, reliable and easy-to-perform chemometric-less method for rice syrup adulterated honey detection using FTIR-ATR

The adulteration of honey (Apis mellifera) is a global problem due to its economic, commercial and health implications. The world's leading beekeeping organisation, APIMONDIA, considers that the detection of adulteration in honey is a problem that has not yet been resolved. This evidence of the importance of the intensive development of analytical techniques that allow the unequivocal detection of adulterants in honey, especially those whose use as honey adulterants has recently emerged. This work aims to develop a fast, easy-to-perform, low-cost analytical method to qualitatively and quantitatively determine rice syrup using the Fourier transform infrared spectroscopy (FTIR) technique with attenuated total reflectance (ATR) mode without complex mathematical procedures and sophisticated sample preparation. This study involved the analysis of 256 intentionally rice-syrup-adulterated honey samples and 92 pure honey samples of bee multifloral honey from Spain. The method, based strictly on the determination of the absorbance directly from the samples, at 1013 cm^-1 The methodology used no need for previous treatments or preparations and demonstrated the scope for the unequivocal detection of rice syrup in adulterated honey containing equal to or higher than 3% (m/m) or more of this adulterant. Using the Exponential Plus Linear model (r = 0.998) shows high accuracy and precision, in terms of relative error (0.32%, m/m) and coefficient of variation (1.4%). The results of this study have led to the establishment of a maximum absorbance threshold of 0.670 for honey without rice syrup.

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.

Fourier transform infrared spectroscopy and chemometrics for the rapid screening of economically motivated adulteration of honey spiked with corn or rice syrup

Due to its high price, increased consumption, and limited production, honey has been a main target for economically motivated adulteration (EMA). An approach combining Fourier-Transform infrared spectroscopy (FTIR) and chemometrics was evaluated to develop a rapid screening tool to detect potential EMA of honey with either rice or corn syrup. A single-class soft independent modeling of class analogy (SIMCA) model was developed using a diverse set of commercial honey products and an authentic set of honey samples collected at four different U.S. Department of Agriculture (USDA) honey sample collection locations. The SIMCA model was externally validated with a set of calibration-independent authentic honey, typical commercial honey control samples, and those spiked with rice and corn syrups in the 1-16% concentration range. The authentic honey and typical commercial honey test samples were correctly predicted with an 88.3% classification rate. High accuracy was found in predicting the rice and corn syrup spiked samples above the 7% concentration range, yielding 97.6% and 94.8% correct classification rates, respectively. This study demonstrated the potential for a rapid and accurate infrared and chemometrics method that can be used to rapidly screen for either rice or corn adulterants in honey in less than 5 min.

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.

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data

Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices.

Rapid and accurate discrimination between pure and adulterated commercial Indian Honey brands using FTIR spectroscopy and principal component analysis

Background:

Four leading commercial Indian honey brands were investigated using FTIR spectroscopy and principal component analysis for rapid and accurate differentiation of pure, mildly adulterated, and highly adulterated honey brand samples.

Methods:

This study is the first to investigate commercial Indian honey brands using FTIR and PCA.

Results:

Hence such methods can investigate adulterations in bulk commercial honey brand samples where sophisticated instrumentations and facilities are not available.

Conclusion:

Thus, the potential of FTIR and PCA can be further used for detecting the presence of adulterations in bulk honey samples without much cost and efforts.

Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics

Honey is a natural product that is considered globally one of the most widely important foods. Various studies on authenticity detection of honey have been fulfilled using visible and near-infrared (Vis-NIR) spectroscopy techniques. However, there are limited studies on stingless bee honey (SBH) despite the increase of market demand for this food product. The objective of this work was to present the potential of Vis-NIR absorbance spectroscopy for profiling, classifying, and quantifying the adulterated SBH. The SBH sample was mixed with various percentages (10−90%) of adulterants, including distilled water, apple cider vinegar, and high fructose syrup. The results showed that the region at 400−1100 nm that is related to the color and water properties of the samples was effective to discriminate and quantify the adulterated SBH. By applying the principal component analysis (PCA) on adulterants and honey samples, the PCA score plot revealed the classification of the adulterants and adulterated SBHs. A partial least squares regression (PLSR) model was developed to quantify the contamination level in the SBH samples. The general PLSR model with the highest coefficient of determination and lowest root means square error of cross-validation (RCV2=0.96 and RMSECV=5.88 %) was acquired. The aquaphotomics analysis of adulteration in SBH with the three adulterants utilizing the short-wavelength NIR region (800−1100 nm) was presented. The structural changes of SBH due to adulteration were described in terms of the changes in the water molecular matrix, and the aquagrams were used to visualize the results. It was revealed that the integration of NIR spectroscopy with aquaphotomics could be used to detect the water molecular structures in the adulterated SBH.

