Determination of fermentative volatile compounds in aged red wines by near infrared spectroscopy

Optimizing the winemaking process: NIR spectroscopy and e-nose analysis for the online monitoring of fermentation

BACKGROUND

In the winemaking process, the rapid determination of specific quality parameters such as sugar content, pH, acidity, concentrations of phenolic compounds, anthocyanins and volatile organic compounds is crucial for high-quality wine production. Traditional analytical methods allow for precise quantification of these parameters but are time-consuming and expensive. This article explores the potential application of non-destructive analytical technique (NDAT) (near infra-red [NIR] and e-nose), as efficient alternatives for online monitoring of fermentation working on two different winemaking tanks and applying chemometrics to develop predictive models to correlate non-destructive and analytical data.

RESULTS

NIR measurements have been used to build principal components regression models, showing good prediction capability for polyphenols, anthocyanins, glucose and fructose. Both offline and online e-nose applications demonstrate good capability of discriminating different fermentation phases, in agreement with aromatic profile changes observed via gas chromatography-mass spectrometry analysis. Moreover, correlation analysis reveals the potential of quartz microbalances, Taguchi Gas Sensors and H2 S sensors in predicting the concentration of compounds of great interest for winemaking (e.g. C6 alcohols, ketones, terpenes and ethyl esters) highlighting the robust connection between sensor data and specific chemical classes.

CONCLUSION

This research aims to showcase the potential employment of NDAT for online monitoring the evolution of must composition during fermentation. The proposed methods could potentially fulfil a longstanding requirement of winemakers, enabling them to closely monitor fermentation allowing the timely making of important technical decisions aimed at achieving oenological objectives in wine production. © 2024 Society of Chemical Industry.

Application of near-infrared spectroscopy for the estimation of volatile compounds in Tempranillo Blanco grape berries during ripening

BACKGROUND

The knowledge of volatile compounds concentration in grape berries is very valuable information for the winemaker, since these compounds are strongly involved in the final wine quality, and in consumer acceptance. In addition, it would allow to set the harvest date according to aromatic maturity, to classify grape berries according to their quality and to make wines with different characteristics, among other implications. However, so far, there are no tools that allow the volatile composition to be measured directly on intact berries, either in the vineyard or in the winery.

RESULTS

In this work, the use of near-infrared (NIR) spectroscopy to estimate the aromatic composition and total soluble solids (TSS) of Tempranillo Blanco grape berries during ripening was evaluated. For this purpose, the spectra in the NIR range (1100-2100 nm) of 240 intact berry samples were acquired in the laboratory. From these same samples, the concentration of volatile compounds was analyzed by thin film-solid-phase microextraction-gas chromatography-mass spectrometry (TF-SPME-GC-MS), and the TSS were quantified by refractometry. These two methods were used as reference methods for model building. Calibration, cross-validation and prediction models were built from spectral data using partial least squares (PLS). Determination coefficients of cross-validation (R2 CV ) above 0.5 were obtained for all volatile compounds, their families, and TSS.

CONCLUSIONS

These findings support that NIR spectroscopy can be successfully use to estimate the aromatic composition as well as the TSS of intact Tempranillo Blanco berries in a non-destructive, fast, and contactless form, allowing simultaneous determination of technological and aromatic maturities. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Monitorization of Varietal Aroma Composition Dynamics during Ripening in Intact Vitis vinifera L. Tempranillo Blanco Berries by Hyperspectral Imaging

The measurement of aromatic maturity during grape ripening provides very important information for determining the harvest date, particularly in white cultivars. However, there are currently no tools that allow this measurement to be carried out in a noninvasive and rapid way. For this reason, in the present work, we have studied the use of hyperspectral imaging (HSI)) to estimate the aromatic composition of Vitis vinifera L. Tempranillo Blanco berries during ripening. A total of 236 spectra in the VIS+short wave near-infrared (VIS+SW-NIR) range (400-1000 nm) of intact berries were acquired contactless under laboratory conditions. As gold standard values, a total of 20 volatile compounds were quantified by gas chromatography-mass spectrometry (GC-MS), and the concentration of total soluble solids (TSS) was measured by refractometry. Calibration, cross-validation, and prediction models were built using partial least squares (PLS). Values of R_CV² ≥ 0.70 were obtained for α-terpineol, p-cymene, β-damascenone, β-ionone, benzaldehyde, benzyl alcohol, hexanal, citral, linalool, 2-phenylethanol, octanoic acid, nonanoic acid, 2-hexenal, 2-hexen-1-ol, (Z)-3-hexen-1-ol, total C₁₃ norisoprenoids, total C6 compounds, total positive compounds (i.e., the sum of all families except C6 compounds), total benzenoids, and total soluble solids (TSS). Therefore, it can be affirmed that HSI in the VIS + SW-NIR range could be a good tool to estimate the aromatic composition of Tempranillo Blanco grape berries in a contactless, fast, and nondestructive way.

