Z Brenn-Struckhofova

Dr Margit Cichna-Markl

Determination of bisphenol A in wine by sol-gel immunoaffinity chromatography, HPLC and fluorescence detection

Keywords: Migration, Bisphenol A, Coatings Bisphenol A, wine, migration, immunoaffinity chromatography, sol-gel, HPLC

Determination of bisphenol A in wine by sol-gel immunoaffinity chromatography, HPLC and fluorescence detection

Zdenka Brenn-Struckhofova, Margit Cichna-

Introduction

Bisphenol A (BPA) is an industrial chemical primarily used in the production of polycarbonates and epoxy resins. Many food containers -like returnable milk and water bottles or baby milk bottles -are made of polycarbonate, while epoxy resins are used as protective coatings in metal food and beverage cans or to line wine storage vats. A number of studies showed a migration of BPA traces from polycarbonate based packagings (Biles et al. 1997a, Brede et al. 2003[START_REF] Nerin | Determination of potential migrants in polycarbonate containers used for microwave ovens by highperformance liquid chromatography with ultraviolet and fluorescence detection[END_REF], can coatings [START_REF] Munguia-Lopez | Effect of heat processing and storage time on migration of bisphenol A (BPA) and bisphenol A-diglycidyl ether (BADGE) to aqueous food stimulant from mexican can coatings[END_REF]Soto-Valdez 2001, Goodson et al. 2004) or wine vat coatings [START_REF] Larroque | Migration des monomères constitutifs des résines époxydiques utilisées pour revêtir les cuves à vin[END_REF]) into food or wine simulants. BPA belongs to the so-called endocrine-disrupting chemicals (EDCs) which can interfere with the function of the hormonal system of humans and animals by binding to the natural estrogen receptor. In animal experiments BPA has been shown to influence mammary gland development [START_REF] Markey | In utero exposure to bisphenol A alters the development and tissue organization of the mouse mammary glands[END_REF]) and cause urethral malformations [START_REF] Timms | Estrogenic chemicals in plastic and oral contraceptives disrupt development of the fetal mouse prostate and urethra[END_REF] or meiotic aneuploidy [START_REF] Hunt | Bisphenol A exposure causes meiotic aneuploidy in the female mouse[END_REF]. Recent studies indicated that BPA can be biologically active at very low doses which had not caused detectable effects in toxicological studies and had therefore been assumed to be safe for humans [START_REF] Timms | Estrogenic chemicals in plastic and oral contraceptives disrupt development of the fetal mouse prostate and urethra[END_REF][START_REF] Ashby | Endocrine disruption occurring at doses lower than those predicted by classical chemical toxicity evaluation: The case of bisphenol A[END_REF][START_REF] Welshons | Large effects from small exposures. I. Mechanisms for endocrine disrupting chemicals with estrogenic activity[END_REF].

Since the food chain is considered as the main source of BPA exposure for humans, several surveys have already been carried out to determine BPA levels in canned foodstuffs and beverages (Biles et al. 1997b[START_REF] Yoshida | Determination of bisphenol A in canned vegetables and fruit by HPLC[END_REF][START_REF] Goodson | Survey of bisphenol A and bisphenol F in canned foods[END_REF], Brotons et al. 1995[START_REF] Thomson | Bisphenol A in canned foods in New Zealand: an exposure assessment[END_REF], Braunrath et al. 2005) but there is a lack of realistic data for wines. Both the "Opinion of the Scientific Committee on Food on BPA" [START_REF] Scf | Opinion of the Scientific Committee on Food on Bisphenol A (Expressed on 17[END_REF] and the"European Union risk assessment report on BPA" (EURAR 2003) were based on exposure models derived from data published by [START_REF] Larroque | Migration des monomères constitutifs des résines époxydiques utilisées pour revêtir les cuves à vin[END_REF] which were, however, obtained in experiments with wine simulants. Following the "Opinion of the Scientific Committee on Food on BPA" the European Commission reduced the Specific Migration Limit for BPA from 3 mg/kg to 0.6 mg/kg (EC 2004).

A recent paper by [START_REF] Lambert | Chromatographic analysis of water and wine samples for phenolic compounds released from food-contact epoxy resins[END_REF] advanced the methodology for the determination of BPA (and other phenolic compounds) in wine which had been in contact with epoxy resins by HPLC and fluorescence detection but due to its high detection limit the method could only be validated by using fortified samples.

The present paper describes a method for the determination of BPA in wine which includes a slightly modified sample clean-up procedure previously developed for the determination of BPA in canned beverages, fruits and vegetables (Braunrath and Cichna 2005). The method offers a detection limit low enough to finally allow the analysis of real wine samples. The application of the method described in this paper provides the first set of data on BPA in wine samples

Experimental

Reagents and Chemicals

Purified polyclonal bisphenol A (BPA) rabbit antibodies (5 mg/240 µl PBS buffer) were a gift from Japan EnviroChemicals, Ltd. (Tokyo, Japan). BPA was bought from Sigma (St. Louis, MO, USA), tetramethoxysilane (TMOS) from Fluka (Buchs, Switzerland). Acetonitrile (ACN), gradient grade for HPLC, was delivered from Fisher Scientific (Leicestershire, UK). The BPA stock solution was prepared by dissolving 20.0 mg of BPA in 20.0 ml ACN. Lower concentrated BPA solutions were obtained by diluting the stock solution with mobile phase. All BPA solutions were stored at 4 °C. Phosphate-buffered saline (PBS), pH 7.6, was prepared by dissolving 12.46 g Na 2 HPO 4 x 2 H 2 O, 1.56 g NaH 2 PO 4 x 2 H 2 O and 8.5 g NaCl in 1 litre of bidistilled water.

