Application of FT-MIR spectroscopy for fast control of red grape phenolic ripening

The content of phenolic compounds determines the state of phenolic ripening of red grapes and is a key criterion in setting the harvest date to produce quality red wines. In this study, the feasibility of Fourier transform mid-infrared (FT-MIR) spectroscopy combined with partial least-squares (PLS) regression to quantify phenolic compounds is reported. The reference methods used for quantifying these compounds (which were evaluated as total phenolic compounds, total anthocyanins, and condensed tannins) were the usual ones used in cellars that employed UV-vis spectroscopy. To take into account the high natural variability of grapes when building the calibration models, fresh grapes from six varieties, at different phenolic ripening states were harvested during three vintages. Destemmed and crushed grapes were subjected to an accelerated extraction process and used as calibration standards. A total of 192 extracts (objects) were obtained, and these were divided into a training set (106 objects) and a test set (86 objects) to evaluate the predictive ability of the models. Among the different MIR regions of the extract raw spectra, those that provided the highest variability on the absorption were selected. The results showed that the best PLS regression model was the one obtained when working in the region of 1168-1457 cm(-1) because it gave the most accurate and robust prediction for total phenolic compounds (RMSEP%=4.3 and RPD=4.5), total anthocyanins (RMSEP%=5.9 and RPD=3.5), and condensed tannins (RMSEP%=5.8 and RPD=3.8). Therefore, it can be concluded that FT-MIR spectroscopy can be a fast and reliable technique for monitoring the phenolic ripening in red grapes during the harvest period.     

Prediction chemical composition and alveograph parameters on wheat by near-infrared transmittance spectroscopy

Moisture, protein, wet gluten, dry gluten, and alveograph parameters (W, P, and P/L) of whole wheat grown in different countries around the world were analyzed using near infrared (NIR) transmittance spectroscopy. Modified partial least squares on NIR spectra (850-1048.2 nm) were developed for each constituent or physical property. The best models were obtained for protein, moisture, wet gluten, and dry gluten with r(2) = 0.99, 0.99, 0.95, and 0.96, respectively. Initial alveograph NIR models proposed for all wheat samples did not perform well. However, when wheat samples were divided in two groups depending on W (deformation energy) values, NIR models were highly improved, showing enough prediction accuracy for screening wheat at the receiving stage at mills or elevators.     

Quantification of phenolic compounds during red winemaking using FT-MIR spectroscopy and PLS-regression

We present a rapid method to quantify phenolic compounds all during the red winemaking process using Fourier transform mid-infrared (FT-MIR) spectroscopy and chemometrics. To get the reference values, we used the usual UV–vis spectroscopy methods, and the compounds studied were evaluated as total phenolic compounds (TPC), total anthocyanins (TA), and condensed tannins (CT). Sampling from five different grape varieties (Merlot, Tempranillo, Syrah, Cari?ena, and Cabernet sauvignon), harvested at different ripening states, and monitored over 10 days of vinification produced a total of 600 spectra. These were used to build and validate four different predictive models by partial least-squares (PLS) regression. The spectral regions selected for each model were between 979 and 2989 cm(–1), and when selecting the most suitable one in each case, good values of performance parameters were obtained (R2(val) > 0.95 and RPD > 4.0 for TPC; R2(val) > 0.90 and RPD > 3.0 for TA; R2(val) < 0.8 and RPD < 3.0 for CT). Furthermore, also more specific PLS regression models for each phenolic parameter and each grape variety were developed using different regions with results similar to those obtained when dealing with all of the grape varieties. It is concluded that FT-MIR spectroscopy together with multivariate calibration could be a rapid and valuable tool for wineries to carry out the monitoring of phenolic compound extraction during winemaking.     

