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Detection of honey adulteration by high fructose corn syrup and maltose syrup using Raman spectroscopy
Journal of Food Composition and Analysis 28 (2012) 69–74
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Original Research Article
Detection of honey adulteration by high fructose corn syrup and maltose syrup using Raman spectroscopy Shuifang Li a,*, Yang Shan b, Xiangrong Zhu b, Xin Zhang c, Guowei Ling a a
College of Science, Central South University of Forestry and Technology, Changsha 410004, China Hunan Agricultural Product Processing Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China c Longping Branch Graduate School, Central South University, Changsha 410025, China b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 26 August 2011 Received in revised form 25 May 2012 Accepted 17 July 2012
Raman spectroscopy was used to detect adulterants such as high fructose corn syrup (HFCS) and maltose syrup (MS) in honey. HFCS and MS were each mixed with authentic honey samples in the following ratios: 1:10 (10%), 1:5 (20%) and 1:2.5 (40%, w/w). Adaptive iteratively reweighted penalized least squares (airPLS) was chosen to remove background of spectral data. Partial least squares-linear discriminant analysis (PLS-LDA) was used to develop a binary classification model. Classification of honey authenticity using PLS-LDA showed a total accuracy of 91.1% (authentic honey vs. adulterated honey with HFCS), 97.8% (authentic honey vs. adulterated honey with MS) and 75.6% (authentic honey vs. adulterated honey with HFCS and MS), respectively. Classification of honey adulterants (e.g. HFCS or MS) using PLS-LDA gave a total accuracy of 84.4%. The results showed that Raman spectroscopy combined with PLS-LDA was a potential technique for detecting adulterants in honey. ß 2012 Elsevier Inc. All rights reserved.
Keywords: Food composition Food analysis Honey Adulteration Raman spectroscopy Adaptive iteratively reweighted penalized least squares (airPLS) Spectral background signal removing Partial least squares-linear discriminant analysis (PLS-LDA)
1. Introduction Honey is a natural product with complex and variable components. According to the definition given by Codex Alimentarius of the Food and Agriculture Organization of the United Nations (FAO), ‘‘Honey is the natural sweet substance, produced by honeybees from the nectar of plants or from secretions of living parts of plants, or excretions of plant-sucking insects on the living parts of plants, which the bees collect, transform by combining with specific substances of their own, deposit, dehydrate, store and leave in honeycombs to ripen and mature’’ (Codex Alimentarius, 2001a,b). The European Union definition is similar but specifies the biological species of honey-producing insects: ‘‘Honey is the natural sweet substance, produced by Apis mellifera bees from the nectar of plants or from secretions of living parts of plants, or excretions of plant-sucking insects on the living parts of plants, which the bees collect, transform by combining with specific substances of their own, deposit, dehydrate, store and leave in honeycombs to ripen and mature.’’ (EU Council, 2002; cf. Official Journal of the European Communities, 2001).
* Corresponding author. Tel.: +86 731 85623352; fax: +86 731 85623352. E-mail address: [email protected] (S. Li). 0889-1575/$ – see front matter ß 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jfca.2012.07.006
Bee honey is a unique sweetening agent that can be used by humans without processing, and it provides significant nutritious and medical benefits (Meda et al., 2005; Schramm et al., 2003; Pe´rez et al., 2007). It is a rich source of readily available sugar, organic acids, amino acids, enzymes, in addition to being a source of many biologically active compounds such as vitamins, flavonoids and so on (Meda et al., 2005; Gonza´lez-miret et al., 2005). China is both a large honey producer and a large worldwide exporter of honey. The annual honey production in China is estimated to exceed 200,000 tones, 50% of which are exported to the EU, USA, Japan and other places. Because of its nutritional and medicinal value, honey continues to be a popular food. However, honey can easily be adulterated with various cheaper sweeteners, such as refined cane sugar beet sugar, high fructose corn syrup and maltose syrup, resulting in higher commercial profits. Stable carbon isotopic ratio mass spectrometry (SCIRA) has generally been used to detect adulterated honey (Martin et al., 1998; Padovana et al., 2003; Simsek et al., 2012), but the technique, while having some potential advantages, is time-consuming, destructive, expensive and requires considerable analytical skills that are hard to meet in routine monitoring analysis. In addition, several other methods have been developed to detect honey adulteration, such as chromatographic (Cordella et al., 2003a, 2005; Cotte et al., 2004;
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Ruiz-Matute et al., 2007; Moralesa et al., 2008), thermal analysis (Cordella et al., 2002, 2003b), nuclear magnetic resonance (Cotte et al., 2007) and statistical correlation between sugar composition and the following compositional properties: moisture, total soluble solids, nitrogen, apparent viscosity, hydroxymethylfurfural (HMF), ash, sodium, calcium, potassium, proline, refractive index and diastatic activity (Abdel-Aal et al., 1993). Although the use of these methods to assess the adulteration of honey has been demonstrated, these methods have the same disadvantages as SCIRA. So it is necessary to develop rapid, non-destructive, easy to use and lowcost analytical methods to detect and quantify adulteration in honey. Recently, middle infrared (MIR) (Gallardo-Vela´zquez et al., 2009; Kelly et al., 2004, 2006a; Sivakesava and Irudayaraj, 2001a,b, 2002; Irudayaraj et al., 2003; Bertelli et al., 2007) and near infrared (NIR) spectroscopy (Kelly et al., 2006b; Downey et al., 2003; Toher et al., 2007; Chen et al., 2011; Zhu et al., 2010) have been applied to authenticating honey. In combination with multivariate data analysis, the spectroscopic methods possess the speed, simplicity, and low cost per analysis required for screening techniques. Raman spectroscopy is another viable method used to rapidly detect food adulteration with the same advantages as MIR and NIR spectroscopic methods, and it has been successfully implemented quantitatively and qualitatively in quality control of food such as cooking oils (Aparicio and Baeten, 1998), olive oil (Zou et al., 2009; Korifi et al., 2011), pork (Olsen et al., 2007), beef (Beattie et al., 2004), coffee (Rubagiza and Meurens, 2005), fruits, vegetables, and juices (Bhosale et al., 2004). Fourier-transform Raman spectroscopy was applied to detecting the adulterants such as beet and inverted cane sugar in honey using canonical variate analysis (CVA) (Paradkar and Irudayaraj, 2001), but only authentic honey samples from the same floral origins were adulterated and studied. In this study, authentic honey samples from ten floral origins were adulterated and studied. The adaptive iteratively reweighted penalized least squares (airPLS) algorithm, which was recently developed and is usually valid for correcting baseline drift in Raman spectroscopy (Zhang et al., 2010), was used to remove background of spectral data, and the partial least squares-linear discriminant analysis (PLS-LDA) algorithms, which is commonly used for binary classification problems and effective in some applications, was used for discriminant analysis. The major aims of this study were to investigate the potential application of Raman spectroscopy to (1) distinguishing between authentic honey and adulterated honey mixed with high fructose corn syrup (HFCS) or maltose syrup (MS), and (2) detecting adulterants (e.g. HFCS or MS) in honey.