Preparation of Sol-Gel Columns

Sol-gel immunoaffinity columns were prepared according to a previously described protocol (Braunrath and Cichna 2005). In brief, TMOS was hydrolysed under slightly acidic conditions in an ultrasonic bath for 30 min. After mixing 1 ml hydrolysate with 1 ml of PBS containing 1 mg of BPA antibody a gel was formed within a few min. When the resulting sol-gel glass had lost 50% of its initial mass it was manually ground in an achate mortar and packed into a 3 ml glass column (Merck, Darmstadt, Germany) equipped with a polytetrafluorethylene frit (Merck). After flushing the immunoaffinity column in sequence with 20 ml of PBS, 15 ml of v/v) and 20 ml of PBS, it was stored in PBS at 4 °C.

Sol-gel filter columns were prepared following the same procedure but by mixing PBS (without BPA antibodies) with pre-hydrolysed TMOS.

Wine samples

59 wine samples were analysed in the present study. 31 samples were obtained from four Austrian vintagers from the Wachau, a famous Austrian wine-growing region. Vintagers kindly provided information about wine storing conditions, e.g. the tank material and storage time. Ten samples were taken directly from vats, 21 had already been filled into glass bottles. Twenty-eight wine samples (17 filled into glass bottles and 11 Tetra Brik wines) were bought in local markets. Since white wine production is more important in Austria (especially in the Wachau) than red wine production the study comprised more white wine (46) than red wine (13) samples.

Sample Preparation

The sample pretreatment procedure is a slight modification of the procedure developed for the analysis of canned beverages, fruits and vegetables (Braunrath and Cichna 2005) which was now adapted to the analysis of wine. It is very simple and consists of just a few steps.

Wine samples were degassed in an ultrasonic bath for 5 min before taking an aliquot of 25 ml which was mixed with 25 ml of PBS. The pH values of these samples were in the range from 3.0 to 3.9. In order to enable interactions of BPA and the antibodies immobilised in the immunoaffinity column the pH value of the sample was adjusted to pH 7.0 by adding diluted sodium hydroxide. To avoid clogging of the immunoaffinity column the samples were filtered through a borosilicate frit. The sample was then applied to a sol-gel pre-column which was on-line coupled with an immunoaffinity column using a flow-rate of about 1 ml/min. After sample application the immunoaffinity column was decoupled from the pre-column and washed with 5 ml of ACN-water (10:90, v/v) before eluting BPA with 4 ml of v/v). The eluate was collected in a 5 ml measuring flask and filled up to the ring mark with bidistilled water. An aliquot of 100 µl was injected into the HPLC system. The immunoaffinity column was regenerated with 20 ml of PBS.

HPLC separation and detection

In the present work two HPLC systems were used operated with different phase systems and detectors. phase A was 0.01 M sodium acetate buffer (pH 4.8, adjusted with acetic acid), mobile phase B ACN. The gradient program was as follows: 0 min: 77% A / 23% B, 10 min: 76.1% A / 23.9% B, 20 min: 73.6% A / 26.4% B, 30 min: 66.8% A / 33.2% B, 40 min: 60% A / 40% B, 62 min: 60% A / 40% B. The flow-rate was 0.6 ml/min, the injection volume 100 µl. Potentials were set at +300, +400, +500, +550, +600, +650 and +700 mV against palladium reference electrodes, the eighth electrode did not work. Peaks were integrated using the CoulArray Win software.

External calibration

HPLC system 1 was calibrated by injecting nine BPA standard solutions in the concentration range from 0.3 to 100.0 ng/ml in mobile phase. Analysis functions were obtained by linear regression of peak areas on standard concentrations.

Standard addition method

In order to determine BPA recoveries the standard addition method was carried out with one white wine (sample 33) and one red wine (sample 55). The nonspiked and three spiked samples (+ 100, 200 and 300 µl of a 100.0 ng/ml BPA standard / 25 ml wine sample) were analysed as described above. Peak areas were plotted against the amount of analyte added. Recoveries were determined by dividing the slope of the linear regression line for the standard addition by the slope of the linear regression line of BPA standard solutions.

Solid phase extraction

Solid phase extraction experiments were carried out using 3 ml C 18 cartridges packed with 500 mg Isolute (International Sorbent Technology, Mid Glamorgan, UK). The C 18 column was flushed with 6 ml of ACN, followed by 6 ml of ACNwater (10:90, v/v). Wine samples were applied at a flow-rate of about 1 ml/min. After washing the column with 10 ml of ACN-water (20:80, v/v) elution was carried out with 4 ml of ACN, collecting the eluate in a 5 ml measuring flask. The eluate was evaporated under a gentle nitrogen stream to about 2 ml before filling up the flask with bidistilled water to the ring mark.

Migration of BPA from plastic stoppers

Each of four plastic stoppers from different manufacturers was stored submerged in a 50 ml centrifuge tube filled with 10 ml of ethanol-water (11:89, v/v). Since in most wine samples the alcohol concentration was in the range from 11 to 12%, migration experiments were carried out with 11% ethanol instead of 10% ethanol conventionally used as alcoholic simulant. At the end of the storage period a 100 µl aliquot of the ethanolic solution was directly injected into HPLC system 1. Method performance characteristics HPLC system 1 was calibrated by injecting 100 µl of BPA standard solutions from 0.3 to 100.0 ng/ml, obtaining a good linearity (r > 0.9998, n=9) between BPA concentration and peak area. The limit of detection (LOD), calculated for a signal-to-noise ratio of 3, was 0.5 ng/ml.

Results and discussion

Sample clean-up

The analytical performance of the whole analysis method was assessed by applying the standard addition method. Correlation coefficients, mean recoveries, standard deviations of the recovery and the LODs (S/N=3) and LOQs (S/N=6) are given in Table 1.

[Insert Table 1 about here] Since the sample preparation procedure results in an analyte enrichment factor of five the whole analysis method offers a LOD and LOQ of 0.1 and 0.2 ng/ml, respectively.