Rheological behavior of honey adulterated with agave, maple, corn, rice and inverted sugar syrups

The aim of this study was to assess the influence of different adulteration agents (agave, maple, corn, rice and inverted sugar) on honey rheology. There was studied the influence of different percentages of adulteration agent on steady state and dynamic state rheology but also on rheology in the negative temperature domain. The authentic honey and adulterated ones behaved as a Newtonian fluid with a liquid-like behavior (G">>G'). Regarding the physicochemical parameters analyzed (moisture and sugar content), significant changes depending on the adulteration agent/degree used were observed. The viscoelastical parameters (η*-complex viscosity, G' -elastic modulus and G"-viscous modulus) and glass transition temperature (Tg) were predicted in function of the chemical composition (moisture content, glucose, fructose, sucrose, maltose, raffinose, trehalose, turanose, melesitose, and F/G ratio) using the PLS-R (partial least square regression). All parameters analyzed had a high regression coefficient for calibration (> 0.810) and validation (> 0.790), except for the elastic modulus.

Effect of long-term and heating storage on honey visible spectrum: an alternative parameter for quality monitoring of bracatinga honeydew honey

Bracatinga (Mimosa scabrella Bentham) honeydew honey (BHH) is a peculiar Brazilian honey. It is produced only every 2 years, which raises concerns about its quality since it can be submitted to different storage conditions until a new harvest is carried out. Therefore, this study investigated the changes in the visible spectrophotometric profile (VSP) of BHH during its storage at room temperature over 24 months and 40 °C for 4 months. Our findings indicated a similar VSP between the BHH samples, but that varied according to the storage condition. These changes were associated with the formation of brown compounds, such as 5-hydroxymethylfurfural, which has a maximum limit established for honeys. Thereby, absorbance above 0.500 absorption units between 380 and 410 nm was proposed as indicative of BHH exposure to prolonged heating with significant loss of its quality. Still, a regression model for absorbance at 380 nm was proposed aiming to predict the BHH storage time at room temperature, since storage time longer than 20 months at average temperatures of 23.0 ± 2.3 °C do not seem to be suitable for BHH. Thus, the VSP showed potential for monitoring BHH quality.

AUTENTIKASI CEPAT MADU HUTAN KALIMATAN TIMUR DENGAN ATR-FTIR SPEKTROSKOPI KOMBINASI ANALISIS KEMOMETRIKA

Honey adulteration is mostly conducted by the addition of sucrose. In this study, the authentication of honey was conducted using ATR-FTIR and chemometrics. Pure honey samples (MA) were collected from nine regions in East Kalimantan. The ATR-FTIR spectra of these samples were then compared to sucrose-adulterated honey (MS), which were prepared in the sucrose concentration from 2.5 to 50% (v / v).The data analysis was performed using chemometrics techniques: 1) Principle Component Analysis (PCA) method, 2) classification with Discriminant Analysis (DA), and 3) regression with (PCR) and (PLS). As a result, PCA was able to visualize the differences between MS and MA. DA analysis was able to distinguish MS and MA at wave numbers from 1200 to 800 cm-1 with 92.5% performance index. Quantitative calibration models of the sucrose-adulterated honey could be obtained from PLS and PCR, while the best calibration model was obtained with the PLS method from the 2nd derivative spectra. In summary, sucrose-adulterated honey from East Kalimantan can be authenticated using ATR-FTIR method in combination with chemometric analysis.

