Article pubs.acs.org/JAFC
Partial Least-Squares-Discriminant Analysis Differentiating Chinese Wolfberries by UPLC−MS and Flow Injection Mass Spectrometric (FIMS) Fingerprints Weiying Lu,† Qianqian Jiang,† Haiming Shi,† Yuge Niu,† Boyan Gao,†,‡ and Liangli (Lucy) Yu*,†,‡ †
Institute of Food and Nutraceutical Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China ‡ Department of Nutrition and Food Science, University of Maryland, College Park, Maryland 20742, United States S Supporting Information *
ABSTRACT: Lycium barbarum L. fruits (Chinese wolfberries) were differentiated for their cultivation locations and the cultivars by ultraperformance liquid chromatography coupled with mass spectrometry (UPLC−MS) and flow injection mass spectrometric (FIMS) fingerprinting techniques combined with chemometrics analyses. The partial least-squares-discriminant analysis (PLS-DA) was applied to the data projection and supervised learning with validation. The samples formed clusters in the projected data. The prediction accuracies by PLS-DA with bootstrapped Latin partition validation were greater than 90% for all models. The chemical profiles of Chinese wolfberries were also obtained. The differentiation techniques might be utilized for Chinese wolfberry authentication. KEYWORDS: Lycium barbarum L., Chinese wolfberry, ultraperformance liquid chromatography, flow injection mass spectrometry, partial least-squares-discriminant analysis
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Recently, fingerprinting techniques such as mass spectrometric and chromatographic fingerprints, combined with chemometrics analyses, have been utilized in differentiating botanical materials with different genotypes, from different growing environment, or from different plant parts.13−18 For instance, high-performance liquid chromatography coupled with ultraviolet spectroscopy (HPLC−UV) and flow injection mass spectrometric (FIMS) fingerprints have been demonstrated in the analyses of peppermints.14 With principal component analysis (PCA), the fingerprints of both methods could differentiate commercial organic and conventional peppermints. In addition, FIMS fingerprinting also was able to effectively differentiate the genotypes (di- and tetraploid) and parts (leaf and whole plant) of Gynostemma pentaphyllum.16 The gas chromatography/mass spectrometry (GC/MS) fingerprints of organically grown basils have been authenticated by the fuzzy rule-building expert system (FuRES) and fuzzy optimal associative memory (FOAM).17,18 Fingerprinting techniques were also combined with statistical approaches to characterize the wolfberries. Two-dimensional infrared spectroscopic fingerprinting was utilized to distinguish eight Lycium species.19 Unique fingerprinting patterns were obtained for different species. However, neither did the results of spectroscopic fingerprinting offer any specific information on individual chemical components nor was the prediction accuracy validated. In 2012, Li et al. demonstrated that clustering analysis of the HPLC−UV fingerprints was able to
INTRODUCTION
Lycium barbarum L. fruits (Chinese wolfberry) have been used in functional foods for thousands of years in China. The flavonoids, polysaccharides, and carotenoids are considered as the major beneficial components of Chinese wolfberries, and their primary health properties may include antioxidant, antiaging, and immune-enhancing activities.1−5 Because of these beneficial effects, Chinese wolfberry holds a large market share in the functional food industry. According to the National Bureau of Statistics, the estimated annual production of the wolfberry in China was 1.7 million tons in 2010, which indicated a wide customer acceptance and large customer demand for wolfberry-related food products.6 It is well accepted that the genotype, growing environment, and the interactions between them may alter the chemical composition and biological properties of a selected botanical.7−10 In 2010, Ningxia, Qinghai, Xinjiang, Gansu, and Inner Mongolia provinces accounted for 92.77% total wolfberry productions in China; Ningxia alone accounted for 41.57%, the highest wolfberry production of all provinces in China.6 The wolfberries produced in Ningxia province of China are considered of the best nutritional and nutraceutical quality with the highest market value.11,12 This has economically motivated Ningxia wolfberry counterfeiting using low-quality wolfberries grown at other locations. Rapid and effective techniques to differentiate Ningxia cultivated wolfberries from those grown at other locations are in high demand. These analytical methods are also needed for wolfberry breeding efforts to obtain improved cultivars with enhanced nutritional and nutraceutical quality and farm gate value for commercial production of Ningxia wolfberries. © 2014 American Chemical Society
Received: Revised: Accepted: Published: 9073
May 6, August August August
2014 21, 2014 25, 2014 25, 2014
