A screening method based on Visible-NIR spectroscopy for the identification and quantification of different adulterants in high-quality honey

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.

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.

Tracing the origin and authenticity of Spanish PDO honey using metagenomics and machine learning

The Protected Designation of Origin (PDO) indication for foods intends to guarantee the conditions of production and the geographical origin of regional products within the European Union. Honey products are widely consumed due to their health-promoting properties and there is a general interest in tracing their authenticity. In this regard, metagenomics sequencing and machine learning (ML) have been proposed as complementary technologies to improve the traceability methods of foods. Therefore, the aim of this study was to analyze the metagenomic profiles of Spanish honeys from three different PDOs (Granada, Tenerife and Villuercas-Ibores), and compare them with non-PDO honeys using ML models (PLS, RF, LOGITBOOST, and NNET). According to the results obtained, non-PDO honeys and Granada PDO showed higher beta diversity values than Tenerife and Villuercas-Ibores PDOs. ML classification of honey products allowed the identification of different microbial biomarkers of the geographical origin of honeys: Lactobacillus kunkeei, Parasaccharibacter apium and Lactobacillus helsingborgensis for PDO honeys and Paenibacillus larvae, Lactobacillus apinorum and Klebsiella pneumoniae for non-PDO honeys. In addition, potential microbial biomarkers of some honey varieties including L. kunkeei for Albaida and Retama del Teide varieties, and P. apium for Tajinaste variety, were identified. ML models were validated on an independent set of samples leading to high accuracy rates (above 90 %). This work demonstrates the potential of ML to differentiate different types of honey using metagenome-based methods, leading to high performance metrics. In addition, ML models discriminate both the geographical origin and variety of products corresponding to different PDOs and non-PDO products. Results here presented may contribute to develop enhanced traceability and authenticity methods that could be applied to a wide range of foods.

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.

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.

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.

Non-Targeted Detection and Quantification of Food Adulteration of High-Quality Stingless Bee Honey (SBH) via a Portable LED-Based Fluorescence Spectroscopy

Stingless bee honey (SBH) is rich in phenolic compounds and available in limited quantities. Authentication of SBH is important to protect SBH from adulteration and retain the reputation and sustainability of SBH production. In this research, we use portable LED-based fluorescence spectroscopy to generate and measure the fluorescence intensity of pure SBH and adulterated samples. The spectrometer is equipped with four UV-LED lamps (peaking at 365 nm) as an excitation source. Heterotrigona itama, a popular SBH, was used as a sample. 100 samples of pure SBH and 240 samples of adulterated SBH (levels of adulteration ranging from 10 to 60%) were prepared. Fluorescence spectral acquisition was measured for both the pure and adulterated SBH samples. Principal component analysis (PCA) demonstrated that a clear separation between the pure and adulterated SBH samples could be established from the first two principal components (PCs). A supervised classification based on soft independent modeling of class analogy (SIMCA) achieved an excellent classification result with 100% accuracy, sensitivity, specificity, and precision. Principal component regression (PCR) was superior to partial least squares regression (PLSR) and multiple linear regression (MLR) methods, with a coefficient of determination in prediction (R2p) = 0.9627, root mean squared error of prediction (RMSEP) = 4.1579%, ratio prediction to deviation (RPD) = 5.36, and range error ratio (RER) = 14.81. The LOD and LOQ obtained were higher compared to several previous studies. However, most predicted samples were very close to the regression line, which indicates that the developed PLSR, PCR, and MLR models could be used to detect HFCS adulteration of pure SBH samples. These results showed the proposed portable LED-based fluorescence spectroscopy has a high potential to detect and quantify food adulteration in SBH, with the additional advantages of being an accurate, affordable, and fast measurement with minimum sample preparation.

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.

