Near infrared spectroscopy applied to the rapid prediction of the floral origin and mineral content of honeys

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.

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.

Determination of the geographic origin of 52 honey samples based on the assessment of anionic content profiling with a new algorithm using monolithic column-based micellar nano-liquid chromatography

In the present study, a new micellar nano LC-UV was, for the first time, reported for the separation and determination of five anions (chloride, nitrite, bromide, sulfate and nitrate) in 52 honey samples. Based on this approach, a graphene oxide-based monolithic column was prepared and applied for the samples. Various amounts of hexadecyltrimethyl-ammonium bromide (HTAB) in the mobile phase were used in order to optimize the separation conditions. The baseline separation was achieved using mobile phase with 25/75% (v/v) ACN/10 mM phosphate buffer at pH 3.4, while the amount of HTAB was optimized as 0.22 mM in the mobile phase. The whole method was validated and it leads to high sensitivity. The LOD values were found in the range of 0.02-0.22 µg/kg, while LOQ values were found in the range of 0.06-0.18 µg/kg. The method allowed to achieve sensitivity analyses of anionic content in 52 honey samples. All data were evaluated using a new algorithm for geographic origin discrimination. K-nearest neighbor algorithm (K-NN), cubic support vector classifier (K-DVS), and K-Mean cluster analysis were used for geographic origin discrimination of honeys. The accuracy of the whole model was calculated as 94.4% with the K-DVS method. The samples from five provinces were classified 100% correctly, while two of them were classified with one misclassification, with an accuracy of 89.9% and 83.3%, respectively. PRACTICAL APPLICATION: The new platforms and advanced technologies are crucial for advanced food analysis. In this article, a novel methodology was attempted for the determination of geographic origin of 52 honey samples. In this sense, micellar nano LC technique with a homemade monolithic nano-column was, for the first time, applied for the anion analysis using a new algorithm.

Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach

Near-Infrared Spectroscopy (NIRS) has shown to be helpful in the study of rice, tea, cocoa, and other foods due to its versatility and reduced sample treatment. However, the high complexity of the data produced by NIR sensors makes necessary pre-treatments such as feature selection techniques that produce compact profiles. Supervised and unsupervised techniques have been tested, creating different subsets of features for classification, which affect the performance of the classifiers based on such compact profiles. In this sense, we propose and test a new covering array feature selection (CAFS) algorithm coupled to the naïve Bayes classifier (NBC) to discriminate among Amazonian cacao nibs from six cacao clones. The CAFS wrapper approach looks for the wavebands that maximize the F₁-score, and then, are more relevant for classification. For this purpose, cacao pods of six varieties were collected, and their grains were extracted and processed (fermented, dried, roasted, and milled) to obtain cacao nibs. Then from each clone NIR spectral profiles in the range of 1100-2500 nm were extracted, and relevant wavebands were selected using the proposed CAFS algorithm. For comparison, two standard feature selection techniques were implemented the multi-cluster feature selection MCFS and the eigenvector centrality feature selection ECFS. Then, based on the different selected variables, three NBCs were built and compared among them through statistical metrics. The results showed that using the wavebands selected by CAFS, the NBC performed an average accuracy of 99.63%; being this superior to the 94.92% and 95.79% for ECFS and MCFS respectively. These results showed that the wavebands selected by the proposed CAFS algorithm allowed obtaining a better fit concerning other feature selection methods reported in the literature.

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.

Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing

There is an increase in the consumption of natural foods with healthy benefits such as honey. The physicochemical composition contributes to the particularities of honey that differ depending on the botanical origin. Botanical and geographical declaration protects consumers from possible fraud and ensures the quality of the product. The objective of this study was to develop prediction models using a portable near-Infrared (MicroNIR) Spectroscopy to contribute to authenticate honeys from Northwest Spain. Based on reference physicochemical analyses of honey, prediction equations using principal components analysis and partial least square regression were developed. Statistical descriptors were good for moisture, hydroxymethylfurfural (HMF), color (Pfund, L and b* coordinates of CIELab) and flavonoids (RSQ > 0.75; RPD > 2.0), and acceptable for electrical conductivity (EC), pH and phenols (RSQ > 0.61; RDP > 1.5). Linear discriminant analysis correctly classified the 88.1% of honeys based on physicochemical parameters and botanical origin (heather, chestnut, eucalyptus, blackberry, honeydew, multifloral). Estimation of quality and physicochemical properties of honey with NIR-spectra data and chemometrics proves to be a powerful tool to fulfil quality goals of this bee product. Results supported that the portable spectroscopy devices provided an effective tool for the apicultural sector to rapid in-situ classification and authentication of honey.