BPA levels in wine samples

In order to get a first impression in the BPA concentrations found in commercially available wine, 59 wine samples were collected, analysed and the results listed together with information on factors which might influence the BPA concentrations. The data obtained are shown in Table 2 andTable 3. [Insert Table 2 and3 about here] In two of ten samples taken directly from wine vats BPA concentrations were below the LOQ (see Table 2a). In seven samples BPA levels ranged from 0.2 to 0.5 ng/ml; only in one sample a significantly higher BPA level of 2.1 ng/ml was found. Mean and median were 0.50 ng/ml and 0.20 ng/ml, respectively. In six of 21 wine samples which were obtained from local vintagers but had already been filled into bottles the BPA concentrations were below the LOQ. In 15 wine samples BPA concentrations ranged from 0.2 ng/ml to 1.5 ng/ml, with a mean of 0.61 ng/ml and a median of 0.50 ng/ml.

In five of the 28 wine samples purchased at local markets the BPA concentration was below the LOQ. In 23 samples BPA levels were in the range from 0.2 to 1.6 ng/ml. The mean was 0.58 ng/ml, the median 0.40 ng/ml.

In general, BPA concentrations were very low. The mean and median for all wine samples with BPA concentrations above the LOQ were 0.58 and 0.40 ng/ml, respectively. In all samples BPA concentrations were far below the Specific Migration Level of 0.6 mg/kg established by the EU Commission in 2004.

The data obtained indicate that neither the material of the vat nor the storage time of the wine has an influence on the BPA concentration in the wine sample. Furthermore, no significant difference has been found between the BPA levels in white wine or red wine samples. In addition there was no relation between the alcohol concentration and the BPA level of the sample. Neither the packaging material (glass bottle or Tetra brik) nor the top of the glass bottle (plastic, metal or cork) had a measurable effect on the BPA level.

Migration of BPA from plastic stoppers.

In order to investigate if BPA can migrate from a plastic stopper four different plastic stoppers were stored in 50 ml centrifuge tubes submerged in 10 ml of ethanol-water (11:89, v/v). Table 4 gives the BPA amounts determined in the ethanolic solutions after storage periods from five to 11 weeks by injecting aliquots of the incubation solutions into HPLC system 1. Figure 3 From two stoppers detectable amounts of BPA were released into the ethanolic solution. In order to confirm the identity of BPA one incubation solution was also injected into HPLC system 2 which was equipped with a coulometric electrode array detector. Figure 4 shows the chromatograms of (A) a BPA standard solution and (B) the incubation solution. Both chromatograms contained a peak with a retention time of 48 min, indicating the presence of BPA in the ethanolic solution. Figure 5 demonstrates the similarity of the current-voltage curves of the 48 min peaks, adding a further evidence for the identity of BPA peaks in the chromatogram of the ethanolic solution and thus migration of BPA from the plastic stopper.

[Insert Figure 4 and Figure 5 about here] However, migration of BPA from plastic stoppers will only lead to insignificant contributions to the BPA concentrations. (The migration of 30 ng BPA from a plastic stopper into a wine sample contained in a 750 ml glass bottle would result in a concentration of about 40 pg BPA / ml wine which is far below the Specific Migration Level of 0.6 mg/kg established by the EU Commission).

BPA exposure due to wine consumption

Due to the lack of literature data on BPA concentrations in wine the results obtained in the present study cannot be compared with previously published ones. The data can, however, be used to reconsider the risk assessment obtained with the help of exposure models based on the results of experiments with wine simulants by [START_REF] Larroque | Migration des monomères constitutifs des résines époxydiques utilisées pour revêtir les cuves à vin[END_REF]. In their study three series of migration experiments were carried out by placing into contact wine simulants with inert materials coated with resins. From the highest BPA migration level obtained (100 mg BPA/kg resin in series 1) the worst-case BPA concentration in wine was calculated to be 650 ng g -1 , assuming that the wine has been stored in a 1500 L vat lined with 10 kg resin. The BPA concentrations obtained in the present paper were far lower than this worst-case estimate. BPA risk assessment by the Scientific Committee on Food on BPA was also based on the migration study of [START_REF] Larroque | Migration des monomères constitutifs des résines époxydiques utilisées pour revêtir les cuves à vin[END_REF]. However, the SCF calculated a significantly lower BPA concentration of 9 ng g-1 by using a lower BPA migration level (10 mg BPA/kg resin) and assuming that the 1500 L wine vat is just lined with 1.4 kg resin.

According to the results obtained in the present study even this lower migration level is an overestimation of the actual BPA concentrations in wine samples.

Based on the highest BPA level found in the present study (2.1 ng/ml), BPA exposure due to wine consumption can be estimated to be lower than 26 ng/ kg body weight/day, assuming a person with a body weight of 60 kg and a wine consumption of 0.75 L per day. This value is about 400 times lower than the Tolerable Daily Intake (TDI) for BPA of 0.01 mg / kg body weight/day proposed by the SCF.

Conclusion

The highly selective analysis method presented allows the determination of traces of BPA in wine samples without the interference of disturbing matrix compounds. Its application to 59 wine samples revealed only very low BPA concentrations. In 13 of the 59 wine samples the BPA concentration was lower than the LOQ (0.2 ng/ml).The mean and median for all wine samples with BPA concentrations above the LOQ were 0.58 and 0.40 ng/ml, respectively. These values are far lower than the estimated BPA levels derived from migration experiments using wine simulants. We wish to thank Heidi Schwartz for carrying out the confirmation studies with the HPLC system equipped with the electrochemical detector We are also grateful to Georg Weingart for supplying wine samples from local vintagers together with data on their storage conditions. The present study would have been impossible without the support of Japan EnviroChemicals, Tokyo, Japan, who provided BPA antibodies. 3); C) red wine (sample 55, Table 3). HPLC system 1, injection volume 100 µl. 2), (B) red wine (sample 55, Table 2).

