Detection of Food Hazards using Fluorescence Fingerprint

4th IFAC Conference on Modelling and Control in Agriculture, Horticulture and Post Harvest Industry August 27-30, 2013. Espoo, Finland

Detection of Food Hazards using Fluorescence Fingerprint Junichi Sugiyama*, Kaori Fujita*, Masatoshi Yoshimura*, Mizuki Tsuta*, Mario Shibata*, Mito Kokawa** *National Food Research Institute, NARO, Tsukuba, Ibaraki 305-8642, JAPAN (e-mail:[email protected]). **The University of Tokyo,Bunkyo, Tokyo 113-8657, JAPAN Abstract: Fluorescence fingerprint technique was applied to detect food hazards quantitatively. It is nondestructive and quick measurement. The acquired three dimensional volume data can be analyzed by multivariate analysis. As examples of food hazards, detection of mycotoxin in wheat flour and prediction of aerobic bacteria population on beef surface are shown. The merit of fluorescence is sensitivity. It could be measured ppm or ppb order for mycotoxin and APC was calibrated between 102 and 108 CFU/cm2. Keywords: fluorescence, spectroscopy, PLS, mycotoxin, aerobic bacteria, wheat, beef This is fluorescence fingerprint(FF). From the top view o the graph(contour map) showed an original pattern which reflects samples is optical property. Conventional fluorescence analysis usually uses only one peak. However, not only peak but also other points could have some related information to the objective. In addition, as all data is recorded in digital value, we can extend the analysing area to the whole area using a powerful computing PC.(2011 Shibata, 2011 Kokawa, 2012 Kokawa, 2012 Ohto)

1. INTRODUCTION Recent development of sensor and computer technologies makes a lot of changes in our surroundings. Sensor technology decreases its price and increases its sensibility. Computer technology increases computation speed drastically, and decrease the cost of device, especially memory medium. Utilization of these developments could introduce innovation in food industry. 1.1 Fluorescence Fingerprint

2. METHODS

Fluorescence is well known technique in analytical chemistry. Its measurement is made by a pair of stimulus and response, which are excitation light and fluorescence spectra. However, if we can have more information from the sample, we could have more precise or more identification at the same time. That is the reason we introduced fluorescence fingerprint(FF), in other word, excitation emission matrix. Fig.1 shows the principle of data acquisition for fluorescence fingerprint. Scanting of excitation wavelength produces a lot of fluorescence spectra. They can be a three dimensional volume data consisting of an excitation wavelength axis, an emission wavelength axis and a fluorescence intensity axis.

2.1 Instrumentation and Measurements FFs were measured using a fluorescence spectrometer (F7000, Hitachi High Technologies Corporation, Tokyo, Japan) equipped with a 150 W Xe arc lamp. The slit widths of both the excitation and emission sides were fixed at 10 nm. The measurement ranges of the excitation and emission wavelengths were 200-900 nm with a wavelength interval of 10 nm. By measuring the fluorescent spectrum while scanning the wavelength of the excitation light, threedimensional volume data of excitation wavelength×emission wavelength×fluorescence intensity, namely, FF, was obtained. Maximum scan speed is 60,000nm/min. So it takes about a few minutes to acquire full range of data with 10nm slit width. 2.2 Pre-processing of Fluorescence Fingerprint Data Fig.2 shows the schematic flow of the pre-processing step of removing nonfluorescent or noisy data from the entire FF data, based on the previous study (Fujita et al.,2010). Fluorescence is an emission with a longer wavelength than excitation. Thus, data whose emission wavelength is shorter the excitation wavelength were removed, as shown in Fig. 2a). Next, the ridge of high intensity in the FF counter map, where the excitation wavelength was equal to the emission wavelength, represented the intensity of scattering light. Also,

Fig.1. Fluorescence fingerprint 978-3-902823-44-1/2013 © IFAC

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10.3182/20130828-2-SF-3019.00035

IFAC AGRICONTROL 2013 August 27-30, 2013. Espoo, Finland

ridges extending from 400, 600, and 800 nm of the emission axis were the second-, third- and fourth-order lights, respectively (Fujita et al., 2010). They were generated by light scattering on the surface of the diffraction grating (Lakowicz,1990) were not fluorescence, and thus were removed (Fig. 2b)). In addition, the data in certain wavelength width were removed as the high-order lights. In addition, because of the low intensity of the Xe lamp and the low sensitivity of the photomultiplier at the short excitation wavelength and long emission wavelengths, respectively, some of the data were noisy and saturated. Although it depends on sample characteristics, typical example is that data included at the excitation wavelength < 250 nm or emission wavelength > 800 nm were removed (Fig. 2c)).

symptoms such as vomiting, diarrhea and headaches upon human and animal ingestion. Such damages are reported around the world. The wheat samples for this experiment were artificially contaminated with fuzarium graminearum in the field. 4 levels of contaminated wheat were harvested. They were ground into flour by the milling machine (Cyclone Sample Mill, UDY Corp., USA). Fig.3 shows FF of each flour. Low, medium, medium-high and high in Fig.3 mean level of contamination.

Fig.2 Data pre-processing

Fig.3 FF of Contaminated Wheat Flours

2.3 Chemometrics

Some fluorescence peaks can be found in Fig.3, however, little difference among the 4 levels. To predict quantitative contamination level, PLS regression was applied. Actual contamination level was measured by HPLC-UV. Fig.4 shows schematic diagram of PLS regression.

