Fluorescence spectroscopy for discrimination of botrytized wines

Accepted Manuscript Fluorescence spectroscopy for discrimination of botrytized wines Jana Sádecká, Michaela Jakubíková, Pavel Májek PII:

S0956-7135(17)30612-6

DOI:

10.1016/j.foodcont.2017.12.033

Reference:

JFCO 5925

To appear in:

Food Control

Received Date: 6 October 2017 Revised Date:

29 November 2017

Accepted Date: 22 December 2017

Please cite this article as: Sádecká J., Jakubíková M. & Májek P., Fluorescence spectroscopy for discrimination of botrytized wines, Food Control (2018), doi: 10.1016/j.foodcont.2017.12.033. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Fluorescence spectroscopy for discrimination of botrytized wines

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Jana Sádecká, Michaela Jakubíková*, Pavel Májek

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Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak

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University of Technology, Radlinského 9, 812 37 Bratislava, Slovak Republic

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*Corresponding author: Tel.: +421-2-59325722; fax: +421-2-52926043; E-mail address:

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[email protected] (M. Jakubíková).

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ACCEPTED MANUSCRIPT Abstract

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Some botrytized wines with “protected designation of origin” have a high market price, thus

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they are prone to adulteration with cheaper alternatives. This work presents the use of

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fluorescence spectroscopy combined with chemometrics as a relatively fast and inexpensive

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tool to discriminate botrytized wines according to two classification criteria: (1)

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distinguishing between botrytized wines of different quality, namely four-, five-, and six butt

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wines, and essence; and (2) distinguishing between unadulterated and adulterated samples.

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Various emission and synchronous fluorescence spectra were recorded and compressed by

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principal component analysis (PCA) and then linear discriminant analysis (LDA) was

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performed. The best PCA-LDA results (the percentage of correct classification for each wine

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category in the prediction step) were obtained with fluorescence spectra recorded on raw

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samples. Regarding wines of different quality, four- and five butt wines as well as essences

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were 100% correctly classified, while six butt wine samples were 80% correctly classified

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using emission spectra excited at 390 or 460 nm as well as synchronous fluorescence spectra

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recorded at wavelength difference of 100 nm. Regarding unadulterated and adulterated

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samples, the percentages of correct classification were 60, 80, 80 and 100% for four-, five-,

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and six butt wines and essence, respectively, while adulterated samples were 100% correctly

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classified, in all cases, using synchronous fluorescence spectra recorded at wavelength

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difference of 100 nm.

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Keywords:

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Botrytized wines; Fluorescence; Chemometrics; Discrimination; Adulterated

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1. Introduction

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Wine labeling commonly provides information on the grape variety and geographical

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designation, and the vintage or age, which frequently determined price. In Europe, some

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wines belonging to the “protected designation of origin” (PDO) category have a high market

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price and they are consequently prone to adulteration with cheaper alternatives. Tokajský

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výber and Tokajská esencia are examples of PDOs (Commission Regulation (EU) No

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401/2010). These wines are produced from grapes infected by Botrytis cinerea (noble rot)

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(Jackson, 2014) in Slovakia, and therefore belong to the botrytized wines. Tokajský výber is

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produced by alcoholic fermentation after pouring of cibebas with must having sugar contents

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of at least 21° NM from the defined vineyard of “vinohradnícka oblasť Tokaj”. According to

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the amount of added cibebas, the Tokajský výber shall be divided into three-, four-, five- and

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six putňový (butt number). This quality is written on the label of the bottle: the more the butts,

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the better the quality, and the higher the price. Much more expensive is esencia (essence)

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because it is made only from separately selected cibebas by slow fermentation of wine. The

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essence shall contain at least 450 g/l of natural sugar and 50 g/l of sugar-free extract. Both

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Tokajská výber and esencia shall mature at least three years, of that at least two years in

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wooden cask (Commission Regulation (EU) No 401/2010). In Hungary, similar three to six

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puttonyos Aszú and Eszencia are produced. It is exclusively white grape varieties that are

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used in the wine-growing area of Tokaj, with Furmint belonging to Vitis vinifera convar

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Pontica being the most important one. It is grown widely to produce both botrytized and non-

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botrytized wines. An example of the latter is varietal Furmint wine, produced by alcohol

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fermentation of the Furmint grape variety.

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In general, compositional profiles of wines regarding amino and/or polyphenolic species can

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be correlated with factors such as grape varieties, geographical origins, organoleptic

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properties and winemaking practices (Arvanitoyannis, Katsota, Psarra, Soufleros, &

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Kallithraka, 1999; Saurina, 2010; Versari, Laurie, Ricci, Laghi, & Parpinello, 2014; Villano et

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al., 2017). The specificity of amine composition, which depends on infection with Botrytis

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cinerea and the wine-making technology, provided the basis for discrimination of Aszú wines

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from Hungarian non-botrytized wines and botrytized wines of other geographic origin (Kiss,

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& Sass-Kiss, 2005). Although botrytized wines are popular products, there is very little

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information available on the differentiation of them according to the butt number and the

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chemometric evaluation of the data is omitted. The effect of the number of butts on the

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biogenic amine content in Aszú wines of the 1993 and 1998 vintages was studied. For the

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ACCEPTED MANUSCRIPT young Aszú wines (1998 vintage), there were differences in the putrescine, cadaverine, and

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phenylethylamine content between the three- and four butt Aszú wines, while spermidine

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content showed significant differences in all the three-, four-, and five butt Aszú wines

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(Hajós, Sass-Kiss, Szerdahelyi, & Bardocz, 2000; Sass-Kiss, Szerdahelyi, & Hajós, 2000).