Injection volume 100 µl, HPLC system 1

  HPLC system 1, used to determine BPA concentrations in wine samples, consisted of a high pressure gradient pump (Model L-6200, Merck), a column thermostat (Column oven 655 A-52, Merck), a six-port injection valve (Model 7161, Rheodyne) equipped with a 100 µl injection loop and a fluorescence detector (Model 1080, Merck) set at 275/305 nm. Wine samples were analysed using a LiChrospher 60 RP-Select B column, 5 µm, 250 x 4.0 mm I.D. (Merck). The mobile phase consisted of water-ACN (70:30, v/v). The flow rate was 1.0 ml/min. Peaks were integrated using the Stratos V 3.0 software (Polymer Laboratories, Darmstadt, Germany). At the end of each HPLC analysis the analytical column was flushed with water/ACN (40:60, v/v) for 10 min. HPLC system 2 was used in order to confirm the identity of BPA when migration experiments with plastic stoppers were carried out. It consisted of a high pressure gradient pump (Model L-6200, Merck), an autosampler (Model AS-2000 A) and a coulometric electrode array detector (ESA, Chelmsford, MA, USA) equipped with two cell blocks consisting of eight working electrodes. The analytical column was a HP-Hypersil PDS column, 5 µm, 250 x 4 mm I.D. Mobile

Fig. 1

 1 Fig.1shows typical chromatograms obtained by injecting (A) a BPA standard solution, (B) a white wine and (C) a red wine sample, both purified by immunoaffinity chromatography. In order to illustrate the increased efficiency in removing matrix components obtained by the immunoaffinity column the same wine samples were alternatively purified by solid-phase extraction using phase systems and operating procedures (as described in the experimental section) which had been optimized for maximum BPA recovery(Braunrath and Cichna 2005). The chromatograms obtained are documented in Fig.2.[Insert Figure1and Figure2about here]

  shows the chromatogram of the ethanolic solution after incubation of stopper 4 for 11 weeks.

Fig. 1 :

 1 Figures:

Fig. 2 :

 2 Fig. 2: Chromatograms obtained after sample preparation by solid phase extraction: (A) white wine (sample 33, Table2), (B) red wine (sample 55, Table

Fig. 3 :

 3 Fig. 3: Migration of BPA from a plastic stopper. Chromatogram obtained by injecting 100 µl aliquots of the ethanolic incubation solution into HPLC system 1.

Fig. 4 :

 4 Fig. 4: Migration of BPA from a plastic stopper. Chromatograms obtained by injecting into HPLC system 2 100 µl aliquots of (A) a BPA standard solution (50 ng/ml in mobile phase) and (B) the ethanolic incubation solution.

Fig. 5 :

 5 Fig. 5: Current-voltage curves of the BPA standard solution (triangle) and the ethanolic incubation solution (square).

  Biles JE, Mc Neal TP, Begley TH, Hollifield HC. 1997a. Determination of bisphenol-A in reusable polycarbonate food-contact plastics and migration to food simulating liquids. Journal of Agricultural and Food Chemistry 45: 3541-3544. Biles, JE, Mc Neal, TP, Begley, TH. 1997b. Determination of bisphenol A migrating from epoxy can coatings to infant formula liquid concentrates. Journal of Agricultural and Food Chemistry 45: 4697-4700. Braunrath R, Cichna M. 2005. Sample preparation including sol-gel immunoaffinity chromatography for determination of bisphenol A in canned beverages, fruits and vegatables. Journal of Chromatography A 1062: 189-198. Braunrath R, Podlipna D, Padlesak S, Cichna-Markl M. 2005. Determination of bisphenol A in canned food by immunoaffinity chromatography, HPLC and fluorescence detection. Journal of Agricultural and Food Chemistry 53: 8911-8917. Brede C, Fjeldal P, Skjevrak I, Herikstad H. 2003. Increased migration levels of bisphenol A from polycarbonate baby bottles after dishwashing, boiling and brushing. Food Additives and Contaminants 20: 684-689. Brotons JA, Olea-Serrano MF, Villalobos M, Pedraza V, Olea N. 1995. Xenoestrogens released from lacquer coatings in food cans. Environmental Health Perspectives 103: 608-612. EC 2004. Off. J. Eur. Communities (2004) L71 (19/EC), 8-21.

Table 1 :

 1 Analytical data

	Wine	Sample	Standard	Mean recovery	LOD (S/N=3)	LOQ
		volume	addition	± SD (%) (n=3)		(S/N=6)
		(ml)	correlation
			coefficient r
	White	25.0	0.9919	74 ± 11	0.1 ng/ml	0.2 ng/ml
	Red	25.0	0.9995	81 ± 8	0.1 ng/ml	0.2 ng/ml

Table 2a :

 2a BPA concentrations in wine samples taken directly from the vat. n.d.: below the LOQ (0.2 ng/ml)

			Food Additives and Contaminants
	Wine sample 1 2 3 4 5 6 7 8 9 10	Wine White White White White White White White White Red Red	Storage time (months) Steel vat Wood vat Plastic vat 10 --10 --6 2 3 8 2 -9.5 --10.5 ---5 5 9.5 ---10 --8 0.25 F o r P e e r R e v i e w BPA concentration (ng/ml) 0.2 0.2 0.4 0.5 0.2 2.1 0.2 n.d. n.d. 0.2	O n l y

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Table 2b :

 2b BPA concentrations in wine samples already filled into glass bottles. n.k.: storage time not known; n.d.: below the LOQ (0.2 ng/ml).

