Classification of simple and complex sugar adulterants in honey by mid-infrared spectroscopy

Adulteration of honey with sugars is the most crucial quality assurance concern to the honey industry. The application of Fourier transform infrared spectroscopy as a screening tool for the determination of the type of sugar adulterant in honey was investigated. Spectra of honey adulterated with simple and complex sugars were recorded in the mid-infrared range using the attenuated total reflectance accessory of a Fourier transform infrared spectrometer. Adulterants considered were sugars (glucose, fructose and sucrose) and invert sugars (cane invert and beet invert). Predictive models were developed to classify the adulterated honey samples using discriminant analysis. Spectral data were compressed using principal component analysis and partial least-square methods. Linear discriminant analysis was used to discriminate the type of adulterant in three different honey varieties. An optimum classification of 100% was achieved for honey samples adulterated with glucose, fructose, sucrose and beet and cane invert sugars. Results demonstrated that discriminant analysis of the spectra of adulterated honey samples could be used for rapid detection of adulteration in honey.     

Detection of inverted beet sugar adulteration of honey by FTIR spectroscopy

A combination of Fourier transform infrared (FTIR) spectroscopy and multivariate statistics as a screening tool for the determination of beet medium invert sugar adulteration in three different varieties of honey is discussed. Honey samples with different concentrations of beet invert sugar were scanned using the attenuated total reflectance (ATR) accessory of the Bio‐Rad FTS‐6000 Fourier transform spectrometer. The spectral wavenumber region between 950 and 1500 cm^?1 was selected for partial least squares (PLS) regression to develop calibration models for beet invert sugar determination in honey samples. Results from the PLS (first derivative) models were slightly better than those obtained with other calibration models. Predictive models were also developed to classify beet sugar invert in three different varieties of honey samples using discriminant analysis. Spectral data were compressed using the principal component method, and linear discriminant and canonical variate analyses were used to detect the level of beet invert sugar in honey samples. The best predictive model for adulterated honey samples was achieved with canonical variate analysis, which successfully classified 88–94 per cent of the validation set. The present study demonstrated that Fourier transform infrared spectroscopy could be used for rapid detection of beet invert sugar adulteration in different varieties of honey. © 2001 Society of Chemical Industry     

Discrimination and classification of beet and cane inverts in honey by FT-Raman spectroscopy

As a natural product, honey has been prone to adulteration. Adulteration of honey by substituting with cheap invert sugars is a critical issue in the honey industry. Fourier Transform (FT) Raman Spectroscopy was used to detect adulterants such as cane and beet invert in honey. FT Ra man spectrum of adulterated samples were characterized and the region between 200 and 1600 cm⁻¹ (representing carbohydrates and amino acid fractions) was used for quantitative and discriminant analysis. Partial least squares, and principal component regression analysis were used for quantitative analysis while linear discriminant analysis and canonical variate analysis (CVA) were used for discriminant analysis. FT-Raman spectroscopy was efficient in predicting beet and cane invert adulterants (R²>0.91) in all three floral types of honey considered. Classification of adulterants in honey using CVA gave a minimum classification accuracy of about 96%.     

Detection of honey adulteration with beet sugar using stable isotope methodology

A usual aspect of our work involves the analysis of honey samples for later sale, following current Spanish legislation. Such analyses essentially consist of studying pollen sediments, and sensory and physicochemical analyses. With this background, it seemed appropriate to investigate possible adulterations due to the addition of sugar (beet and cane). To do this, we selected 49 samples of honey obtained from 14 floral types and used them for pollinic and sensory analyses and to detect possible adulterations due to the addition of beet sugar products (treating the oligosaccharide fraction contained in the honey with the galactose oxidase reaction) or due to corn syrup addition (with normal δ¹³C stable carbon isotope ratios). After classifying the samples according to the results of the pollen and sensory analyses, further assays were conducted. From the results it was concluded that 15% of the samples had been adulterated with beet sugar and 4% with cane sugar. The implementation of many analyses for each sample means that the results can be intercorrelated very well.     

Rapid Determination of Invert Cane Sugar Adulteration in Honey Using FTIR Spectroscopy and Multivariate Analysis

ABSTRACT: Fourier transform infrared spectroscopy with an attenuated total reflection sampling accessory was combined with multivariate analysis to determine the level (1% to 25%, wt/wt) of invert cane sugar adulteration in honey. On the basis of the spectral data compression by principal component analysis and partial least squares, linear discriminant analysis (LDA), and canonical variate analysis (CVA), models were developed and validated. Two types of artificial neural networks were applied: a quick back propagation network (BPN) and a radial basis function network (RBFN). The prediction success rates were better with LDA (93.75% for validation set) and BPN (93.75%) than with CVA (87.50%) and RBFN (81.25%).     