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.

Multielemental characterization of honey using inductively coupled plasma mass spectrometry fused with chemometrics

Honey is considered a desirable ingredient in a range of different foodstuffs because of its nutrient and therapeutic effect. The honey characteristics mainly depend on the type of vegetation visited by the bees and the climatic conditions in which the plants are growing. Therefore, the purity, floral and geographical origin and authenticity are important factors influencing the overall perception of honey and honey-based products in terms of quality and price. An important parameter in this picture is the elemental composition of honey because it can be linked with the floral type of honey, floral plant density and the botanical origin of nectar and pollens. In this work, the concentration range variation of 18 elements (Al, As, Ba, Ca, Cd, Co, Cr, Cu, Mg, Mn, Na, Ni, K, Pb, Sr, Ti, V and Zn) was investigated in four varieties of honey (linden, acacia, rape, and sunflower) originating from Romania, because the elemental profile of honey may give important information to differentiate its geographical and varietal origin for authenticity purpose. All the determinations were carried out by inductively coupled plasma quadrupole mass spectrometry (ICP-Q-MS). The most abundant minerals decreased in the following order: K > Ca > Mg > Na, having the mean values of 248.70, 59.97, 20.54 and 11.92 mg kg^-1 , respectively. The mineral content marks the differences in honey samples from different botanical origin and can be used as a tool for authentication purposes and also extends its applicability to assess the traceability of honey. Analysis of variance showed the preliminary relationships between the elements and samples. Further, the discrimination between different studied honey samples was achieved by principal component analysis (PCA). The multivariate analysis of the data allowed us to separate the honey samples into distinct groups according to their macroelement and microelement composition, emphasizing the origin of variation of element concentrations by honey type. Therefore, this approach might be potentially useful for the control of honey quality, origin or authenticity, and even to use the honey as environmental tracer.

Characterization of Novel Protein Component as Marker for Floral Origin of Jujube (Ziziphus jujuba Mill.) Honey

Jujube (Ziziphus jujuba Mill.) honey, one of the most valuable honey varieties from China with unique characteristics, is vulnerable to being the target of adulteration and deliberate mislabeling of botanical origin. This study investigated the typical protein component of jujube honey to authenticate the floral source by SDS-PAGE analysis combined with LC-MS/MS identification, and its stability to heating was also evaluated. One band and two adjacent but independent bands, both with molecular weights of ∼19 kDa, were notably observed in Coomassie brilliant blue- and silver-stained SDS-PAGE gels, respectively, for jujube honey from different geographic origins, whereas that was not present for the other five botanical honey varieties, suggesting this protein component was suitable as a marker for jujube honey. LC-MS/MS identification revealed that it was constituted by one Z. jujuba-derived protein (gene number:Zj.jz016003045) and two A. mellifera-derived proteins (an uncharacterized protein with accession number tr|A0A088AC16 and a cleavage fragment from major royal jelly protein-1), and the existence of plant-derived protein was attributed to the special neutral pH of jujube honey. Additionally, these protein markers exhibited good stability to heating below 85 °C/30 min. This study provided a simple method to characterize jujube honey and first identified a protein indicator to determine the botanical origin of honey.

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

According to European Union regulations, honey is a pure product and adding to or removing from it any kind of substance is illegal. Nevertheless, its adulteration by adding inexpensive and artificial adulterants is a common practice. This paper deals with the use of visible and near-infrared spectroscopy (Vis-NIRS) combined with chemometric tools as a screening technique for the identification and quantification of different types of adulterants (inverted sugar, rice syrup, brown cane sugar and fructose syrup) added to high-quality honey (Granada Protected Designation of Origin, Spain) at different levels (5%-50%). A complete discrimination between non-adulterated and adulterated samples was achieved. A general regression model to quantify the adulteration levels was developed as well as specific models for each adulterant. The coefficients of determination were higher than 0.96 for all the models. These results demonstrate the capacity of Vis-NIRS combined with chemometric tools for honey quality control.