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of 50% acetonitrile (v/v) with sonication at ambient temperature for 5 min, and then centrifuged at 13400g for 10 min to remove the nondissolvable impurities. The supernatant was subjected to UPLC− MS and FIMS analysis. Ultraperformance Liquid Chromatography−Mass Spectrometry (UPLC−MS). An Acquity UPLC BEH C18 column (2.1 mm i.d. × 100 mm, 1.7 μm) attached with a VanGuard precolumn (2.1 mm i.d. × 5 mm, 1.7 μm) (Waters, Milford, MA, USA) was used. A binary solvent system of 0.1% (v/v) formic acid in deionized water (solvent A) and 0.1% (v/v) formic acid in acetonitrile (solvent B) was used. It has been studied that the addition of formic acid as a mobile phase modifier would reduce peak tailing and improve the performance of separation for flavonoids and phenolic acids, the major functional components of wolfberries.21 The gradient elution program was 0−8 min, 10−100% B with a concave (curve parameter was 8); 8−13 min, 100% B; 13−15 min, 100−10% B with a linear gradient; and 15−16.5 min, 10% B. The column temperature was 40 °C, and the flow rate was 0.4 mL/min. Based on the previous liquid chromatography−mass spectrometry (LC−MS) studies of Chinese wolfberries, the negative ion mode was better at characterizing the major functional components such as flavonoids and phenolic acids in wolfberries than the positive ion mode.20,21 The electrospray ionization (ESI) source was operated under the negative ion mode without applying collision energy. The capillary voltage was 2.5 kV, and the cone voltage was 40 V. The ionization temperature was 120 °C, and desolvation temperature was 250 °C. The sodium formate (0.34 g/L in 90% isopropanol, v/v) was used for QTOF-MS startup calibration under the same ESI conditions for sample measurement, and leucine enkephalin (2 mg/L in 50% acetonitrile, v/v) was used for QTOF-MS lockspray calibration. The sodium formate was introduced prior to the ESI interface during the startup calibration. The mass spectra from 0.5 to 8.5 min were recorded. Two channels of MS measurements were recorded. The first channel was for the data collection of MS signals ranging from 100 to 1500 Da without collision energy. The second channel offered additional fragmentation information for the compound identification, which collected mass fragments of 100 to 1000 Da with a 35 eV collision energy. The scanning speed was 0.3 s/scan. Each sample was injected in triplicate by a sequence of random block design. Three blocks were injected sequentially, and each block contained a replicate of 29 samples in a randomized order. The methanol blank solution was injected at the beginning and the end of each block to reduce sample cross-contaminations. The entire UPLC−MS sequence ran for two consecutive days. Flow Injection Mass Spectrometry (FIMS). The same MS instrumentation and method were applied in FIMS fingerprinting as that described in the UPLC−MS analysis section, without the UPLC column. The solvent was 0.1% (v/v) formic acid in 50% acetonitrile:water (v/v). The solvent pH was 3.0 with a flow rate of 0.5 mL/min. The MS signals from 100 to 1000 Da were collected from 0 to 0.5 min after injection. Data Analysis. The peak areas and PLS-DA plots were calculated using the Waters MarkerLynx software (version 4.1). For UPLC−MS data, the component peaks were extracted with a chromatogram mass window of 0.01 Da, and a retention time window of 0.2 min. Twenty characteristic peaks appearing in all NX samples were selected manually, in addition with five characteristic peaks in QH sample to validate peak selection. For each FIMS spectrum, peaks were extracted by a mass window of 1 Da. The peak areas and FIMS spectra were preprocessed by dividing the mass of each sample weighed before homogenization, followed by the autoscaling transformation, i.e., each feature was subtracted by its mean value, and divided by their standard deviation. The number of classes was 2 for wolfberries produced in NX and other locations. When differentiating NX cultivars, the number of classes was 4. The bootstrapped Latin partition22 of 10 bootstraps and 5-fold Latin partition was applied for validation to evaluate the model prediction power. The bootstrapping applied resampling, and achieved multiple evaluations with a statistical power similar to permutation. Also, Latin partition is an improved method derived from cross-
identify different cultivation locations of eight wolfberry samples.8 Although HPLC−UV fingerprints were obtained, no compositional information was related to the clustering tree. The mass spectrometric technique offers mass-to-charge ratio measurements that generally furnished greater specificity for differentiation and component characterization than the UV fingerprints. Bondia-Pons et al. applied ultraperformance liquid chromatography coupled with mass spectrometry (UPLC− MS) with partial least-squares-discriminant analysis (PLS-DA) to differentiate the geographic origin of wolfberries.20 However, samples from only three origins (Tibet, Northern China, and Mongolia) were analyzed. It is interesting to test whether the UPLC−MS and FIMS may be applied to differentiate Chinese wolfberries produced in Ningxia and other locations, or the different wolfberry cultivars from a selected growing region. In this study, the PLS-DA of UPLC−MS and FIMS fingerprints was tested for possible differentiation of Chinese wolfberries produced in Ningxia from those grown in other locations. The PLS-DA was also utilized to possibly distinguish the four wolfberry cultivars grown in Ningxia. The prediction accuracies for both approaches were calculated. In addition, the phytochemical compositions of the wolfberries were examined. The results from this study may serve as a scientific foundation for the authentication of Chinese wolfberries.