	Wine	Wine	Volum		Alcohol	Top of the		Storage time (months)		BPA concentration (ng/ml)
	sample		e (L)		(%)	glass bottle
							Steel	Wood	Plastic	Glass
							vat	vat	vat	bottle
	11	White	0.75		12.5	Plastic	5	-	-	11	n.d.
	12 13 14 15	White White White White F 0.75 0.75 0.75 0.75 o F o	12.5 12.5 12.5 13.0	Cork Cork Plastic Cork	5 8 8 7	----	----	11 5 5 8	n.d. n.d. n.d. 1.0
	16	White	1.00 r 11.5 r	Metal	8	-	-	5	0.2
	17 18 19 20 21 22 23	White White White White White White White	0.75 0.75 0.75 0.75 1.00 1.00 1.00	12.5 13.5 13.5 P P 12.5 12.0 11.5 11.5 e Cork Cork Plastic Plastic Metal Cork Cork e r e e r	5 6 6 5 3 -n.k.	----2 3.5 -	-----3.5 -	8 6 6 10 9 7 n.k.	1.2 0.5 0.4 n.d. 0.6 0.6 0.5
	24 25 26 27 28 29 30 31	White White White White Red Red Red Red	0.75 0.75 0.5 1.00 0.75 0.75 1.00 0.75		12.0 13.5 12.5 12.5 12.5 13.0 11.5 13.0	Cork Plastic Plastic Cork Cork Cork Metal Plastic R e n.k. n.k. n.k. ---9 -v i e w ----11 n.k. -n.k. R e v i e w	---n.k. -0.25 --	n.k. n.k. n.k. n.k. 2 n.k. 6 n.k.	1.5 0.3 0.2 0.3 n.d. 0.7 0.8 0.4
								O n O n
									l y l y

Table 3 :

 3 BPA concentrations in wine samples bought in local markets. n.d.: below the LOQ (0.2 ng/ml).

	Wine	Wine	Container Volume	Alcohol	Top	BPA concentration
	sample			(L)	(%)		(ng/ml)
	32	White	Glass	1.00	12.5	Plastic	1.6
	33	White	Glass	0.75	12.0	Plastic	0.2
	34	White	Glass	0.25	11.5	Metal	0.3
	35	White	Glass	0.25	11.5	Metal	0.3
	36	White	Glass	0.38	12.5	Metal	0.3
	37	White	Glass	0.75	11.5	Plastic	0.4
	38	White	Glass	0.75	12.5	Metal	n.d.
	39	White	Glass	0.75	12.0	Cork	0.3
	40	White	Glass	0.75	11.5	Plastic	0.8
	41	White	Glass	1.00	12.5	Cork	1.2
	42	White	Tetra Brik 1.00	10.0	-	0.2
	43	White	Tetra Brik 1.50	11.0	-	0.2
	44	White	Tetra Brik 1.00	12.0	-	0.8
	45	White	Tetra Brik 1.00	10.0	-	0.6
	46	White	Tetra Brik 1.50	11.5	-	0.4
	47	White	Tetra Brik 1.00	11.5	-	0.4
	48	White	Tetra Brik 1.00	11.0	-	1.0
	49	White	Tetra Brik 1.00	11.5	-	0.6
	50	White	Tetra Brik 1.00	11.0	-	0.3
	51	White	Tetra Brik 1.00	11.5	-	n.d.
	52	White	Tetra Brik 1.00	11.5	-	0.4
	53	Red	Glass	0.75	12.5	Cork	n.d.
	54	Red	Glass	0.75	12.0	Plastic	0.5
	55	Red	Glass	1.00	12.0	Metal	0.6
	56	Red	Glass	0.75	13.0	Cork	n.d.
	57	Red	Glass	0.75	13.5	Cork	1.2
	58	Red	Glass	0.75	12.5	Cork	0.7
	59	Red	Glass	0.75	12.0	Metal	n.d.

Table 4 :

 4 Migration of BPA from plastic stoppers. n.d.: below the LOD (0.5 ng/ml).

	Page 23 of 28
	Stopper	Incubation	Mass of BPA migrated from the
		period (weeks)	stopper (ng)
	1	6	32
	2	5	n.d.
	3	11	n.d.
	4	11	31

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uk Food Additives and Contaminants

Acknowledgement


Ryszard ; Siuda
email: siuda@atr.bydgoszcz.pl

Graŝyna Balcerowska

Czesław Sadowski

Comparison of different spectral ranges within uv-vis-nir region for the determination of the content of scab damaged component in blended samples of ground wheat

Keywords: Analysis -NIR, Chemometrics, Quality assurance Additives/Contaminants: Mycotoxins -fusarium flour, wheat, scab, fusarium head blight, diffuse reflectance spectroscopy

Comparison of the usability of different spectral ranges within uv-vis-nir region for determination