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Although there were differences in the histamine, putrescine, and spermidine content between

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the five- and six butt Aszú wines, Csomós, and Simon-Sarkadi (2002) concluded that the free

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amino acid and biogenic amine content of Aszú wines depended on the vineyards the wines

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originated from and not on the number of butts. As a consequence of the action of Botrytis

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cinerea, botrytized grapes of Furmint variety contained more amine compounds than normal

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grapes, with predominating spermidine. Also, the biogenic amine content of botrytized wines

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was higher than that of non-botrytized varietal Furmint wines, phenylethylamine was the most

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abundant amine (Hajós et al., 2000; Sass-Kiss et al., 2000).

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Five butt Aszú wines from vintages between 1999 and 1993 showed decreased amounts of

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polyphenols and also antioxidative activity due to much longer aging period in which more

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oxidative degradation could have occurred. Consequently, lower amounts of polyphenols and

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antioxidative activity were determined for six butt Aszú wine from 1981 season (Pour

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Nikfardjam, László, & Dietrich, 2003; Pour Nikfardjam, László, & Dietrich, 2006). The five

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and six butt Aszú wines as well as Furmint wines contained gallic, protocatechuic, caftaric,

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and coutaric acids as well as catechin in the mg/L range, higher total polyphenol content and

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antioxidative activity were observed for butt Aszú wines than Furmint wines (Pour

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Nikfardjam et al., 2003). Recently amount of polyphenols and the antioxidant activity were

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compared for three-, four-, five-, and six butt wines and Furmint wines; the highest

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antioxidant activity and also amount of polyphenols were observed in six butt wine. However,

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there was no information on vintage year (Ballová, Eftimová, Kurhajec, & Eftimová, 2016).

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High resolution 1H NMR spectroscopy showed the very similar relative integral intensities

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for four-, five-, and six butt wines as well as essence of 1999 and 2000 vintages,

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corresponding to the phenolic compounds (Mazur, Husáriková, Kaliňák, & Valko, 2015).

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Authors concluded that the 1H NMR spectra can be utilized as the unique “fingerprints” of

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the Slovak Tokaj wines.

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The combination of instrumental techniques with multivariate statistics have allowed the

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successful classification of wines according to grape varieties, geographical origin, and

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certain aspects of the winemaking process (Arvanitoyannis et al., 1999; Saurina, 2010;

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Versari et al., 2014; Villano et al., 2017). However, the use of fluorescence spectroscopy for

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such purposes has been scarcely explored. Interestingly, an attention was mainly focused on

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ACCEPTED MANUSCRIPT the red wines (Airado-Rodríguez, Durán-Merás, Galeano-Díaz, & Wold, 2011; Airado-

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Rodríguez, Galeano-Díaz, Durán-Merás, & Wold, 2009; Dufour, Letort, Laguet, Lebecque, &

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Serra, 2006; Saad, Bouveresse, Locquet, & Rutledge, 2016; Tan et al., 2016; Yin, Li, Ding, &

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Wang, 2009). Only recently, three varieties of white wine (Chardonnay, Sauvignon Blanc,

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and Torrontés) were distinguished using discriminant analysis by unfolded partial least

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squares or successive projection algorithm, based on excitation-emission matrices (EEMs)

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(Azcarate et al., 2015). Parallel factor analysis (PARAFAC) of EEMs of white Chardonnay

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wines indicated that sulfur dioxide treatment of a must subsequently influenced mainly the

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first two PARAFAC components. Distinct component combinations revealed either SO2

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dependent or vintage-dependent signatures (Coelho et al., 2015). Thirteen white wines were

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differentiated by using a sensor array based on fluorescence quenching of two oppositely

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charged poly(p-phenyleneethynylene)s and their complexes; the colored tannins and other

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polyphenols were the main quenchers (Han, Bender, Seehafer, & Bunz, 2016). No record has

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been found in the literature on application of fluorescence spectroscopy for the discrimination

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of botrytized wines.

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In view of the mentioned above, the main objective of this study was to assess for the first

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time the potential of fluorescence spectroscopy using multivariate statistical methods to

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discriminate selected botrytized wines. Two classification criteria were evaluated: (1)

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distinguishing between wines of different quality, namely four-, five-, and six butt wines, and

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essence; and (2) distinguishing between unadulterated and adulterated samples.

2. Material and methods

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2.1. Samples

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A total of 60 samples were analyzed, comprising 15 samples of each category (i.e. four butt

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wine, five butt wine, six butt wine, essence) which were obtained from the Slovak Tokaj

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region (vintages 2000 − 2015). The content of residual natural sugar and of sugar free extract

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(Table 1) was determined according to the Commission Regulation No. 2676/90 (European

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Economic Community (EEC) 1990). Tokaj wine Furmint was chosen as adulterant since it

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represents about 70% of the cultivars used in Tokaj (Pour Nikfardjam et al., 2003). Samples

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were stored at 4 °C, and equilibrated at 20 °C before measurement. Fluorescence spectra were

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immediately registered after opening the bottle.

