NIR detection of honey adulteration reveals differences in water spectral pattern

Rapid detection of honey adulteration using machine learning on gas sensor data

Honey has long been an essential component of human nutrition, valued for its health benefits and economic significance. However, honey adulteration poses a significant challenge, whether by adding sweeteners or mixing high-value single-flower honey with lower-quality multi-flower varieties. Traditional detection methods, such as melissopalynological analysis and chromatography, are often time-consuming and costly. This study proposes an artificial intelligence-based approach using the BME688 gas sensor to detect honey adulteration rapidly and accurately. The sensor captures the gas composition of honey mixtures, creating a unique digital fingerprint that can be analysed using machine learning techniques. Experimental results demonstrate that the proposed method can detect adulteration with high precision, distinguishing honey mixtures with up to 5% resolution. The findings suggest that this approach can provide a reliable, efficient, and scalable solution for honey quality control, reducing dependence on expert analysis and expensive laboratory procedures.

Discrimination on potential adulteration of honey by differential scanning calorimetry (DSC) and graph-based semi-supervised learning (GSSL)

Honey is a valuable natural food product, prized for its nutritional and therapeutic properties. However, the widespread issue of honey adulteration, often involving the addition of plant-based syrups, poses significant challenges to global markets. This study utilized differential scanning calorimetry (DSC), a thermal-analytical technique, to characterize the thermal profiles of 43 honey samples, including both authentic and adulterated samples with high-fructose corn syrup (HFCS) and varying syrup concentrations. Principal component analysis (PCA) and graph-based semi-supervised learning (GSSL) were applied to classify the samples, achieving high accuracy. Results indicated that increasing adulteration levels led to higher water content and decreased glass transition temperature (Tg) and heat capacity difference (ΔCp). Furthermore, the established K-Nearest Neighbor (KNN) graph and Kullback-Leibler (KL) divergence effectively visualized relationships among samples. The integration of DSC with GSSL presents a cost-efficient and resource-effective approach for detecting honey adulteration with minimal experimental effort while maintaining high classification accuracy. This method holds promise for addressing honey adulteration in the food industry.

Aquaphotomics study of fresh cannabis inflorescence: near infrared spectral analysis of water matrix structures

Aquaphotomics is an approach that describes the water-light interactions in aqueous solutions or biological systems and retrieves information about the nature of the underlying water-related interactions. We evaluated the water spectral pattern (WASP) and water matrix structure of freshly harvested cannabis inflorescence from seven different chemovars using near-infrared (NIR) spectral data coupled with chemometric models. Six activated water bands-1342, 1364, 1384, 1412, 1440, and 1462 nm, occurred consistently in all of the spectrum exploration steps as well as in the partial least squares-discriminant analysis (PLS-DA) steps. However, according to major class and chemovar aquagram values, the largest spectral variation was associated with the following bands: 1412, 1364, 1374, 1384, 1488, and 1512 nm. A strong positive correlation between 1364, 1374, and 1384 nm aquagram values and a strong negative correlation between 1412 and 1512 nm aquagram values were observed through all aquagram analysis steps. These water activated bands were found to serve as good discriminators and classifiers according to either major class or chemovar. Furthermore, significant differences in the water matrix structure of different cannabis chemovars were observed, with the highest variations associated with the presence of free water molecules, small molecule solvation shells, extent of strongly bound water, and the number of hydrogen bonds per water molecule. Minor cannabinoids and terpenes such as cannabigerolic acid and (-)-guaiol displayed relatively high correlations with these bands. The results of this study suggest that the most accurate way to explore the cannabis inflorescence water matrix spectral pattern is by chemovars and not by major classes.

Phytochemical profiling of oak bark extract: A combined approach using near-infrared spectroscopy and liquid chromatography-mass spectrometry