variations in sugar concentration. Adulterants (also produced at 65 8Brix) were generated by diluting commercially sourced, HFCS (92% fructose and glucose) and MS (50% maltose) with distilled water, which was obtained directly from the producer (Hunan Runtao Biological Technology Company Ltd.). HFCS was mixed with 44 of 74 authentic honey samples, respectively, in the ratios of 1:10 (10%), 1:5 (20%) and 1:2.5 (40%, w/w) to obtain 132 HFCS-adulterated honeys. MS was mixed with 50 of 74 authentic honey samples, respectively, in the ratios of 1:10 (10%), 1:5 (20%) and 1:2.5 (40%, w/w) to obtain 150 MS-adulterated honeys. In order to distinguish between the authentic honey and honey adulterated with HFCS or MS and detected adulterants (e.g. HFCS or MS), four sample sets were developed. The first sample set (set 1) consisted of 74 authentic honey samples and 76 randomly from 132 HFCS-adulterated honey; the second sample set (set 2) consisted of 74 authentic honey samples and 75 chosen randomly from 150 MS-adulterated honey; the third sample set (set 3) consisted of 74 authentic honey samples and 75 adulterated honey samples, of which 38 were randomly selected from 76 HFCS-adulterated honey and 37 randomly selected from 75 MS-adulterated honey; the fourth sample set (set 4) consisted of 76 HFCS-adulterated honey samples and 75 MS-adulterated honey samples. This set was specifically for an analysis based on the type of adulterant (i.e. HFCS or MS). When choosing adulterated honey samples, to avoid the bias toward samples at the highest levels of adulteration, we chose 24–26 samples at random for corresponding sample sets at each percentage (10, 20, 40%, w/w), and thus the proportion of adulterated honey samples for each percentage (10, 20, 40%, w/ w) was about 1/3 of total adulterated samples in every sample set. 2.2. Spectral data collection Spectra were recorded using an i-Raman spectrometer (BWS 415-785H, B&W TEK Inc., USA) equipped with a fiber-optic Raman probe, a thermoelectric cooled CCD detector with 2048 pixels and a 785 nm laser with a maximum output power of 495 mW in the signal range of 175–2600 cm 1. The instrumental spectral resolution, which was measured as full width at half maximum (FWHM) of the signal wavelength, was 3 cm 1. About 10 g of each sample was poured into a clean quartz cell placed in the Raman sample holder and the laser was focused on the center of the sample. Samples were scanned at an increment of 10 mm. Integration time was 15 s. The system was operated using the BWSpec TM 3.26 software provided by the manufacturer. Three measurements were performed on the same samples. Mean spectra were used in all subsequent calculations.
2. Materials and methods 2.3. Chemometrics and data analysis 2.1. Honey samples Seventy-four authentic honey samples from ten floral origins were obtained directly from bee-keepers in Hunan, Hubei, Guangxi, Sichuan, Ningxia, Guizhou, Shangxi, Shanxi, Yunnan and Henan province of China from 2008 to 2010. The ten floral origins were Brassica spp., Zizyphus spp., Citrus spp., Robinia pseudoacacia L., Vitex negundo Linn. Var. heterophylla (Franch.) Rehd., Lycium chinense Mill., Litchi chinensis Sonn., Malus spp., Helianthus sp. and Chrysanthemum indicum L. The water content of samples was 17.1–35.7% (w/w). None of the samples were filtered, and they were stored at 6–8 8C in the laboratory before analysis. Prior to spectral measurement, honey were liquefied in a water bath at 55 8C, manually stirred to ensure homogeneity and adjusted to a standard solid content (65 8Brix) with distilled water to avoid spectral complications from naturally occurring
The airPLS algorithm worked by iteratively changing weights of sum squares errors (SSE) between fitted baseline and original signals, and the weights of SSE were obtained adaptively using difference between previously fitted baseline and original signals. This method proved to be fast, flexible and valid (Chen et al., 2010; Zhang et al., 2010). PLS-LDA has been shown effective in some classification problems (Yi et al., 2007; Li et al., 2011). Using the PLS algorithm, we first decomposed the spectral matrix X and value matrix y and got the principal component matrix T of X. Then we carried out linear discriminant analysis using matrix T and y and obtained the discriminant function that could potentially discriminate groups of the test samples. The optimal number of latent variables was estimated using 10-fold cross-validation of PLS-LDA models. Discrimination models were established using latent variables
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selected by cross validation. In this study, two classes of the samples were assigned numeric value of 1 (authentic honey samples) and 1 (adulterated honey samples) respectively. PLSLDA models were developed to make predictions on the test samples. All computations were performed in Matlab 7.0 (Mathworks, USA) under Windows XP. The airPLS and PLS-LDA algorithm codes were provided by Research Center of Modernization of Chinese Medicines, Central South University, China (PLS-LDA and airPLS Matlab codes can also be respectively downloaded free of charge from http://code.google.com/p/cars2009 and http://code.google.com/p/airpls/).
