Food Anal. Methods DOI 10.1007/s12161-016-0735-8
The Effect of Path Length on the Measurement Accuracies of Wine Chemical Parameters by UV, Visible, and Near-Infrared Spectroscopy Nevse Molla 1 & Ivan Bakardzhiyski 2 & Yana Manolova 1 & Valentin Bambalov 3 & Daniel Cozzolino 4 & Liudmil Antonov 1
Received: 10 November 2016 / Accepted: 14 November 2016 # Springer Science+Business Media New York 2016
Abstract The use of spectral measurements using either UV, visible (VIS), or near-infrared (NIR) spectroscopy to characterize wines or to predict wine chemical composition has been extensively reported. However, little is known about the effect of path length on the UV, VIS, and NIR spectrum of wine and the subsequent effect on the performance of calibrations used to measure chemical composition. Several parameters influence the spectra of organic molecules in the NIR region, with path length and temperature being one of the most important factors affecting the intensity of the absorptions. In this study, the effect of path length on the standard error of UV, VIS, and NIR calibration models to predict phenolic compounds was evaluated. Nineteen red and 13 white wines were analyzed in the UV, VIS, and NIR regions (200–2500 nm) in transmission mode using two effective path lengths 0.1 and 1 mm. Principal component analysis (PCA) and partial least squares (PLS) regression models were developed using full cross validation (leave-one-out). These models were used to interpret the spectra and to develop calibrations for phenolic compounds. These results indicated that path length has an effect on the standard * Daniel Cozzolino
[email protected]
1
Institute of Organic Chemistry with Centre of Phytochemistry, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bldg. 9, 1113 Sofia, Bulgaria
2
Department of Technology of Wine and Beer, University of Food Technologies Plovdiv, 26 Maritza Blvd, 4002 Plovdiv, Bulgaria
3
Department of Viticulture, Agricultural University Plovdiv, 12 Mendeleev Blvd, 4000 Plovdiv, Bulgaria
4
School of Medical and Applied Sciences, CQIRP (Central Queensland Innovation and Research Precinct), Central Queensland University (CQU), Bruce Highway, North Rockhampton, QLD 4701, Australia
error of cross validation (SECV) absolute values obtained for the PLS calibration models used to predict phenolic compounds in both red and white wines. However, no statistically significant differences were observed (p > 0.05). The practical implication of this study was that the path length of scanning for wines has an effect on the calibration accuracies; however, they are non-statistically different. Main differences were observed in the PCA score plot. Overall, well-defined protocols need to be defined for routine use of these methods in research and by the industry. Keywords Path length . Near infrared . UV . Visible . Wine . Phenolic compounds
Introduction Meticulous and continuous controls are required to maintain the quality of wine in terms of chemical composition, varieties and regions of origin, and authenticity, among others (Arvanitoyannis 1999; Downey 2016; Espiñeira and Santaclara 2016). For example, the authenticity of wine is guaranteed by strict guidelines laid down by the responsible national authorities, which include official sensory evaluation, chemical analyses, and examination of the register kept by the wine producer (Arvanitoyannis 1999; Downey 2016; Espiñeira and Santaclara 2016). Historically, sensory evaluation is the most direct and widely applied method to assess wine quality as well as to authenticate wine products. However, these methods are subjective and time consuming and may easily lead to incorrect conclusions about the quality and composition of the wine (Cozzolino et al. 2005; Sivertsen et al. 1999). As a consequence, objective methods, including routine chemical and instrumental methods, based on the chemical composition of
Food Anal. Methods
wines have been introduced as an alternative by several researchers and wineries (Jacobson 2006; Compendium of International Methods of Wine and Must Analysis 2014). These studies employ advanced chromatographic (highperformance liquid chromatography, gas chromatography) and/or spectroscopic (nuclear magnetic resonance, mass spectrometry (Picó & Barceló 2015), mid-infrared (MIR) spectroscopy (Rodriguez-Saona et al. 2016), and multi-element ICPMS (Stafilov and Karajova 2006)) techniques. Most instrumental techniques require high initial investment and maintenance costs and complicated pre-treatment procedures, which limit their wide application in the wine industry. Spectral measurements such as UV, visible (VIS), and near-infrared (NIR) techniques have been recognized as a rapid and nondestructive technique and have been widely applied in the analysis of wines (Burns and Ciurczak 2008; Ciurczak and Igne 2015; Workman and Weyer 2008). The number of applications in the analysis of liquid samples such as alcoholic and non-alcoholic beverages, juices, milk, and dairy products is sharply growing during the last years (Cozzolino 2016). Although spectroscopy (UV, VIS, NIR) has been used for many years as a method in routine analysis of wine, no robust information on the effect of path length on the UV, VIS, and NIR spectra of wines has been reported. Most of the conventional methods reported the use of 1-mm cells (cuvettes). The current study compared the effect of the path length on the measurements of phenolic compounds in a set of red and white Bulgarian wines. It is worth to mention that, although it is believed that the Balkan Peninsula is one of the cradles of the winemaking in the antiquity, there are no systematic investigations concerning the composition, authenticity, and traceability of Bulgarian wines. Only few scattered studies (Angelov and Stalev 2011; Bambalov et al. 1995; Jakubec et al. 2012; Lakatošová et al. 2016; Tsanova-Savova et al. 2002; Tsanova-Savova and Ribarova 2002; Yanev et al. 1989) mainly based on the antioxidant capacity of Bulgarian red wines are available in the literature.