The aim of the presented study was to qualitatively and quantitatively determine the chemical composition of oak bark extracts in order to gain insights into the effectiveness as alternative medication for various diseases. The primary emphasis was on developing a near-infrared spectroscopy (NIRS) method for precise quantification of two key polyphenolic compounds, specifically gallic acid and catechin, in form of a fast and non-destructive quality control. A comprehensive dataset consisting of 48 samples from various production batches was analyzed throughout this research. Qualitative analysis was conducted using High Performance Liquid Chromatography coupled with a mass detector (LC-MS) to separate and identify individual components of the oak bark extract. Individual components were identified, confirmed and quantified using existing literature combined with appropriate standard references. Whereas the predominant nature of identified substances was of polyphenolic nature. Furthermore, a semi-quantitative assessment was additionally performed for eight identified constituents to identify their chemical stability or possible occurring transformations during storage, utilizing quantification via internal standard met in order to identify fluctuations and chemical variability within oakbark, five key components were precisely quantified using LC-MS and corresponding standard substances. For this purpose, HPLC measurements coupled to an Ultraviolet/Visible (UV/Vis) detector were utilized as reference method. NIRS measurements were performed on a FT-NIR benchtop device in transmission mode. Partial least squares regression (PLSR) was then applied for model building, after identifying the optimal spectral pretreatment. Model evaluation was performed using leave-one-out-cross validation followed by evaluation of an independent test set. The model proved promising results for the quantification of gallic acid on the benchtop device with a standard error of cross validation (SECV) of 13.41 mg/L and a standard error of prediction (SEP) of 19.33 mg/L, while the absolute concentrations of the different batches analyzed ranged from 126.49 to 332.54 mg/L. For the quantification of catechin the SECV was reported at 23.61 mg/L, the SEP at 32.35 mg/L with sample concentrations falling between 13.50 and 383.72 mg/L. In this study, we introduce various analytical methodologies for both qualitative and quantitative assessment of a complex phytochemical sample, specifically oak bark extract, aimed at identifying and confirming the presence of active compounds within the extract.

Nondestructive Determination of Tocopherol and Tocotrienol in Vitamin E Powder Using Near- and Mid-Infrared Spectroscopy

Vitamin E is an essential nutrient, but its poor water solubility limits food and pharmaceutical applications. The usability of vitamin E can be enhanced via modification methods such as encapsulation, which transforms the physical state of vitamin E from a liquid to a powder. This study examined the efficacy of near-infrared (NIR) and mid-infrared (MIR) spectroscopy in identifying and predicting various vitamin E derivatives in vitamin E-encapsulated powder (VEP). An MIR analysis revealed the fundamental C-H vibrations of vitamin E in the range of 2700-3250 cm-1, whereas an NIR analysis provided information about the corresponding combination, first, and second overtones in the range of 4000-9000 cm-1. The MIR and NIR data were analyzed using a principal component analysis to characterize the VEP. Partial least squares (PLS) regression was applied to predict the content of individual vitamin E derivatives. PLS cross-validation revealed that NIR analysis provides more reliable predictive accuracy and precision for the contents of vitamin E derivatives, achieving a higher coefficient of determination for prediction (Q2) (0.92-0.99) than MIR analysis (0.20-0.85). For test set validation, the NIR predictions exhibited a significant level of accuracy, as indicated by a high ratio of prediction to deviation (RPD) and Q2. Furthermore, the PLS models developed using the NIR data had statistically significant predictive performance, with a high RPD (1.54-3.92) and Q2 (0.66-0.94). Thus, NIR spectroscopy is a valuable nondestructive technique for analyzing vitamin E samples, while MIR spectroscopy serves as a useful method for confirming its presence.

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.

Advances in Vibrational Spectroscopic Techniques for the Detection of Bio-Active Compounds in Virgin Olive Oils: A Comprehensive Review

Vibrational spectroscopic techniques have gained significant attention in recent years for their potential in the rapid and efficient analysis of virgin olive oils, offering a distinct advantage over traditional methods. These techniques are particularly valuable for detecting and quantifying bio-active compounds that contribute to the nutritional and health benefits of virgin olive oils. This comprehensive review explores the latest advancements in vibrational spectroscopic techniques applied to virgin olive oils, focusing on the detection and measurement of key bio-active compounds such as unsaturated fatty acids, phenolic compounds, and other antioxidant compounds. The review highlights the improvements in vibrational spectroscopy, data processing, and chemometric techniques that have significantly enhanced the ability to accurately identify these compounds compared to conventional analytical methods. Additionally, it addresses current challenges, including the need for standardized methodologies and the potential for integrating vibrational spectroscopy with other analytical techniques to improve accuracy and reliability. Finally, findings over the last two decades, in which vibrational spectroscopy techniques were effectively used for the detailed characterization of bio-active compounds in virgin olive oils, are discussed.

Honey Adulteration Detection via Ultraviolet-Visible Spectral Investigation Coupled with Chemometric Analysis

Any change in the composition or physicochemical parameters of honey outside the standardized intervals may be deemed fraud, irrespective of direct introduction of certain substances or feeding honeybees with syrups. Simple and rapid tools along with more sophisticated ones are required to monitor fraudulent practices in the honey trade. In this work, UV-Vis spectroscopy was used to identify and quantify six Romanian honey types (five monofloral and one polyfloral) mixed with commercially available corn syrup, corn syrup with plant extracts, inverted syrup, and fruit syrup at different concentrations (5%, 10%, 20%, 30%, 40%, and 50%). Relevant spectral features were used to develop a neural model, which was able to pinpoint adulteration, regardless of the honey and adulterant type. The proposed model was able to detect adulteration levels higher than 10%, thereby serving as a cost-effective and reliable tool to monitor honey quality.