3. Results and discussion 3.1. Raman spectra of authentic honey and adulterated honey Fig. 1 shows raw Raman spectra of analyzed honey. Because there was no relevant information in the spectral channel of 0–300 and 1300–2048, the spectral channel of 300–1300 (the signal range of 270–1820 cm 1) was selected for analysis. Fig. 2 shows raw spectra of a randomly selected authentic honey and the same honey adulterated with HFCS (40%, w/w) and MS (40%, w/w) in the spectral range 270–1820 cm 1. HFCS-adulterated and MS-adulterated honey from Fig. 2 showed some characteristic Raman shifts, which were used to detect the adulterants. As shown in Figs. 1 and 2, the signals of authentic and adulterated honey showed characteristics bands around 351, 425, 517, 592, 629, 705, 778, 824, 865, 915, 981, 1065, 1127, 1264, 1373 and 1461 cm 1. The characteristic groups were similar to those reported by Paradkar and Irudayaraj (2001). Some characteristic groups and their corresponding vibration modes were well established except 351, 425, 517, 592, 629, 778 and 981 cm 1, which are unknown (Paradkar and Irudayaraj, 2001; Batsoulis et al., 2005). A signal around 705 cm 1 corresponds to the stretching of CO and CCO, OCO bending; 865 and 824 cm 1 were found to be caused by the vibration of CH and C(1)H, CH2; a signal around 915 cm 1 was associated with the vibration of C(1)H and COH; a signal around 1065 cm 1 might be caused by a major contribution by the bending vibration of C(1)–H and COH in carbohydrates and a minor contribution by the vibration of C–N bond in proteins and amino acids; a signal around 1127 cm 1 could be a combination of stretching vibration of C–O bond (major) and vibration of C–N bond of protein and amino acids (minor); a signal around 1264 cm 1 was associated with the vibration of C(6)–OH and C(1)–OH; a signal around 1373 cm 1 corresponded to the bending of C–H and O–H bonds; and a signal around 1461 cm 1 was caused by a combination of bending vibration of CH2 group (major) and the vibration of COO group. 3.2. The choice of spectral data pretreatment Different spectral data pretreatments, including autoscaling, mean-centered (MC), first and second derivatives, multiplicative scattering correction (MSC), airPLS and their combination, were employed to optimize the PLS-LDA model. Comparing the total classification accuracy based on 10-fold cross-validation for PLSLDA models of every sample set using different pretreatments based on different number of PLS components, we found that airPLS combined with autoscaling always had the best performance with a small number of PLS components. Thus, the pretreatment of airPLS combined with autoscaling was selected before PLS-LDA modeling. Autoscaling equalizes variances of all variables and gives the same weight to all the variables (Li et al., 2012).
Fig. 1. Raw Raman spectra of analyzed honey: (A) authentic honey; (B) highfructose corn syrup (HFCS)-adulterated honey; (C) maltose syrup (MS)-adulterated honey.
Fig. 3 shows the raw and corrected spectra of a representative sample by airPLS. The useful information (peak shape of the spectrum) was reserved while the background signal was effectively reduced.
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Fig. 2. Raw Raman spectra of a randomly selected authentic honey sample and the same honey sample adulterated with high fructose corn syrup (40%, w/w) and maltose syrup (40%, w/w).
3.3. PLS-LDA models of authentic honey and adulterated honey Sample sets 1, 2 and 3 were divided into training set (70%) and test set (30%) using a Kennard–Stone (KS) algorithm (Kennard and Stone, 1969). Three PLS-LDA models based on the optimal spectral data pretreatment were also developed to evaluate the efficacy of Raman spectroscopy in distinguishing between authentic honey and adulterated honey. Table 1 shows the classification accuracy using the PLS-LDA model. Based on 10-fold cross-validation for training set, the classification accuracy of authentic honey, adulterated honey and total honey were 84.9% (45/53), 82.7% (43/52) (10%, w/w: 80.0%, 20%, w/w: 82.4%, 40%, w/w: 86.7%), 83.8% (88/105) for set 1, 100% (56/56), 93.8% (45/48) (10%, w/w: 93.3%, 20%, w/w: 93.3%, 40%, w/ w: 94.4%), 97.1% (101/104) for set 2, and 91.8% (45/49), 72.7% (40/ 55) (10%, w/w: 70.6%, 20%, w/w: 73.7%, 40%, w/w: 73.7%), 81.7% (85/104) for set 3, respectively, so the sensitivity and the specificity were 83.33% and 84.31% for set 1, 94.92% and 100% for set 2, and 75.00% and 90.91% for set 3, respectively. While for test set, the classification accuracy of authentic honey, adulterated honey and total honey were 95.2% (20/21), 87.5% (21/24) (10%, w/w: 83.3%, 20%, w/w: 87.5%, 40%, w/w: 90%), 91.1% (41/45) for set 1, 100% (18/ 18), 96.3% (26/27) (10%, w/w: 90.0%, 20%, w/w: 100.0%, 40%, w/w: 100.0%), 97.8% (44/45) for set 2 and 92.0% (23/25), 55.0% (11/20)
Fig. 3. The raw and corrected spectra of a representative sample using airPLS.