Materials and Methods
Ottonel, Viognier, and Muscat Blanc à Petits Grains. The detailed geographic origin of the samples is shown in Table 1. Chemical Analyses The concentration of wine phenolics was estimated by analyzing total phenol content by the Folin-Ciocalteu procedure (Singleton and Rossi 1965), as implemented in method OIVMA-AS2-10 (Compendium of International Methods of Wine and Must Analysis 2014). Total phenols, phenolic acids, and flavonoids were determined according to Sommers (Somers and Ziemelis 1985). Anthocyanins and catechins were evaluated according to the method reported by Ribérau-Gayon and Stonestreet (Ribéreau-Gayon and Stonestreet 1965) and Pompei and Peri (Pompei and Peri 1971), respectively. The alcohol content, pH value, and acidity have been determined according to the methods OIV-MA-AS312-01B, OIV-MAF1-06, and OIV-MA-AS313-01 of the International Organisation of Vine and Wine (Compendium of International Methods of Wine and Must Analysis 2014), respectively. All used chemicals and solvents were of analytical grade. UV-VIS-NIR Spectroscopy The UV-VIS-NIR spectra were recorded on Jasco V570 double beam scanning spectrophotometer (Jasco Inc., Japan), equipped with a thermostatic cell holder and Huber MPC-K6 thermostat (Huber Kältemaschinenbau AG, Germany) with precision 1 °C, in the range 200–2500 nm with resolution 2 nm. The measurements and data collection were controlled by Spectra Manager version 1.54 (Jasco Inc., Japan). Transmission mode was used with scanning speed 200 nm/ min, and the level of the noise was reduced by using slow response mode of the detectors, which corresponds to the average of four measurements at a single spectra measurements. The spectra were measured against air by using quartz cells with cell thickness of 1 and 0.1 mm (Hellma Analytics, Germany). Before the measurements, the wine samples were filtered through 20-μm membrane filters (CHROMAFIL®Xtra Polyester, Macherey-Nagel, Germany).