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades

During food quality control, NIR technology enables the rapid and non-destructive determination of the typical quality characteristics of food categories, their origin, and the detection of potential counterfeits. Over the past 20 years, the NIR results for a variety of food groups-including meat and meat products, milk and milk products, baked goods, pasta, honey, vegetables, fruits, and luxury items like coffee, tea, and chocolate-have been compiled. This review aims to give a broad overview of the NIRS processes that have been used thus far to assist researchers employing non-destructive techniques in comparing their findings with earlier data and determining new research directions.

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.

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.

Simple and inexpensive microwave setup for industrial based applications: Quantification of flower honey adulteration as a case study

A simple and inexpensive microwave measurement setup based on measurements of magnitudes of transmission properties (| S 21 | dB ) is proposed for industrial-based microwave aquametry (moisture or water content) applications. An easy-to-apply calibration procedure based on normalization is implemented to eliminate systematic errors in the measurement system. As a case study, we applied this setup for the quantification of water-adulteration in flower honey. After validating this system by distilled water and pure flower honey measurements,| S 21 | dB measurements of the pure flower honey with various adulteration percentages ( δ ) up to 9% are conducted to examine the performance of the measurement setup for quantification of water adulteration. A multi-dimensional fitting procedure is implemented to predict δ using the proposed inexpensive microwave measurement setup. It is shown that it is possible to quantify an adulteration level with an accuracy better than ∓ 1 % by the proposed measurement setup and the applied multi-dimensional fitting procedure.

Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy

Introduction

Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming.

Methods

To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models.

Results

The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix).

Discussion

This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.

Rapid prediction method of ZIF-8 immobilized Candida rugosa lipase activity by near-infrared spectroscopy

Candida rugosa lipase (CRL, EC3.1.1.3) is one of the main enzymes synthesizing esters, and ZIF-8 was chosen as an immobilization carrier for lipase. Enzyme activity testing often requires expensive reagents as substrates, and the experiment processes are time-consuming and inconvenient. As a result, a novel approach based on near-infrared spectroscopy (NIRs) was developed for predicting CRL/ZIF-8 enzyme activity. The absorbance of the immobilized enzyme catalytic system was evaluated using UV-Vis spectroscopy to investigate the amount of CRL/ZIF-8 enzyme activity. The powdered samples' near-infrared spectra were obtained. The sample's enzyme activity data were linked with each sample's original NIR spectra to establish the NIR model. A partial least squares (PLS) model of immobilized enzyme activity was developed by coupling spectral preprocessing with a variable screening technique. The experiments were completed within 48 h to eliminate inaccuracies between the reduction in enzyme activity with increasing laying-aside time throughout the test and the NIRs modeling. The root-mean-square error of cross-validation (RMSECV), the correlation coefficient of validation set (R) value, and the ratio of prediction to deviation (RPD) value were employed as assessment model indicators. The near-infrared spectrum model was developed by merging the best 2nd derivative spectral preprocessing with the Competitive Adaptive Reweighted Sampling (CARS) variable screening method. This model's root-mean-square error of cross-validation (RMSECV) was 0.368 U/g, the correlation coefficient of calibration set (R_cv) value was 0.943, the root-mean-square error of prediction (RMSEP) set was 0.414 U/g, the correlation coefficient of validation set (R) value was 0.952, and the ratio of prediction to deviation (RPD) was 3.0. The model demonstrates that the fitting relationship between the predicted and the reference enzyme activity value of the NIRs is satisfactory. The findings revealed a strong relationship between NIRs and CRL/ZIF-8 enzyme activity. As a result, the established model could be implemented to quantify the enzyme activity of CRL/ZIF-8 quickly by including more variations of natural samples. The prediction method is simple, rapid, and adaptable to be the theoretical and practical basis for further studying other interdisciplinary research work in enzymology and spectroscopy.

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.

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

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

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.

Aquaphotomics—Exploring Water Molecular Systems in Nature

Since its birth in 2005, when introduced by Prof [...]