(10%, w/w: 50.0%, 20%, w/w: 57.1%, 40%, w/w: 60.0%), 75.6% (34/ 45) for set 3, respectively, so the sensitivity and the specificity were 86.96% and 95.45% for set 1, 94.74% and 100% for set 2, and 71.88% and 84.62% for set 3, respectively. In terms of classification accuracy, set 2 had better performance than set 1, and set 3 had the worst performance (Table 1). The reason may be that the major content in authentic honey is monosaccharide, e.g., glucose and fructose, which can amount up to about 70%, and the dominant content in HFCS is also glucose and fructose (92%), which is somewhat similar to the content in authentic honey, meanwhile, the dominant content in MS is maltose (50%), which is quite dissimilar to the main content in honey; set 3 had the worst performance because adulterated honey of set 3 was composed of two different types of adulterated samples, while those of set 1 and 2 were comprised of only one type of adulterated samples. 3.4. Discrimination of honey adulterants In this study, a PLS-LDA model for the discrimination of honey adulterants was developed using set 4. All 151 samples were divided into training set (106) and test set (45) using the KS algorithm. Table 2 shows the results of the model. For the training set, the classification accuracy of HFCS-adulterated honey, MS-adulterated honey and total honey were 98.1% (51/52),
Table 1 Classification of authentic honey and adulterated honey using PLS-LDA model based on the spectral data pretreatment of airPLS. Classification accuracy of training set (%)a
Set 1 Set 2 Set 3 a
Classification accuracy of test set (%)
Authentic honey
Adulterated honey
Total honey
Authentic honey
Adulterated honey
Total honey
84.9 100 91.8
82.7 93.8 72.7
83.8 97.1 81.7
95.2 100 92.0
87.5 96.3 55.0
91.1 97.8 75.6
Based on 10-fold cross-validation.
Table 2 Discrimination of adulterants in honey using PLS-LDA model based on the spectral data pretreatment of airPLS. Classification accuracy of training set (%)a
Set 4 a
Classification accuracy of test set (%)
HFCS-adulterated honey
MS-adulterated honey
Total honey
HFCS-adulterated honey
MS-adulterated honey
Total honey
98.1
85.2
91.5
95.8
71.4
84.4
Based on 10-fold cross-validation.
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85.2% (46/54) and 91.5% (97/106) respectively based on the 10fold cross-validation. For the test set, 1 of 24 HFCS-adulterated honey samples was wrongly predicted as MS-adulterated honey and 6 of 21 MS-adulterated honey samples were wrongly predicted as HFCS-adulterated honey. The classification accuracy of HFCS-adulterated honey, MS-adulterated honey and total honey were 95.8% (23/24), 71.4% (15/21) and 84.4% (38/45), respectively. The above results have shown that Raman spectroscopy coupled with chemometrics methods seems to be an efficient technique for discriminating adulterants such as HFCS and MS in honey. 4. Conclusion Raman spectroscopy combined with PLS-LDA was successfully applied to detecting adulterants in honey. The airPLS was an effective pretreatment for diminishing fluorescence background of the Raman spectroscopy. Raman spectroscopy combined with PLSLDA seems more successful in detecting MS than HFCS in honey. The method is simple and efficient, and it does not need sample preprocessing; therefore it is suitable for on-site testing in field applications. Acknowledgements We thank the staff at the Research Center of Modernization of Chinese Medicines, Central South University for providing airPLS and PLS-LDA Matlab codes. This study was supported by the Scientific and Technological Innovation Projects of Hunan Academy of Agricultural Sciences (2010), the Natural Science Foundation of China (Project No.: 20875065). References Abdel-Aal, E.-S.M., Ziena, H.M., Youssef, M.M., 1993. Adulteration of honey with high fructose corn syrup: detection by different methods. Food Chemistry 48 (2), 209–212. Aparicio, R., Baeten, V., 1998. Fats and oils authentification by FT-Raman. OCLOleagineux Corps Gras Lipides 5 (4), 293–295. Batsoulis, A.N., Siatis, N.G., Kimbaris, A.C., Alissandrakis, E.K., Pappas, C.S., Tarantilis, P.A., Harizanis, P.C., Polissiou, M.G., 2005. FT-Raman spectroscopic simultaneous determination of fructose and glucose in honey. Journal of Agricultural and Food Chemistry 53 (2), 207–210. Beattie, R.J., Bell, S.J., Farmer, L.J., Moss, B.W., Patterson, D., 2004. Preliminary investigation of the application of Raman spectroscopy to the prediction of the sensory quality of beef silverside. Meat Science 66 (4), 903–913. Bertelli, D., Plessi, M., Sabatini, A.G., Lolli, M., Grillenzoni, F., 2007. Classification of Italian honeys by mid-infrared diffuse reflectance spectroscopy (DRIFTS). Food Chemistry 101 (4), 1565–1570. Bhosale, P., Ermakov, I.V., Ermakova, M., Gellermann, W., Bernatein, P.S., 2004. Resonance Raman quantification of nutritionally important carotenoids in fruits, vegetables, and their juices in comparison to high-pressure liquid chromatography analysis. Journal of Agricultural and Food Chemistry 52 (11), 3281–3285. Chen, L.Z., Xue, X.F., Ye, Z.H., Zhou, J.H., Chen, F., Zhao, J., 2011. Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy? Food Chemistry 128 (4), 1110–1114. Chen, S., Li, X.N., Liang, Y.Z., Zhang, Z.M., Liu, Z.X., Zhang, Q.M., Ding, L.X., Ye, F., 2010. Raman spectroscopy fluorescent background correction and its application in clustering analysis of medicines. Spectroscopy and Spectral Analysis 30 (8), 143–146. Codex Alimentarius Commission Standards, 2001. Codex Standard for Sugars: Standard 12-1981, Rev. 1, 1987. Codex Alimentarius, 2001. Draft revised standard for honey. Alinorm 01/25 19-26. Food and Agriculture Organization of the United Nations, Rome. Cordella, C., Antinelli, J.F., Aurieres, C., Faucon, J.-P., Cabrol-Bass, D., Sbirrazzuoli, N., 2002. Use of differential scanning calorimetry (DSC) as a new technique for detection of adulteration in honey. 1. Study of adulteration effect on honey thermal behavior. Journal of Agricultural and Food Chemistry 50 (1), 203–208. Cordella, C.B.Y., Milita˜o, J.S.L.T., Cle´ment, M.C., Cabrol-Bass, D., 2003. Honey characterization and adulteration detection by pattern recognition applied on HPAEC-PAD profiles. 1. Honey floral species characterization. Journal of Agricultural and Food Chemistry 51 (11), 3234–3242.