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For discrimination between wine categories, the total population of samples was divided

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randomly and proportionally to wine category into a calibration set (consisting of two thirds

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of the samples selected from each category) and a prediction set (consisting of the remaining

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one third of the samples). The calibration and prediction set thus, contained forty (four butt

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wine, n = 10; five butt wine, n = 10; six butt wine, n = 10; essence, n = 10) and twenty (5

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samples of each category) samples, respectively. Undiluted and diluted (0.2% w/w in water)

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wine samples were used. Water was purified by a Milli-Q system (Millipore, USA).

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For discrimination between unadulterated and adulterated samples, the calibration and

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prediction sets were prepared individually for each wine category. Undiluted wine samples

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were used. The calibration set contained the same samples used as calibration set for

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discrimination between wine categories plus a group of ten mixtures of, e.g., four butt wines

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containing 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10% (w/w) of Furmint. The mixtures were prepared as

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follows: each sample of, e.g., four butt wine was used for preparing one mixture. The

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prediction set contained the same samples used as prediction set for discrimination between

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wine categories plus a group of five mixtures of, e.g., four butt wines containing 2.5, 4.5, 6.5,

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8.5, and 9.5% (w/w) of Furmint. Again, each sample of, e.g., four butt wine was used for

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preparing one mixture.

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2.2. Instrument

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Fluorescence spectra were obtained using the Perkin-Elmer LS 50 Luminescence

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Spectrometer equipped with the Xenon lamp. Samples were placed in a conventional

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10 × 10 × 45 mm quartz cell. The widths of both the excitation slit and emission slit were set

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at 5 nm. The acquisition interval and integration time were set at 1 nm and 0.1 s, respectively.

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Scan speed was 200 nm·min-1. FL Data Manager Software (Perkin-Elmer, USA) was used for

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spectral acquisition and data processing. Fluorescence measurements were done in triplicate

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for each sample.

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Total luminescence spectra of the bulk (diluted) wine samples were obtained by recording of

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the emission spectra in the 250−600 nm (250−500 nm) range at excitation wavelengths in the

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250−500 nm (250−350 nm) range, spaced by 10 nm intervals in the excitation domain. This

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means that twenty-six and eleven emission spectra were recorded for each bulk and diluted

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sample, respectively. Fully corrected emission spectra were then concatenated into the

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ACCEPTED MANUSCRIPT excitation-emission matrices and contour maps of total luminescence were subsequently

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constructed such that the x and y axis represented the emission and the excitation

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wavelengths, respectively, while the contours linked points of equal fluorescence intensity.

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Synchronous fluorescence spectra were obtained by simultaneously scanning the excitation

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and emission monochromators with constant wavelength differences ∆λ of 10, 20, 30, 40, 50,

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60, 70, 80, 90, and 100 nm between them, the excitation wavelength ranges were 250–500 nm

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and 250−350 nm, respectively, for bulk and diluted wine samples. Fluorescence intensities

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were plotted as a function of the excitation wavelength. Synchronous fluorescence spectra

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were then concatenated into the matrices and contour maps of synchronous fluorescence were

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subsequently constructed such that the x and y axis represented the excitation wavelength and

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the wavelength difference ∆λ, respectively, while the contours linked points of equal

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synchronous fluorescence intensity. Contours maps were plotted using the Origin software,

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version 9.0 (OriginLab, USA).

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2.3. Data analysis

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Data analysis was performed with the Microsoft Office Excel 2016 software (Microsoft

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Office, USA) and STATISTICA, version 12.0 (StatSoft, USA). The parts of the spectra

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including the scatter and noise were discarded before applying classification analysis. A

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mean-centering preprocessing was applied to the spectra.

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Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) was used as

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classification method. PCA reduces the data dimensionality by explaining the most relevant

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part of the information present in the original data using synthetic factors, called principal

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components (PCs). The number of PCs is based on the eigenvalue criterion and the total

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variance explained. The loading plot shows the variables mainly contributing to each of the

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principal components. The values that represent the samples in the space defined by the PCs

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are the component scores, which can be used as input to LDA, instead of the original

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variables. This is because LDA requires that the number of variables (wavelengths) should

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not exceed the number of samples in each group. LDA is concerned with determining the so-

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called discriminant functions, which maximize the ratio of between-class variance and

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minimize the ratio of within-class variance (Berrueta, Alonso-Salces, & Héberger, 2007). The

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variables mainly contributing to the discrimination were selected by the F test of Wilks'

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lambda to conduct stepwise discriminant analysis and consequently calculate a discriminant

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equation.

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ACCEPTED MANUSCRIPT The performance of the calibration models was evaluated using the leave-one-out-cross-

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validation approach in which the calibration set, itself, was used to validate the model. The

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model was repeatedly refit leaving out a single sample and then used to derive a prediction for

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the left-out sample (Arlot, & Celisse, 2010). Leave-one-out-cross-validation was used

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because it is the best alternative for small sample sizes of fewer than 50 cases (Molinaro,

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Simon, & Pfeiffer, 2005).

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The performance of the model was estimated by calculating the percentage of samples that

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were classified correctly in the calibration, cross-validation, and prediction steps. This was

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obtained as the percentage of correct classification for each wine category, or as the number

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of total correct classifications (independently of the category) divided by the total number of

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samples, presented as a percentage. 100% is the best a model can achieve.