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning

Confusing low-molecular-weight hyaluronic acid (LMWHA) from acid degradation and enzymatic hydrolysis (named LMWHA-A and LMWHA-E, respectively) will lead to health hazards and commercial risks. The purpose of this work is to analyze the structural differences between LMWHA-A and LMWHA-E, and then achieve a fast and accurate classification based on near-infrared (NIR) spectroscopy and machine learning. First, we combined nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, two-dimensional correlated NIR spectroscopy (2DCOS), and aquaphotomics to analyze the structural differences between LMWHA-A and LMWHA-E. Second, we compared the dimensionality reduction methods including principal component analysis (PCA), kernel PCA (KPCA), and t-distributed stochastic neighbor embedding (t-SNE). Finally, the differences in classification effect of traditional machine learning methods including partial least squares-discriminant analysis (PLS-DA), support vector classification (SVC), and random forest (RF) as well as deep learning methods including one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were compared. The results showed that genetic algorithm (GA)-SVC and RF were the best performers in traditional machine learning, but their highest accuracy in the test dataset was 90%, while the accuracy of 1D-CNN and LSTM models in the training dataset and test dataset classification was 100%. The results of this study show that compared with traditional machine learning, the deep learning models were better for the classification of LMWHA-A and LMWHA-E. Our research provides a new methodological reference for the rapid and accurate classification of biological macromolecules.

Aquaphotomics Monitoring of Lettuce Freshness during Cold Storage

Fresh-cut leafy vegetables are one of the most perishable products because they readily deteriorate in quality even during cold storage and have a relatively short shelf life. Since these products are in high demand, methods for rigorous quality control and estimation of freshness that are rapid and non-destructive would be highly desirable. The objective of the present research was to develop a rapid, non-destructive near-infrared spectroscopy (NIRS)-based method for the evaluation of changes during cold storage of lettuce using an aquaphotomics approach to monitor the water molecular structure in lettuce leaves. The reference measurements showed that after 6 days of dark, cold storage, the weight and water activity of lettuce leaves decreased and β-carotene decreased, while chlorophylls slightly increased. Aquaphotomics characterization showed large differences in the lettuce leaves' spectra depending on their growth zone. Difference spectra, principal component analysis (PCA) and linear discriminant analysis (LDA) confirmed the differences in the inner and outer leaves and revealed that spectra change as a function of storage time. Partial least squares regression (PLSR) allowed the prediction of the time spent in storage with a coefficient of determination of R² = 0.80 and standard error of RMSE = 0.77 days for inner, and R² = 0.86 and RMSE = 0.66 days for outer leaves, respectively. The following water absorbance bands were found to provide the most information in the spectra: 1348, 1360, 1373, 1385, 1391, 1410, 1416, 1422, 1441, 1447, 1453, 1466, 1472, 1490, 1503, 1515, 1521, 1534 and 1571 nm. They were further used as water matrix coordinates (WAMACs) to define the water spectral patterns (WASPs) of lettuce leaves. The WASPs of leaves served to succinctly describe the state of lettuces during storage. The changes in WASPs during storage reveled moisture loss, damage to cell walls and expulsion of intracellular water, as well as loss of free and weakly hydrogen-bonded water, all leading to a loss of juiciness. The WASPs also showed that damage stimulated the defense mechanisms and production of vitamin C. The leaves at the end of the storage period were characterized by water strongly bound to collapsed structural elements of leaf tissues, mainly cellulose, leading to a loss of firmness that was more pronounced in the outer leaves. All of this information was reflected in the changes of absorbance in the identified WAMACs, showing that the water molecular structure of lettuce leaves accurately reflects the state of the lettuce during storage and that WASPs can be used as a multidimensional biomarker to monitor changes during storage.

Hyperspectral Microscopy Technology to Detect Syrups Adulteration of Endemic Guindo Santo and Quillay Honey Using Machine-Learning Tools

Honey adulteration is a common practice that affects food quality and sale prices, and certifying the origin of the honey using non-destructive methods is critical. Guindo Santo and Quillay are fundamental for the honey production of Biobío and the Ñuble region in Chile. Furthermore, Guindo Santo only exists in this area of the world. Therefore, certifying honey of this species is crucial for beekeeper communities-mostly natives-to give them advantages and competitiveness in the global market. To solve this necessity, we present a system for detecting adulterated endemic honey that combines different artificial intelligence networks with a confocal optical microscope and a tunable optical filter for hyperspectral data acquisition. Honey samples artificially adulterated with syrups at concentrations undetectable to the naked eye were used for validating different artificial intelligence models. Comparing Linear discriminant analysis (LDA), Support vector machine (SVM), and Neural Network (NN), we reach the best average accuracy value with SVM of 93% for all classes in both kinds of honey. We hope these results will be the starting point of a method for honey certification in Chile in an automated way and with high precision.