Wine Samples Statistical and Multivariate Data Analysis A set of red (19 samples) and white (13 samples) wines from different Bulgarian regions (vintage 2015) were collected and analyzed using both reference and spectroscopy methods. The wines were obtained from both widespread and local cultivars. The red wines set contains Cabernet Sauvignon (5 samples), Merlot (3 samples), Syrah (3 samples), Pinot Noir (2 samples), Cabernet Franc (2 samples), and 1 sample of each Marselan, Egiodola, Melnik 55, and Shiroka Melnishka Loza. The collection of white wines contains Chardonnay (7 samples), Sauvignon Blanc (3 samples), and 1 sample of each Muscat
Spectra were exported from the Jasco software in csv format to The Unscrambler software (Version X, CAMO ASA, Oslo, Norway) for chemometric analysis. Principal component analysis (PCA) was performed to examine the dominant patterns in the spectral data. Calibration models between chemical composition and UV, VIS, and NIR spectra were developed using partial least squares (PLS) with full cross validation (Cozzolino et al. 2007; Cozzolino et al. 2006). The spectra were transformed using the second derivative (Savitzky-
0.4 1.5 1.0 1.3 0.4 1.7
2661.4 2934.6 2967.4 3564.9 3022.1 3910.9
2614.1 3262.5 3324.4 2803.5 2628.6 2927.3 2956.5 3539.4 3011.1 3900.0
2606.8 3240.6 3309.9 2799.8 2697.8 2938.3 2974.7 3583.1 3036.6 3921.9
2625.0 3284.4 3342.6 2810.8 28.4 5.2 7.9 18.6 10.7 8.9
7.9 17.8 13.6 5.2
1697.2 2550.3 2586.2 2729.9
14.7 57.3 12.7 7.3
2617.7 3630.4 3976.5 3335.4
2635.9 3674.1 4009.3 3353.6
7.9 18.6 13.6 7.9
237.5 271.5 302.6 257.9 241.5 290.7
217.1 239.2 257.9 217.1
2
238.6 271.5 302.6 260.2 242.6 290.7
217.1 240.3 259.0 218.3
3
0.5 0.0 0.0 0.9 0.5 0.0
0.0 0.5 0.5 0.5
4
0.2 0.0 0.0 0.4 0.2 0.0
0.0 0.2 0.2 0.2
5
52.5 41.7 44.3 55.1 47.2 57.5
44.6 51 51.2 41.6
1
0.3 0.5 0.3 0.2
243.7 327.5 330.9 215.4
243.2 325.3 329.8 214.9
244.3 329.2 332.0 216.0
0.5 1.7 0.9 0.5
0.2 0.5 0.3 0.2
41.6 55.1 62.2 53.6
0.7 5.7 1.3
0.3 355.4 2.4 282.6 0.4 453.8
337.2 278.9 450.2
366.4 286.2 461.1
417.4 388.2 217.0 344.5 446.5 391.9 497.5 450.2 366.4 555.8
53.0 57.5 33.7 55.8 76.2 64.3 79.0 79.0 50.7 105.0
13.0 3.6 47.3 3.0 1.1 54.1 5.2 1.1 46.2
10.7 3.0 7.9 10.7 10.7 10.7 7.9 3.0 13.6 7.9 46.2 53.5 45.6
51.8 56.9 33.1 54.7 75.0 63.7 79.0 78.4 49.6 104.5 49.0 54.7 46.7
54.1 58.1 34.3 56.4 77.3 64.8 79.0 79.6 51.8 105.6
1.7 0.8 1.4 1.4 1.2 0.7 0.0 0.6 1.8 0.4
7.5 9 6.5 7.8 9.6 9.3 10.5 10 8.2 10.8 1.2 2.6 8 0.5 0.9 8.2 0.5 1.0 9.1
0.9 0.5 0.5 0.8 0.9 0.5 0.0 0.5 0.9 0.5
1609.7 1762.7 1658.3 2433.0 1975.2 2652.8
1675.3 2214.4 2203.5 1863.5
2
1674.1 1773.6 1676.5 2467.0 1995.8 2674.6
1693.5 2253.3 2231.4 1876.8
3
26.4 5.2 7.9 14.8 8.8 8.9
7.9 15.9 11.7 5.5
4
1574.5 2235.1 2561.7 2408.7
1587.8 2266.6 2584.8 2429.4
6.0 12.9 9.8 9.5
191.7 365.5 274.7 303.0 213.4 287.5
232.4 197.5 240.6 298.0
1
190.1 364.7 273.5 301.1 212.6 286.7
231.2 196.3 239.0 297.6
2
192.8 366.7 275.9 304.2 213.8 287.9
234.7 198.3 241.7 298.8
3
1.1 0.8 1.0 1.4 0.5 0.5
1.6 0.8 1.1 0.5
4
0.6 0.2 0.3 0.5 0.3 0.2
0.7 0.4 0.5 0.2
5
1101.3 759.5 512.7 987.4 835.5 1424.1
930.4 1443.1 1329.1 1025.3
1
1063.3 721.5 493.7 949.4 816.5 1367.1