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3. Results and discussion

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3.1. Spectra

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The total luminescence spectra of undiluted wine samples (Fig. 1A-D) show two relatively

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intense bands, one with excitation maximum at about 400 nm and emission maximum at

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about 480–490 nm and the second with excitation maximum at about 450–460 and emission

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maximum at about 530–540 nm. The exact positions of the maxima vary slightly between

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various wines, which may result from differences in the composition. The first band is close

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to the excitation and emission profiles of the first PARAFAC component recently calculated

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for cava sparkling wines (Elcoroaristizabal et al., 2016). The second band has yet been

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reported for brandy samples, corresponding to the first PARAFAC component (Markechová,

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Májek, Kleinová, & Sádecká, 2014) as well as for cava sparkling wines, corresponding to the

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third PARAFAC component (Elcoroaristizabal et al., 2016). High correlation between the

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third PARAFAC component (465/530 nm) and the commonly used non-enzymatic browning

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indicators was observed. This component could also be related to vitamin B2 or riboflavin

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(Christensen, Nørgaard, Bro, & Engelsen, 2006; Elcoroaristizabal et al., 2016).

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Undiluted wine samples exhibited a high UV/VIS absorption from 4 to 0.1 absorbance units

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when scanning from 200 to 700 nm (Fig. S1), thus the fluorescence was affected by the inner

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filter phenomena − an apparent decrease in fluorescence intensity and/or a distortion of band

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shape as a result of the absorption of excitation and/or emitted light by the sample matrix or

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the fluorophore itself (Miller, 1981; Christensen et al., 2006). The highest UV/VIS absorption

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ACCEPTED MANUSCRIPT and consequently the highest inner filter effects were observed for six butt wine. One way to

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overcome these phenomena is to use concentrations that result in absorbance values of 0.1 or

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lower, simply to dilute the sample with an appropriate solvent. Bulk samples were diluted

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with water because fluorescence of diluted samples was high enough to record spectra. In

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addition, water was without fluorescent interferences. On the other hand, the fluorescence

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spectra of the purest grade of available ethanol or ethanol:water mixture showed little

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fluorescence at the used wavelength ranges. Moreover, differences in the fluorescence spectra

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for different ethanol-water ratios were observed (Liu, Luo, Shen, Lu, & Ni, 2006). Upon

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dilution with water, the total luminescence spectrum of the wine was gradually varied.

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Different bands behaved differently for various dilutions until the wine was diluted to 0.2%

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w/w. From this level the shape of fluorescence spectrum stabilized (Fig. S1) and fluorescence

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intensity depended linearly on the dilution (Table 2).

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The total luminescence spectra of diluted wine samples (0.2% w/w in water) (Fig. 1E-H)

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exhibit an intense band at around 270–280/350 nm and a weak one at 300−320/430−450 nm

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in excitation/emission. Some phenolic acids and monomeric catechins present fluorescent

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characteristics, that could be related with band at around 270–280/350 nm (Airado-Rodríguez

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et al., 2011; Azcarate et al., 2015; Bravo, Silva, Coelho, Vilas Boas, & Bronze, 2006). Indeed,

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the five and six butt Aszú wines showed high contents (in the mg/L range) of gallic,

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protocatechuic, caftaric, and coutaric acids as well as catechin (Pour Nikfardjam et al., 2003).

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The corresponding excitation/emission wavelength pairs found in the literature are 278/360,

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270/358, 280/320, 280/320, and 278/360 nm, respectively (Airado-Rodríguez et al., 2011;

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Bravo et al., 2006). Other phenolic acids like gentisic acid and p-coumaric acid characterized

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by excitation/emission wavelength pairs 318/442 and 302/420 nm (Stallings, & Schulman,

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1975; Putschögl, Zirak, & Penzkofer, 2008), respectively, can contribute to a weak

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fluorescence of wines at 300−320/430−450 nm in excitation/emission. Gentisic acid was the

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major hydroxybenzoic acid constituent in Chardonnay, Semillon and Italian Riesling white

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wines (Ma et al., 2014), while p-coumaric acid was found in Aszú wines (Pour Nikfardjam et

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al., 2003), both of which were in the mg/L range. Besides phenolic acids, wines contain many

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other fluorescent compounds (e.g., phenolic aldehydes, trans-stilbenes, flavonoids, tyrosol,

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vitamins and proteins), thus it is quite obvious that each spectral band of wine corresponds to

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a related group of fluorescent compounds, and not necessarily to a single fluorophore.

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ACCEPTED MANUSCRIPT Total synchronous fluorescence spectra (Fig. 1I-P) are presented for the same undiluted as

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well as diluted wine samples, to enable comparison with the total luminescence spectra.

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Regarding undiluted wine samples, two characteristic maxima can be seen in total

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synchronous spectra (Fig. 1I-L), one at the excitation wavelength of 400 nm with the ∆λ at

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80−90 nm, corresponding to the band at 400/480–490 nm in total luminescence spectra, and

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the other at the excitation wavelength range of 450−460 nm with the ∆λ at 70−90 nm,

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corresponding to the band at 450−460/530−540 nm in total luminescence spectra. Total

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synchronous spectra recorded on the diluted wine samples (Fig. 1M-P) again show two

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characteristic maxima, one at the excitation wavelength range of 270−280 nm (∆λ at 70−80

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nm), and the other at the excitation wavelength range of 300−320 nm (∆λ > 100 nm),

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corresponding to total luminescence bands at 270–280/350 nm and 300−320/430−450 nm,

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respectively.