Aquaphotomic Study of Effects of Different Mixing Waters on the Properties of Cement Mortar

The mixing water used for cement concrete has a significant effect on the physical properties of the material after hardening; however, other than the upper limit for the mixed impurities, not enough consideration has been given to the functions and characteristics of water at the molecular level. In this study, we investigated the effect of four different types of water (two spring-, mineral waters, tap water and distilled water) on the drying shrinkage of the hardened cement by comparing the material properties of the concrete specimens and analyzing the molecular structure of the water and cement mortar using aquaphotomics. The near infrared (NIR) spectra of waters used for mixing were acquired in the transmittance mode using a high-precision, high-accuracy benchtop spectrometer in the range of 400-2500 nm, with the 0.5 nm step. The NIR spectra of cement paste and mortar were measured in 6.2 nm increments in the wavelength range of 950 nm to 1650 nm using a portable spectrometer. The measurements of cement paste and mortar were performed on Day 0 (immediately after mixing, cement paste), 1 day, 3 days, 7 days, and 28 days after mixing (cement mortar). The spectral data were analyzed according to the aquaphotomics' multivariate analysis protocol, which involved exploration of raw and preprocessed spectra, exploratory analysis, discriminating analysis and aquagrams. The results of the aquaphotomics' analysis were interpreted together with the results of thermal and drying shrinkage measurements. Together, the findings clearly demonstrated that the thermal and drying shrinkage properties of the hardened cement material differed depending on the water used. Better mechanical properties were found to be a result of using mineral waters for cement mixing despite minute differences in the chemical content. In addition, the aquaphotomic characterization of the molecular structure of waters and cement mortar during the initial hydration reaction demonstrated the possibility to predict the characteristics of hardened cement at a very early stage. This provided the rationale to propose a novel evaluation method based on aquaphotomics for non-invasive evaluation and monitoring of cement mortar.

Aquaphotomics for monitoring of groundwater using short-wavelength near-infrared spectroscopy

Water spectrum of any aqueous system contains information about OH covalent and hydrogen bonds that are highly influenced by the environment and the rest of the molecules in the system. When aquaphotomics is used to analyze the water near infrared (NIR) spectra, the information about the water molecular structure can be obtained as a function of internal and external factors. The objective of this research is to apply aquaphotomics analysis to evaluate different groundwaters by using their NIR unique spectral pattern, robust to external influences of temperature and humidity, that can potentially be used for water type identification and screening practice. Two groundwaters obtained at different depths and their mixture, differing in mineral content and molecular structure were monitored on a daily basis using portable visible/NIR (vis/NIR) spectrometer during three consecutive years. The spectra were pre-processed by smoothing and multiplicative scatter correction (MSC) to remove noise and baseline effects. Results showed that NIR spectral patterns of groundwater samples were affected by changes in environmental factors - temperature, humidity, time and others. The water absorbance bands which are highly influenced by humidity and temperature in short wavelength NIR region were identified. Their avoidance resulted in obtaining consistent spectral patterns during the entire monitoring period, unique for each groundwater, that can be used as its fingerprint and monitored over time. Consistency and uniqueness of the spectral pattern for each groundwater provide a potential to use the deviation of spectral pattern as an indicator of changes in the water. These results confirm that vis/NIR spectral pattern can be used as an integrative marker of water status, stable over time, providing the basis for an efficient cost-effective method for monitoring of water functionality.

Water as a Probe for Standardization of Near-Infrared Spectra by Mutual-Individual Factor Analysis

The standardization of near-infrared (NIR) spectra is essential in practical applications, because various instruments are generally employed. However, standardization is challenging due to numerous perturbations, such as the instruments, testing environments, and sample compositions. In order to explain the spectral changes caused by the various perturbations, a two-step standardization technique was presented in this work called mutual–individual factor analysis (MIFA). Taking advantage of the sensitivity of a water probe to perturbations, the spectral information from a water spectral region was gradually divided into mutual and individual parts. With aquaphotomics expertise, it can be found that the mutual part described the overall spectral features among instruments, whereas the individual part depicted the difference of component structural changes in the sample caused by operation and the measurement conditions. Furthermore, the spectral difference was adjusted by the coefficients in both parts. The effectiveness of the method was assessed by using two NIR datasets of corn and wheat, respectively. The results showed that the standardized spectra can be successfully predicted by using the partial least squares (PLS) models developed with the spectra from the reference instrument. Consequently, the MIFA offers a viable solution to standardize the spectra obtained from several instruments when measurements are affected by multiple factors.