892.4 1405.1 1272.2 968.4
2
1158.2 797.5 531.7 1044.3 854.4 1462.0
949.4 1481.0 1367.1 1101.3
3
mg/dm3 (±) catechin
Catechins
41.0 31.0 15.5 41.0 15.5 41.0
26.9 31.0 41.0 55.9
4
3.7 4.1 3.0 4.2 1.9 2.9
2.9 2.1 3.1 5.5
5
0.4 0.6 0.4 0.4
328.6 386.8 466.8 284.4
327.9 385.7 465.6 282.5
329.4 388.4 467.9 285.6
0.6 1.1 1.0 1.4
0.2 0.3 0.2 0.5
987.4 1746.9 2031.7 2221.5
968.4 1689.9 1993.7 2183.6
1025.3 1822.8 2088.6 2259.5
26.9 55.9 41.0 31.0
2.7 3.2 2.0 1.4
0.1 346.5 344.2 348.8 1.9 0.5 1595.0 1557.0 1670.9 53.7 3.4
1.6 0.3 0.5 0.6 0.4 0.3
0.5 0.7 0.5 0.3
5
mg/dm3 anthocyanins
Anthocyanins
152.5 50.5 255.7
179.2 138.0 61.5 94.2 108.8 105.2 147.7 107.6 134.3 97.9
139.2 49.3 252.1
169.5 134.3 56.6 84.5 99.1 93.0 140.4 101.5 121.0 89.4
162.2 51.7 260.6
185.3 144.0 70.0 102.7 114.9 113.7 158.6 111.2 144.0 102.7
9.8 1.0 3.6
6.9 4.3 6.0 7.5 6.9 8.8 7.9 4.3 9.8 6.0
2254.5 2243.6 2266.6 9.5 3295.2 3287.9 3300.0 5.2 95.5 91.8 101.5 4.3
6.4 2.0 1.4
3.9 3.1 9.8 7.9 6.4 8.4 5.3 4.0 7.3 6.2
42.5 42.5 33.4
48.6 54.9 57.7 51.6 57.7 55.4 67.6 53.2 62.3 38.7
40.3 41.0 31.9
47.1 53.2 53.9 49.4 56.2 53.2 66.1 50.9 60.8 36.5
45.6 44.8 34.9
50.9 56.2 60.0 54.7 58.5 59.2 68.4 54.7 64.6 41.0
2.2 1.6 1.2
1.6 1.3 2.7 2.2 1.1 2.7 1.1 1.6 1.6 1.9
5.3 3.9 3.7
3.4 2.3 4.7 4.3 1.9 4.9 1.6 3.1 2.6 4.8
0.4 196.7 195.2 199.0 1.7 0.9 1879.8 1841.8 1936.7 41.0 2.2 0.2 292.2 291.0 293.7 1.1 0.4 1310.1 1234.2 1367.1 55.9 4.3 4.5 6.6 6.2 7.4 0.5 8.3 1898.8 1841.8 1936.7 41.0 2.2
2936.9 2916.3 2961.2 18.5 0.6 281.7 280.9 283.2 1.1 0.4 2316.5 2278.5 2392.4 53.7 2.3
1579.3 2250.9 2575.1 2422.1
2535.0 2531.4 2538.6 3.0
1640.1 1770.0 1669.2 2453.6 1983.7 2663.7
1682.6 2233.9 2215.6 1869.6
1
mg/dm3 catechin equivalents
Flavonoids
Collecting sites: 1—Suvorovo (Varna Province); 2—Pirgovo (Rouse Province); 3—Orjahovo (Vratsa Province); 4—Levunovo (Blagoevgrad Province); 5—Topoli dol (Pazardzhik Province); 6—Brestnik (Plovdiv Province); 7—Starosel (Plovdiv Province); 10—Tsernodab (Haskovo Province); 11—Gen. Todorov (Blagoevgrad Province); and 12—Vranja (Blagoevgrad Province). According to Bulgarian Wine and Spirit Drinks Act (2014, https://www.mi.government.bg/library/index/download/lang/en/fileId/83), the collecting sites belong to wine growing zone North BDanubian Plain^ (2, 3), East BBlack Sea^ (1), South BThracian Plain^ (5, 6, 7, 10), and Southeast BStruma Valley^ (4, 11, 12)
230.8 242.5 328.7
391.9 380.9 198.8 319.0 421.0 366.4 479.3 442.9 333.6 537.6
229.0 229.9 326.0
406.4 384.6 206.1 333.6 435.6 380.9 486.6 446.5 351.8 548.5
229.9 238.0 327.8
2.1 0.4 2.5 0.3 0.9 0.2 0.9 0.8 1.1 0.9
2.6 0.8 3.8 3.2 2.5 2.8 1.6 0.7 3.9 1.4
5.7 1.3 6.3 0.7 3.2 0.7 3.2 2.9 3.8 2.9
264.9 317.0 242.5 273.0 363.7 331.4 363.7 368.2 332.3 333.2
269.4 318.8 251.4 273.9 366.4 332.3 368.2 371.8 337.6 336.8
277.5 319.7 255.9 274.8 370.9 333.2 370.9 375.4 340.3 340.3
0.2 247.7 246.6 248.8 0.9 0.4 49.1 0.1 250.5 250.0 251.1 0.5 0.2 63.5 0.5 114.1 113.5 114.7 0.5 0.4 11.1
0.0 4111.3 4085.8 4140.4 22.5 0.5 273.7 272.6 274.9 0.9 0.3 62.8
2625.0 3655.9 3994.7 3346.3
238.1 271.5 302.6 259.0 242.0 290.7
217.1 239.8 258.5 217.7
1
Index of total phenols (a.u.)