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MATERIALS AND METHODS
Materials and Reagents. Twenty-nine wolfberry samples harvested in 2012 were obtained from Professor Jinming Gao at Shaanxi Engineering Center of Bioresource Chemistry & Sustainable Utilization, College of Science, Northwest A&F University. The sample set included 19 Ningxia (NX) wolfberry samples and 10 samples from five other provinces of China, including three Inner Mongolia (IM), three Qinghai (QH), two Gansu (GS), one Hebei (HB), and one Xinjiang (XJ) samples, respectively. All samples were L. barbarum fruits, except that one was Lycium ruthenicum Murr. fruits from QH. Out of all 19 NX samples, the cultivars of 16 NX L. barbarum fruit were recorded, including five NX wolfberry No. 1 (NX1), five NX wolfberry No. 4 (NX4), two NX wolfberry No. 5 (NX5), and four NX wolfberry No. 7 (NX7) samples. Three other samples had no cultivar information and were removed in the statistical analysis related to wolfberry cultivars. The fruits were airdried immediately after harvest and stored in zip-lock plastic bags at −20 °C. Deionized water was obtained using a Milli-Q purification system (Millipore Laboratory, Bedford, MA, USA). The HPLC grade methanol and acetonitrile were purchased from Merck (Darmstadt, Germany). The high-performance liquid chromatographic grade formic acid was purchased from Sigma-Aldrich (St. Louis, MO, USA). The analytical grade ethanol was purchased from Yangyuan Chemical (Changshu, Jiangsu, China). Other chemicals or solvents were of the highest commercial grade and used without further purification. Instrumentation. A Precellys 24 Dual homogenizer (Bertin Technologies, Bretonneux, France) was used for sample homogenization. A KH7200 sonicator was used for sonication during sample extraction (Kunshan Hechuang Ultrasonic Corporation, Kunshan, Jiangsu, China). UPLC−MS analysis was performed by an Acquity UPLC coupled with a Xevo G2 quadrupole time-of-flight (QTOF) mass spectrometer (Waters, Milford, MA, USA). Sample Preparation. The dried fruits (dry weight: 0.512 ± 0.010 g) were placed in a 7 mL homogenizing tube with 1 mL of deionized water. The homogenization was performed twice at a speed of 5500 rpm. Each cycle lasted for 20 s with a 5 s hold time between cycles. Then 4 mL of analytical grade ethanol was added. The extraction was performed at ambient temperature for 30 min using 80% (v/v) ethanol under sonication. After the mixture was centrifuged at 3901g for 5 min, 1 mL of supernatant was transferred to a clean tube, and the solvent was removed with nitrogen flow. The extract was redissolved in 1 mL 9074
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Figure 1. UPLC−MS base peak chromatograms of Chinese wolfberries. QH, HB, XJ, GS, and IM represent samples from Qinghai, Hebei, Xinjiang, Gansu, and Inner Mongolia, respectively. NX1, NX4, NX5, and NX7 represent Ningxia (NX) wolfberry No. 1, No. 4, No. 5, and No. 7 cultivars, respectively. validation, in which the ratio between different classes is maintained. The validation result was reported as the percentage of correct predictions to reflect the prediction accuracy. Afterward, a two-way analysis of variance (ANOVA) was applied to determine whether the prediction models were statistically different from each other. In addition, the average regression coefficients of PLS-DA could be plotted with confidence intervals. Positive and negative coefficients represent the relationships of the peaks to different classes. The importance of each peak could be quantified by the absolute magnitude of each regression coefficient. The peak would be insignificant for classification, if the confidence interval crossed zero. The PLS-DA model validation and ANOVA were performed by an inhouse program using MATLAB R2013b (The MathWorks, Natick, MA, USA). The number of latent variables to build the PLS-DA model is an important parameter that affects its prediction ability. If the number of latent variables is too large, overfitting may occur, and underfitting may occur otherwise. The optimized number of latent variables was determined by achieving the best mean prediction accuracy when applying the same bootstrapped Latin partition procedure (10 bootstraps and 5-fold Latin partition) on each training partition in the validation process. The algorithm of PLS was the nonlinear iterative partial least-squares (NIPALS), and the binary encoding was applied to handle the multiclass problem.