基于代谢组学技术的神农百花蜜真实性鉴别

目的 通过非靶向代谢组学鉴定真实神农百花蜜特征标志物,并建立基于偏最小二乘(partial least squares,PLS)的神农百花蜜判别模型。方法 采用基于超高效液相色谱-四极杆串联飞行时间质谱(ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry,UPLC-Q-TOF-MS)的非靶向代谢组学方法分析神农百花蜜样本,利用V+S图、聚类热图和受试者工作特征(receiver operating characteristic,ROC)曲线确定真实神农百花蜜特征标志物,利用神农百花蜜特征标志物构建PLS判别模型。结果 多元统计分析结果显示,主成分分析(principal component analysis,PCA)和正交偏最小二乘-判别分析(orthogonal partial least squares-discriminant analysis,OPLS-DA)可以实现真实神农百花蜜和对照组蜂蜜的区分;分别在正、负离子模式下筛选出了19种和27种差异代谢物,其中13...     

蜂蜜品种鉴别方法的研究进展

本研究利用氨基酸分析仪对我国3个不同品种单花蜜洋槐蜜、椴树蜜和油菜蜜,共计110个蜂蜜样品中17种氨基酸含量进行分析,并基于氨基酸含量结合统计分析方法进行蜂蜜种类鉴别分析。结果表明,油菜蜜中水解氨基酸含量高于椴树蜜和洋槐蜜,16种氨基酸含量在三个蜂蜜品种间存在差异。主成分分析（principle component analysis,PCA）结果表明不同植物源蜂蜜具有聚类趋势,偏最小二乘法判别分析（Partial least squares discriminant analysis,PLS-DA）结果表明油菜蜜可以和洋槐蜜和椴树蜜区分开来。线性判别分析（Linear discriminant analysis,LDA）结果表明3种蜂蜜整体判别率为92.7%,油菜蜜的判别率为92.3%。本研究为油菜蜜、洋槐蜜和椴树蜜分类鉴别提供数据支撑和参考依据。     

Determination of important biochemical properties of honey to discriminate pure and adulterated honey with sucrose (Saccharum officinarum L.) syrup

The aims of the present study were to determine biochemical properties of honey samples and to discriminate pure and adulterated honey produced by the standard bee feeding method (control honey), the shaking method (pure blossom honey), and overfeeding (100 kg/colony syrup) with sucrose syrup (adulterated honey). The biochemical properties evaluated were moisture, ash, acidity, hydroxymethylfurfural (HMF), specific sugars (i.e. fructose, glucose, fructose–glucose, sucrose, and maltose), diastase activity, δ¹³C value (honey), δ¹³C value (protein), electrical conductivity, potassium, vitamin C, and proline. Fifteen honey samples were analyzed by discriminant analysis stepwise method. Proline, electrical conductivity and sucrose were found as discriminative characters of samples. Based on these three properties 100% of original group cases (samples) correctly classified in their real group. We found that the honey produced by feeding with 100 kg sucrose syrup per colony contained the sucrose as low as pure blossom honey. Therefore, the sugar (sucrose, fructose and glucose) content of honey cannot be used to distinguish between adulterated (sucrose syrup) and pure blossom honey.     

Verification test of sensory analyses of comb and strained honeys produced as pure and feeding intensively with sucrose (Saccharum officinarum L.) syrup

The aims of the study were to discriminate comb and strained honeys produced by the standard beekeeping method (control), shaking method (pure blossom honey), and feeding intensively (100 kg/colony) with sucrose (adulterated honey) syrup by using sensory analysis and to develop a method to be used in identification of unknown or suspicion honey samples. In the study, twenty trained panelists assessed honey samples in relation to their properties including taste, odor, color, aroma, viscosity, dissolution in mouth, inflammation in throat, attractiveness, flavor and general impression during four months. There were no differences in odor, viscosity, and dissolution in mouth between comb and strained honey samples which produced by different methods (P > 0.05). Discrimination of strained honey by sensory analysis was more reliable when compared to comb honey. The ratio of correctly classified sample was 78.3% for comb and 86.7% for strained honey. The more honey was pure the more discrimination of honey sample by sensory analysis was reliable. In verification test five unknown honey samples were classified 100% in their real groups by using canonical discriminant function Coefficients of each properties evaluated and the projections of the sample points on the plane of the canonical function-1 and function-2.     