Nondestructive prediction of total phenolics, flavonoid contents, and antioxidant capacity of rice grain using near-infrared spectroscopy

Phytochemicals such as phenolics and flavonoids, which are present in rice grains, are associated with reduced risk of developing chronic diseases such as cardiovascular disease, type 2 diabetes, and some cancers. The phenolic and flavonoid compounds in rice grain also contribute to the antioxidant activity. Biofortification of rice grain by conventional breeding is a way to improve nutritional quality so as to combat nutritional deficiency. Since wet chemistry measurement of phenolic and flavonoid contents and antioxidant activity are time-consuming and expensive, a rapid and nondestructive predictive method based on near-infrared spectroscopy (NIRS) would be valuable to measure these nutritional quality parameters. In the present study, calibration models for measurement of phenolic and flavonoid contents and antioxidant capacity were developed using principal component analysis (PCA), partial least-squares regression (PLS), and modified partial least-squares regression (mPLS) methods with the spectra of the dehulled grain (brown rice). The results showed that NIRS could effectively predict the total phenolic contents and antioxidant capacity by PLS and mPLS methods. The standard errors of prediction (SEP) were 47.1 and 45.9 mg gallic acid equivalent (GAE) for phenolic content, and the coefficients of determination ( r (2)) were 0.849 and 0.864 by PLS and mPLS methods, respectively. Both PLS and mPLS methods gave similarly accurate performance for prediction of antioxidant capacity with SEP of 0.28 mM Trolox equivalent antioxidant capacity (TEAC) and r (2) of 0.82. However, the NIRS models were not successful for flavonoid content with the three methods ( r (2) < 0.4). The models reported here are usable for routine screening of a large number of samples in early generation screening in breeding programs.     

前表面荧光光谱法鉴别不同冷藏时间的大黄鱼鲜度

为研究前表面荧光光谱法在水产品品质评价方面的应用,利用前表面荧光对不同冷藏时间的大黄鱼肌肉进行扫描,对色氨酸和烟酰胺腺嘌呤二核苷酸(NADH)的荧光光谱数据进行主成分分析(PCA)和Fisher线性判别分析(FLDA),并运用偏最小二乘回归(PLSR)建立了大黄鱼鱼肉荧光光谱数据和冷藏时间的预测模型。结果表明,用PCA方法提取色氨酸和NADH荧光光谱的有效信息,所建模型可区分不同冷藏时间(0~8 d)的大黄鱼样品,且色氨酸作为内源荧光探针的分析效果更好;用FLDA方法分析色氨酸和NADH荧光光谱,留一法(leave-one-out)交叉验证的判别正确率分别为100%和98%,对不同冷藏时间的大黄鱼区分效果优于PCA方法;PLSR模型中色氨酸和NADH荧光光谱的校正集和预测集的相关系数均大于0.9,交互验证均方根误差(RMSECV)分别约为1.13、0.41,校正集均方根误差(RMSEC)/预测集均方根误差(RMSEP)分别约为0.53、0.99,通过NADH荧光光谱建立的PLSR模型预测能力较好。前表面荧光光谱法结合化学计量学技术能够对不同冷藏时间的大黄鱼进行有效区分。本研究结果为前表面荧光光谱技术在大黄鱼冷藏保鲜中对冷藏时间的预测提供了一定的理论依据。     

Authentication of the botanical and geographical origin of honey by front-face fluorescence spectroscopy

Front-face fluorescence spectroscopy, directly applied on honey samples, was used for the authentication of 11 unifloral and polyfloral honey types (n = 371 samples) previously classified using traditional methods such as chemical, pollen, and sensory analysis. Excitation spectra (220-400 nm) were recorded with the emission measured at 420 nm. In addition, emission spectra were recorded between 290 and 500 nm (excitation at 270 nm) as well as between 330 and 550 nm (excitation at 310 nm). A total of four different spectral data sets were considered for data analysis. Chemometric evaluation of the spectra included principal component analysis and linear discriminant analysis; the error rates of the discriminant models were calculated by using Bayes' theorem. They ranged from <0.1% (polyfloral and chestnut honeys) to 9.9% (fir honeydew honey) by using single spectral data sets and from <0.1% (metcalfa honeydew, polyfloral, and chestnut honeys) to 7.5% (lime honey) by combining two data sets. This study indicates that front-face fluorescence spectroscopy is a promising technique for the authentication of the botanical origin of honey and may also be useful for the determination of the geographical origin within the same unifloral honey type.     