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Journal of Food Composition and Analysis journal homepage: www.elsevier.com/locate/jfca
Original Research Article
Detection of honey adulteration by high fructose corn syrup and maltose syrup using Raman spectroscopy Shuifang Li a,*, Yang Shan b, Xiangrong Zhu b, Xin Zhang c, Guowei Ling a a
College of Science, Central South University of Forestry and Technology, Changsha 410004, China Hunan Agricultural Product Processing Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China c Longping Branch Graduate School, Central South University, Changsha 410025, China b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 26 August 2011 Received in revised form 25 May 2012 Accepted 17 July 2012
Raman spectroscopy was used to detect adulterants such as high fructose corn syrup (HFCS) and maltose syrup (MS) in honey. HFCS and MS were each mixed with authentic honey samples in the following ratios: 1:10 (10%), 1:5 (20%) and 1:2.5 (40%, w/w). Adaptive iteratively reweighted penalized least squares (airPLS) was chosen to remove background of spectral data. Partial least squares-linear discriminant analysis (PLS-LDA) was used to develop a binary classification model. Classification of honey authenticity using PLS-LDA showed a total accuracy of 91.1% (authentic honey vs. adulterated honey with HFCS), 97.8% (authentic honey vs. adulterated honey with MS) and 75.6% (authentic honey vs. adulterated honey with HFCS and MS), respectively. Classification of honey adulterants (e.g. HFCS or MS) using PLS-LDA gave a total accuracy of 84.4%. The results showed that Raman spectroscopy combined with PLS-LDA was a potential technique for detecting adulterants in honey. ß 2012 Elsevier Inc. All rights reserved.
Keywords: Food composition Food analysis Honey Adulteration Raman spectroscopy Adaptive iteratively reweighted penalized least squares (airPLS) Spectral background signal removing Partial least squares-linear discriminant analysis (PLS-LDA)
1. Introduction Honey is a natural product with complex and variable components. According to the definition given by Codex Alimentarius of the Food and Agriculture Organization of the United Nations (FAO), ‘‘Honey is the natural sweet substance, produced by honeybees from the nectar of plants or from secretions of living parts of plants, or excretions of plant-sucking insects on the living parts of plants, which the bees collect, transform by combining with specific substances of their own, deposit, dehydrate, store and leave in honeycombs to ripen and mature’’ (Codex Alimentarius, 2001a,b). The European Union definition is similar but specifies the biological species of honey-producing insects: ‘‘Honey is the natural sweet substance, produced by Apis mellifera bees from the nectar of plants or from secretions of living parts of plants, or excretions of plant-sucking insects on the living parts of plants, which the bees collect, transform by combining with specific substances of their own, deposit, dehydrate, store and leave in honeycombs to ripen and mature.’’ (EU Council, 2002; cf. Official Journal of the European Communities, 2001).
* Corresponding author. Tel.: +86 731 85623352; fax: +86 731 85623352. E-mail address: [email protected] (S. Li). 0889-1575/$ – see front matter ß 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jfca.2012.07.006
Bee honey is a unique sweetening agent that can be used by humans without processing, and it provides significant nutritious and medical benefits (Meda et al., 2005; Schramm et al., 2003; Pe´rez et al., 2007). It is a rich source of readily available sugar, organic acids, amino acids, enzymes, in addition to being a source of many biologically active compounds such as vitamins, flavonoids and so on (Meda et al., 2005; Gonza´lez-miret et al., 2005). China is both a large honey producer and a large worldwide exporter of honey. The annual honey production in China is estimated to exceed 200,000 tones, 50% of which are exported to the EU, USA, Japan and other places. Because of its nutritional and medicinal value, honey continues to be a popular food. However, honey can easily be adulterated with various cheaper sweeteners, such as refined cane sugar beet sugar, high fructose corn syrup and maltose syrup, resulting in higher commercial profits. Stable carbon isotopic ratio mass spectrometry (SCIRA) has generally been used to detect adulterated honey (Martin et al., 1998; Padovana et al., 2003; Simsek et al., 2012), but the technique, while having some potential advantages, is time-consuming, destructive, expensive and requires considerable analytical skills that are hard to meet in routine monitoring analysis. In addition, several other methods have been developed to detect honey adulteration, such as chromatographic (Cordella et al., 2003a, 2005; Cotte et al., 2004;
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Ruiz-Matute et al., 2007; Moralesa et al., 2008), thermal analysis (Cordella et al., 2002, 2003b), nuclear magnetic resonance (Cotte et al., 2007) and statistical correlation between sugar composition and the following compositional properties: moisture, total soluble solids, nitrogen, apparent viscosity, hydroxymethylfurfural (HMF), ash, sodium, calcium, potassium, proline, refractive index and diastatic activity (Abdel-Aal et al., 1993). Although the use of these methods to assess the adulteration of honey has been demonstrated, these methods have the same disadvantages as SCIRA. So it is necessary to develop rapid, non-destructive, easy to use and lowcost analytical methods to detect and quantify adulteration in honey. Recently, middle infrared (MIR) (Gallardo-Vela´zquez et al., 2009; Kelly et al., 2004, 2006a; Sivakesava and Irudayaraj, 2001a,b, 2002; Irudayaraj et al., 2003; Bertelli et al., 2007) and near infrared (NIR) spectroscopy (Kelly et al., 2006b; Downey et al., 2003; Toher et al., 2007; Chen et al., 2011; Zhu et al., 2010) have been applied to authenticating honey. In combination with multivariate data analysis, the spectroscopic methods possess the speed, simplicity, and low cost per analysis required for screening techniques. Raman spectroscopy is another viable method used to rapidly detect food adulteration with the same advantages as MIR and NIR spectroscopic methods, and it has been successfully implemented quantitatively and qualitatively in quality control of food such as cooking oils (Aparicio and Baeten, 1998), olive oil (Zou et al., 2009; Korifi et al., 2011), pork (Olsen et al., 2007), beef (Beattie et al., 2004), coffee (Rubagiza and Meurens, 2005), fruits, vegetables, and juices (Bhosale et al., 2004). Fourier-transform Raman spectroscopy was applied to detecting the adulterants such as beet and inverted cane sugar in honey using canonical variate analysis (CVA) (Paradkar and Irudayaraj, 2001), but only authentic honey samples from the same floral origins were adulterated and studied. In this study, authentic honey samples from ten floral origins were adulterated and studied. The adaptive iteratively reweighted penalized least squares (airPLS) algorithm, which was recently developed and is usually valid for correcting baseline drift in Raman spectroscopy (Zhang et al., 2010), was used to remove background of spectral data, and the partial least squares-linear discriminant analysis (PLS-LDA) algorithms, which is commonly used for binary classification problems and effective in some applications, was used for discriminant analysis. The major aims of this study were to investigate the potential application of Raman spectroscopy to (1) distinguishing between authentic honey and adulterated honey mixed with high fructose corn syrup (HFCS) or maltose syrup (MS), and (2) detecting adulterants (e.g. HFCS or MS) in honey.