0.1 266.4 266.4 266.4 0.0 0.0 55.3
1.1 0.2 0.3 0.5 0.4 0.2
0.3 0.5 0.4 0.2
5
mg/dm3 caffeic equivalents
Phenolic acids
2334.8 2254.0 2397.7 60.0 2.6 3317.1 3306.2 3324.4 7.9 3080.1 3062.2 3098.1 14.7 0.5 4369.9 4362.7 4377.2 5.9 1849.9 1796.0 1930.7 58.2 3.1 584.9 581.3 588.6 3.0
2972.4 2972.4 2972.4 0.0
1661.3 2424.6 2559.3 2712.0
0.9 2.3 0.5 0.3
7.3 26.4 19.4 33.6 7.3 48.1
1.5 0.3 0.3 1.5
4
1679.3 2469.5 2568.3 2720.9
1966.6 1814.0 1903.8 2541.3 2038.5 2810.7
32.0 7.3 7.3 26.4
3
0.3 3677.8 3674.1 3681.4 3.0
1948.7 1751.1 1858.9 2460.5 2020.5 2694.0
1957.6 1787.0 1876.8 2505.4 2029.5 2747.9
2155.2 2334.8 2361.7 1822.9
2
2685.0 2676.0 2694.0 7.3
2083.4 2316.8 2343.8 1760.1
2110.3 2325.8 2352.8 1796.0
5
1
4
1
3
mg/dm3 catechin equivalents
mg/dm3 galic acid
2
Total phenols
Total phenols
Characteristics of the wine samples (1 = average value; 2 = min value; 3 = max value; 4 = standard deviation; 5 = CV)
Red wines Pinot Noir3 Pinot Noir3 Merlot3 Cabernet Sauvignon3 Cabernet Franc3 Syrah3 Marselan3 Egiodola3 Merlot4 Cabernet Sauvignon4 Cabernet Sauvignon5 Syrah5 Cabernet Franc6 Syrah6 Cabernet Sauvignon7 Cabernet Sauvignon10 Merlot10 Melnik 5511 Shiroka Melnishka Loza12 White wines Sauvignon Blanc1 Chardonnay1 Muscat Ottonel2 Sauvignon Blanc3 Chardonnay3 Chardonnay3 Chardonnay3 Chardonnay3 Viognier3 Muscat Blanc à Petits Grains5 Chardonnay5 Sauvignon Blanc6 Chardonnay6
Wine sample
Table 1
Food Anal. Methods
Food Anal. Methods
Golay transformation (10-point smoothing and second-order filtering)) before calibration models were developed. In order to evaluate the effect of the path length on the UV, VIS, and NIR calibration for phenolic compounds, the resulting standard error of cross validation (SECV) of the calibration was compared using a Fisher’s test (F value) (Cozzolino et al. 2007; Cozzolino et al. 2006). The F value was calculated as F ¼ SECV2=SECV1; where SECV1 < SECV2 The calculated F value was compared with the confidence limit F critical (1 − α, n1 − 1, n2 − 2), obtained from the distribution F table, where α is the test significance level (α = 0.05 in this experiment), n1 the sample number measured at the 1-mm path length, and n2 the sample number measured using the 0.1 path length (n1, n2… = 10 in this experiment). The differences between the SECV are significant when F > F limit (Cozzolino et al. 2007; Cozzolino et al. 2006). To quantify the proportions of the total spectral variability explained by path length and wine variety, scores from the PCA were analyzed statistically as follows. After PCA analysis, the scores from the first four principal components (PCs), which accounted for more than 95% of the total spectral variability in the raw spectra, were analyzed using ANOVA (Systat, USA). The sum of variances of a specific factor (e.g., path length and variety) or an interaction term (e.g., path length × variety) of the PCs can be interpreted as the expected variance of future samples taken from the whole population (Cozzolino et al. 2007; Cozzolino et al. 2006).