In Situ Determination of Cannabidiol in Hemp Oil by Near-Infrared Spectroscopy

Cannabidiol (CBD, 1) is an active component of hemp oil and many other products that offers diverse health benefits. Near-infrared spectroscopy (NIRS) coupled with chemometrics was utilized to quantify the CBD (1) concentration in the hemp oil through the containing glass vial. NIRS provided a fast and cost-effective tool to measure chemical profiles for the hemp oil samples with various concentrations of CBD (1) and its acid precursor, i.e., cannabidiolic acid (CBDA, 2). The measured NIR spectra were transformed by using a Savitzky-Golay first-derivative filter to remove baseline drift. Two self-optimizing chemometric methods, super partial least-squares regression (sPLSR) and self-optimizing support vector elastic net (SOSVEN), were applied to construct automatically multivariate models that predict the concentrations of CBD (1) and total CBD (sum of 1 and 2 concentrations) of the hemp oil samples. The SOSVEN had validation errors of 6.4 mg/mL for the prediction of CBD (1) concentration and 6.6 mg/mL for the prediction of total CBD concentration, which are significantly lower than the errors given by sPLSR. Other than the lower validation errors, SOSVEN has another advantage over sPLSR in that it builds a multivariate model while selecting spectral features at the same time. These results demonstrated that NIR spectroscopy combined with chemometrics can be used as a rapid and cost-effective approach to determine the CBD (1) and total CBD concentrations in hemp oil. Manufacturers would benefit from the fast and reliable approach in quality assurance.

Imaging of bee honey sugar crystals by second-harmonic generation microscopy

Bee honey is an exceptionally nutritious food with unique chemical and mineral contents. This report introduces the use of the second-harmonic generation (SHG) microscopy for imaging honey sugar crystals' morphology as an alternative for its authentication process. The crystals and their boundaries are clearly observed with SHG compared with bright-field microscopy, where the liquid honey avoids the visualization of a sharp image. Four different honey samples of Mexico's various floral origins and geographical regions are analyzed in our study. These samples are representative of the diversity and valuable quality of bee honey production. The SHG image information is complemented with Raman spectroscopy (RS) analysis, since this optical technique is widely used to validate the bee's honey composition stated by its floral origin. We relate the SHG imaging of honey crystals with the well-defined fructose and glucose peaks measured by RS. Size measurement is introduced using the crystal´s length ratio to differentiate its floral origin. From our observations, we can state that SHG is a promising and suitable technique to provide a sort of optical fingerprint based on the floral origin of bee honey.

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.

A Laser-Based Method for the Detection of Honey Adulteration

In the present work, laser-induced breakdown spectroscopy, aided by some machine learning algorithms (i.e., linear discriminant analysis (LDA) and extremely randomized trees (ERT)), is used for the detection of honey adulteration with glucose syrup. In addition, it is shown that instead of the entire LIBS spectrum, the spectral lines of inorganic ingredients of honey (i.e., calcium, sodium, and potassium) can be also used for the detection of adulteration providing efficient discrimination. The constructed predictive models attained high classification accuracies exceeding 90% correct classification.

Application of ANOVA-simultaneous component analysis to quantify and characterise effects of age, temperature, syrup adulteration and irradiation on near-infrared (NIR) spectral data of honey

NIR spectroscopy combined with chemometric analysis has proven to be a rapid and cost-effective screening tool for the detection of syrup-adulterated honey. Processing and storage conditions which alter the chemical and physical state of honey may affect the spectra. The effects of age, storage temperature, syrup adulteration (10 and 20% w/w) and irradiation treatment on the NIR spectra of honey were investigated as a function of time with ANOVA-simultaneous component analysis (ASCA), an experimental design-focused exploratory data analysis method. The factors 'time', 'temperature' and 'adulteration' were found to have significant effects (p < 0.05), but no significant effect was observed for irradiation treatment. A significant interaction effect was found between factors 'time' and 'adulteration', with the greatest disparity between authentic and adulterated class signals found immediately after adulteration and decreasing within three months thereafter.