variations in sugar concentration. Adulterants (also produced at 65 8Brix) were generated by diluting commercially sourced, HFCS (92% fructose and glucose) and MS (50% maltose) with distilled water, which was obtained directly from the producer (Hunan Runtao Biological Technology Company Ltd.). HFCS was mixed with 44 of 74 authentic honey samples, respectively, in the ratios of 1:10 (10%), 1:5 (20%) and 1:2.5 (40%, w/w) to obtain 132 HFCS-adulterated honeys. MS was mixed with 50 of 74 authentic honey samples, respectively, in the ratios of 1:10 (10%), 1:5 (20%) and 1:2.5 (40%, w/w) to obtain 150 MS-adulterated honeys. In order to distinguish between the authentic honey and honey adulterated with HFCS or MS and detected adulterants (e.g. HFCS or MS), four sample sets were developed. The first sample set (set 1) consisted of 74 authentic honey samples and 76 randomly from 132 HFCS-adulterated honey; the second sample set (set 2) consisted of 74 authentic honey samples and 75 chosen randomly from 150 MS-adulterated honey; the third sample set (set 3) consisted of 74 authentic honey samples and 75 adulterated honey samples, of which 38 were randomly selected from 76 HFCS-adulterated honey and 37 randomly selected from 75 MS-adulterated honey; the fourth sample set (set 4) consisted of 76 HFCS-adulterated honey samples and 75 MS-adulterated honey samples. This set was specifically for an analysis based on the type of adulterant (i.e. HFCS or MS). When choosing adulterated honey samples, to avoid the bias toward samples at the highest levels of adulteration, we chose 24–26 samples at random for corresponding sample sets at each percentage (10, 20, 40%, w/w), and thus the proportion of adulterated honey samples for each percentage (10, 20, 40%, w/ w) was about 1/3 of total adulterated samples in every sample set. 2.2. Spectral data collection Spectra were recorded using an i-Raman spectrometer (BWS 415-785H, B&W TEK Inc., USA) equipped with a fiber-optic Raman probe, a thermoelectric cooled CCD detector with 2048 pixels and a 785 nm laser with a maximum output power of 495 mW in the signal range of 175–2600 cm 1. The instrumental spectral resolution, which was measured as full width at half maximum (FWHM) of the signal wavelength, was 3 cm 1. About 10 g of each sample was poured into a clean quartz cell placed in the Raman sample holder and the laser was focused on the center of the sample. Samples were scanned at an increment of 10 mm. Integration time was 15 s. The system was operated using the BWSpec TM 3.26 software provided by the manufacturer. Three measurements were performed on the same samples. Mean spectra were used in all subsequent calculations.
2. Materials and methods 2.3. Chemometrics and data analysis 2.1. Honey samples Seventy-four authentic honey samples from ten floral origins were obtained directly from bee-keepers in Hunan, Hubei, Guangxi, Sichuan, Ningxia, Guizhou, Shangxi, Shanxi, Yunnan and Henan province of China from 2008 to 2010. The ten floral origins were Brassica spp., Zizyphus spp., Citrus spp., Robinia pseudoacacia L., Vitex negundo Linn. Var. heterophylla (Franch.) Rehd., Lycium chinense Mill., Litchi chinensis Sonn., Malus spp., Helianthus sp. and Chrysanthemum indicum L. The water content of samples was 17.1–35.7% (w/w). None of the samples were filtered, and they were stored at 6–8 8C in the laboratory before analysis. Prior to spectral measurement, honey were liquefied in a water bath at 55 8C, manually stirred to ensure homogeneity and adjusted to a standard solid content (65 8Brix) with distilled water to avoid spectral complications from naturally occurring
The airPLS algorithm worked by iteratively changing weights of sum squares errors (SSE) between fitted baseline and original signals, and the weights of SSE were obtained adaptively using difference between previously fitted baseline and original signals. This method proved to be fast, flexible and valid (Chen et al., 2010; Zhang et al., 2010). PLS-LDA has been shown effective in some classification problems (Yi et al., 2007; Li et al., 2011). Using the PLS algorithm, we first decomposed the spectral matrix X and value matrix y and got the principal component matrix T of X. Then we carried out linear discriminant analysis using matrix T and y and obtained the discriminant function that could potentially discriminate groups of the test samples. The optimal number of latent variables was estimated using 10-fold cross-validation of PLS-LDA models. Discrimination models were established using latent variables
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selected by cross validation. In this study, two classes of the samples were assigned numeric value of 1 (authentic honey samples) and 1 (adulterated honey samples) respectively. PLSLDA models were developed to make predictions on the test samples. All computations were performed in Matlab 7.0 (Mathworks, USA) under Windows XP. The airPLS and PLS-LDA algorithm codes were provided by Research Center of Modernization of Chinese Medicines, Central South University, China (PLS-LDA and airPLS Matlab codes can also be respectively downloaded free of charge from http://code.google.com/p/cars2009 and http://code.google.com/p/airpls/).