Introduction

Fusarium spp. invading grain is known to cause a grain disease called fusarium head blight or scab. Scab damaged kernels differ from sound kernels in many respects, first of all in chemical composition [START_REF] Bechtel | The effects of Fusarium graminearum on wheat kernels[END_REF][START_REF] Teich | Soft white winter wheat cultivar field-resistance to scab and deoxynivalenol accumulation[END_REF][START_REF] Wasowicz | Changes of chemical grain components, especially lipids, during their deterioration by fungi[END_REF][START_REF] Boyacioglu | Changes in some Biochemical Components of Wheat Grain that was Infected with Fusarium graminearum[END_REF][START_REF] Matthäus | Progression of mycotoxin and nutrient concentrations in wheat after inoculation with Fusarium culmorum[END_REF]) that results in a lowering of grain quality. Changes in chemical composition are accompanied with changes in physical properties (Seitz andBechtel 1985, Tkaczuk et al. 1991), e.g. such as kernels discolouring and decreasing mass (weight). Change in kernels colour and other visible symptoms are the main features that grain inspectors use in visual evaluation of the grain damage caused by scab. Quantitative evaluation of the same features supplemented with evaluations extended into the near infrared region was found useful for detection of scab damaged single kernels [START_REF] Dowell | Predicting scab, vomitoxin, and ergosterol in single wheat kernels using near-infrared spectroscopy[END_REF], Delwiche and Kim 2000[START_REF] Delwiche | Classification of scab-and other mold-damaged wheat kernels by nearinfrared reflectance spectroscopy[END_REF][START_REF] Delwiche | Detection of Scab-Damaged Hard Red Spring Wheat Kernels by Near-Infrared Reflectance[END_REF], and can also be used for development of automatic systems for corn single kernel classification (Pasikatan andDowell 2001, Pearson et al. 2004). Many authors reported a correlation of scab damage and contamination of damaged grain with mycotoxins, first of all with deoxynivalenol (DON) (see, e.g., [START_REF] Teich | Soft white winter wheat cultivar field-resistance to scab and deoxynivalenol accumulation[END_REF][START_REF] Ruckenbauer | Food safety of cereals: A chain-wide approach to reduce Fusarium mycotoxins[END_REF], Dexter and Nowicki 2003, and references therein). As food and feed contaminated with mycotoxins is known to cause both human and livestock diseases, determination of mycotoxins content as well as scab intensity are often the common target of research. Again optical methods are recognised as a rapid and cost-effective tool for scab and/or mycotoxins detection in both whole [START_REF] Dowell | Predicting scab, vomitoxin, and ergosterol in single wheat kernels using near-infrared spectroscopy[END_REF][START_REF] Dowell | Reflectance and Transmitance Spectroscopy Applied to Detecting Fumonisin in Single Kernels Infected with Fusarium verticillioides[END_REF][START_REF] Pettersson | Rapid estimation of deoxynivalenol and Fusarium by near infrared spectroscopy[END_REF][START_REF] Delwiche | Classification of scab-and other mold-damaged wheat kernels by nearinfrared reflectance spectroscopy[END_REF] and ground grain [START_REF] Kos | Fourier transform mid-infrared spectroscopy with attenuated total reflection (FT-IR/ATR) as a tool for the detection of Fusarium fungi on maize[END_REF][START_REF] Delwiche | Classification of scab-and other mold-damaged wheat kernels by nearinfrared reflectance spectroscopy[END_REF]and 2004).

An obvious strategy for mycotoxins detection is to detect contamination at the earliest stage of the "farm to table" chain in food production. In practice, a natural option for grain based products is grain examination before storage or/and milling, as it gives a possibility to prevent the contamination all products derived from processing of the grain. The cited above papers meet this demand. However grain examination at this stage is not a common practice.

Moreover, fungi can develop when storage conditions are not optimal. In addition, it has been reported that flour and flour-based commodities are often contaminated with mycotoxins (see, e.g., Rasmussen et al. 2003). Therefore, a question of value seems to be whether optical methods can be useful in examination for scab contamination at later stages of grain-based food products, such as flour. Direct detection of mycotoxins needs the mid-infrared spectral region to be used [START_REF] Kos | Fourier transform mid-infrared spectroscopy with attenuated total reflection (FT-IR/ATR) as a tool for the detection of Fusarium fungi on maize[END_REF], while most popular spectrophotometers work at the visible to near infrared spectral range, i.e. at the range where scab damage can be detected, whereas direct contamination with mycotoxins can not be detected. However a rapid method for detection of scab damage in flour could be used as, at least, a screening procedure before further examination for contamination with mycotoxins. This paper aims to extend the search for spectral wavelengths most useful for scab detection into the near UV region and to determine what is the potential efficiency in detection of scab damage when diffuse reflectance measurements are made over this range when ground wheat samples are fractioned and the size of particles is close to the size in flour.

Materials and methods

Materials

Two lots of wheat (Kris variety) grain were obtained from two plots. One lot was harvested from a plot where the crop grew in natural conditions (control material), while the other one was harvested from a plot where infestation of the heads with a suspension of F. culmorum conidia was carried out during the flowering stage. After harvesting and threshing, both lots were dried in air at room temperature. Grain from both categories was ground (1.0 mm screen, ZM1, Retsch, Haan, Germany) and sieved. The fractions with size of the particles lower than 0.18 mm from both samples were taken and blended in different proportions. This particular size of particles was selected because it is close to the size of the largest particles of white bread flour and to the size of smaller particles of whole grain wheat flour. Moreover, the small size of particles enabled even small volume blended samples to be homogeneous in composition. As a result of blending, 18 samples with the content of infested material ranging from 0% to 83.97% were prepared (the percentage was determined from weights of infested and healthy constituents and defined as the ratio of the mass of infested material to the total mass of the sample).

Methods

UV-VIS-NIR spectrophotometer (Cary5000, Varian) was used for recording diffuse reflectance spectra in log(1/R) mode in the wavelengths range from 200 to 2500 nm, every 2 nm. For each sample 10 scans at two crossed positions of the cup were taken and averaged for the sake of further analysis. Averaged spectra of four samples, considerably differing in the content of damaged material (5.2, 12.4, 22.6 and 57.2 %), were subdivided from the full set of spectra and left out as a test set. The remaining 14 spectra were used for building multivariate calibration models with PLS method using commercial software PLS_Toolbox3.0 (Eigenvector Research, Inc, USA). Within a given range of wavelengths and a method of spectra pre-processing, the best model was selected based on the minimum of the RMSECV (root mean square error of cross-validation) with a leave-one-out scheme of cross-validation.