Results and Discussion In routine food analysis using spectroscopy methods, the main drawback of calibration models developed for a specific food or beverage is their lack of robustness. The lack of robustness in a calibration can be originated from several causes such as variable sample temperature, spectrophotometer temperature, ambient stray light, and path length (Cozzolino et al. 2007; Cozzolino et al. 2006). Table 1 shows the average, range (minimum and maximum), standard deviation, and coefficient of variation for the measurement of flavonoids, anthocyanins, total phenols, and catechins in the set of wine samples analyzed. A wide range in composition was observed in the set of wines analyzed as reported by other authors (Lakatošová et al. 2016; Tsanova-Savova et al. 2002; Tsanova-Savova and Ribarova 2002; Yanev et al. 1989). It was therefore considered to be a representative set of samples on which to develop NIR calibration models in order to test the effect of temperature on both the spectra and calibration robustness (Cozzolino et al. 2007; Cozzolino et al. 2006). Note that the aim of this study was not to develop robust UV, VIS, and NIR calibrations for
the analysis of wine phenolic compounds but rather to test the effect of the path length on the accuracy of such models. In order to quantify the influence of path length on the spectra of wine as well as the effect on the accuracy of UV, VIS, and NIR calibration models for phenolic compounds, the SECV values for the different UV, VIS, and NIR calibration models for spectra collected at two different path lengths and the chemical composition were statistically compared. Table 2 compares the SECV obtained for the prediction of total phenolic and index of total phenolic and anthocyanins in the set of red and white wines analyzed using two path lengths. It was observed that the analysis of wine samples using 1-mm path length tends to increase the error of prediction (measured as SECV). Overall, no statistically significant differences were used between the two path lengths used to analyze the wine samples. The other aspect analyzed in this study was the effect of the path length on the discrimination of samples according to wine. Figures 1, 2, and 3 show the principal component analysis (scores, eigenvectors, and amount of explained variance) for the set of red and white wines analyzed using UV, VIS, and NIR ranges. The best separation among wines was observed when the UV and VIS ranges were used while the UV-VISNIR and the NIR range along did not achieve a good separation between wines. The interpretation of the eigenvectors derived from the analysis of both wine styles indicated some changes in the absorption at specific wavelength changes developed in the system. The highest eigenvectors observed in PC1 were related to pigments (VIS region) mainly associated with phenolic compounds in red wines (between 450 and 550 nm) and in the UV region with bands associated with phenolic in the set of white wine samples (between 250 and 300 nm) (Somers and Ziemelis 1985; Ribéreau-Gayon and Stonestreet 1965;
Table 2 Standard error in cross validation for phenolic compounds measured in red and white wine samples using two different path lengths
Index TP 0.1 mm 1 mm TP 0.1 mm 1 mm Antho 0.1 mm 1 mm
Red wines (n = 19)
White wines (n = 13)
3.15a 4.81a
0.38a 0.55a
154.9a 192.2a
0.30a 0.55a
70.2a 66.5a
n/a n/a
Numbers connected with the same letter denote no statistical significant differences TP total phenolics, Antho anthocyanins, n/a not applicable
Food Anal. Methods
Fig. 1 Principal component analysis of wine samples using the UV-VIS-NIR wavelength range
Pompei and Peri 1971). In addition, the UV and VIS ranges contain information regarding phenolic compounds (Uríčková
and Sádecká 2015), such as benzoic acids in the wavelength range 235–305 nm, hydroxycinnamic acids (227–245 and
Fig. 2 Principal component analysis of wine samples using the UV and VIS ranges
Food Anal. Methods
Fig. 3 Principal component analysis of wine samples using the NIR range
310–332 nm), flavonols (250–270 and 350–390 nm), anthocyanins (267–275 and 475–545 nm), and catechins (280 nm). The difference between red and white wine is observed in the range 400–650 nm, where only red wine exhibits an absorbance peak associated with anthocyanins. Several important wine components are fluorescent compounds most of which are polyphenols. All wines fluoresce in the emission region 300–400 nm, when they are excited at wavelengths below 290 nm (e.g., benzoic acids, anthocyanins, and flavanols). Another emission region is found between 350 and 450 nm, when the samples are excited at wavelengths longer than 290 nm (hydroxycinnamic acids and stilbenes) (Uríčková and Sádecká 2015). Generally, PC