Rapid determination of the total content of oleanolic acid and ursolic acid in Chaenomelis Fructus using near-infrared spectroscopy

Chaenomelis Fructus is a widely used traditional Chinese medicine with a long history in China. The total content of oleanolic acid (OA) and ursolic acid (UA) is taken as an important quality marker of Chaenomelis Fructus. In this study, quantitative models for the prediction total content of OA and UA in Chaenomelis Fructus were explored based on near-infrared spectroscopy (NIRS). The content of OA and UA in each sample was determined using high-performance liquid chromatography (HPLC), and the data was used as a reference. In the partial least squares (PLS) model, both leave one out cross validation (LOOCV) of the calibration set and external validation of the validation set were used to screen spectrum preprocessing methods, and finally the multiplicative scatter correction (MSC) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLS regression, and the characteristic spectrum range was determined as 7,500-4,250 cm^-1, with 14 optimal ranks. In the back propagation artificial neural network (BP-ANN) model, the scoring data of 14 ranks obtained from PLS regression analysis were taken as input variables, and the total content of OA and UA reference values were taken as output values. The number of hidden layer nodes of BP-ANN was screened by full-cross validation (Full-CV) of the calibration set and external validation of the validation set. The result shows that both PLS model and PLS-BP-ANN model have strong prediction ability. In order to evaluate and compare the performance and prediction ability of models, the total content of OA and UA in each sample of the test set were detected under the same HPLC conditions, the NIRS data of the test set were input, respectively, to the optimized PLS model and PLS-BP-ANN model. By comparing the root-mean-square error (RMSEP) and determination coefficient (R ²) of the test set and ratio of performance to deviation (RPD), the PLS-BP-ANN model was found to have better performance with RMSEP of 0.59 mg·g^-1, R ² of 95.10%, RPD of 4.53 and bias of 0.0387 mg·g^-1. The results indicated that NIRS can be used for the rapid quality control of Chaenomelis Fructus.

Novel Application of NIR Spectroscopy for Non-Destructive Determination of 'Maraština' Wine Parameters

This study investigates the colour and standard chemical composition of must and wines produced from the grapes from Vitis vinifera L., 'Maraština', harvested from 10 vineyards located in two different viticultural subregions of the Adriatic region of Croatia: Northern Dalmatia and Central and Southern Dalmatia. The aim was to explore the use of NIR spectroscopy combined with chemometrics to determine the characteristics of Maraština wines and to develop calibration models relating NIR spectra and physicochemical/colour data. Differences in the colour parameters (L*, a*, hue) of wines related to the subregions were confirmed. Colour difference (ΔE) of must vs. wine significantly differed for the samples from the Maraština grapes grown in both subregions. Principal component regression was used to construct the calibration models based on NIR spectra and standard physicochemical and colour data showing high prediction ability of the 13 studied parameters of must and/or wine (average R² of 0.98 and RPD value of 6.8). Principal component analysis revealed qualitative differences of must and wines produced from the same grape variety but grown in different subregions.

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.

Revealing the Effect of Heat Treatment on the Spectral Pattern of Unifloral Honeys Using Aquaphotomics

In this study we aimed to investigate the effect of heat treatment on the spectral pattern of honey using near infrared spectroscopy (NIRS). For the research, sunflower, bastard indigo, and acacia honeys were collected from entrusted beekeepers. The honeys were not subject to any treatment before. Samples were treated at 40 °C, 60 °C, 80 °C, and 100 °C for 60, 120, 180, and 240 min. This resulted in 17 levels, including the untreated control samples. The 5-hydroxymethylfurfural (HMF) content of the honeys was determined using the Winkler method. NIRS spectra were recorded using a handheld instrument. Data analysis was performed using ANOVA for the HMF content and multivariate analysis for the NIRS data. For the latter, PCA, PCA-LDA, and PLSR models were built (using the 1300–1600 nm spectral range) and the wavelengths presenting the greatest change induced by the perturbations of temperature and time intervals were collected systematically, based on the difference spectra and the weights of the models. The most contributing wavelengths were used to visualize the spectral pattern changes on the aquagrams in the specific water matrix coordinates. Our results showed that the heat treatment highly contributed to the formation of free or less bonded water, however, the changes in the spectral pattern highly depended on the crystallization phase and the honey type.

NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends

Coffee is both a vastly consumed beverage and a chemically complex matrix. For a long time, an arduous chemical analysis was necessary to resolve coffee authentication issues. Despite their demonstrated efficacy, such techniques tend to rely on reference methods or resort to elaborate extraction steps. Near infrared spectroscopy (NIRS) and the aquaphotomics approach, on the other hand, reportedly offer a rapid, reliable, and holistic compositional overview of varying analytes but with little focus on low concentration mixtures of Robusta-to-Arabica coffee. Our study aimed for a comparative assessment of ground coffee adulteration using NIRS and liquid coffee adulteration using the aquaphotomics approach. The aim was to demonstrate the potential of monitoring ground and liquid coffee quality as they are commercially the most available coffee forms. Chemometrics spectra analysis proved capable of distinguishing between the studied samples and efficiently estimating the added Robusta concentrations. An accuracy of 100% was obtained for the varietal discrimination of pure Arabica and Robusta, both in ground and liquid form. Robusta-to-Arabica ratio was predicted with R2CV values of 0.99 and 0.9 in ground and liquid form respectively. Aquagrams results accentuated the peculiarities of the two coffee varieties and their respective blends by designating different water conformations depending on the coffee variety and assigning a particular water absorption spectral pattern (WASP) depending on the blending ratio. Marked spectral features attributed to high hydrogen bonded water characterized Arabica-rich coffee, while those with the higher Robusta content showed an abundance of free water structures. Collectively, the obtained results ascertain the adequacy of NIRS and aquaphotomics as promising alternative tools for the authentication of liquid coffee that can correlate the water-related fingerprint to the Robusta-to-Arabica ratio.

Preliminary Study for Inspecting Moisture Content, Dry Matter Content, and Firmness Parameters of Two Date Cultivars Using an NIR Hyperspectral Imaging System

The assessment and assurance of the quality attributes of dates is a key factor in increasing the competitiveness and consumer acceptance of this fruit. The increasing demand for date fruits requires a rapid and automated method for monitoring and analyzing the quality attributes of date fruits to replace the conventional methods used by inspection which limits the production and involves human errors. Moisture content (MC), dry matter content (DMC), and firmness (F) are three important quality attributes for two date cultivars (Khalas and Sukkari) that have been inspected using the hyperspectral imaging (HSI) technique based on the reflectance mode. Images of intact date fruits at the maturity stage Tamr were obtained within the wavelength range of 950–1750 nm. Monitoring and assessment of MC, DMC, and F [first maximum rupture force (MF, N)] were performed using a partial least squares regression model. Accurate prediction models were attained. The results highlight that the coefficients of determination (R²_Prediction) are estimated to be 0.91 and 0.89 for MC, DMC, and F (N) with the lowest values of the standard error of prediction (SEP) equal to 0.82, 0.81 (%), and 4.12 (N), respectively, and the residual predictive deviation (RPD) values were 3.65, 3.69, and 3.42 for MC, DMC, and F (N), respectively. The results obtained from this preliminary study indicate the great potential of applying HSI for the assessment of physical, chemical, and sensory quality attributes of date fruits overall in the five maturity stages.

Determination of starch and amylose contents in various cereals using common model of near-infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used to determine the total starch and amylose contents in various kinds of cereals namely wheat, waxy rice, non-waxy rice, millet, sorghum, waxy maize, buckwheat, barley, and hulless oat. The partial least-squares (PLS) analysis and principal component regression (PCR) were used to establish the calibration models. PLS model achieved a better effect than PCR at 1100 - 2500 nm, and the coefficient of determination (R2) of the calibration and prediction sets were both higher than 0.9 after the best pre-treatment method, first derivative plus Savitzky-Golay. Additionally, the root mean square error (RMSE) was lower than 2.50, and the root mean square error of cross-validation (RMSECV) was less than 3.50 for starch. By comparing PLS models at different waveband regions, the optimal determination results for starch and amylose were obtained at 1923 - 1961 and 1724 - 1818 nm, respectively. NIRS was found to be a successful method to determine of the starch and amylose contents in various cereals.

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.

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.

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

Plasmon mediated spectrally selective and sensitivity-enhanced uncooled near-infrared detector

Here, we present a high performance uncooled near-infrared (NIR) detector comprising of a giga hertz (GHz) solidly mounted resonator (SMR) and gold nanorods (GNRs) arrays. By coupling the localized surface plasmon resonances of GNRs, the resonator system exhibits optimized optical response to vis-NIR region. Both simulation and experiments demonstrate the hybrid GNRs-SMR exhibit significantly enhanced optical responsive sensitivity of NIR, the tunable aspect ratios (AR) of GNRs enable resonator respond sensitively to selected light. Specially, taking advantage of the acoustofluidic effect of SMR, the GNRs can be controllably and precisely modified on the microchip surface in an ultra-short time, which addresses one of the most fundamental challenges in the localized functionalization of micro/nano scale surface. The presented work opens new directions in development of novel miniaturized, tunable NIR detector.

