Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification

Physicochemical and antioxidant properties of Apis cerana honey from Lombok and Bali Islands

Limited honey production worldwide leads to higher market prices, thus making it prone to adulteration. Therefore, regular physicochemical analysis is imperative for ensuring authenticity and safety. This study describes the physicochemical and antioxidant properties of Apis cerana honey sourced from the islands of Lombok and Bali, showing their unique regional traits. A comparative analysis was conducted on honey samples from Lombok and Bali as well as honey variety from Malaysia. Moisture content was found slightly above 20% in raw honey samples from Lombok and Bali, adhering to the national standard (SNI 8664:2018) of not exceeding 22%. Both honey types displayed pH values within the acceptable range (3.40-6.10), ensuring favorable conditions for long-term storage. However, Lombok honey exhibited higher free acidity (78.5±2.14 meq/kg) than Bali honey (76.0±1.14 meq/kg), surpassing Codex Alimentarius recommendations (≤50 meq/kg). The ash content, reflective of inorganic mineral composition, was notably lower in Lombok (0.21±0.02 g/100) and Bali honey (0.14±0.01 g/100) compared to Tualang honey (1.3±0.02 g/100). Electric conductivity, indicative of mineral content, revealed Lombok and Bali honey with lower but comparable values than Tualang honey. Hydroxymethylfurfural (HMF) concentrations in Lombok (14.4±0.11 mg/kg) and Bali (17.6±0.25 mg/kg) were slightly elevated compared to Tualang honey (6.4±0.11 mg/kg), suggesting potential processing-related changes. Sugar analysis revealed Lombok honey with the highest sucrose content (2.39±0.01g/100g) and Bali honey with the highest total sugar content (75.21±0.11 g/100g). Both honeys exhibited lower glucose than fructose content, aligning with Codex Alimentarius guidelines. The phenolic content, flavonoids, and antioxidant activity were significantly higher in Lombok and Bali honey compared to Tualang honey, suggesting potential health benefits. Further analysis by LC-MS/MS-QTOF targeted analysis identified various flavonoids/flavanols and polyphenolic/phenolic acid compounds in Lombok and Bali honey. The study marks the importance of characterizing the unique composition of honey from different regions, ensuring quality and authenticity in the honey industry.

Classification of oils and margarines by FTIR spectroscopy in tandem with machine learning

This study assessed the combined utility of ATR-FTIR spectroscopy and machine learning (ML) techniques for identifying and classifying pure njangsa seed oil (NSO), palm kernel oil (PKO), coconut oil (CCO), njangsa seed oil-palm kernel oil (NSOPKO) and njangsa seed oil-coconut oil (NSOCCO) margarine. Additionally, it quantified the degree of adulteration in each oil and margarine using ML regression models and sunflower oil and canola-flaxseed oil margarine as adulterants. Fingerprints of the oils and the margarines derived in the spectra region 4000-600 cm-1 were combined with ML models. The first two principal components explained 99.4% and 98% of the variance of pure oils and margarines and 90.1, 88.3, 88, 97.3 and 98.3% of adulterated PKO, NSO, CCO, NSOCCO and NSOPKO, respectively while enabling visualization. Pure margarines were classified accurately (100%) in all models. KNN was most effective in classifying pure oil at 97% followed by LR (93%), SVM (83%), LightGBM (53%) and DT (50%). The R2 obtained from all the models for adulterated PKO, NSO, CCO, NSOPKO and NSOCCO ranged from 59-99%, 55-99%, 45-94%, 69-98% and 59-94%, respectively. SVM and DT underperformed, while KNN was the best model.

Detection of barley malt syrup as an adulterant in honey by 1H NMR profile

Currently, Barley Malt Syrup (BMS) is one of the forms of growing adulteration in honey. However, there have been no reports regarding its identification by NMR. In this aspect, we proposed a 1H NMR profiling method to discriminate between authentic and honey adulterated with BMS. The authenticated honey samples were artificially adulterated with varying percentages of BMS. It was found that a marker peak primarily falling around the 5.40 ppm region exhibited discrimination between pure and adulterated samples. Furthermore, NMR data of the samples were analyzed using statistical models. The findings demonstrate that NMR sugar profiles region, when combined with PCA analysis, can effectively detect varying degrees of adulteration. Despite qualitative nature of the outcomes, spiking studies have revealed that approach can reliably identify sugar addition at levels as low as 5-10%. Overall, NMR-based approach proves to be effective in detecting BMS as an adulterant in honey.

Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors

Nuclear magnetic resonance (NMR) is a crucial technique for analyzing mixtures consisting of small molecules, providing non-destructive, fast, reproducible, and unbiased benefits. However, it is challenging to perform mixture identification because of the offset of chemical shifts and peak overlaps that often exist in mixtures such as plant flavors. Here, we propose a deep-learning-based mixture identification method (DeepMID) that can be used to identify plant flavors (mixtures) in a formulated flavor (mixture consisting of several plant flavors) without the need to know the specific components in the plant flavors. A pseudo-Siamese convolutional neural network (pSCNN) and a spatial pyramid pooling (SPP) layer were used to solve the problems due to their high accuracy and robustness. The DeepMID model is trained, validated, and tested on an augmented data set containing 50,000 pairs of formulated and plant flavors. We demonstrate that DeepMID can achieve excellent prediction results in the augmented test set: ACC = 99.58%, TPR = 99.48%, FPR = 0.32%; and two experimentally obtained data sets: one shows ACC = 97.60%, TPR = 92.81%, FPR = 0.78% and the other shows ACC = 92.31%, TPR = 80.00%, FPR = 0.00%. In conclusion, DeepMID is a reliable method for identifying plant flavors in formulated flavors based on NMR spectroscopy, which can assist researchers in accelerating the design of flavor formulations.

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.

Enabling honey quality and authenticity with NMR and LC-IRMS based platform

Honey has been known for economically motivated adulteration around the world, because of its high demand and short supply. As consequence increasing honey production using the deliberate addition of sugar syrups while claiming a fictitious origin and diversifying it to increase its value. Generally, honey testing is supervised by a set of guidelines and quality parameters to ensure its quality and authenticity. As per the many regulatory bodies, current honey scams have been challenging to identify with conventional methods, so quality control labs require sophisticated technology. With these paradigm shifts, the aim of the present review is focused on the authenticity of honey through two important cutting-edge methods viz LC-IRMS and NMR. The LC-IRMS aids in the detection of added C₃ and C₄ sugars. Whereas NMR has provided a potent solution by allowing the classification of botanical varieties and geographical origin along with the quantification of a set of quality parameters in a single experiment.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

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.

Physicochemical Composition of Local and Imported Honeys Associated with Quality Standards

The compliance with honey standards is crucial for its validity and quality. The present study evaluated the botanical origin (pollen analysis) and physicochemical properties: moisture, color, electrical conductivity (EC), free acidity (FA), pH, diastase activity, hydroxymethylfurfural (HMF), and individual sugar content of forty local and imported honey samples. The local honey exhibited low moisture and HMF (14.9% and 3.8 mg/kg, respectively) than imported honey (17.2% and 23 mg/kg, respectively). Furthermore, the local honey showed higher EC and diastase activity (1.19 mS/cm and 11.9 DN, respectively) compared to imported honey (0.35 mS/cm and 7.6 DN, respectively). The mean FA of local honey (61 meq/kg) was significantly naturally higher than that of imported honey (18 meq/kg). All local nectar honey that originated from Acacia spp. exhibited naturally higher FA values that exceeded the standard limit (≤50 meq/kg). The Pfund color scale ranged from 20 to 150 mm in local honey and from 10 to 116 mm in imported honey. The local honey was darker, with a mean value of 102.3 mm, and was significantly different from imported honey (72.7 mm). The mean pH values of local and imported honey were 5.0 and 4.5, respectively. Furthermore, the local honey was more diverse in pollen grain taxa compared to imported honey. Local and imported honey elicited a significant difference regarding their sugar content within individual honey type. The mean content of fructose, glucose, sucrose, and reducing sugar of local honey (39.7%, 31.5%, 2.8%, and 71.2%, respectively) and imported honey (39.2%, 31.8%, 0.7%, and 72.0%, respectively) were within the permitted quality standards. This study indicates the necessity of increasing the awareness regarding quality investigations for healthy honey with good nutritional value.