composition could occur due to planting season, or the interactions between factors such as season and location, or season and genotype. Peaks 7, 8, 10, 13, and 19 were the typical components in this QH sample. Peaks 13 and 19 were tentatively identified as quercetin-di(rhamno)-hexoside and cirsimaritin. The overall profiles of HB were also different from the NX samples. This result was consistent with a previous UPLC−UV fingerprinting study in which HB samples furnished the largest dissimilarity compared to other samples from eight locations.8 However, it was difficult to predict the growing location of a commercial wolfberry sample based only on direct observation of its base peak chromatogram. For all NX wolfberries, most of the peaks were identical except for variations in their relative concentrations. This observation was consistent with the previous genetic diversity studies on NX wolfberry cultivars that demonstrated high genetic similarities of NX wolfberries,24 indicating a challenging objective to distinguish the cultivars by manually inspecting the chromatogram. Differentiating Samples from Ningxia and Other Locations by UPLC−MS Peak Areas. Figure 2A depicts the PLS-DA scores plot of the UPLC−MS for the 29 wolfberry samples using the peak areas of 20 and 5 peaks selected from NX and the wild grown QH sample, respectively. Most samples from NX were separated from the samples grown in other locations regardless of cultivar differences. Figure 1 demonstrated that the wolfberry samples from locations outside NX may contain many characteristic components to help differentiation. However, it is not possible to perform an exhaustive compositional analysis on all species of Lycium. Consequently, it should be verified that 20 primary peaks selected from NX samples alone was adequate for differentiation. Comparing the PLS-DA scores plots which included (Figure 2A) or excluded (Figure 2B) peaks 7, 8, 10, 13, and 19 that were only detected in wild grown QH samples, a similar clustering trend was observed. In both plots, the wild
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RESULTS AND DISCUSSION UPLC−MS Chromatograms. Figure 1 reports the representative UPLC−MS base peak chromatograms of the nine Chinese wolfberry extracts. The base peak chromatogram plots the intensity of the most abundant ion, the base peak intensity, in each MS scan. More components can be observed in the base peak chromatograms than in the total ion chromatogram, because the base peak intensity is less influenced by noise than the total intensity. The peak profiles of a sample from QH significantly differed from samples grown in other locations. This probably was because this QH sample was a wild grown Lycium species (L. ruthenicum), which has a unique composition according to a previous study.23 Besides location and genotype, significant differences in the chemical 9075
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12 was 1,3-dihydro-2-benzofuran. In addition, 18 components were tentatively identified according to the high-resolution measurements with QTOF-MS (Table 1). The phenolic acids including quercetin-rhamno-dihexoside, quercetin-di(rhamno)hexoside, quercetin-3-O-rutinoside, kaempferol-3-O-rutinoside, and isorhamnetin-3-O-rutinoside were consistent with the findings of a previous study using a liquid chromatography with ultraviolet detection.21 Citric acid, citric acid glycoside, coumaric acid glycoside, ferulic acid glycoside, scopoletin glycoside, isocoumaran, trihydroxy octadecenoic acid, dihydroxy octadecadienoic acid, hydroperoxy octadecatrienoic acid, dihydroxy octadecadienoic acid, and hydroxy linoleic acid were not reported in wolfberry previously. Differentiating Cultivars from a Selected Location by UPLC−MS Peak Areas. With the application of PLS-DA on the subset of 16 NX samples, different cultivars were also separated into distinctive clusters (Figure 3A). Data points representing NX1 were located in the center of the plot, indicating that less characteristic components were extracted compared to the other three cultivars. Because NX1 was the earliest commercial breed of NX wolfberry cultivar, all other cultivars from NX were developed from NX1. The cultivation process led to the occurrences of characteristic components and variations in concentrations. The locations of peaks differed in Figure 2C, suggesting a different component profile of NX cultivars as compared to the fruits outside NX province. Specifically, peak 