Differentiation of the spectra, when applied, was performed with an eleven-points Savitzky-Golay first derivative smoothing filter [START_REF] Savitzky | Smoothing and Differentiation of Data by Simplified Least Squares Procedures[END_REF][START_REF] Madden | Comments on Savitzky-Golay Convolution Method for Least-Squares Fit Smoothing and Differentiation of Digital Data[END_REF]. Once the best model was selected, it was applied for interpretation of the spectra from the test set, and the root mean square error of prediction (RMSEP) determined. Assignments of spectral bands were based on two-dimensional covariance maps provided by a technique called twodimensional correlation spectroscopy [START_REF] Noda | Two-dimensional correlation spectroscopy -applications in vibrational and optical spectroscopy[END_REF]Ozaki 2004, Ozaki et al. 2001). Calculations needed to obtain relevant two-dimensional plots were based on a description of the method given by [START_REF] Barton | Two-Dimensional Vibrational Spectroscopy: Correlation of Mid-and Near-Infrared Regions[END_REF]), and performed with our own procedure developed in Matlab.

Results and discussion

Raw spectra

The set of original spectra is shown in Figure 1. The most striking feature of the spectra is the considerable shift observed in the range of lower wavelengths, while in the typical NIR range this effect is almost absent. In light of the well known phenomenon of multiple scattering of the NIR radiation in granulated samples [START_REF] Osborne | Practical NIR spectroscopy with application in food and beverage analysis[END_REF], the latter finding is not typical, however it can be explained as a result of the high uniformity of the size of particles in the samples due to sieving of the materials before blending. Inspection of the plot of correlation [Insert Figure 1 about here] coefficient, calculated between the variance of the content of infested component and the variance in the set of spectra at a given wavelength, versus wavelength (c.f. Figure 1), suggested the two sub-ranges of different general properties of the spectra can be subdivided within the whole measured UV-VIS-NIR range. The border between the sub-ranges was located at ca. 1400 nm. This suggested analysing the spectra either in the whole range or in two sub ranges that can be split from the whole range at 1400 nm. Apart from modelling in the whole range, modelling in the shorter wavelengths range of 200 to 1400 nm, and in the longer wavelengths range of 1400 to 2500 nm were performed. Modelling in the above defined ranges was tried with three typical methods for spectra preprocessing, i.e. with multiple scatter correction -MSC, single differentiation -Diff, and double differentiation -2-Diff with and without centring (C) in all cases. For evaluation of the quality of PLS calibration models the following performance parameters were calculated: the coefficient of determination (R 2 ), the root mean square errors of: calibration (RMSEC), cross-validation (RMSECV) and prediction (RMSEP). The values of errors themselves, however, give no direct information on the precision of the model nor whether or not the model is over fitted. Therefore two other parameters, more directly referred to both latter characteristics of the model, were calculated. The first one is the so called ratio of the range of the modelled quantity (i.e. the percentage of scab damaged material) to the RMSEP, denoted as RER. The value of RER can serve as general classification of the calibration models (see e.g. [START_REF] Batten | Plant analysis using near infrared reflectance spectroscopy: the potential and the limitations[END_REF]). The second of the introduced parameters is the ratio of RMSEC to the RMSECV, RCV. The lower the RCV value is, the more likely it is that the model is over fitted. Therefore the models with RCV close to unity are preferred, provided the remaining performance parameters are satisfactory. In practice, the RCV values are considerably lower than unity and no firm theoretical indication for the minimal acceptable value for RCV exists.

Multivariate calibration

From our experience it follows that models with RCV not far from 0.5 work well.

The results obtained for all the above mentioned ranges of wavelengths and variants for spectra pre-processing are listed in Table I The choice for the best model for a given range has to be made between differentiated only or differentiated and centred spectra. The latter choice seems to be optimal because of better (higher) RCV and/or lower complexity (lower number of latent factors involved) of the model. For the set of spectra pre-processed in this way, two models were of very good performance, i.e. the models for the whole range and for the sub range of shorter wavelengths.

The former model allowed one to predict the content of damaged material with the accuracy of ca. 1.5%, while the latter model gave a possibility for the accuracy of prediction to be even twice better (0.76%). Therefore the final choice is modelling within the shorter wavelengths [Insert Figures 2 about here] range based on single differentiated and centred set of spectra, even though modelling within the whole range needs a lower number of latent variables. In order to illustrate the properties of the best models within particular spectral ranges, Figure 2 presents the plots of predicted versus actual (calculated from weights of blended materials) content of damaged component in the samples for the models.

Spectral bands assignments and analysis

The dependence of correlation on wavelength shown in Figure 1 suggests that two spectral ranges of different character and usability in interpretation of the changes caused by scab infestation could be distinguished. The longer wavelengths range covers a part of typical NIR region and it can be expected to be useful for determination of possible changes in chemical composition of the samples, while visible and ultraviolet ranges together can monitor the changes in colour and, perhaps, absorbance due to transitions involving the valence electrons of some molecules. To get a better insight into details, two tools can be used, i.e. the plot of the dependence of correlation (between the content of the infested constituent and variability at a particular wavelength) on the wavelength, and two-dimensional maps provided from the so-called two-dimensional correlation spectroscopy (2DCOS) [START_REF] Noda | Two-dimensional correlation spectroscopy -applications in vibrational and optical spectroscopy[END_REF]Ozaki 2004, Ozaki et al. 2001). While the former tool measures the strength of correlation between the changes in the content of the analyte (scab damaged component) and variability observed in the set of spectra at a given wavelength, the latter one gives an opportunity to diagnose the internal structure of the set of spectra in light of covariance between signals measured at any pair of wavelengths. In general, the observed intensity changes in spectral bands can not necessarily be attributed to the change in the content of scab damaged component. Therefore, inspection of the correlation with the content of scab damaged component should help in determining spectral regions and/or bands where variability present in the spectra is correlated to the content of this constituent (cf. plot of correlation in Figure 1), while 2DCOS can provide insight into covariance between signals for any two wavelengths independently of whether the covariance is, or not, correlated to the content of infested constituent. In other words, 2DCOS enables one to exhibit covariances in internal structure of the set of spectra, while correlation with the content of damaged constituent allows one to distinguish covariances related to this content. A 2D map of wavelength-wavelength covariances provides two types of peaks: auto peaks located on the diagonal of the map that give information about the variance at a particular wavelength, and cross-peaks located off the diagonal that provide the covariance between the signals measured at two different wavelengths. 2DCOS was first applied to the whole wavelength range, then to the longer wavelength range, in both cases after MSC pre-treatment of the spectra. The latter range has been chosen because it contains bands characteristic for compounds typical for the chemical composition of wheat. Figure 3 shows a 2D map calculated within the whole spectral region. It displays strong autopeaks in the UV range. The most prominent autopeaks are observed [Insert Figure 3 about here] within the range of 210-260nm, where a very high correlation with the infested constituent takes place (cf. Figure 1), while cross-peaks are located mainly near 1450 nm and 1940nm, and are negatively correlated with peaks from the UV region. Bands at 1450 nm and 1940nm are commonly attributed to the content of moisture [START_REF] Osborne | Practical NIR spectroscopy with application in food and beverage analysis[END_REF][START_REF] Shenk | Application of NIR Spectroscopy to Agricultural Products[END_REF].