Identification of characteristic aroma and bacteria related to aroma evolution during long-term storage of compressed white tea

Compressed white tea (CWT) is a reprocessed tea of white tea. Long-term storage has greatly changed its aroma characteristics, but the material basis and transformation mechanism of its unique aroma are still unclear. In this study, flavor wheel, headspace gas chromatography ion mobility spectroscopy, chemometrics, and microbiomics were applied to study the flavor evolution and important aroma components during long-term storage of CWT, and core functional bacteria were screened. During long-term storage, the aroma of CWT gradually changed from sweet, fruity and floral to stale flavor, woody and herbal. A total of 56 volatile organic compounds (VOCs) were identified, 54 of which were significantly differences during storage. The alcohols content was the highest during 1-5 years of storage, the esters content was the highest during 7-13 years of storage, and the aldehydes content was the highest during 16 years of storage. Twenty-nine VOCs were identified as important aroma components, which were significantly correlated with 6 aroma sub-attributes (P < 0.05). The functional prediction of bacterial community reminded that bacterial community could participate in the transformation of VOCs during storage of CWT. Twenty-four core functional bacteria were screened, which were significantly associated with 29 VOCs. Finally, 23 characteristic differential VOCs were excavated, which could be used to identify CWT in different storage years. Taken together, these findings provided new insights into the changes in aroma characteristics during storage of CWT and increased the understanding of the mechanism of characteristic aroma formation during storage.

Application of near-infrared spectroscopy/artificial neural network to quantify glycosylated norisoprenoids in Tannat grapes

Grape variety, vinification, and ageing are factors conditioning the aroma of a wine, with volatile secondary metabolites responsible for the so-called grape varietal character. Particularly, grape glycosylated norisoprenoids are mostly responsible for the sensory profile of Tannat wines, making relevant the use of fast instrumental tools to evaluate their concentration, allow classifying grapes and defining the optimum maturity for harvest. NIR spectroscopy is a fast, non-destructive technique, which requires minimal sample preparation. However, its quantitative applications need chemometric models for interpretation. In this work, a NIR-ANN calibration was developed to quantify norisoprenoids in Vitis vinifera cv. Tannat grapes during maturation and harvesting. Glycosidated norisoprenoids were determined by GC-MS. The ANN adjustments showed better performance than linear models such as PLS, while the best calibration was obtained by homogenising grape samples when comparing to grape juice; making possible to fit a model with an error of 146 μg/kg.

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometric analysis for detection and quantification of adulteration in lavender and citronella essential oils

INTRODUCTION

The growing consumer interest in "naturals" led to an increased application of essential oils (EOs). The market outbreak induced the intensification of EO adulterations, which could affect their quality.

OBJECTIVES

Nowadays, little is known about the illegal practice of adulteration of EOs with vegetable oils. Therefore, the application of mid-infrared spectroscopy coupled with chemometrics was proposed for the detection of EO counterfeits.

MATERIALS AND METHODS

Two EOs, three seed oils, and their mixtures were selected to build the adulteration model. EO-adulterant mixtures for model calibration and validation were analyzed by attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. The spectral data were analyzed with principal component analysis (PCA) and partial least-squares (PLS) regression.

RESULTS

PCA allowed the discrimination of the EO and adulterant percentages by explaining 97.47% of the total spectral variance with two principal components. A PLS regression model was generated with three factors explaining 97.73% and 99.69% of the total variance in X and Y, respectively. The root mean square error of calibration and the root mean square error of cross-validation were 0.918 and 1.049, respectively. The root mean square error of prediction value obtained from the external validation set was 1.588 and the coefficients of determination R² _CAL and R² _CV were 0.997 and 0.996, respectively.

CONCLUSIONS

The results highlighted the robustness of the developed method in quantifying counterfeits in the range from 0 to 50% of adulterants, disregarding the type of EO and adulterant employed. The present work offers a research advance and makes an important impact in phytochemistry, revealing an easily applicable method for EO quality assessment.

Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling

Wine aroma is an important quality trait in wine, influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, including the water status of grapevines, canopy management, and the effects of climate change, such as increases in ambient temperature and drought. In this study, a low-cost and portable electronic nose (e-nose) was used to assess wines produced from grapevines exposed to different levels of smoke contamination. Readings from the e-nose were then used as inputs to develop two machine learning models based on artificial neural networks. Results showed that regression Model 1 displayed high accuracy in predicting the levels of volatile aromatic compounds in wine (R = 0.99). On the other hand, Model 2 also had high accuracy in predicting smoke aroma intensity from sensory evaluation (R = 0.97). Descriptive sensory analysis showed high levels of smoke taint aromas in the high-density smoke-exposed wine sample (HS), followed by the high-density smoke exposure with in-canopy misting treatment (HSM). Principal component analysis further showed that the HS treatment was associated with smoke aroma intensity, while results from the matrix showed significant negative correlations (p < 0.05) were observed between ammonia gas (sensor MQ137) and the volatile aromatic compounds octanoic acid, ethyl ester (r = -0.93), decanoic acid, ethyl ester (r = -0.94), and octanoic acid, 3-methylbutyl ester (r = -0.89). The two models developed in this study may offer winemakers a rapid, cost-effective, and non-destructive tool for assessing levels of volatile aromatic compounds and the aroma qualities of wine for decision making.

Aged fragrance formed during the post-fermentation process of dark tea at an industrial scale

Although aged fragrance is the most outstanding quality characteristic of dark tea, its formation still is not much clear. Thus, the volatiles of Qingzhuan tea (QZT) during the whole post-fermentation process were investigated at an industrial scale. The results showed that most of volatiles increased during pile-fermentation of QZT and weakened during aging storage, but some new volatiles were produced through aging storage. Hexanal, (E)-2-hexenal, (E)-2-decenal, 2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde, heptanal, (E)-2-octenal, (R)-5,6,7,7a-tetrahydro-4,4,7a-trimethyl-2(4H)-benzofuranone, ionone, 2-heptanone, 3-ethyl-4-methyl-1H-pyrrole-2,5-dione, (R,S)-5-ethyl-6-methyl-3-hepten-2-one, cis-5-ethenyltetrahydro-5-trimethyl-2-furanmethanol, and linalool generated by pile-fermentation should be the basic volatiles of aged fragrance in QZT, and 4-(2,4,4-trimethyl-cyclohexa-1,5-dienyl)-but-3-en-2-one, 6-methyl-5-heptene-2-one, safranal, guaiene, trans-2-(2-propynyloxy)-cyclohexanol, nonanal, and 4-(2,6,6-trimethyl-1-cyclohexen-1-yl)-2-butanone formed during aging storage should be the transformed volatiles of aged fragrance in QZT, which together constitute the characteristic components of aged fragrance. Notably, 4-(2,6,6-trimethyl-1-cyclohexen-1-yl)-2-butanone, 6-methyl-5-heptene-2-one, and safranal were selected as the key volatiles of QZT. These results contribute to understand better the formation of agedfragrance in dark tea.

Rapid detection of quality of Japanese fermented soy sauce using near-infrared spectroscopy

This study investigated the feasibility of rapidly evaluating the final quality of Japanese fermented soy sauce (shoyu) using NIR spectroscopy and partial least-squares (PLS) regression. In total, 110 shoyu samples that had been entered in the annual soy sauce competition from 2016 to 2018 were collected and analyzed. The transmittance spectra (400-1800 nm) and the transflectance spectra (680-2500 nm) of these samples were acquired and processed by different pre-treatments. PLS regression was applied to the raw and processed spectra to construct models based on a calibration set (76 shoyu samples from 2016 and 2017) and to evaluate these models using a validation set (34 shoyu samples from 2018), according to their values for bias and root mean square error of prediction (RMSEP). The results showed that the models constructed using the full spectra of transflectance performed better than those using transmittance spectra. Comparing the influence of different regions in the transflectance spectra enabled the accuracy of the models to be improved. The model constructed from transflectance spectra from the 1800 to 2500 nm region using pre-treatment of second derivative was superior to the other models, with a bias value of -2 and the lowest RMSEP value of 13 in the validation set. To further narrow the wavelength range, the models constructed using the spectral region from 2050 to 2400 nm also showed a better performance for predicting the sensory quality of soy sauce products. This study has demonstrated that the NIR spectroscopy technique could be used as an alternative routine quality control procedure, which can rapidly and economically classify the quality of soy sauce products.

The application of near infrared spectroscopy to wine analysis: An innovative approach using lyophilization to remove water bands interference

The implementation of near-infrared spectroscopy as an analytical method for the quantification of major wine parameters is limited due to the aqueous nature of wines. Water molecules contribute to a poor signal-to-noise ratio and to suppress important groups' vibrations frequencies, preventing the quantification of most chemical compounds present. This paper proposes an alternative approach for the quantification of major wine indicators based on near infrared spectroscopy using lyophilized wine samples. A diversity of wine samples, including red, white and rosé, were lyophilized and analyzed by NIR spectroscopy. The parameters quantified were: alcoholic degree, volumic mass, total dry extract, total sugars, total acidity, volatile acidity, pH, free sulfur dioxide and total sulfur dioxide. Calibrations using partial least squares (PLS) regression were performed against the results obtained by reference methods. Spectra collected within 10,000 to 4000 cm^-1 range were randomly divided in two sets: one for the optimization of the PLS models and the remaining for external testing. The PLS models obtained were able to accurately quantify total sugars, pH, volumic mass and total dry extract with a range-error-ratio above 10. The quantification of the remaining parameters yielded unsatisfactory results. This methodology proved to be an interesting alternative for the quantification of major wine quality descriptors by circumventing the interference of water bands. Further studies exploring different lyophilization conditions and additional wine chemical compounds present at low concentrations are needed.