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It is clear that the spectra of the Tokaj wines (Fig. 1) are quite similar and difficult to

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distinguish by visual inspection. Therefore, chemometric method such as PCA and LDA were

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used.

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3.2. Discrimination between wine categories

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PCA was carried out on the emission/synchronous spectra to investigate grouping the samples

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based on wine category. In general, no clear grouping was observed in the PCA score plots.

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Thus, LDA was applied to the first PCs of the PCA performed on the spectra. The number of

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PCs was based on the eigenvalue criterion, the total variance explained, and the percentage of

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correct classification of cross-validation in LDA. The results of PCA obtained for the best

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experimental conditions, as discussed below, are shown in Fig. 2. The most relevant

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wavelengths with the highest loadings that largest contribute to the discrimination can be

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selected from the loading plots.

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Firstly, the PCA was carried out on the emission spectra. Regarding undiluted samples, good

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total classification was achieved using the first three PCs (account for more than 99.6% of the

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ACCEPTED MANUSCRIPT total variance) of the PCA performed on the emission spectra recorded at the excitation

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wavelengths 370, 390, 400, and 460 nm in the range of 380−600, 400−600, 415−600 and

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470−600 nm, respectively. The percentage of total correct classification was > 90% in

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calibration, cross-validation and prediction for all selected excitation wavelengths (Fig. 3A).

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Using the first three PCs corresponding to the excitation wavelength 390 nm or 460 nm (Fig.

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2A, B), the best classification was obtained (97.5%, calibration; 95% cross-validation, 95%

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prediction, Fig. 3A). Four and five butt wines as well as essences were classified correctly in

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all cases. On the other hand, some of six butt wines were classified as belonging to essence

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group. The emission wavelengths that largest contributed to the discrimination were around

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470 and 530 nm (Fig. 2A, B).

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Regarding diluted samples, total correct classification > 75% was achieved using the first

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three PCs (account for more than 99.5% of the total variance) of the PCA carried out on the

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emission spectra recorded at the excitation wavelengths 270, 280, 290, and 300 nm in the

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range of 280−500, 290−500, 300−500, and 340−500 nm, respectively (Fig. 3B). For these

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four spectral regions, the percentage of correct classification increased with increasing

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excitation wavelength, thus, using the first three PCs corresponding to the excitation

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wavelength 300 nm (Fig. 2C), the best total classification (100%, calibration; 100% cross-

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validation, 90% prediction, Fig. 3B) was achieved. Six butt wines and essences were

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classified correctly in all cases. However, some of four butt wines were classified as

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belonging to five butt wines group, and vice versa.

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Secondly, the PCA was applied to the synchronous fluorescence data sets. Similarly, to the

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emission data sets, no meaningful grouping was observed in the PCA score plots, and

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therefore, LDA was applied to the first PCs. The percentages of correct classification of PCA-

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LDA models corresponding to different ∆λ values are shown in Fig. 3C, D.

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For undiluted samples, the PCA-LDA models were calculated on synchronous fluorescence

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spectra in the range of 250−500 nm. Using the first five PCs (> 99.8% captured variance) of

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the PCA performed on the synchronous fluorescence spectra recorded at ∆λ = 20, 40, 60, and

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80 nm, total correct classification oscillated around 66% (Fig. 3C). Interestingly, essence

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samples were classified correctly in all calibration, cross-validation and prediction steps,

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regardless of ∆λ value. On the other hand, the best total classification (97.5%, calibration;

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95% cross-validation, 95% prediction, Fig. 3C) was obtained using the first five PCs of the

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PCA calculated on the synchronous fluorescence spectra recorded at ∆λ = 100 nm (Fig. 2D).

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Four- and five butt wines as well as essences were classified correctly in all calibration, cross-

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ACCEPTED MANUSCRIPT validation and prediction steps. However, some of six butt wines were classified as belonging

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to essence group. The most discriminating excitation wavelengths shifted from 420 to 390 nm

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and from 480 to 460 nm with increasing ∆λ value (Fig. S2). Thus, they were 390 and 460 nm

342

for ∆λ = 100 nm loading plot (Fig. 2D).

343

Regarding diluted samples, the PCA-LDA models were calculated in the range of 250−350

344

nm. Using the first three PCs (> 99.7% captured variance) of the PCA carried out on the

345

synchronous fluorescence spectra recorded at ∆λ = 20, 40, and 60 nm, total correct

346

classification was around 75% (Fig. 3D). Essence and six butt wine samples were classified

347

correctly in all calibration, cross-validation and prediction steps, regardless of ∆λ value, with

348

exception of ∆λ = 60 nm for six butt wines. The first three PCs corresponding to ∆λ = 80 or

349

100 nm (Fig. 2E, F) provided the best total classification (100%, calibration; 95% cross-

350

validation, 90% prediction, Fig. 3D). Six butt wines and essences were classified correctly in

351

all cases. However, some of four butt wines were classified as belonging to five butt wines

352

group, and vice versa. The most discriminating excitation wavelengths were around 280 nm

353

(Fig. 2E, F and Fig. S3), corresponding to phenolic acids and monomeric catechins (Airado-

354

Rodríguez et al., 2011; Stallings et al., 1975; Putschögl et al., 2008).