Authentication of the botanical origin of honey by front-face fluorescence spectroscopy. A preliminary study

The potential of front-face fluorescence spectroscopy for the authentication of unifloral and polyfloral honey types (n = 57 samples) previously classified using traditional methods such as chemical, pollen, and sensory analysis was evaluated. Emission spectra were recorded between 280 and 480 nm (excit: 250 nm), 305 and 500 nm (excit: 290 nm), and 380 and 600 nm (excit: 373 nm) directly on honey samples. In addition, excitation spectra (290-440 nm) were recorded with the emission measured at 450 nm. A total of four different spectral data sets were considered for data analysis. After normalization of the spectra, chemometric evaluation of the spectral data was carried out using principal component analysis (PCA) and linear discriminant analysis (LDA). The rate of correct classification ranged from 36% to 100% by using single spectral data sets (250, 290, 373, 450 nm) and from 73% to 100% by combining these four data sets. For alpine polyfloral honey and the unifloral varieties investigated (acacia, alpine rose, honeydew, chestnut, and rape), correct classification ranged from 96% to 100%. This preliminary study indicates that front-face fluorescence spectroscopy is a promising technique for the authentication of the botanical origin of honey. It is nondestructive, rapid, easy to use, and inexpensive. The use of additional excitation wavelengths between 320 and 440 nm could increase the correct classification of the less characteristic fluorescent varieties.     

Origin of French virgin olive oil registered designation of origins predicted by chemometric analysis of synchronous excitation-emission fluorescence spectra

The authentication of virgin olive oil samples requires usually the use of sophisticated and very expensive analytical techniques, so there is a need for fast and inexpensive analytical techniques for use in a quality control methodology. Virgin olive oils present an intense fluorescence spectra. Synchronous excitation-emission fluorescence spectroscopy (SEEFS) was assessed for origin determination of virgin olive oil samples from five French registered designation of origins (RDOs) (Nyons, Vallée des Baux, Aix-en-Provence, Haute-Provence, and Nice). The spectra present bands between 600 and 700 nm in emission due to chlorophylls a and b and pheophytins a and b. The bands between 275 and 400 nm in emission were attributed to alpha-, beta-, and gamma-tocopherols and to phenolic compounds, which characterize the virgin olive oils compared to other edible oils. The chemometric treatment (PLS1) of synchronous excitation-emission fluorescence spectra allows one to determine the origin of the oils from five French RDOs (Baux, Aix, Haute-Provence, Nice, and Nyons). Results were quite satisfactory, despite the similarity between two denominations of origin (Baux and Aix) that are composed by some common cultivars (Aglandau and Salonenque). The interpretation of the regression coefficients shows that RDOs are correlated to chlorophylls, pheophytins, tocopherols, and phenols compounds, which are different for each origin. SEEFS is part of a global analytic methodology that associates spectroscopic and chromatographic techniques. This approach can be used for traceability and vindicates the RDOs.     

Laser-based spectroscopic methods to evaluate the humification degree of soil organic matter in whole soils: a review

Purpose

The objective of this review is to survey critically the results obtained by the application of laser-induced fluorescence spectroscopy (LIFS) and laser-induced breakdown spectroscopy (LIBS) to the evaluation of the humification degree (HD) of soil organic matter (SOM) directly in untreated, intact whole soils.

Materials and methods

A large number of soils of various origin and nature, either native or under various cultivations, land use, and management, at various depths, have been studied to evaluate the HD of their SOM directly in intact whole samples. The LIFS spectra were obtained by either a bench or a portable argon laser apparatus that emits UV-VIS light of high power, whereas the LIBS spectra were obtained using a Q-switched Nd:YAG laser at 1064 nm.

Results and discussion

The close correlations found by comparing H_LIF values of whole soil samples with values of earlier proposed humification indexes confirmed the applicability of LIFS to assess the HD of SOM in whole soils. The high correlation found between HD_LIBS values and H_LIF values showed the promising potential of LIBS for the evaluation HD of SOM.

Conclusions

The LIFS technique shows to be a valuable alternative to evaluate the HD of SOM by probing directly the whole solid soil sample, thus avoiding the use of any previous chemical and/or physical treatments or separation procedures of SOM from the mineral soil matrix. The emerging application of LIBS to evaluate the HD of SOM in whole soils appears promising and appealing due to its sensitivity, selectivity, accuracy, and precision.