21 (tentatively identified as hydroperoxy octadecatrienoic acid) and peaks 12, 16, and 18 (tentatively identified as 1,3-dihydro-2-benzofuran, kaempferol-3-O-rutinoside, and trihydroxy octadecenoic acid, respectively) were respectively related to NX5 and NX7. The loading plots (Figures 2C and 3B) clearly demonstrated UPLC−MS advantages in discovering the relationships between phytochemical compounds and different sample classes, with the trade-off of analysis time. The PCA was also performed. Similar clustering trends could be observed by comparing Figures 2B and 3A with the principal component scores plots (Figures S1 and S2 in the Supporting Information). As a supervised method, the PLS-DA achieved better separations between different sample classes than PCA. FIMS Fingerprints. FIMS fingerprinting is a novel technique without chromatographic separation. It is suitable for a high-throughput assay of food materials. In the present study, FIMS fingerprinting was compared to the conventional UPLC−MS base peak fingerprints under the same instrumentation configurations. Nine representative FIMS fingerprints were plotted in Figure 4. Distinctively unique patterns of wild grown QH and HB samples were observed in Figure 4, in good agreement with the chromatograms obtained by UPLC−MS (Figure 1). The peaks at m/z 191.0192 and 191.0555 were tentatively identified as citric acid and scopoletin, respectively. The peaks at m/z 111.0082 and 133.0136 were tentatively identified as the fragments of citric acid, which were consistent with the UPLC−MS measurements of peak 1 (Table 1). Differentiating Samples from Ningxia and Other Locations by FIMS Fingerprints. Figure 5 is the PLS-DA plot to differentiate samples from Ningxia and other locations by their FIMS fingerprints. The scores plots (Figure 5A) indicated a similar clustering trend as the UPLC−MS plots (Figure 2B). The wild grown QH samples presented in Figure 5A were located at the bottom of the plot away from the other samples, consistent with the FIMS fingerprints in Figure 4 and the scores plot of Figure 2B. There were many variables in the
Figure 2. PLS-DA scores and loading plots differentiating wolfberries produced in NX and other locations using UPLC−MS peak areas: (A) the scores plot by 20 and 5 peaks respectively selected from NX and wild grown Qinghai (QH) sample; (B) the scores plot by 20 peaks only selected from NX samples; and (C) the corresponding loading plot of B. In both A and B, the wild grown QH sample is circled.
grown QH samples were away from all other samples and respectively positioned at the top-left and bottom-left corners. As a result, any peaks observed only in non-NX samples were excluded for further analysis. The loading plot (Figure 2C) reflects the importance of each compound for each data cluster. The closer a peak was to the respective sample response, the more characteristic it was in that class of sample. It can be observed that peaks 4, 6, 11, 12, and 15 were mainly related to NX samples. According to the tentative identification by QTOF-MS, peaks 4 and 6 were glycosides of coumaric acid; peak 11 was scopoletin; and peak 9076
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Table 1. Tentatively Identified Compounds by UPLC−MSa [M − H]− tR (min)
compound
formula
exptl
theor
1 2
0.71 0.92
citric acid citric acid glycoside
C6H8O7 C39H55O17
191.0187 794.3354
191.0192 794.3361
3 4 5 6 7
1.03 1.15 1.32 1.95 2.16
unknown coumaric acid glycoside quercetin-rhamno-dihexoside coumaric acid glycoside unknown
C46H53O12 C9H8O3 C33H40O21 C15H17O8 C27H37O7
796.3470 163.0393 771.1987 325.0928 472.2447
796.3459 163.0395 771.1984 325.0923 472.2461
8
2.39
unknown
C27H35O7
470.2290
470.2305
9 10
2.51 2.62
ferulic acid glycoside unknown
C10H10O4 C27H35O7
193.0500 470.2281
193.0501 470.2305
11 12
2.64 3.62
C10H8O4 C8H8O
191.0340 119.0495
191.0344 119.0497
13 14 15 16 17 18 19 20 21 22 23 24
3.64 4.21 4.48 4.73 4.81 6.66 7.18 7.47 7.53 7.72 7.95 8.03
scopoletin 1,3-dihydro-2-benzofuran (isocoumaran) quercetin-di(rhamno)-hexoside quercetin-3-O-rutinoside (rutin) unknown kaempferol-3-O-rutinoside isorhamnetin-3-O-rutinoside trihydroxy octadecenoic acid cirsimaritin dihydroxy octadecadienoic acid hydroperoxy octadecatrienoic acid dihydroxy octadecadienoic acid hydroxy linoleic acid unknown
C33H40O20 C27H30O16 C8H14O C27H30O15 C28H32O16 C18H34O5 C17H14O6 C18H32O4 C18H30O4 C18H32O4 C18H32O3 C48H78O10
755.2031 609.1456 245.0926 593.1519 623.1610 329.2320 313.0714 311.2224 309.2058 311.2220 295.2271 814.5580