Characterization of honeys by their botanical and geographical origins based on physico-chemical properties and chemo-metrics analysis

Honey, because of its nutritional and medicinal values, is in high demand and has become one of the important commodities. However, the issue of its quality and authenticity remain as important factors in consumption and marketing of honey. To assess the possibility of discriminating honeys by their geographical and botanical origins; 30 fresh honey samples of different botanical and geographical origins were collected and their major physico-chemical properties such as: total dissolved sugar (TDS), total ash, sugar profile, acidity, metallic ions and electric conductivity (EC) were investigated. The data was subjected to different chemo-metric (Hierarchical Cluster, Principal components and stepwise discriminant) analysis. Among the 23 characters used in the analysis; only 11 (TDS, EC, acidity, total ash, colour, and some specific metallic ions) characters have showed significant variations among different origin honeys. According to the stepwise discriminant analysis; 11 variables confirmed the grouping of the honey samples into four cluster groups based on their botanical and geographical origins. The clustering of the honeys associated with dominant plant source & climatic conditions of their origins. The study generally revealed the successful discrimination of honeys into their botanical and geographical provenances using fewer physico-chemical characters supported with melissopalynological data through applying suitable chemo-metric analysis.     

Discrimination and classification of adulterants in maple syrup with the use of infrared spectroscopic techniques

Food adulteration is a profit‐making business for some unscrupulous manufacturers. Maple syrup is a soft target for adulterators owing to its simplicity of chemical composition. The use of infrared spectroscopic techniques such as Fourier transform infrared (FTIR) and near‐infrared (NIR) as a tool to detect adulterants such as cane and beet invert syrups as well as cane and beet sugar solutions in maple syrup was investigated. The FTIR spectra of adulterated samples were characterised and the regions of 800–1200 cm^?1 (carbohydrates) and 1200–1800 and 2800–3200 cm^?1 (carbohydrates, carboxylic acids and amino acids) were used for detection. The NIR spectral region between 1100 and 1660 nm was used for analysis. Linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for discriminant analysis, while partial least squares (PLS) and principal component regression (PCR) were used for quantitative analysis. FTIR was more accurate in predicting adulteration using the two different regions (R² > 0.93 and 0.98) compared with NIR (R² > 0.93). Classification and quantification of adulterants in maple syrup show that both NIR and FTIR can be used for detecting adulterants such as pure beet and cane sugar solutions, but FTIR was superior to NIR in detecting invert syrups. © 2002 Society of Chemical Industry     

Discrimination and classification of adulterants in maple syrup with the use of infrared spectroscopic techniques

Food adulteration is a profit‐making business for some unscrupulous manufacturers. Maple syrup is a soft target of adulterators owing to its simplicity of chemical composition. In this study the use of Fourier transform infrared (FTIR) spectroscopy and near‐infrared (NIR) spectroscopy to detect adulterants such as cane and beet invert syrups as well as cane and beet sugar solutions in maple syrup was investigated. The FTIR spectrum of adulterated samples was characterised and the regions 800–1200 cm^?1 (carbohydrates) and 1200–1800 and 2800–3200 cm^?1 (carbohydrates, carboxylic acids and amino acids) were used for detection. The region between 1100 and 1660 nm in the NIR spectrum was used for analysis. Linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for discriminant analysis, while partial least squares (PLS) and principal component regression (PCR) were used for quantitative analysis. FTIR was more accurate in predicting adulteration using two different regions (R² > 0.93 and >0.98) compared with NIR (R² > 0.93). Classification and quantification of adulterants in maple syrup show that NIR and FTIR can be used for detecting adulterants such as pure beet and cane sugar solutions, but FTIR was superior to NIR in detecting invert syrups. © 2003 Society of Chemical Industry     

Differentiation of blossom and honeydew honeys using multivariate analysis on the physicochemical parameters and sugar composition

Multivariate analysis was applied on physicochemical parameters (moisture, water activity, electric conductivity, colour, hydroxymethyl furfural, acidity, pH, proline, diastase and invertase), sugar composition (fructose, glucose, sucrose, maltose, isomaltose, trehalose, turanose and melezitose) and palinological parameters determined in blossom and suspected honeydew honeys in order to differentiate them. The majority of the physicochemical, sugar composition and palinological parameters evaluated presented significant differences in the mean values between the suspected honeydew and blossom honeys, with the exception of moisture, water activity, diastase, fructose and maltose. Blossom honey samples tend to differentiate from the suspected honeydew honeys after applying factor analysis on the physicochemical parameters and sugar composition. Stepwise linear discriminant analysis allows the correct classification of all blossom honeys, and only one honeydew honey was erroneously included as blossom honey. So, the use of multivariate analysis on physicochemical parameters and sugar composition can be a useful tool to differentiate these types of honeys.     

Classification of Italian honeys by mid-infrared diffuse reflectance spectroscopy (DRIFTS)

Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) and multivariate statistical analysis methods were used for the identification and classification of honey from different floral sources. The 82 honey samples (robinia, chestnut, citrus, polyfloral) were scanned by DRIFTS in the region 4000–600 cm⁻¹ and also transformed in 1st and 2nd derivatives. Spectral data were analyzed by principal component analysis, general discriminant analysis and classification tree analysis. Classification accuracy near 100% was obtained by discriminant and classification tree analyses. Classification models were successfully validated with one-third leave out method and a classification of about 100% were achieved.     

