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     

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     

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     

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     

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%).     

Prediction of Inverted Cane Sugar Adulteration of Honey by Fourier Transform Infrared Spectroscopy

Fourier transform infrared (FTIR) spectroscopy with an attenuated total reflection (ATR) sampling accessory has been used to determine the cane medium invert sugar in 3 different varieties of honey. Predictive models were developed to classify the cane sugar-adulterated honey samples, using discriminant analysis. Linear discriminant and canonical variate analysis were used to discriminate adulterated honey samples. The optimum classification of 88 to 96.4% was achieved in a validation set, using linear discriminant analysis with the partial least squares (PLS) data compression technique. Calibrations developed to predict the spiked inverted cane sugar concentration in honey with PLS-1st derivative method gave standard error of prediction (SEP) between 2.8 to 3.6 % w/w.     

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 Differentiation of Listeria monocytogenes Epidemic Clones III and IV and Their Intact Compared with Heat‐Killed Populations Using Fourier Transform Infrared Spectroscopy and Chemometrics

The objectives of this study were to determine if Fourier transform infrared (FT‐IR) spectroscopy and multivariate statistical analysis (chemometrics) could be used to rapidly differentiate epidemic clones (ECs) of Listeria monocytogenes, as well as their intact compared with heat‐killed populations. FT‐IR spectra were collected from dried thin smears on infrared slides prepared from aliquots of 10 μL of each L. monocytogenes ECs (ECIII: J1‐101 and R2‐499; ECIV: J1‐129 and J1‐220), and also from intact and heat‐killed cell populations of each EC strain using 250 scans at a resolution of 4 cm^?1 in the mid‐infrared region in a reflectance mode. Chemometric analysis of spectra involved the application of the multivariate discriminant method for canonical variate analysis (CVA) and linear discriminant analysis (LDA). CVA of the spectra in the wavelength region 4000 to 600 cm^?1 separated the EC strains while LDA resulted in a 100% accurate classification of all spectra in the data set. Further, CVA separated intact and heat‐killed cells of each EC strain and there was 100% accuracy in the classification of all spectra when LDA was applied. FT‐IR spectral wavenumbers 1650 to 1390 cm^?1 were used to separate heat‐killed and intact populations of L. monocytogenes. The FT‐IR spectroscopy method allowed discrimination between strains that belong to the same EC. FT‐IR is a highly discriminatory and reproducible method that can be used for the rapid subtyping of L. monocytogenes, as well as for the detection of live compared with dead populations of the organism.     

Rapid evaluation and discrimination of γ‐irradiated carbohydrates using FT‐Raman spectroscopy and canonical discriminant analysis

Chemical changes induced by gamma irradiation of selected sugar systems—honey and fructose—were investigated through their molecular fingerprint using Fourier transform Raman spectroscopy (FT‐Raman). Generalized two‐dimensional (2‐D) correlation spectroscopy was applied to FT‐Raman spectra of the control and 17 kGy irradiated fructose to elucidate changes in the chemical structure upon irradiation. The irradiation induced changes in the ring (below 700 cm^?1) and conformational structure (800–1500 cm^?1) of fructose were identified by means of a 2‐D FT‐Raman correlation spectroscopy. The irradiation damage depicted from the C? H stretch region (2800–3000 cm^?1) of the FT‐Raman spectra of honey was used to develop a pattern recognition model for classifying honey based on the irradiation dose. A hybrid partial least squares (PLS)–canonical variate analysis (CVA) with the optimum number of factors from PLS was used for rapid discrimination of honeys irradiated at 1, 5, 10 or 17 kGy. The present study demonstrated that FT‐Raman spectroscopy, together with chemometrics, could be a rapid tool for classification of foodstuffs with high sugar content and provides a viable option to explore radiation‐induced modifications to sugar systems subjected to irradiation processing. Copyright © 2007 Society of Chemical Industry     

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.     

Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beef

This study aimed to evaluate the potential of near infrared spectroscopy (NIRS) as a fast and non-destructive tool for detecting and quantifying different adulterants in fresh and frozen-thawed minced beef. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R_P²) of 0.96, 0.94 and 0.95 with standard error of prediction (SEP) of 5.39, 5.12 and 2.08% (w/w) for minced beef adulterated by pork, fat trimming and offal, respectively. The performance of the developed models declined when the samples were in a frozen-thawed condition, yielding R_P² of 0.93, 0.82 and 0.95 with simultaneous augments in the SEP of 7.11, 9.10 and 2.38% (w/w), respectively. Linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA) and non-linear regression models (logistic, probit and exponential regression) were developed at the most relevant wavelengths to discriminate between the pure (unadulterated) and adulterated minced beef. The classification accuracy resulting from both types of models was quite high, especially the LDA, PLS-DA and exponential regression models which yielded 100% accuracy. The current study demonstrated that the VIS-NIR spectroscopy can be utilized securely to detect and quantify the amount of adulterants added to the minced beef with acceptable precision and accuracy.     