Quantification of Hydrogen Peroxide in Cretan Honey and Correlation with Physicochemical Parameters

The aim of the present study is to quantify hydrogen peroxide, generated from various types of honey produced in Crete, as a potent antimicrobial agent, and establish any correlation with their physicochemical parameters. The basic physicochemical parameters (diastase activity, HMF content, moisture, electrical conductivity, color, and sugars) of 30 authentic honey samples were determined. The concentration of hydrogen peroxide in all samples was found to be within the range 0.010-0.092 mM. The known correlation between the electrical conductivity and the color of honey was confirmed in this study. Univariate and multivariate statistics applied to the results indicate that the results can be used to discriminate honey sample groups of different botanical origins.

Simultaneous sex and species classification of silkworm pupae by NIR spectroscopy combined with chemometric analysis

BACKGROUND

Most studies only focus on the sex discrimination of silkworm pupae. However, species differentiation of silkworm pupae is also needed in sericulture. To classify the sex and species at the same time, the present study adopts near infrared (NIR) spectroscopy combined with multivariate analysis.

RESULTS

First, spectra samples were acquired using an NIR sensor, comprising female and male silkworm pupae from three species. Second, three different variables selection approaches were used, including a successive projections algorithm, competitive adaptive reweighted sampling (CARS) and interval partial least squares (iPLS). Third, identification models were built based on random forest and partial least squares discriminant analysis (PLSDA). The experimental results show that iPLS-PLSDA model (95.24%) gives a high performance when using the one of the three variable selection methods alone. To further increase the performance, the variable selection methods are optimized. The accuracy of the iPLS-CARS-PLSDA model is as high as 98.41%.

CONCLUSION

The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for sex and species identification of silkworm pupae. © 2020 Society of Chemical Industry.

Detection of acacia honey adulteration with high fructose corn syrup through determination of targeted α‑Dicarbonyl compound using ion mobility-mass spectrometry coupled with UHPLC-MS/MS

High-value acacia honey is often adulterated with inexpensive high fructose corn syrup (HFCS), due to their similar color and sugar composition. α‑Dicarbonyl compounds formed by Maillard reaction or caramelization during heat treatment or storage, differ between HFCS and honey due to differences in starting materials and processing methods. In this study, we compared α-dicarbonyl compounds in acacia honey and HFCS by Ion Mobility-Mass Spectrometry and multivariate statistical analysis. Through α-dicarbonyl compound derivatization with o-phenylenediamine, we screened a marker with 189.1023 m/z and 139.3 Å² Collision Cross-Section that can distinguish HFCS from acacia honey. Nuclear magnetic resonance spectra identified this marker compound as 3,4-dideoxypentosulose. We then used chromatography-coupled tandem mass spectrometry to quantitate 3,4-dideoxypentosulose in market samples of honey and HFCS and found that 3,4-dideoxypentosulose was negligible (<0.098 mg/kg) in honey, but prevalent in HFCS (≧1.174 mg/kg), indicating 3,4-dideoxypentosulose can serve as an alternative indicator of HFCS adulteration of acacia honey.

The Toxic Impact of Honey Adulteration: A Review

Honey is characterized as a natural and raw foodstuff that can be consumed not only as a sweetener but also as medicine due to its therapeutic impact on human health. It is prone to adulterants caused by humans that manipulate the quality of honey. Although honey consumption has remarkably increased in the last few years all around the world, the safety of honey is not assessed and monitored regularly. Since the number of consumers of honey adulteration have increased in recent years, their trust and interest in this valuable product has decreased. Honey adulterants are any substances that are added to the pure honey. In this regard, this paper provides a comprehensive and critical review of the different types of adulteration, common sugar adulterants and detection methods, and draws a clear perspective toward the impact of honey adulteration on human health. Adulteration increases the consumer's blood sugar, which can cause diabetes, abdominal weight gain, and obesity, raise the level of blood lipids and can cause high blood pressure. The most common organ affected by honey adulterants is the liver followed by the kidney, heart, and brain, as shown in several in vivo research designs.