Impact of botanical source and processing conditions on physicochemical properties and antioxidant activity of honey in the northern part of Thailand

Phenolics, melissopalynological analysis, and selected physicochemical characteristics could differentiate floral honey origin. Botanical sources (longan, multifloral and Manuka) had more pronounced effect on honey properties than processing conditions. The conductivity was related to ash content and colour of honey. Total sugar, fructose and glucose content in all samples were not significantly different. Melissopalynological analysis was used to verify the botanical source of honey samples. Predominant pollen was identified. Results characterising antioxidant properties (DPPH radicals, hydroxyl radicals, and carbon-centred radicals scavenging activity) were confirmed via both Electron Spinning Resonance and oxygen radical absorbance capacity (ORAC). They showed that only hydroxyl radicals scavenging activity and ORAC were significantly different. Type and amount of phenolics in honey samples identified using HPLC could differentiate botanical source. Gallic acid and kaempferol were potential marker phenolics for longan honey. Multivariate statistical analysis allowed discrimination of honey from different botanical sources based on their phenolic profiles.     

Detection of Clostridium botulinum in natural sweetening.

Various sugar products were examined for contamination with C. botulinum spores. Type A, B and C spores were detected in three of 56 samples of sugar for apiculture, which may attest the significance of bee-feed as a source of contamination of honey. The heavy contamination of honey with C. botulinum spores sometimes encountered, however, can not be explained unless some other factors, e.g., that allowing germination and multiplication of the spores somewhere during honey production, are found. Type A spores were detected in some samples of raw sugar and molasses and also in two of 41 samples of brown sugar lump, but not in refined sugar or other various samples taken at a sugar factory or in sugar cane left on the field in Okinawa. The fact that some natural sweetenings are contaminated with C. botulinum spores, even in low concentrations, may be food-hygienically important.     

Fast aroma profiling to detect invert sugar adulteration with zNose™

Rapid aroma profiling of food products is a potential technique for at‐line food quality evaluation. In this work the potential of zNose^?, a surface acoustic wave‐based sensor, was tested for honey quality assessment. Buckwheat honey was purposely adulterated with different levels of beet and cane invert sugar, and its aroma profile was measured after different periods of headspace equilibration. PCA using the relative peak areas as well as the full zNose^? spectra resulted in a clear separation between honey, and beet and cane invert sugar adulterants in the mixtures. PLS models were developed for quantitative estimation of adulterants using the entire spectra as well as the relative peak areas. Better predictions were obtained with the PLS models based on spectra than with those based on relative peak areas. A correlation of validation of 0.98 was obtained between predicted and measured percentage of adulteration. This model was also successfully validated with an external set of honey mixtures, resulting in an average deviation of 3% adulteration between the predicted and reference values. Copyright © 2004 Society of Chemical Industry     

Near infrared spectral fingerprinting for confirmation of claimed PDO provenance of honey

Near infrared spectroscopic fingerprints of Corsican honey samples were analysed by a range of chemometric tools to confirm their claimed provenance. Authentic, unfiltered honeys (n = 373; 219 Corsican and 154 non-Corsican) were collected over 2 production seasons, the goal being to create a specific spectral fingerprint for Corsican honey. Following preliminary data examination by principal component analysis, the multivariate method studied for provenance confirmation was partial least squares regression; various spectral pre-treatments were investigated. Best PLS discriminant models developed using full cross-validation, a variable selection algorithm and a 2nd derivative data pre-treatment, gave correct classification results of 90.0% and 90.3% for the Corsican and non-Corsican honey samples respectively. Using separate calibration and validation samples from this same honey collection, highest correct classification values of 90.4% and 86.3% for Corsican and non-Corsican honey samples respectively were obtained again using a variable selection procedure.     

Characterization and Discrimination of the Floral Origin of Sidr Honey by Physicochemical Data Combined with Multivariate Analysis

Sidr honey represents one of the most expensive monofloral honeys worldwide. The quality control of such honey types usually depends on pollen analysis or comparison of physicochemical characters. In the presented work, 38 different honey samples of which 13 represented genuine Sidr (Ziziphus spina-christy) honey samples were collected from various areas of Yemen. All samples were characterized by physicochemical parameters including moisture content, pH, electrical conductivity, and free acidity. The physicochemical data was subjected to multivariate data analysis including principal component analysis (PCA) and hierarchical cluster analysis (HCA). The development of partial least square discriminant analysis (PLS-DA) model on validation gave 100 % correct classification of the test set samples. All tested honey samples were within the level permitted by the international standards for honey quality. The application of the discriminant technique PLS-DA presented excellent potential for discriminating the botanical origin of Yemeni Sidr honey from other non-Sidr samples and may serve as a discriminant model to be applied to other honey types worldwide.