Biological activities and chemical composition of three honeys of different types from Anatolia

In this study, we investigated and compared some chemical properties and in vitro biological activities of three different types of Turkish honey. The first two honey samples were monofloral from chestnut and rhododendron flowers, collected from the east Black Sea region, and the third sample was the heterofloral form of astragalus (Astragalus microcephalus Willd.), thyme (Thymus vulgaris) and other several mountain flowers, collected from Erzincan in Eastern Anatolia. The chemical properties of the honey samples, such as total moisture, ash, total protein, sucrose, invert sugar, diastase activity, hydroxymethylfurfural content and acidity, were determined. Total phenolics, superoxide radical- and peroxynitrite-scavenging activities, and ferric reducing/antioxidant power measurements were used as antioxidant capacity determinants with ±-catechin, butylated hydroxytoluene, ascorbic acid, and trolox^® used as reference. The antimicrobial activity was studied by the agar diffusion method, using eight bacteria and two yeasts. The mineral contents were also determined by an AAS method. The chestnut flower honey had the highest phenolic content, superoxide radical-scavenging activity and reducing power, while the heterofloral honey sample exhibited the highest peroxynitrite-scavenging activity. The antioxidant activities were also found to be related to the sample concentrations. The mineral content of the chestnut honey was much higher than the others. The samples showed moderate antimicrobial activity against some microorganisms, especially Helicobacter pylori ATCC 49503, Staphylococcus aureus ATCC 25923, Bacillus subtilis ATCC 6633, Candida tropicalis ATCC 13803 and Candida albicans ATCC 10231. The honey samples studied proved to be a good source of antioxidants and antimicrobial agents that might serve to protect health and fight against several diseases.     

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.     

Detection and Quantification of Adulteration in Honey through Near Infrared Spectroscopy

Honey is one of the important traditional medicines since ancient times. In this article, a case study was carried out using near infrared spectroscopy techniques with Chemometrics to detect the Jaggery adulterants in the honey. Jaggery was used to prepare adulterant solution of different proportionate by manually mixing with four types of different honey samples. In total, 160 spectra were collected using the XDS^TM Optiprobe analyzer reflection type spectrometer and a calibration model was built using partial least square regression. The honey adulteration was predicted statistically with the calibration error 0.00751 and coefficient of determination R² of 0.9924.     

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.     

Sensory Differences between Product Matrices Made with Beet and Cane Sugar Sources

Although beet and cane sugar sources have nearly identical chemical compositions, the sugars differ in their volatile profiles, thermal behaviors, and minor chemical components. Scientific evidence characterizing the impact of these differences on product quality is lacking. The objective of this research was to determine whether panelists could identify a sensory difference between product matrices made with beet and cane sugar sources. Sixty‐two panelists used the R‐index by ranking method to discern whether there was a difference between 2 brands of beet and 2 brands of cane sugars in regard to their aroma and flavor, along with a difference in pavlova, simple syrup, sugar cookies, pudding, whipped cream, and iced tea made with beet and cane sugars. R‐index values and Friedman's rank sum tests showed differences (P < 0.05) between beet and cane sugars in regard to their aroma and flavor. Significant differences between the sugar sources were also identified when incorporated into the pavlova and simple syrup. No difference was observed in the sugar cookies, pudding, whipped cream, and iced tea. Possible explanations for the lack of difference in these products include: (1) masking of beet and cane sensory differences by the flavor and complexity of the product matrix, (2) the relatively small quantity of sugar in these products, and (3) variation within these products being more influential than the sugar source. The findings from this research are relevant to sugar manufacturers and the food industry as a whole, because it identifies differences between beet and cane sugars and product matrices in which beet and cane sugars are not directly interchangeable.     

Origin Identification and Quantitative Analysis of Honeys by Nuclear Magnetic Resonance and Chemometric Techniques

The combination of ¹H NMR spectroscopy and multivariate statistical analysis has become a promising method for the discrimination of food origins. In this paper, this method has been successfully employed to analyze 70 Chinese honey samples from eight botanic origins, three geographical origins, and five production dates. Thirty-three components in honey samples were detected and identified from their ¹H NMR spectra, and 20 of them were accurately quantified by comparing their integral area with that of internal standards with relaxation time correction. Nontargeted principal component analysis (PCA) has been applied to distinguish the honeys from different botanical and geographical origins. The variations of components in the honeys, including saccharides and all kind of amino and organic carboxylic acids, confirmed their clustering according to their origins in PCA scores plots. Orthogonal partial least squares discriminant analysis (OPLS-DA) based on the NMR data for the different pairwise honey samples allows to identify the compositional variations contributed to geographical discrimination and storage time. Hence, NMR spectroscopy coupled with chemometric techniques offers an efficient tool for quality control of honey, and it could further serve to the classification, qualitative and quantitative control of other foods.     

拉曼光谱法快速鉴别蜂蜜中掺入甜菜糖浆的可行性研究

对蜂蜜用各种廉价的糖浆掺假,一直是蜂蜜行业的严重问题。本文利用拉曼光谱结合化学计量学方法对蜂蜜中掺入甜菜糖浆进行鉴别。用airPLS法扣除拉曼荧光背景,用自归一化法预处理光谱,在全谱范围(4000～10000cm^-1)内建立偏最小二乘-线性判别分析（PLS-LDA）模型,并进行预测。训练集10折交互验证的判别总正确率为90.4%,预测集的判别总正确率为95.4%。实验结果表明拉曼光谱结合化学计量学方法可快速鉴别蜂蜜中掺入的甜菜糖浆。