3. Results and discussion 3.1. Raman spectra of authentic honey and adulterated honey Fig. 1 shows raw Raman spectra of analyzed honey. Because there was no relevant information in the spectral channel of 0–300 and 1300–2048, the spectral channel of 300–1300 (the signal range of 270–1820 cm 1) was selected for analysis. Fig. 2 shows raw spectra of a randomly selected authentic honey and the same honey adulterated with HFCS (40%, w/w) and MS (40%, w/w) in the spectral range 270–1820 cm 1. HFCS-adulterated and MS-adulterated honey from Fig. 2 showed some characteristic Raman shifts, which were used to detect the adulterants. As shown in Figs. 1 and 2, the signals of authentic and adulterated honey showed characteristics bands around 351, 425, 517, 592, 629, 705, 778, 824, 865, 915, 981, 1065, 1127, 1264, 1373 and 1461 cm 1. The characteristic groups were similar to those reported by Paradkar and Irudayaraj (2001). Some characteristic groups and their corresponding vibration modes were well established except 351, 425, 517, 592, 629, 778 and 981 cm 1, which are unknown (Paradkar and Irudayaraj, 2001; Batsoulis et al., 2005). A signal around 705 cm 1 corresponds to the stretching of CO and CCO, OCO bending; 865 and 824 cm 1 were found to be caused by the vibration of CH and C(1)H, CH2; a signal around 915 cm 1 was associated with the vibration of C(1)H and COH; a signal around 1065 cm 1 might be caused by a major contribution by the bending vibration of C(1)–H and COH in carbohydrates and a minor contribution by the vibration of C–N bond in proteins and amino acids; a signal around 1127 cm 1 could be a combination of stretching vibration of C–O bond (major) and vibration of C–N bond of protein and amino acids (minor); a signal around 1264 cm 1 was associated with the vibration of C(6)–OH and C(1)–OH; a signal around 1373 cm 1 corresponded to the bending of C–H and O–H bonds; and a signal around 1461 cm 1 was caused by a combination of bending vibration of CH2 group (major) and the vibration of COO group. 3.2. The choice of spectral data pretreatment Different spectral data pretreatments, including autoscaling, mean-centered (MC), first and second derivatives, multiplicative scattering correction (MSC), airPLS and their combination, were employed to optimize the PLS-LDA model. Comparing the total classification accuracy based on 10-fold cross-validation for PLSLDA models of every sample set using different pretreatments based on different number of PLS components, we found that airPLS combined with autoscaling always had the best performance with a small number of PLS components. Thus, the pretreatment of airPLS combined with autoscaling was selected before PLS-LDA modeling. Autoscaling equalizes variances of all variables and gives the same weight to all the variables (Li et al., 2012).
Fig. 1. Raw Raman spectra of analyzed honey: (A) authentic honey; (B) highfructose corn syrup (HFCS)-adulterated honey; (C) maltose syrup (MS)-adulterated honey.
Fig. 3 shows the raw and corrected spectra of a representative sample by airPLS. The useful information (peak shape of the spectrum) was reserved while the background signal was effectively reduced.
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Fig. 2. Raw Raman spectra of a randomly selected authentic honey sample and the same honey sample adulterated with high fructose corn syrup (40%, w/w) and maltose syrup (40%, w/w).
3.3. PLS-LDA models of authentic honey and adulterated honey Sample sets 1, 2 and 3 were divided into training set (70%) and test set (30%) using a Kennard–Stone (KS) algorithm (Kennard and Stone, 1969). Three PLS-LDA models based on the optimal spectral data pretreatment were also developed to evaluate the efficacy of Raman spectroscopy in distinguishing between authentic honey and adulterated honey. Table 1 shows the classification accuracy using the PLS-LDA model. Based on 10-fold cross-validation for training set, the classification accuracy of authentic honey, adulterated honey and total honey were 84.9% (45/53), 82.7% (43/52) (10%, w/w: 80.0%, 20%, w/w: 82.4%, 40%, w/w: 86.7%), 83.8% (88/105) for set 1, 100% (56/56), 93.8% (45/48) (10%, w/w: 93.3%, 20%, w/w: 93.3%, 40%, w/ w: 94.4%), 97.1% (101/104) for set 2, and 91.8% (45/49), 72.7% (40/ 55) (10%, w/w: 70.6%, 20%, w/w: 73.7%, 40%, w/w: 73.7%), 81.7% (85/104) for set 3, respectively, so the sensitivity and the specificity were 83.33% and 84.31% for set 1, 94.92% and 100% for set 2, and 75.00% and 90.91% for set 3, respectively. While for test set, the classification accuracy of authentic honey, adulterated honey and total honey were 95.2% (20/21), 87.5% (21/24) (10%, w/w: 83.3%, 20%, w/w: 87.5%, 40%, w/w: 90%), 91.1% (41/45) for set 1, 100% (18/ 18), 96.3% (26/27) (10%, w/w: 90.0%, 20%, w/w: 100.0%, 40%, w/w: 100.0%), 97.8% (44/45) for set 2 and 92.0% (23/25), 55.0% (11/20)
Fig. 3. The raw and corrected spectra of a representative sample using airPLS.