M AN U

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355

357

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Under optimal experimental conditions, PCA-LDA models from the diluted or undiluted

359

samples gave similar results, slightly better for undiluted four- and five butt wines compared

360

to diluted, but slightly worse for undiluted six butt wines compared to diluted. Similarly, the

361

synchronous fluorescence spectra of undiluted beer samples appeared to be more appropriate

362

than those of diluted beers, but some of beers were better classified using diluted samples

363

(Sikorska, Górecki, Khmelinskii, Sikorski, & De Keukeleire, 2004). Comparison of results for

364

undiluted and diluted samples is not straightforward. The complex fluorescence pattern for

365

undiluted sample results from its fluorescence characteristics as well as its absorbing, inner

366

filter and quenching abilities, thus several phenomena can influence classification results. The

367

dilution decreases the concentration of fluorophores at the level to be approximately linearly

368

related to the fluorescence intensity, changes interactions of the matrix as well as decreases

369

‘non-fluorescence phenomena’.

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ACCEPTED MANUSCRIPT 372

3.3. Discrimination between unadulterated and adulterated samples

373

Synchronous fluorescence spectroscopy shows important advantages over conventional

375

fluorescence spectroscopy, such as spectral simplification, decreased light-scattering

376

interference, and selectivity improvement. It detects multiple fluorophores with a much

377

smaller number of excitation and emission wavelengths, thus reducing data acquisition and

378

processing times. Therefore, this method was used to discriminate adulterated samples.

379

Fig. 4 shows total synchronous fluorescence spectrum of Furmint wine, which was used as

380

adulterant, characterized by two maxima at the excitation wavelength of 390 nm (∆λ = 70

381

nm) and 453 nm (∆λ = 60 nm). The maxima were observed at lower values of ∆λ as

382

compared to any of the butt wines or essences. Based on the Fig. 1I-L and Fig. 4, synchronous

383

fluorescence spectra of the mixtures were collected in the 350–500 nm range, using ∆λ = 20,

384

40, 60, 80, and 100 nm. PCA-LDA applied separately to the data acquired at each of ∆λ

385

resulted in the percentages of correct classification given in Fig. 5. The results of PCA

386

obtained for the best classification conditions, as discussed below, are shown in Fig. 6.

M AN U

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387 388

390

391

393

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392

TE D

389

The ability to discriminate between four butt wines and adulterated samples was the best at

395

∆λ = 100 nm; using the first three PCs (account for 99.7% of the total variance, Fig. 6A), the

396

percentages of correct classification were 100, 60 and 80% for adulterated, unadulterated and

397

total samples, respectively, in prediction step (Fig. 5A).

398

The discrimination of five butt and adulterated wines was better at ∆λ = 60 or 100 nm, rather

399

than 20, 40 or 80 nm. Using the first three PCs (99.7% captured variance, Fig. 6B), the correct

400

classifications were 100, 80 and 90% for adulterated, unadulterated and total samples,

401

respectively, in prediction step at ∆λ = 60 or 100 nm (Fig. 5B).

402

The same percentages of the correct classification were also obtained in discrimination

403

between six butt wine and adulterated samples using the first three PCs (account for more

404

than 99.9 % of the total variance, Fig. 6C) and ∆λ in the range from 40 to 100 nm (Fig. 5C),

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ACCEPTED MANUSCRIPT or between essence and adulterated samples using the first three PCs (> 99.7% captured

406

variance) and ∆λ = 20 and 40 nm (Fig. 5D). Using ∆λ = 60, 80 and 100 nm (the first three

407

PCs, > 99.8% captured variance), the percentages of correct classification were 100% for the

408

essence samples as well as for the adulterated ones (Fig. 5D). Based on the loadings plots

409

(Fig. 6A-D), the most discriminating excitation wavelengths were around 390 and 460 nm for

410

all the mixtures.

411

The discrimination of adulterated samples (mixtures) was 100% and independent of the

412

percentage of adulteration for five butt wines and essence, irrespective of ∆λ, for six butt

413

wines, using ∆λ = 40−100 nm as well as for four butt wine, using ∆λ = 100 nm. In other cases

414

(six butt wine, ∆λ = 20 nm; four butt wine, ∆λ = 20−80 nm), the mixtures not correctly

415

discriminated corresponded to those that have lower adulteration rates − 1 and 2% w/w in

416

cross-validation and 2.5% w/w in prediction (Table S1).

417

Compared

418

unadulterated and adulterated samples (identification of adulterated samples) required fewer

419

PCs and was less dependent on ∆λ value. Explanation can be such that four-, five-, and six

420

butt wines and essence spectra resemble each other more than any of them resembles Furmint

421

wine spectrum, and this is true regardless of ∆λ (Fig. S4). Since essence and six butt wine

422

differ from Furmint wine much more than four- and five butt wine, better classification was

423

obtained for unadulterated six butt wine and essence.

424

426

4. Conclusions

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between

wine

categories,

discrimination

between

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425

discrimination

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with

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The results showed that the fluorescence spectroscopy can be a very powerful method for

428

distinguishing groups of samples that have very similar spectral characteristic such as Tokaj

429

wine categories or their adulterated samples. The best PCA-LDA models were based on the

430

fluorescence spectra recorded on undiluted (raw) samples. Regarding Tokaj wine categories,

431

the best PCA-LDA models (excitation wavelength, 390 or 460 nm; ∆λ = 100 nm) produced a

432

total correct classification of 95% in prediction step. Four- and five butt wines as well as

433

essences were 100% correctly classified, while six butt wines were 80% correctly classified.