755.2035 609.1456 245.0966 593.1506 623.1612 329.2328 313.0712 311.2222 309.2066 311.2220 295.2273 814.5595
25
8.27
unknown
C34H44O9
595.2890
595.2907
fragment ions and formula − b
111.0088 (C5H3O3 ), 133.0127 (C4H5O5−)b 191.0191 (C6H7O7−), 470.2286 (C27H34O7−),b 632.2822 (C33H44O12−) 634.2954 (C40H42O7−), 472.2437 (C27H34O7−) 353.0867 (C16H17O9−) 301.0364 (C15H9O7−),b 609.1465 (C27H29O16−)b 163.0397 (C9H7O3−) 228.1711 (C13H24O3−)b, 308.1979 (C18H28O4−)b, 350.2081 (C20H30O5−)b 135.0450 (C8H7O2−),b 291.1702 (C21H23O−),b 334.1760 (C19H26O5−)b 355.1003 (C16H19O9−) 135.0448 (C8H7O2−),b 291.1712 (C21H23O−),b 334.1774 (C19H26O5−)b nd nd nd 300.0274 (C15H8O7−)b 203.1826 (C16H11−) 285.0311 (C15H9O6−) 315.0501 (C16H11O7−) nd 253.0509 (C15H9O4−), 283.0970 (C17H15O4−) 293.2123 (C18H29O3−) nd nd nd 566.3455 (C31H50O9−), 506.3423 (C29H46O7−), 293.2114 (C18H29O3−) 241.0114 (C13H5O5−),b 279.2318 (C18H31O2−)b
tR, retention time; exp [M − H]− and theor [M − H]− were experimental and theoretical m/z of molecular ions, respectively; nd, not detected; the formulas were determined by achieving the smallest mass error between theoretical and experimental m/z, taking the fragment ions into consideration; bThe fragment ions observed in the second mass spectral channel. a
loading plots (Figure 5B), but few m/z peaks could be related to differentiation by the FIMS fingerprints. For the FIMS method, the PCA was not able to effectively differentiate the production location (Figures S3 in the Supporting Information). Differentiating Cultivars from a Selected Location by FIMS Fingerprints. Figure 6A is the PLS-DA scores plot to differentiate cultivars of a selected location. Different cultivars were separated, except NX1 and NX4 were overlapped. This was probably due to the greatest similarity between NX1 and NX4, compared with other cultivars. This close relationship was also observed in the loading plot in Figure 6B. Similar to Figure 5B, the extraction of characteristic components was relatively difficult due to the large amount of variables in the FIMS fingerprints. Compared with the conventional chromatographic methods, the FIMS fingerprinting reduced the analysis time significantly while retaining enough information for differentiation. On the other hand, the UPLC−MS method has advantages in providing detailed component information. The PCA results indicated less degree of separation between the cultivars (Figure S4 in the Supporting Information), compared to the PLS-DA scores plot (Figure 6A). Partial Least-Squares-Discriminant Analysis (PLS-DA) Validation. A quantitative evaluation of the accuracies for differentiation is necessary since there were no definite
boundaries between different classes of samples in the scores plots for both UPLC−MS and FIMS fingerprints (Figures 2, 3, 5, and 6). The PLS-DA with bootstrapped Latin partition was applied as a classifier to achieve the automatic prediction. Table 2 is the validation result. The FIMS fingerprinting yielded 71% averaged prediction accuracy when differentiating the NX cultivars, since the autoscaling data transformation elevated noise in the FIMS spectra, especially when handling the multiclass problem. For UPLC−MS, because the peak-selection step ensured the removal of most noises, the averaged prediction accuracy did not decrease with the application of autoscaling. The scaling step was removed and the model was re-evaluated afterward for differentiating the NX cultivars using FIMS fingerprints. To achieve an unbiased spectrometric comparison to the predictions, the total ion spectra (TIS) obtained from UPLC−MS by averaging all MS spectra from 100 to 1000 Da across retention times were also evaluated. The autoscaling was also applied to TIS because the spectrometric noise was reduced by signal averaging. All prediction accuracies were above 90%. The TIS and UPLC−MS achieved higher prediction accuracies than the FIMS fingerprinting method. The two-way ANOVA was performed on the prediction accuracies. The result indicated that the prediction accuracies using UPLC−MS, TIS, or FIMS methods were statistically different at a 95% confidence level. The results proved the 9077
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Figure 3. PLS-DA scores (A) and loading (B) plots differentiating NX cultivars using UPLC−MS peak areas. NX1, NX4, NX5, and NX7 respectively represent NX wolfberry No. 1, No. 4, No. 5, and No. 7 cultivars, respectively.