     

Characterization of phenolic compounds in strawberry (Fragaria x ananassa) fruits by different HPLC detectors and contribution of individual compounds to total antioxidant capacity

Phenolic compounds in strawberry (Fragaria x ananassa) fruits were identified and characterized by using the complementary information from different high-performance liquid chromatography detectors: diode array, mass spectrometer in positive and negative mode, and coulometric array. Electrochemical profiles obtained from the coulometric array detector contributed to the structural elucidation suggested from the UV-vis and mass spectra. About 40 phenolic compounds including glycosides of quercetin, kaempferol, cyanidin, pelargonidin, and ellagic acid, together with flavanols, derivatives of p-coumaric acid, and ellagitannins, were described, providing a more complete identification of phenolic compounds in strawberry fruits. Quercetin-3-malonylhexoside and a deoxyhexoside of ellagic acid were reported for the first time. Antioxidative properties of individual components in strawberries were estimated by their electrochemical responses. Ascorbic acid was the single most important contributor to electrochemical response in strawberries (24%), whereas the ellagitannins and the anthocyanins were the groups of polyphenols with the highest contributions, 19 and 13% at 400 mV, respectively.     

Near‐infrared spectroscopy for rapid estimation of microbial properties in reclaimed mine soils

Several chemical and microbial properties of mine soils need to be measured for comprehensive assessment of the reclamation success. The objective of this study was to evaluate the ability of NIR spectroscopy to predict organic C (C_org), total N (N_t), and several microbial properties of mine soils reclaimed for forestry. Soils samples (n = 154) were collected at two reclaimed areas in central and S Poland, and their spectra in the NIR region (including the visible range, 400–2500 nm) were recorded. A half of the samples was used to develop calibration equations, and another half was used for validation. The modified partial least squares regression was applied to build calibration equations using the whole spectrum (0 to 2nd derivative). The best predictions were obtained for C_org and N_t (ratio of standard deviation to standard error of prediction in the validation stage [RPD] = 3.4 and 4.1; the regressions coefficients [a] of linear regression [measured against predicted values] = 0.94 and 0.96; correlation coefficients [r] = 0.96 and 0.97, respectively). Very well predictive models applicable for quantitative measurements were obtained also for microbial biomass, basal respiration, and the activities of dehydrogenase and acid phosphatase (RPD = 2.3–2.5, a = 0.90–0.99, r = 0.90–0.92). Prediction of urease activity was slightly worse (RPD = 2.1, a = 0.88, r = 0.87) but sufficient for rough estimation. The obtained results indicated the ability of NIR spectroscopy to predict complex soil microbial properties. Therefore, application of this analytical method may improve the assessment of recovery of microbial functions in reclaimed post‐mining barrens.     

基于近红外光声光谱的土壤有机质含量定量建模方法

该研究的目的在于应用近红外光声光谱技术结合不同的定量分析方法实现5种不同类型土壤有机质含量的快速估测。对中国中、东部地区5种不同类型土壤风干样本进行光谱扫描,经过多元散射校正、一阶导数、二阶导数及平滑等预处理后,应用逐步多元回归(SMLR)、主成分分析(PCR)、偏最小二乘法(PLS)和偏最小二乘法-反向传播神经网络(PLS-BPNN)等方法建立土壤有机质含量的定量估测模型。结果显示,不同预处理方法对所建土壤有机质含量估测模型的预测精度有较大影响,总体表现为多元散射校正+Norris一阶导数>多元散射校正>Norris一阶导数>标准正态化>Norris二阶导数>吸光度>Savitzky-Golay平滑后一阶导数>Savitzky-Golay平滑后二阶导数。对于4种不同建模方法,均以多元散射校正+Norris一阶导数滤波平滑后的光谱建模精度最高,其中采用PLS-BPNN方法建模效果最好,其次是PLS、SMLR和PCR。采用PLS-BPNN建立有机质校正模型具有极高的预测精度,建模决定系数和均方根偏差分别为0.97和1.88,模型测试决定系数和均方根偏差分别为0.97和1.72。因此,基于多元散射校正+Norris一阶导数光谱建立的PLS-BPNN模型可能是土壤有机质含量估测建模的最优方法。     

A Comparative Study of Fourier Transform Raman and NIR Spectroscopic Methods for Assessment of Protein and Apparent Amylose in Rice