Integration of Non-targeted Proteomics Mass Spectrometry with Machine Learning for Screening Cooked Beef Adulterated Samples

The degradation of ingredients in heat-processed meat products makes their authentication challenging. In this study, protein profiles of raw beef, chicken, duck, pork, and binary simulated adulterated beef samples (chicken-beef, duck-beef, and pork-beef) and their heat-processed samples were obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Heat-stable characteristic proteins were found by screening the overlapping characteristic protein ion peaks of the raw and corresponding heat-processed samples, which were discovered by partial least-squares discriminant analysis. Based on the 36 heat-stable characteristic proteins, qualitative classification for the raw and heat-processed meats was achieved by extreme gradient boosting. Moreover, quantitative analysis via partial least squares regression was applied to determine the adulteration ratio of the simulated adulterated beef samples. The validity of the approach was confirmed by a blind test with the mean accuracy of 97.4%. The limit of detection and limit of quantification of this method were determined to be 5 and 8%, respectively, showing its practical aspect for the beef authentication.

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.

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.

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.

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.

Fast discrimination and quantification analysis of Curcumae Radix from four botanical origins using NIR spectroscopy coupled with chemometrics tools

Curcumae Radix (Yujin) is a multi-origin herbal medicine with excellent clinical efficacy. For fast discrimination and quantification analysis of Yujin from four botanical origins (Guiyujin, Huangyujin, Lvyujin and Wenyujin), near infrared (NIR) spectroscopy combined with chemometrics tools was employed in this study. Based on NIR data, principal component analysis (PCA) could only realize the separation between Guiyujin and Wenyujin samples, and the partial least squares-discrimination analysis (PLS-DA), support vector machine (SVM) and k-nearest neighbors (KNN) models achieved the complete discrimination of the four species of Yujin with 100% accuracy. Moreover, the method for the simultaneous determination of six bioactive compounds in Yujin was developed by HPLC. Germacrone, curdione and curcumenol could be found in all samples, and curcumin, demethoxycurcumin and bisdemethoxycurcumin were only observed in Huangyujin samples. Then, the support vector machine regression (SVMR) model for the prediction of germacrone content was successfully constructed. And the coefficients of determination were 0.88 and 0.89 for calibration and validation sets, respectively. The present work proposes a quick, economic and reliable method for the discrimination of Yujin from four botanical origins and the prediction of germacrone content, which will contribute to its quality control researches.

A Rheological Analysis of Biomaterial Behaviour as a Tool to Detect the Dilution of Heather Honey

Heather honey is a valuable and rheologically special type of honey. Its above-average selling price may motivate its intentional violation with a mixture of honey from another botanical origin, the price of which is lower on the market. This work deals with the rheological properties of such devalued heather honey in order to determine the changes in the individual rheological parameters depending on the degree of dilution of the heather honey. For this purpose, a differently diluted heather honey sample series was created and the following rheological parameters were determined: hysteresis area, n-value, yield stress (τ₀), parameter B (Weltman model), parameter ϕ, or parameter C (model describing the logarithmic dependence of the complex viscosity on the angular frequency). Part of the work was research into whether the set parameters can be used as comparative parameters. It was found that the hysteresis area does not appear to be a suitable relative comparison parameter due to the high variability. The parameters that appear to be suitable are the relative parameters n-value and the parameter ϕ, which showed the greatest stability. The change in the determined rheological parameters is, depending on the degree of dilution, non-linear with a step change between the samples containing 40% (w/w) and 60% (w/w) of a heather honey.

Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors

Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a single-sensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

A convenient and rapid method for detecting d-glucose in honey used smartphone

Information concerning food composition, including information on its glucose content, is essential for modern food industry due to greater consumer awareness and expectations. In this work, the gene encoding d-glucose dehydrogenase (GDH) from Bacillus Natto was expressed in Escherichia coli BL21(DE3) firstly. Ni-IDA column was used for the purification of GDH. Then, the purified GDH was used to construct a color system with stable and effective measurement of concentration of d-glucose. The smart phone photographing and the software Microsoft Photoshop have been used in the system for determination of the color. The enzymatic analysis system can detect the concentration of d-glucose from 5 mM to 40 mM, and other various sugars has no interference to the system. The system was used to quantitatively detect the concentration of d-glucose in honey. The system can be used for convenient and rapid detection of d-glucose in food, especially for large numbers of samples.