Therefore, the 2D map shown in Figure 3 allows one to state that the content of moisture decreases with the increase of infested constituent. However, at the wavelengths around 1450 and 1950nm there are also spectral bands of several biochemical compounds of grain, such as starch, protein, cellulose, and carbohydrates [START_REF] Osborne | Practical NIR spectroscopy with application in food and beverage analysis[END_REF][START_REF] Shenk | Application of NIR Spectroscopy to Agricultural Products[END_REF]). As a consequence, the resultant bands are broad and of complex structure since they consist of several overlapping bands coming from particular compounds. The 2D map in Figure 3 also shows the presence of less distinct cross-peaks at wavelengths close to 2100 and 2460-2490nm, which are typically recognised as common for starch and cellulose [START_REF] Osborne | Practical NIR spectroscopy with application in food and beverage analysis[END_REF][START_REF] Shenk | Application of NIR Spectroscopy to Agricultural Products[END_REF]. In order to get a more detailed insight into changes in the content of the main biochemical compounds, a 2D map of covariances has been calculated within the range of 1400 to 2500 nm. This map is shown in Figure 4. The map reveals two autopeaks close to 1940 and 2220 nm. At the former wavelength, cross-peaks can also be seen at 1450 nm (positive covariance) and much weaker, but still recognisable, structures at 1720 and 1840 nm (both with negative covariance). The peak at 1720 nm can be attributed to the change in the content of lipids [START_REF] Roussel | NIRS analysis reveals temporal trends in the chemical composition of French bread wheat accessions cultivated between 1800 and 2000[END_REF], while the band near 1840 nm has no chemical assignment in most references, except for in the paper by Diffee [START_REF] Diffee | Tobacco Analysis by NIR Spectroscopy[END_REF], where the author attributes it to cellulose. Another series of cross-peaks appears because of covariance [Insert Figure 4 about here] with the variability at the wavelengths around 2220 nm. The changes in this region are caused by changes in proteins content since many bands in the range of 2150-2200 nm characterise protein absorption and are well recognised [START_REF] Roussel | NIRS analysis reveals temporal trends in the chemical composition of French bread wheat accessions cultivated between 1800 and 2000[END_REF]. As it can be seen, these bands reveal negative correlation with the moisture changes. Moreover, a much weaker but still recognisable negative correlation is observed with the band around 2100 nm, and is due to changes in starch and cellulose content. The above analysis allows one to conclude that the content of protein and lipids increased with the increase of the scab damaged constituent, whereas the content of moisture and starch decreased. It should be noted, however, that it is difficult to make an unambiguous conclusion regarding the content of cellulose. The above findings are in agreement with reports of other authors (Wasowicz 1991, Boyacioglu and[START_REF] Boyacioglu | Changes in some Biochemical Components of Wheat Grain that was Infected with Fusarium graminearum[END_REF].

Conclusions

Of three spectral regions used for PLS modelling of the content of scab damaged constituent, the most usable region was 200-1400nm, covering the near ultraviolet, visible and very near infrared ranges. However, the whole spectral range where the spectra were collected (200-2500 nm) also supplied a model of very good performance. Of six methods for pre-processing This study has also demonstrated the capability of correlation analysis combined with two dimensional correlation spectroscopy to effectively assignment spectral bands. The results show that the regions where differences in chemical composition appear can be successfully determined. The information obtained by 2DCOS enabled deduction about the relative trends between the biochemical compounds of grains such as protein, lipids, starch, and cellulose as well as moisture. Table I. Values of parameters needed to assess the performance of PLS calibration models obtained for three ranges of wavelengths and several methods of spectra pre-processing. Bold figures distinguish best models in particular spectral ranges. For more explanations see the text.

  prior to modelling, single differentiation followed by centring led to best models in all spectral ranges.

Figure 1 .

 1 Figure 1. The set of original spectra (solid lines) recorded from 18 samples differing in the content of damaged component and the absolute value of correlation coefficient (broken line), calculated between the variance of the content of infested component and the variance in a set of spectra at given wavelength, versus wavelength.

Figure 2 .

 2 Figure 2. Plots of predicted versus actual content of scab damaged component for the best models in three ranges of wavelengths: a) whole (200 to , b) shorter, c)longer. Dots are for the samples used in calibration, triangles -in prediction. Inserts give the performance parameters

Figure 4 .

 4 Figure 4. Two-dimensional map of wavelength-wavelength covariance calculated for spectra recorded within the NIR region and pre-processed with MSC.

F

  Figure 2a.

  Figure 2b.

  . It can be seen from the Table that RER values typical for the distinguished wavelength regions noticeably differ. One can also see from the Table that superior models within a particular range were obtained for single differentiated spectra.

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http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants

Acknowledgments

The work has been supported from research projects 2P06R 113 26 and BS-16/2005.