Fourier‐transform Raman (FT‐Raman) spectroscopy and near‐infrared (NIR) reflectance spectroscopy were used to compare calibration models for determining rice cooking quality parameters such as apparent amylose and protein. Samples from two seasons were used in each calibration set. The laboratory values ranged from 4.89 to 12.48% for protein and from 0.2 to 25.7% for amylose. The data for both FT‐Raman and NIR were preprocessed with orthogonal signal correction (OSC) for standardization. For both spectroscopic methods, five models were optimized by partial least squares regression (PLSR) and by Martens' uncertainty regression (MUR), including no processing, smoothing, normalization, first derivative (D1), and second derivative (D2). Based solely on standard error of cross‐validation (SECV), the FT‐Raman method was superior to the NIR method for protein. For amylose, the FT‐Raman and NIR methods resulted in similar calibration statistics with a high precision, with the FT‐Raman requiring fewer factors. The best FT‐Raman models were generated from OSC preprocessing with MUR for protein (SECV 0.15%, five factors) and from OSC without MUR for amylose (SECV 0.70%, seven factors). The best NIR models were obtained with D2 transform of OSC spectra for protein (SECV 0.22%, four factors) and with OSC spectra for amylose (SECV 0.57%, 11 factors).     

Front-face fluorescence spectroscopy as a rapid and nondestructive tool for differentiating various cereal products: a preliminary investigation

The potential of intrinsic fluorescence spectroscopy was investigated for differentiating between processed grains (flours, pasta, and semolinas) of different wheat cereal products. A total of 59 samples (e.g., 20 complete Kamut, semicomplete Kamut, and soft wheat flours, 28 pasta, and 11 semolinas manufactured from complete Kamut, semicomplete Kamut, and hard wheat flours) were analyzed by front-face fluorescence spectroscopy. Tryptophan fluorescence spectra were scanned between 305 and 400 nm on samples following excitation at 290 nm. The principal component analysis (PCA) performed on flour spectra clearly differentiated complete Kamut and semicomplete Kamut samples from those produced from complete and semicomplete soft wheat flours. The PCA performed on pasta spectra discriminated samples manufactured from complete Kamut and complete hard wheat flours from those made with semicomplete Kamut and semicomplete hard wheat flours. The best discrimination was obtained from tryptophan spectra recorded on semolinas since the four groups were well discriminated. Correct classification amounting to 61.9% was obtained for pasta spectra. A better classification was obtained for flour and semolina spectra since correct classification amounted to 86.7% and 87.9%, respectively. Front-face fluorescence spectroscopy has the potential to be a rapid, low-cost, and efficient method for the authentication of cereal products.     

Comparison of different extraction solvent mixtures for characterization of phenolic compounds in strawberries

Eight different solvent mixtures containing acetone or methanol pure or combined with an acid (acetic, formic, hydrochloric) were tested for their efficiency for extraction of phenolic compounds from strawberries belonging to five groups of polyphenols: anthocyanins, flavonols, flavan-3-ols, hydroxycinnamic acid derivatives and conjugated forms of ellagic acid. Twenty-eight compounds from these five groups have been detected and quantified using HPLC-DAD-ESI-MS(n). The yield of each phenolic compound and group was evaluated with regard to the extraction solvent composition. Acetone containing extraction mixtures were superior to the ones containing methanol for extraction yield of total phenolic compounds, which was especially pronounced for the groups of flavan-3-ols and conjugated forms of ellagic acid. The mixture acetone/acetic acid (99:1, v/v) gave the best results for the qualitative and quantitative assay of the polyphenols present in strawberries since all 28 compounds were detected only in these extracts in quantities higher or comparable to the other extraction solvents tested.     

Simultaneous fluorometric determination of chlorophylls a and B and pheophytins a and B in olive oil by partial least-squares calibration

The resolution of quaternary mixtures of chlorophylls a and b and pheophytins a and b has been accomplished by partial least-squares (PLS) multivariate calibration, applied to the fluorescence signals of these pigments. The total luminescence information of the compounds has been used to optimize the spectral data set to perform the calibration. After preliminary studies, a method is described in acetone media, to avoid emulsions with the olive oil samples. Different scanning paths have been selected for each method. For the simultaneous determination of the pigments in olive oil samples, a comparative study of the results found by using excitation, emission, and synchronous spectral data, as analytical signal, was performed. The excitation spectra were selected as the better analytical signals for the determination of the pigments in olive oil samples. The optimum wavelength range to record the excitation spectra (lambda(em) = 662 nm) was selected to minimize the contribution of pheophytin a and to maximize the contribution of the other pigments, which are the minor constituents in olive oil. Determination of these pigments in olive oil samples was effected from the excitation spectra of dissolutions o suitable aliquots in acetone. Recovery values from olive oil, spiked with chlorophylls a and b and pheophytins a and b, were in the ranges of 70-112, 71-111, 76-105, and 82-109%, respectively.     