Primary Structure of Glycans by NMR Spectroscopy

Glycans, carbohydrate molecules in the realm of biology, are present as biomedically important glycoconjugates and a characteristic aspect is that their structures in many instances are branched. In determining the primary structure of a glycan, the sugar components including the absolute configuration and ring form, anomeric configuration, linkage(s), sequence, and substituents should be elucidated. Solution state NMR spectroscopy offers a unique opportunity to resolve all these aspects at atomic resolution. During the last two decades, advancement of both NMR experiments and spectrometer hardware have made it possible to unravel carbohydrate structure more efficiently. These developments applicable to glycans include, inter alia, NMR experiments that reduce spectral overlap, use selective excitations, record tilted projections of multidimensional spectra, acquire spectra by multiple receivers, utilize polarization by fast-pulsing techniques, concatenate pulse-sequence modules to acquire several spectra in a single measurement, acquire pure shift correlated spectra devoid of scalar couplings, employ stable isotope labeling to efficiently obtain homo- and/or heteronuclear correlations, as well as those that rely on dipolar cross-correlated interactions for sequential information. Refined computer programs for NMR spin simulation and chemical shift prediction aid the structural elucidation of glycans, which are notorious for their limited spectral dispersion. Hardware developments include cryogenically cold probes and dynamic nuclear polarization techniques, both resulting in enhanced sensitivity as well as ultrahigh field NMR spectrometers with a ¹H NMR resonance frequency higher than 1 GHz, thus improving resolution of resonances. Taken together, the developments have made and will in the future make it possible to elucidate carbohydrate structure in great detail, thereby forming the basis for understanding of how glycans interact with other molecules.

Off-Line SPE LC-LRMS Polyphenolic Fingerprinting and Chemometrics to Classify and Authenticate Spanish Honey

The feasibility of non-targeted off-line SPE LC-LRMS polyphenolic fingerprints to address the classification and authentication of Spanish honey samples based on both botanical origin (blossom and honeydew honeys) and geographical production region was evaluated. With this aim, 136 honey samples belonging to different botanical varieties (multifloral and monofloral) obtained from different Spanish geographical regions with specific climatic conditions were analyzed. Polyphenolic compounds were extracted by off-line solid-phase extraction (SPE) using HLB (3 mL, 60 mg) cartridges. The obtained extracts were then analyzed by C18 reversed-phase LC coupled to low-resolution mass spectrometry in a hybrid quadrupole-linear ion trap mass analyzer and using electrospray in negative ionization mode. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were employed to assess the pattern recognition capabilities of the obtained fingerprints to address honey classification and authentication. In general, a good sample discrimination was accomplished by PLS-DA, being able to differentiate both blossom-honey and honeydew-honey samples according to botanical varieties. Multiclass predictions by cross-validation for the set of blossom-honey samples showed sensitivity, specificity, and classification ratios higher than 60%, 85%, and 87%, respectively. Better results were obtained for the set of honeydew-honey samples, exhibiting 100% sensitivity, specificity, and classification ratio values. The proposed fingerprints also demonstrated that they were good honey chemical descriptors to deal with climatic and geographical issues. Characteristic polyphenols of each botanical variety were tentatively identified by LC-MS/MS in multiple-reaction monitoring mode to propose possible honey markers for future experiments (i.e., naringin for orange/lemon blossom honeys, syringic acid in thyme honeys, or galangin in rosemary honeys).

Physicochemical Properties of a New Green Honey from Banggi Island, Sabah

Green honey is exclusively available on the island of Banggi in Sabah, and its uniqueness sees the commodity being sold at a high market price. Therefore, green honey is prone to adulteration by unscrupulous individuals, possibly compromising the health of those consuming this food commodity for its curative properties. Moreover, an established standard for reducing sugar in green honey is unavailable. Ipso facto, the study aimed to profile green honey’s physical and chemical properties, such as its pH, moisture content, free acidity, ash content, electroconductivity, hydroxymethylfurfural (HMF), total phenolic content, total flavonoid content, DPPH, colour, total sugar content, total protein content, and heavy metals as well as volatile organic compounds, the data of which are profoundly valuable in safeguarding consumers’ safety while providing information for its quality certification for local consumption and export. The results revealed that the honey’s physicochemical profile is comparable to other reported kinds of honey. The honey’s naturally green colour is because of the chlorophyll from the nectar from various flowers on the island. The raw honey showed free acidity between 28 and 33 Meq/100 g, lower than the standard’s 50 Meq/100 g. The hydroxymethylfurfural content is the lowest compared to other reported honey samples, with the total phenolic content between 16 and 19 mg GAE/100 g. The honey’s reducing sugar content is lower (~37.9%) than processed ones (56.3%) because of water removal. The protein content ranged from 1 to 2 gm/kg, 4- to 6-fold and 2-fold higher than local and manuka honey, respectively. The exceptionally high content of trans-4-hydroxyproline in raw honey is its source of collagen and other healing agents. Interestingly, low levels of arsenic, lead, nickel, cadmium, copper, and cobalt were detected in the honey samples, presumably due to their subterranean hives. Nevertheless, the honey is fit for general consumption as the concentrations were below the maxima in the Codex Alimentarius Commission of 2001.