Discrimination of Tetrastigma hemsleyanum according to geographical origin by near-infrared spectroscopy combined with a deep learning approach

Recently, deep learning has presented as a powerful approach to overcome the deficiencies of the conventional biochemical approaches. In this study, a method for discriminating medicinal plant Tetrastigma hemsleyanum from different origins was proposed using near-infrared spectroscopy (NIRS) and deep learning models. Support vector machine (SVM), self-adaptive evolutionary extreme learning machine (SAE-ELM), and convolutional neural network (CNN) were used to process the near-infrared spectral data (4000-5600 cm^-1). The results indicated that the average recognition accuracy of SVM on the test set samples (n = 60) reached 90%. The average recognition accuracy of SAE-ELM was 98.3%, while CNN correctly discriminated 100% of T. hemsleyanum from different origins. Notably, CNN avoids tedious redundant data preprocessing and is also able to save the trained model for the next call to achieve rapid detection. As above, this study provides an effective deep learning-based method for discriminating the geographical origins of T. hemsleyanum as well as providing a convenient and satisfactory approach to ensure the famous-region of other medicinal plants.

Simultaneous Determination of Metal Ions in Zinc Sulfate Solution Using UV-Vis Spectrometry and SPSE-XGBoost Method

Excessive discharge of heavy metal ions will aggravate environment pollution and threaten human health. Thus, it is of significance to real-time detect metal ions and control discharge in the metallurgical wastewater. We developed an accurate and rapid approach based on the singular perturbation spectrum estimator and extreme gradient boosting (SPSE-XGBoost) algorithms to simultaneously determine multi-metal ion concentrations by UV–vis spectrometry. In the approach, the spectral data is expanded by multi-order derivative preprocessing, and then, the sensitive feature bands in each spectrum are extracted by feature importance (VI score) ranking. Subsequently, the SPSE-XGBoost model are trained to combine multi-derivative features and to predict ion concentrations. The experimental results indicate that the developed “Expand-Extract-Combine” strategy can not only overcome problems of background noise and spectral overlapping but also mine the deeper spectrum information by integrating important features. Moreover, the SPSE-XGBoost strategy utilizes the selected feature subset instead of the full-spectrum for calculation, which effectively improves the computing speed. The comparisons of different data processing methods are conducted. It outcomes that the proposed strategy outperforms other routine methods and can profoundly determine the concentrations of zinc, copper, cobalt, and nickel with the lowest RMSEP. Therefore, our developed approach can be implemented as a promising mean for real-time and on-line determination of multi-metal ion concentrations in zinc hydrometallurgy.

Detection of fraud in high-quality rice by near-infrared spectroscopy

A key feature of food fraud is the use of a lower value ingredient to imitate an authentic product. This study was based on near-infrared spectroscopy (NIRS) analysis technology, partial least squares discriminant analysis (PLS-DA), and a support vector machine (SVM) to detect whether high-quality rice was mixed with other varieties of rice. As an aid to qualitative discrimination, PLS was used to establish the quantitative analysis model to assist in the recognition of the degree of fraud. Due to the direct correlation between the results of NIRS analysis and the homogeneity of the samples, four groups of samples with different physical forms (full granules, 40 mesh, 70 mesh, and 100 mesh) were prepared, each group consisted of 20 pure samples and 140 mixed samples, and the mixing ratio was between 5% and 50%, with an interval of 5%. Regarding qualitative analysis, the performance of the model has no obvious relationship with the physical state of the sample, the qualitative model of PLS-DA and SVM can detect the fraudulent rice with a 5% detection limit, respectively. Regarding quantitative analysis, the performance of the prediction model was closely related to the particle size of the samples: 100 mesh > 70 mesh > 40 mesh > full grains. The determination coefficient and root mean square errors of the optimal prediction result were 0.96 and 2.93, respectively. These results demonstrate that NIRS analysis technology is a reliable and fast tool to determine whether high-quality rice contains other varieties of rice. PRACTICAL APPLICATION: The work of this article is based on the current background of increasingly serious rice fraud, using near-infrared spectroscopy to quickly identify fraudulent rice, to a certain extent, and effectively alleviate the rice fraud. This technology can serve for the supervision of food regulatory agencies on rice fraud, and can also be used in food factories to ensure the authenticity of raw materials of rice.

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.