434

Notice that better discrimination between six butt wines and essences was achieved for

435

diluted samples, however, at the expense of decreasing the correct classification of four- and

436

five butt wines. For discrimination between unadulterated and adulterated samples, the

437

percentages of correct classifications were 60, 80, 80 and 100% for four-, five-, six butt wines

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14

ACCEPTED MANUSCRIPT and essences, respectively, while adulterated samples were 100% correctly classified in all the

439

cases, using synchronous fluorescence spectra recorded at wavelength difference of 100 nm.

440

However, considering the relatively low number of samples and the similarities between them

441

due to climatic, soil and technological characteristics, caution is recommended in the use of

442

the PCA-LDA models to predict new samples in routine analysis until further work involving

443

more samples is realized.

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438

444

Acknowledgments

446

This research was supported by the Slovak Research and Development Agency under the

447

contract No. APVV-15-0355.

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448 449

Conflict of interest

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Jana Sádecká declares that she has no conflict of interest.

452

Michaela Jakubíková declares that she has no conflict of interest.

453

Pavel Májek declares that he has no conflict of interest.

454

This article does not contain any studies with human or animal subjects.

456 457

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Airado-Rodríguez, D., Galeano-Díaz, T., Durán-Merás, I., & Wold, J. P. (2009). Usefulness

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Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection.

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multiple wavelength differences. Australian Journal of Grape and Wine Research, 22, 358–

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ACCEPTED MANUSCRIPT 566

Figure captions

567

Fig. 1 Total luminescence (A-H) and synchronous fluorescence (I-P) spectra of undiluted (A-

568

D, I-L) or diluted (E-H, M-P) botrytized wine samples. (A, E, I and M, four butt wine; B, F, J

569

and N, five butt wine; C, G, K and O, six butt wine; D, H, L and P, essence).

570

Fig. 2 Results of principal component analysis carried out with emission (A, λex = 390 nm; B,

572

λex = 460 nm; C, λex = 300 nm) and synchronous fluorescence (D, ∆λ = 100 nm; E, ∆λ = 80

573

nm; F, ∆λ = 100 nm) spectral data of undiluted (A, B, D) or diluted (C, E, F) botrytized wine

574

samples. The loadings of the first principal components (PC1-PC3 or PC1-PC5) as well as the

575

corresponding explained variances (%) are shown.

SC

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576

Fig. 3 Percentage of correct classification of PCA-LDA models corresponding to different

578

excitation wavelengths, λex, (A, B) and ∆λ (C, D). (Orange, 4, four butt wine; Pink, 5, five

579

butt wine; Violet, 6, six butt wine; Purple, E, essence; Green, T, total. Grid, calibration;

580

parallel lines, cross-validation; no pattern, prediction).

M AN U

577

581 582

Fig. 4 Total synchronous fluorescence spectrum of Furmint wine.

TE D

583

Fig. 5 Percentage of correct classification of PCA-LDA models for (A) four-, (B) five-, and

585

(C) six butt wines and (D) essences, and their admixtures containing 1−10% (w/w) of Furmint

586

wine corresponding to different ∆λ values. (Gray, M, mixture; Orange, 4, four butt wine;

587

Pink, 5, five butt wine; Violet, 6, six butt wine; Purple, E, essence; Green, T, total. Grid,

588

calibration; parallel lines, cross-validation; no pattern, prediction).

AC C

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584

590

Fig. 6 Results of principal component analysis carried out with synchronous fluorescence

591

(∆λ = 100 nm) spectral data of (A) four-, (B) five-, and (C) six butt wines and (D) essence,

592

and their admixtures containing 1−10 % (w/w) of Furmint wine. The loadings of the first

593

three principal components (PC1-PC3) as well as the corresponding explained variances (%)

594

are shown.

595 596

19

ACCEPTED MANUSCRIPT 597

Table 1

598

The content of residual natural sugar and of sugar free extract in wine samples (mean±SD). Residual natural sugar (g/L)

Sugar free extract (g/L)

Four butt

110 ± 17

32 ± 7

Five butt

145 ± 20

38 ± 12

Six butt

170 ± 19

42 ± 4

Essence

468 ± 15

54 ± 8

Furmint

10.2 ± 1.8

22 ± 2

RI PT

Wine

599

SC

600

Table 2

602

Effect of the dilution on the fluorescence of six butt wine. λex (nm)

Dilution (% w/w) Undiluted

λem (nm)

Fluorescence intensity

492

48

536

51

468

347

361

431

440

204

270

349

217

270

351

109

400 460

20

390

2

270

EP

603

AC C

0.1

TE D

310

0.2

M AN U

601

20

ACCEPTED MANUSCRIPT Fig. 1 450

400

400

350

250

250 250

450

500

550

600

300

300

350

400

F

340

G

300

260

260

260

400

450

500

250

300

350

400

450

500

100

K

60

∆λ (nm)

80

60

40

450

500

300

350

λex (nm)

400

450

λex (nm)

N

60

∆λ (nm)

80

60

40

40

20

280

300

320

340

400

450

500

300

λex (nm)