Fourier Transform Near-Infrared Spectroscopy and Chemometrics To Predict Zygosacchromyces rouxii in Apple and Kiwi Fruit Juices

This study investigated the capability of near-infrared spectroscopy (NIRS) to predict the concentration of Zygosaccharomyces rouxii in apple and kiwi fruit juices. The yeast was inoculated in fresh kiwi fruit juice ( n = 68), reconstituted kiwi juice ( n = 85), and reconstituted apple juice ( n = 64), followed by NIR spectra collection and plate counting. A principal component analysis indicated direct orthogonal signal correction preprocessing was suitable to separate spectral samples. Parameter optimization algorithms increased the performance of support vector machine regression models developed in a single variety juice system and a multiple variety juice system. Single variety juice models achieved accurate prediction of Z. rouxii concentrations, with the limit of quantification at 3 to 15 CFU/mL ( R² = 0.997 to 0.999), and the method was also feasible for Hanseniaspora uvarum and Candida tropicalis. The best multiple variety juice model obtained had a limit of quantification of 237 CFU/mL ( R² = 0.961) for Z. rouxii. A Bland-Altman analysis indicated good agreement between the support vector machine regression model and the plate counting method. It suggests that NIRS can be a high-throughput method for prediction of Z. rouxii counts in kiwi fruit and apple juices.

Terahertz time-domain attenuated total reflection spectroscopy applied to the rapid discrimination of the botanical origin of honeys

A new technique to identify the floral resources of honeys is demanded. Terahertz time-domain attenuated total reflection spectroscopy combined with chemometrics methods was applied to discriminate different categorizes (Medlar honey, Vitex honey, and Acacia honey). Principal component analysis (PCA), cluster analysis (CA) and partial least squares-discriminant analysis (PLS-DA) have been used to find information of the botanical origins of honeys. Spectral range also was discussed to increase the precision of PLS-DA model. The accuracy of 88.46% for validation set was obtained, using PLS-DA model in 0.5-1.5THz. This work indicated terahertz time-domain attenuated total reflection spectroscopy was an available approach to evaluate the quality of honey rapidly.

Authenticity determination of honeys with non-extractable proteins by means of elemental analyzer (EA) and liquid chromatography (LC) coupled to isotope ratio mass spectroscopy (IRMS)

The present work aims to systematically demonstrate the authenticity of honeys with non-extractable proteins for the first time, by means of EA-IRMS and LC-IRMS. Fifty-three pure honeys of various botanical and geographical origins were studied and a criterion on the basis of the stable carbon isotope ratio characterization of total honey and the main sugars was established for pure honeys. Parameters such as δ¹³C values of total honey and the main sugars were well utilized to identify honeys with non-extractable proteins. Thirty-five honeys from which protein could not be extracted were all identified as adulterated with C-4 sugars or C-3 sugars. The use of isotopic compositions and some systematic differences permit the honeys with non-extractable proteins to be reliably identified. The findings obtained in this work could supplement the AOAC 998.12 C-4 sugar method, with regard to honeys from which protein cannot be extracted.

Identification of monofloral honeys using HPLC-ECD and chemometrics

A total of 77 jujube, longan and chaste honey samples were collected from 18 different areas of China. Thirteen types of phenolic acids in the honey samples were analysed using high-performance liquid chromatography with electrochemical detection (HPLC-ECD). Moreover, HPLC-ECD fingerprints of the monofloral honey samples were established. From the analysis of the HPLC-ECD fingerprints, common chromatography peak information was obtained, and principal component analysis and discriminant analysis were performed using selected common chromatography peak areas as variables. By comparing with phenolic acids as variables, using a chemometric analysis which is based on the use of common chromatography peaks as variables, 36 honey samples and 41 test samples could be correctly identified according to their floral origin.