(10%, w/w: 50.0%, 20%, w/w: 57.1%, 40%, w/w: 60.0%), 75.6% (34/ 45) for set 3, respectively, so the sensitivity and the specificity were 86.96% and 95.45% for set 1, 94.74% and 100% for set 2, and 71.88% and 84.62% for set 3, respectively. In terms of classification accuracy, set 2 had better performance than set 1, and set 3 had the worst performance (Table 1). The reason may be that the major content in authentic honey is monosaccharide, e.g., glucose and fructose, which can amount up to about 70%, and the dominant content in HFCS is also glucose and fructose (92%), which is somewhat similar to the content in authentic honey, meanwhile, the dominant content in MS is maltose (50%), which is quite dissimilar to the main content in honey; set 3 had the worst performance because adulterated honey of set 3 was composed of two different types of adulterated samples, while those of set 1 and 2 were comprised of only one type of adulterated samples. 3.4. Discrimination of honey adulterants In this study, a PLS-LDA model for the discrimination of honey adulterants was developed using set 4. All 151 samples were divided into training set (106) and test set (45) using the KS algorithm. Table 2 shows the results of the model. For the training set, the classification accuracy of HFCS-adulterated honey, MS-adulterated honey and total honey were 98.1% (51/52),
Table 1 Classification of authentic honey and adulterated honey using PLS-LDA model based on the spectral data pretreatment of airPLS. Classification accuracy of training set (%)a
Set 1 Set 2 Set 3 a
Classification accuracy of test set (%)
Authentic honey
Adulterated honey
Total honey
Authentic honey
Adulterated honey
Total honey
84.9 100 91.8
82.7 93.8 72.7
83.8 97.1 81.7
95.2 100 92.0
87.5 96.3 55.0
91.1 97.8 75.6
Based on 10-fold cross-validation.
Table 2 Discrimination of adulterants in honey using PLS-LDA model based on the spectral data pretreatment of airPLS. Classification accuracy of training set (%)a
Set 4 a
Classification accuracy of test set (%)
HFCS-adulterated honey
MS-adulterated honey
Total honey
HFCS-adulterated honey
MS-adulterated honey
Total honey
98.1
85.2
91.5
95.8
71.4
84.4
Based on 10-fold cross-validation.
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85.2% (46/54) and 91.5% (97/106) respectively based on the 10fold cross-validation. For the test set, 1 of 24 HFCS-adulterated honey samples was wrongly predicted as MS-adulterated honey and 6 of 21 MS-adulterated honey samples were wrongly predicted as HFCS-adulterated honey. The classification accuracy of HFCS-adulterated honey, MS-adulterated honey and total honey were 95.8% (23/24), 71.4% (15/21) and 84.4% (38/45), respectively. The above results have shown that Raman spectroscopy coupled with chemometrics methods seems to be an efficient technique for discriminating adulterants such as HFCS and MS in honey. 4. Conclusion Raman spectroscopy combined with PLS-LDA was successfully applied to detecting adulterants in honey. The airPLS was an effective pretreatment for diminishing fluorescence background of the Raman spectroscopy. Raman spectroscopy combined with PLSLDA seems more successful in detecting MS than HFCS in honey. The method is simple and efficient, and it does not need sample preprocessing; therefore it is suitable for on-site testing in field applications. Acknowledgements We thank the staff at the Research Center of Modernization of Chinese Medicines, Central South University for providing airPLS and PLS-LDA Matlab codes. This study was supported by the Scientific and Technological Innovation Projects of Hunan Academy of Agricultural Sciences (2010), the Natural Science Foundation of China (Project No.: 20875065). References Abdel-Aal, E.-S.M., Ziena, H.M., Youssef, M.M., 1993. Adulteration of honey with high fructose corn syrup: detection by different methods. Food Chemistry 48 (2), 209–212. Aparicio, R., Baeten, V., 1998. Fats and oils authentification by FT-Raman. OCLOleagineux Corps Gras Lipides 5 (4), 293–295. Batsoulis, A.N., Siatis, N.G., Kimbaris, A.C., Alissandrakis, E.K., Pappas, C.S., Tarantilis, P.A., Harizanis, P.C., Polissiou, M.G., 2005. FT-Raman spectroscopic simultaneous determination of fructose and glucose in honey. Journal of Agricultural and Food Chemistry 53 (2), 207–210. Beattie, R.J., Bell, S.J., Farmer, L.J., Moss, B.W., Patterson, D., 2004. Preliminary investigation of the application of Raman spectroscopy to the prediction of the sensory quality of beef silverside. Meat Science 66 (4), 903–913. Bertelli, D., Plessi, M., Sabatini, A.G., Lolli, M., Grillenzoni, F., 2007. Classification of Italian honeys by mid-infrared diffuse reflectance spectroscopy (DRIFTS). Food Chemistry 101 (4), 1565–1570. Bhosale, P., Ermakov, I.V., Ermakova, M., Gellermann, W., Bernatein, P.S., 2004. Resonance Raman quantification of nutritionally important carotenoids in fruits, vegetables, and their juices in comparison to high-pressure liquid chromatography analysis. Journal of Agricultural and Food Chemistry 52 (11), 3281–3285. Chen, L.Z., Xue, X.F., Ye, Z.H., Zhou, J.H., Chen, F., Zhao, J., 2011. Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy? Food Chemistry 128 (4), 1110–1114. Chen, S., Li, X.N., Liang, Y.Z., Zhang, Z.M., Liu, Z.X., Zhang, Q.M., Ding, L.X., Ye, F., 2010. Raman spectroscopy fluorescent background correction and its application in clustering analysis of medicines. Spectroscopy and Spectral Analysis 30 (8), 143–146. Codex Alimentarius Commission Standards, 2001. Codex Standard for Sugars: Standard 12-1981, Rev. 1, 1987. Codex Alimentarius, 2001. Draft revised standard for honey. Alinorm 01/25 19-26. Food and Agriculture Organization of the United Nations, Rome. Cordella, C., Antinelli, J.F., Aurieres, C., Faucon, J.-P., Cabrol-Bass, D., Sbirrazzuoli, N., 2002. Use of differential scanning calorimetry (DSC) as a new technique for detection of adulteration in honey. 1. Study of adulteration effect on honey thermal behavior. Journal of Agricultural and Food Chemistry 50 (1), 203–208. Cordella, C.B.Y., Milita˜o, J.S.L.T., Cle´ment, M.C., Cabrol-Bass, D., 2003. Honey characterization and adulteration detection by pattern recognition applied on HPAEC-PAD profiles. 1. Honey floral species characterization. Journal of Agricultural and Food Chemistry 51 (11), 3234–3242.
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