320

300

250

300

350

350

400

450

500

250

P

300

350

260

280

300

λex (nm)

320

340

400

450

500

λex (nm)

100

60

EP

500

20

300

80

AC C

450

60

60

1

400

100

80

340

600

40

100

TE D

λex (nm)

280

550

80

20

260

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λem (nm)

40

20

260

350

L

250

O

450

340

λex (nm)

100

80

500

∆λ (nm)

100

400

260

300

20

250

350

λem(nm)

M AN U

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40

20

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250 250

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80

20

550

λem (nm)

60

40

500

320

250

80

∆λ (nm)

∆λ (nm)

J

450

H

λem (nm)

100

300

400

300

280

250

350

320

280

350

300

300

340

280

300

350

λem(nm)

340

λem (nm)

∆λ (nm)

250 250

600

λex (nm)

300

250

M

550

320

λex (nm)

λex (nm)

320

I

500

λem(nm)

λem(nm)

E

450

λex (nm)

400

350

∆λ (nm)

350

500

400

300

300

D

450

300

250

500

RI PT

350

C

450

λex(nm)

λex(nm)

λ ex(nm)

400

500

SC

450

∆λ (nm)

B

500

λex(nm)

A

40

20

260

280

300

λex (nm)

320

340

ACCEPTED MANUSCRIPT Fig.2 A

6

B

PC1 ( 93.2 %) PC2 ( 6.0 %) PC3 ( 0.6 %)

3

2

Loadings

Loadings

PC1 (89.3%) PC2 (9.9%) PC3 (0.6%)

4

0

0

-2

-4

-6 400

-6 450

500

550

600

480

510

λem (nm)

C

540

λem (nm)

PC1 (73.6%) PC2 (24.0%) PC3 (2.0%)

4

D

Loadings

Loadings

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M AN U

-4 -2

-6

-8 360

400

440

480

λem (nm)

-4 250

300

350

400

450

500

λex (nm)

6

PC1 (95.9%) PC2 (3.7%) PC3 (0.3%)

4

F

6

PC1 (97.4%) PC2 (2.1%) PC3 (0.3%)

4

2

0

TE D

Loadings

2

0

-2

-2

-4

-4 260

280

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340

EP

λex (nm)

AC C

Loadings

600

SC

4 0

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570

PC1 (85.3%) PC2 (9.5%) PC3 (4.2%) PC4 (0.8%) PC5 (0.1%)

6

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λex (nm)

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ACCEPTED MANUSCRIPT Fig. 3

4 5 6 ET

4 5 6 ET

4 5 6 ET

Diluted samples

100

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4 5 6 ET

4 5 6 ET

270 nm

280 nm

60 40 20 0

390 nm

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λex

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300nm

100

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100 nm

20 nm

EP

TE D

∆λ

AC C

Correct classification (%)

100

456ET

290 nm

λex

Undiluted samples

C

4 5 6 ET

80

0 370 nm

4 5 6 ET

RI PT

4 5 6 ET

Correct classification (%)

100 Correct classification (%)

B

Undiluted samples

A

3

40 nm

60 nm

∆λ

80 nm

100 nm

ACCEPTED MANUSCRIPT Fig. 4 100

60

RI PT

40

20

250

300

350

400

450

500

EP

TE D

M AN U

SC

λex (nm)

AC C

∆λ (nm)

80

4

ACCEPTED MANUSCRIPT Fig. 5

M 4 T

M 4 T

M 4 T

B100

M 4 T

80 60 40 20

M 5 T

M 5 T

20 nm

40 nm

60 nm

80 nm

20 nm

40 nm

40 20

100 nm

80 nm

100 nm

M 6 T

M 6 T

D 100

M 6 T

M E T

M E T

M E T

60 40 20 0

80

M E T

M E T

M AN U

Correct classification (%)

80

60 nm

80 nm

100 nm

SC

M 6 T

60 nm

∆λ

60 40 20

0

20 nm

40 nm

60 nm

80 nm

100 nm

20 nm

EP

TE D

∆λ

AC C

Correct classification (%)

M 6 T

M 5 T

60

∆λ

C 100

M 5 T

80

0

0

M 5 T

RI PT

M 4 T

Correct classification (%)

Correct classification (%)

A 100

5

40 nm

∆λ

ACCEPTED MANUSCRIPT Fig. 6 A

6

PC1 (96.7%) PC2 (2.8%) PC3 (0.2%)

B

PC1 (97.7%) PC2 (1.5%) PC3 (0.5%)

6

3

Loadings

0

-3

0

-3

-6

-6 390

420

450

480

390

420

λex (nm)

PC1 (93.5%) PC2 (5.6%) PC3 (0.8%)

D

6

3

0

0

-3

-6

PC1 (93.6%) PC2 (5.7%) PC3 (0.6%)

-3

M AN U

Loadings

3

480

SC

6

-6

-9

-9

390

420

450

480

390

420

450

λex (nm)

EP

TE D

λex (nm)

AC C

Loadings

C

450

λex (nm)

RI PT

Loadings

3

6

480

ACCEPTED MANUSCRIPT Highlights • Emission and synchronous fluorescence spectra with chemometrics were used. • Classification of botrytized wines according to different quality.

AC C

EP

TE D

M AN U

SC

RI PT

• The best PCA-LDA results were obtained with synchronous fluorescence spectra.

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