Two new Monascus metabolites with strong blue fluorescence isolated from red yeast rice

Red yeast rice obtained as cultures of Monascus AS3.4444 on rice was extracted and analyzed by high-performance liquid chromatography (HPLC). Two new Monascus metabolites with similar fluorescence spectra (lambda ex = 396 nm, lambda em = 460 nm) and UV absorption spectra (lambda max = 386 nm) were detected. They were isolated by rechromatography on a silica gel column and semipreparative HPLC, and two strong blue fluorescent compounds were obtained. Their structures were elucidated by electrospray ionization mass spectrometry (ESI-MS), electrospray ionization tandem mass spectrometry (ESI-MS/MS), intensive ESI-MS, and nuclear magnetic resonance spectroscopy ( (1)H NMR, (13)C NMR, COSY, and HMBC) studies. High-resolution mass spectrometry indicated the molecular formulas C 21H 24O 5 and C 23H 28O 5. The two new compounds, named monasfluore A and monasfluore B, respectively, contain a alkyl side chain, gamma-lactone, and propenyl group, whereas the more lipophilic compound, monasfluore B, is a higher homologue of monasfluore A, with the more lipophilic octanoyl instead of the hexanoyl side chain.     

Parent and harvest year effects on near-infrared reflectance spectroscopic analysis of olive (Olea europaea L.) fruit traits

The influence of parent and harvest year on the determination of oil, moisture, oleic acid, and linoleic acid contents in intact olive fruit was studied by near-infrared spectroscopy (NIRS). Spectral data from 400 to 1700 nm were recorded on 437 fruit samples collected in 1996 and 1997 from seedling plants derived from three different female parents. Partial least squares models were developed using samples for each year and for each female parent separately and were validated against the other groups. Calibration models were accurate enough to predict all constituents in new samples from a different female parent but were not transferable across years. However, a calibration equation of sufficient accuracy was obtained from the combined data set (r values of 0.94, 0.93, 0.84, and 0.88 and RMSECV values of 1.33, 1.88, 4.73, and 2.91 for oil, moisture, oleic acid, and linoleic acid contents, respectively). These results demonstrate the utility of NIRS as a selection tool in olive breeding programs.     

可见/近红外光谱技术无损检测果实坚实度的研究

该研究的目的是建立可见/近红外光谱与梨果实坚实度之间的数学模型,评价可见/近红外光谱技术无损测量梨果实坚实度的应用价值。在可见/近红外光谱区域(350～1800 nm),试验对比分析了不同测量部位、不同光谱预处理方法和不同校正建模算法的梨果实坚实度校正模型。结果表明：赤道部位吸光度一阶微分光谱的偏最小二乘回归所建梨果实坚实度校正模型的预测性能较优,其校正和预测相关系数分别为0.8779和0.8087,校正和预测均方误差分别为1.0804 N和1.4455 N。研究表明：可见/近红外光谱技术无损检测梨果实坚实度是可行的。     

Quantitative analysis of red wine tannins using Fourier-transform mid-infrared spectrometry

Tannin content and composition are critical quality components of red wines. No spectroscopic method assessing these phenols in wine has been described so far. We report here a new method using Fourier transform mid-infrared (FT-MIR) spectroscopy and chemometric techniques for the quantitative analysis of red wine tannins. Calibration models were developed using protein precipitation and phloroglucinolysis as analytical reference methods. After spectra preprocessing, six different predictive partial least-squares (PLS) models were evaluated, including the use of interval selection procedures such as iPLS and CSMWPLS. PLS regression with full-range (650-4000 cm(-1)), second derivative of the spectra and phloroglucinolysis as the reference method gave the most accurate determination for tannin concentration (RMSEC = 2.6%, RMSEP = 9.4%, r = 0.995). The prediction of the mean degree of polymerization (mDP) of the tannins also gave a reasonable prediction (RMSEC = 6.7%, RMSEP = 10.3%, r = 0.958). These results represent the first step in the development of a spectroscopic methodology for the quantification of several phenolic compounds that are critical for wine quality.