Feasibility of NIR spectroscopy detection of moisture content in coco-peat substrate based on the optimization characteristic variables

Moisture content is an important index to evaluate the water content in substrate. Near-infrared (NIR) spectroscopy was used for rapid quantitative detection of moisture content of coco-peat substrate. The different spectral pretreatment methods were adopted to pre-process the spectral data. Successive projection algorithm (SPA), elimination of uninformative variables algorithm (UVE) and synergy interval partial least squares algorithm (Si-PLS) were used to screen characteristic variables of coco-peat substrate original spectral data and different pretreatment spectral data. The partial least squares (PLSR) and multiple linear regression (MLR) were used to establish the relationship model between the spectral data and reference measurement value of moisture content. In comparison, the best and simplest spectral prediction model was established when SPA was used to screen the characteristic variables of Savitzky-Golay (S-G) smoothing spectral data and MLR was used to establish the model. And the corresponding correlation coefficient and root mean square error of calibration set were 0.9976 and 1.0989%, respectively; the correlation coefficient and root mean square error of prediction set were 0.9963 and 1.4029%, respectively, and RPD was 11.28. The results of this study provided a feasible method for the rapid detection of moisture content of coco-peat substrate.

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview

Amid today's stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Modern Methods for Assessing the Quality of Bee Honey and Botanical Origin Identification

This paper is a summary of the latest literature on methods for assessing quality of natural bee honey. The publication briefly characterizes methods recommended by the International Honey Commission, published in 2009, as well as newer methods published in the last 10 years. Modern methods of assessing honey quality focus mainly on analyzing markers of individual varieties and classifying them into varieties, using, among others, near infrared spectroscopy techniques (NIR), potentiometric tongue, electronic nose, nuclear magnetic resonance (NMR), zymography, polymerase chain reaction (PCR), DNA metabarcoding, and chemometric techniques including partial least squares (PLS), principal component analysis (PCA) and artificial neural networks (ANN). At the same time, effective techniques for analyzing adulteration, sugar, and water content, hydroxymethylfurfural (HMF), polyphenol content, and diastase activity are being sought. Modern techniques enable the results of honey quality testing to be obtained in a shorter time, using the principles of green chemistry, allowing, at the same time, for high precision and accuracy of determinations. These methods are constantly modified, so that the honey that is on sale is a product of high quality. Prospects for devising methods of honey quality assessment include the development of a fast and accurate alternative to the melissopalynological method as well as quick tests to detect adulteration.

Fast Quantification of Honey Adulteration with Laser-Induced Breakdown Spectroscopy and Chemometric Methods

Honey adulteration is a major issue in food production, which may reduce the effective components in honey and have a detrimental effect on human health. Herein, laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to fast quantify the adulterant content. Two common types of adulteration, including mixing acacia honey with high fructose corn syrup (HFCS) and rape honey, were quantified with univariate analysis and partial least squares regression (PLSR). In addition, the variable importance was tested with univariable analysis and feature selection methods (genetic algorithm (GA), variable importance in projection (VIP), selectivity ratio (SR)). The results indicated that emissions from Mg II 279.58, 280.30 nm, Mg I 285.25 nm, Ca II 393.37, 396.89 nm, Ca I 422.70 nm, Na I 589.03, 589.64 nm, and K I 766.57, 769.97 nm had compact relationship with adulterant content. Best models for detecting the adulteration ratio of HFCS 55, HFCS 90, and rape honey were achieved by SR-PLSR, VIP-PLSR, and VIP-PLSR, with root-mean-square error (RMSE) of 8.9%, 8.2%, and 4.8%, respectively. This study provided a fast and simple approach for detecting honey adulteration.

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.