Figure 5. PLS-DA scores (A) and loading (B) plots differentiating wolfberries produced in NX and other locations using FIMS fingerprints. In A, the wild grown QH sample is circled.
Figure 4. FIMS fingerprints of Chinese wolfberries. QH, HB, XJ, GS, and IM represent samples from Qinghai, Hebei, Xinjiang, Gansu, and Inner Mongolia, respectively. NX1, NX4, NX5, and NX7 respectively represent NX wolfberry No. 1, No. 4, No. 5, and No. 7 samples, respectively. 9078
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as well as the different cultivars grown at a selected location. Compared with each other, FIMS fingerprinting has advantages in reducing the analysis time significantly, and UPLC−MS offers more information when relating the differentiation model to chemical compositions. Both methods could be developed for the quality assurance, quality control, and authentication of wolfberry and wolfberry-containing foods.
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ASSOCIATED CONTENT
S Supporting Information *
Principal component scores plots and PLS-DA model regression coefficients. This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*Tel: (86)-21-3420-5828. Fax: (86)-21-3420-4107. E-mail:
[email protected]. Funding
This work was supported by National High Technology Research and Development Program of China (Grant Nos. 2013AA102202; 2013AA102207); a special fund for Agroscientific Research in the Public Interest (Grant No. 201203069); and SJTU startup fund for young talent (Grant No. 13X100040047), and SJTU 985-III disciplines platform and talent fund (Grants TS0414115001; TS0320215001). Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS Prof. Jinming Gao at Shaanxi Engineering Center of Bioresource Chemistry & Sustainable Utilization, College of Science, Northwest A&F University, is thanked for kindly providing us the wolfberry samples for this study.
Figure 6. PLS-DA scores (A) and loading (B) plots differentiating NX cultivars using FIMS fingerprints. NX1, NX4, NX5, and NX7 respectively represent NX wolfberry No. 1, No. 4, No. 5, and No. 7 samples, respectively.
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Table 2. Statistical Validation Results of PLS-DA Classification Model by Bootstrapped Latin Partitiona UPLC−MS cultivation location NX cultivar
APA (%) nLVs APA (%) nLVs
94 17 93 16
± ± ± ±
3 1 6 2
TIS 97 51 95 28
± ± ± ±
2 10 4 3
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FIMS 93 47 91 25
± ± ± ±
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4 12 4b 8
a The deviations were reported as 95% confidence interval; APA, averaged prediction accuracy; nLVs, number of latent variables. b Calculated by the mean-centering preprocessing method.
effectiveness of the PLS-DA projection plots as a convenient visualization technique for the differentiation. In addition, the average regression coefficients with confidence limits from all the latent variables in the PLS-DA model were calculated and plotted (Figure S5−S8 in the Supporting Information). Each extracted chromatographic peak or m/z measurement can be estimated for its statistical significance in the PLS-DA model for each class of sample. For example, in Figure S8 in the Supporting Information, peaks of m/z 792 and 794 were positively related to cultivars NQ1 and NQ7, and negatively related to cultivars NQ4 and NQ5. In summary, the UPLC−MS chromatogram and the FIMS fingerprints combined with PLS-DA projection plots were quantitatively validated for their potential utilization in differentiating wolfberries from Ningxia and other locations, 9079
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