score distributions indicate the degree of similarities between the sample spectra. Thus, the scores for some of the components are spread irregularly as a function of the path length (red wine measured with 0.1-mm and white wines measured with 0.1- and 1-mm path length). It is very likely that the PC is capturing only noise or other non-deterministic variations and does not describe physically significant changes. It is interesting to note that the scores were arranged in a linear pattern in the direction of PC2 and follow the differences in path length. It was observed that PC1 captures all the dominant changes in the spectra related to the interaction between red wine samples measured using 1 and 0.1 mm. While in the set of white wine samples, PC2 reflects the changes associated with path length. The eigenvectors in the PC1 are mainly related to wine pigments (anthocyanins, 540 nm). Both PC2 in the case of red wines and PC1 in the case of white wine samples explain
related changes in the spectra associated with the differences in path length. Changes in the NIR region are mainly associated with PC1, where the main eigenvectors around the O–H bonds (1900 nm) are the most dominant. However, the most important finding of this study was that for both sets of wines, PC1 was associated with the changes due to path length where differences in electronic transitions due to wine pigments and due to shifts in anthocyanin absorption related to co-pigmentation might explain the differences (Cozzolino et al. 2007; Cozzolino et al. 2006; Murray and Cove 2004). The results suggested that some compositional characteristics or parameters of the red wine matrix could be more affected than others (e.g., pigments, phenolic compounds). Particularly, the use of different path lengths can affect the response of particular chromophores to the light and therefore have an effect on the accuracy of the models. This is of particular importance as the path length used can be defined as the so-called apparent path length due to the fact that the optical density of the sample can vary with different packing of the same physical volume (Murray and Cove 2004). This can indicate that the volume of wine used to perform the analysis can have an effect, in particularly when compounds such as phenolics and flavonoids are measured. Therefore, the selection of the path length is the most important consideration in designing the sample presentation system when transmission is used (Cozzolino et al., 2007; Cozzolino et al., 2006; Murray and Cove, 2004). In addition, the use of different path lengths can lead to opposite conclusions when these types of technologies are applied to target classification or
Food Anal. Methods
discrimination issues. More importantly, they can bias results in issues related to authenticity or origin. Kemeny (2007) suggested that one of the most important parameters in liquid sample analysis such as wine is the optimum section of the path length in order to achieve the most precise analytical results. This study has highlighted the importance of the path length in the prediction of phenolic compounds in a set of red and white wines. In addition, the importance of the adequate path length was highlighted when this type of methods are considered to classify different styles of wines.
Conclusions These results indicated that path length has an effect on the standard error of cross validation (SECV) absolute values obtained for the PLS calibration models used to predict phenolic compounds in both red and white wines. However, no statistically significant differences were observed (p > 0.05). The practical implication of this study was that the path length of scanning for wines has an effect on the calibration accuracies; however, they are non-statistically different. Main differences were observed in the PCA score plot. Overall, well-defined protocols need to be defined for routine use of these methods in research and by the industry. Acknowledgements The financial support from the Bulgarian Science Fund (Project DFNI B02/22 BTraditional Bulgarian Wines – Characteristics and identification^) is gratefully acknowledged. Compliance with Ethical Standards Conflict of Interest Nevse Molla declares that he has no conflict of interest. Ivan Bakardzhiyski declares that he has no conflict of interest. Yana Manolova declares that she has no conflict of interest. Valentin Bambalov declares that he has no conflict of interest. Daniel Cozzolino declares that he has no conflict of interest. Liudmil Antonov declares that he has no conflict of interest. Ethical Approval This article does not contain any studies with human or animal subjects. Informed Consent Not applicable.
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