Early Diagnosis of Type 2 Diabetes Based on Near-Infrared Spectroscopy Combined With Machine Learning and Aquaphotomics

Early diagnosis is important to reduce the incidence and mortality rate of diabetes. The feasibility of early diagnosis of diabetes was studied via near-infrared spectra (NIRS) combined with a support vector machine (SVM) and aquaphotomics. Firstly, the NIRS of entire blood samples from the population of healthy, pre-diabetic, and diabetic patients were obtained. The spectral data of the entire spectra in the visible and near-infrared region (400–2,500 nm) were used as the research object of the qualitative analysis. Secondly, several preprocessing steps including multiple scattering correction, variable standardization, and first derivative and second derivative steps were performed and the best pretreatment method was selected. Finally, for the early diagnosis of diabetes, models were established using SVM. The first overtone of water (1,300–1,600 nm) was used as the research object for an aquaphotomics model, and the aquagram of the healthy group, pre-diabetes, and diabetes groups were drawn using 12 water absorption patterns for the early diagnosis of diabetes. The results of SVM showed that the highest accuracy was 97.22% and the specificity and sensitivity were 95.65 and 100%, respectively when the pretreatment method of the first derivative was used, and the best model parameters were c = 18.76 and g = 0.008583.The results of the aquaphotomics model showed clear differences in the 1,400–1,500 nm region, and the number of hydrogen bonds in water species (1,408, 1,416, 1,462, and 1,522 nm) was evidently correlated with the occurrence and development of diabetes. The number of hydrogen bonds was the smallest in the healthy group and the largest in the diabetes group. The suggested reason is that the water matrix of blood changes with the worsening of blood glucose metabolic dysfunction. The number of hydrogen bonds could be used as biomarkers for the early diagnosis of diabetes. The result show that it is effective and feasible to establish an accurate and rapid early diagnosis model of diabetes via NIRS combined with SVM and aquaphotomics.

Influence of steroids on hydrogen bonds in membranes assessed by near infrared spectroscopy

The covalent OH bonds of water vibrate and absorb radiation in the near infrared (NIR) region at wavelengths that vary according to the strength of the bonds which, at the same time, are sensitive to the number and/or strength of hydrogen bonds. By means of multivariate analytical tools, such spectral shift was exploited to study the effect of temperature, 25-hydroxycholesterol and progesterone on the H-bonded network of water in DMPA membranes. Temperature was found as the dominating factor altering the NIR spectra of water and then the H-bonds. Increasing temperatures disrupt the H-bonds network, strengthening the OH covalent bonds. The disruption of the H-bonds along the 13-58 °C range was noticeably greater than that caused by lipids or steroids at 500 μM. The H-bonded network of the interfacial water in DMPA membranes was disrupted by the presence of 25-hydroxycholesterol, but no significant disruption was observed in the presence of progesterone. The reduction of the H-bonds entails a reduction in the aggregation of the interfacial water by a reduction in the number of H-bonded molecules. It is proposed that the number of water molecules bonded with two H-bonds diminishes and the number of molecules with no H-bond increases roughly at similar proportions, with a constant population of molecules with one H-bond. The opposed effects of steroids are discussed in the context of their opposed effects on the phase state of membranes, the membrane water content and the steroid molecular structure.

Classification of Bee Pollen and Prediction of Sensory and Colorimetric Attributes-A Sensometric Fusion Approach by e-Nose, e-Tongue and NIR

The chemical composition of bee pollens differs greatly and depends primarily on the botanical origin of the product. Therefore, it is a crucially important task to discriminate pollens of different plant species. In our work, we aim to determine the applicability of microscopic pollen analysis, spectral colour measurement, sensory, NIR spectroscopy, e-nose and e-tongue methods for the classification of bee pollen of five different botanical origins. Chemometric methods (PCA, LDA) were used to classify bee pollen loads by analysing the statistical pattern of the samples and to determine the independent and combined effects of the above-mentioned methods. The results of the microscopic analysis identified 100% of sunflower, red clover, rapeseed and two polyfloral pollens mainly containing lakeshore bulrush and spiny plumeless thistle. The colour profiles of the samples were different for the five different samples. E-nose and NIR provided 100% classification accuracy, while e-tongue > 94% classification accuracy for the botanical origin identification using LDA. Partial least square regression (PLS) results built to regress on the sensory and spectral colour attributes using the fused data of NIR spectroscopy, e-nose and e-tongue showed higher than 0.8 R² during the validation except for one attribute, which was much higher compared to the independent models built for instruments.