The quantification models were developed using partial least squares (PLS) regression with leave-one-out cross validation to the FF data of the calibration samples. The performance of PLS models depends on the number of latent variables (LVs) used. The optimum number of LVs was determined by minimizing the root-mean-square error of the prediction of cross-validation. The calibration model was applied to the validation dataset to evaluate the accuracy of the model. The fitting of the calibration model to the calibration and validation datasets was finally evaluated by the coefficient of determination (R2), standard error of calibration (SEC), and standard error of prediction (SEP) . 3. RESULTS and DISCUSSION 3.1 Detection of Mycotoxin in Wheat Major mycotoxin in wheat is deoxynivarenol(DON). There are other mycotoxin , nivarenol(NIV) and zeararenon(ZEA). They appear in major crops such as wheat, corn and other cereal grains. They reduce yield and quality. They caused

Fig.4 PLS Regression

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IFAC AGRICONTROL 2013 August 27-30, 2013. Espoo, Finland

Fig.5 is the prediction of DON concentration in contaminated wheat flour. Both calibration and validation datasets show significant correlations between actual values and predicted values.

3.2 Prediction of Aerobic Bacteria Population on Beef surface 60 lean beef pieces, consisting of two lots (15 pieces / lot) each of Australian and Japanese cattle, were purchased from a local meat store (Ibaraki, Japan) and they were cut into 45 x 45 x 8 mm pieces at the store. Samples were stored aerobically by putting them into sterilized plastic Petri dishes with lids. Each lot (15 pieces) of lean beef samples were stored in an incubator at 15 °C and analyzed after 0, 12, 24, 36 and 48 hours of storage. For analysis, three samples were used for both fluorescence fingerprint measurement and microbial determination. The sample was placed between a quartz plate 0.5 mm thick and an acrylic plate 1 mm thick (Fig. 7(a)) and mounted in the sample holder in the spectrophotometer. A Fluorescence spectrophotometer (F-7000, Hitachi High-Technology Corp.) mounted with a front-surface sample holder was used to measure FF. FFs were measured in the range of 200 ~ 900 nm for both excitation and emission wavelengths with 10 nm intervals. Four locations (Fig. 7(b), cross marks, No.1 - 4) were measured for one sample at room temperature. A total of 240 FFs (4 lots x 5 different time of storage x 3 samples x 4 positions) were collected.

It is well known that not only DON but also other mycotoxins like NIV, ZEA were also contaminated at the same time. However, degree of contamination is different from DON. FF also reflects on these contaminations. There could be created the model to predict for NIV and ZEA from the same FF.

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Fig.5 Prediction of DON

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Fig.7 Sample preparation for FF and APC measurement (Cross mark No.1 ~ 4: FF measurement position, Shaded area: swab both surfaces of meat and quartz plate for APC measurement) After FF measurements, 40 mm squared areas on both quartz plate and beef sample were wiped with a sterile swab (Fig.7(b), shaded area). To ensure adequate sampling, the sample was swabbed in a horizontal pattern and again in a vertical pattern, while being rotated between the index finger and thumb in a back and forth motion. Serial dilutions of the swab sample were prepared with the phosphate buffer solution in which the swab was immersed, then aerobic plate count (APC [CFU/cm2], CFU: Colony forming unit) were determined by incubating 1 ml of appropriate dilution on PetrifilmTM Aerobic count plates (Sumitomo 3M Ltd.) for 48 hr at 35 °C . A total of 60 APCs (4 lots x 5 different time of storage x 3 samples) were determined through the entire experiment.

Fig.6 Prediction of NIV and ZEA Fig.6 shows the results of NIV and ZEA prediction. Both results have good correlations. Especially, the remarkable point is sensitivity to predict NIV. The order is almost ppb levels. It is too little, the conventional chemical analysis cannot detects it. So actual value of NIV and ZEA was measured by LC/ MS/MS. As a result, it is clear that the FF can predict DON, NIV, ZEA at the same time.

Fig. 8 shows the time variation of aerobic plate count determined in Australian beef sample (cross and triangle symbols) and Japanese beef (circle and square symbols). Both initial aerobic plate count and growth rate varied among the lots. PLS regression was applied to FF to develop a model for the 72

IFAC AGRICONTROL 2013 August 27-30, 2013. Espoo, Finland

prediction of aerobic plate count (APC) . Fig. 9 shows the result of PLS regression. In this case for the beef meat, prediction model for the aerobic plate count was made with seven latent variables (LV), which gave best result with highest correlation and lowest SEC. From the result for validation set (Fig. 9(a)), good correlation (R2 = 0.819) and small SEP (SEP = 0.752 log [CFU/cm2]) was obtained and the accuracy of the model was verified.

The distribution of the regression coefficient of this model is shown in Fig. 10. The wavelength conditions with high regression coefficient value are considered to contribute largely to the model. High regression coefficient values are observed in the fluorophores related regions (A - D) . It seems that each peak was caused by the following intrinsic fluorophores (wavelength condition of excitation and emission maximum in FF is shown in parentheses) (Prased, 2003): (A) tryptophan (Excitation (Ex) 290 nm / Emission (Em) 330 & 660 nm), (B) NAD(P)H (Ex 320 nm / Em 460 nm), (C) Porphyrins (Ex 430 nm / Em 600 nm), and (D) Flavins (Ex 460 nm / Em 520 nm).

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As a result, the regression model was build depending on the information of these fluorophores.

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Fig.8 Time variation of the aerobic plate count on the surface of beef meat (Japanese 1 & 2: samples of Japanese cattle, Australian 1 & 2: samples of Australian cattle) (a)

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Calibration（LV 7）

Y = 0.889 * X + 0.472 2 R = 0.889 RMSEC = 0.548

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Fig.10 Distribution of the regression coefficient of PLS model

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Fig.9 PLS regression for APC on beef 73

IFAC AGRICONTROL 2013 August 27-30, 2013. Espoo, Finland

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