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

Asian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world's priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a robust and simple method for determining the adulteration of luwak coffee. In this research, the use of UV-Visible spectra combined with PLSR was evaluated to establish rapid and simple methods for quantification of adulteration in luwak-arabica coffee blend. Several preprocessing methods were tested and the results show that most of the preprocessing spectra were effective in improving the quality of calibration models with the best PLS calibration model selected for Savitzky-Golay smoothing spectra which had the lowest RMSECV (0.039) and highest RPD_cal value (4.64). Using this PLS model, a prediction for quantification of luwak content was calculated and resulted in satisfactory prediction performance with high both RPD _p and RER values.

Determination of polyphenolic compounds of red wines by UV-VIS-NIR spectroscopy and chemometrics tools

Spectral analysis is a quick and non-destructive method to analyse wine. In this work, trans-resveratrol, oenin, malvin, catechin, epicatechin, quercetin and syringic acid were determined in commercial red wines from DO Rías Baixas and DO Ribeira Sacra (Spain) by UV-VIS-NIR spectroscopy. Calibration models were developed using principal component regression (PCR) or partial least squares (PLS) regression. HPLC was used as reference method. The results showed that reliable PLS models were obtained to quantify all polyphenols for Rías Baixas wines. For Ribeira Sacra, feasible models were obtained to determine quercetin, epicatechin, oenin and syringic acid. PCR calibration models showed worst reliable of prediction than PLS models. For red wines from mencía grapes, feasible models were obtained for catechin and oenin, regardless the geographical origin. The results obtained demonstrate that UV-VIS-NIR spectroscopy can be used to determine individual polyphenolic compounds in red wines.

Determination of amino acid nitrogen in tuber mustard using near-infrared spectroscopy with waveband selection stability

Near-infrared (NIR) spectroscopy was successfully applied to the rapid chemical-free determination of amino acid nitrogen (AAN) in tuber mustard. Moving window partial least squares, combined with Savitzky-Golay smoothing, was used for the waveband selection. Based on the various divisions in the calibration and prediction sets, an effective modeling approach with good stability was proposed. The results confirmed that the long-wave NIR region contains sufficient information for the quantification of AAN in tuber mustard. An appropriate waveband (5874-4,258 cm(-1)) located in the combination region of NIR was selected. The validation root mean square error of prediction and the validation correlation coefficients of prediction were 0.380 mg/g and 0.962, respectively, both indicating good validation effect and stability. The results provided a reliable NIR model and can serve as valuable references for designing spectroscopic instruments for quality evaluation of tuber mustard.

Technical solutions for analysis of grape juice, must, and wine: the role of infrared spectroscopy and chemometrics

Information about constituents of grape juice, must, and wine can be used for management and decision support systems in order to improve, monitor, and adapt grape and wine production to new challenges. Numerous sensors that gather this information are either currently available or in development. Nevertheless there is still a need to adapt these sensors to special requirements, for example robustness, calibration and maintenance, operating costs, duration, sensitivity, and specificity to a particular application. The sensors commonly used by the wine industry are those that are based on mid-infrared (MIR), near-infrared (NIR), visible (VIS) and ultraviolet (UV) spectroscopy. This article reviews some recent technical solutions for analysis of juice, must and wine based on the combination of infrared spectroscopy and chemometrics.

Determination of amino acids in Chinese rice wine by fourier transform near-infrared spectroscopy

Chinese rice wine is abundant in amino acids. The possibility of quantitative detection of 16 free amino acids (aspartic acid, threonine, serine, glutamic acid, proline, glycine, alanine, valine, methionine, isoleucine, leucine, tyrosine, phenylalanine, lysine, histidine, and arginine) in Chinese rice wine by Fourier transform near-infrared (NIR) spectroscopy was investigated for the first time in this study. A total of 98 samples from vintage 2007 rice wines with different aging times were analyzed by NIR spectroscopy in transmission mode. Calibration models were developed using partial least-squares regression (PLSR) with high-performance liquid chromatography (HPLC) by postcolumn derivatization and diode array detection as a reference method. To validate the calibration models, full cross (leave-one-out) validation was employed. The results showed that the calibration statistics were good (rcal>0.94) for all amino acids except proline, histidine, and arginine. The correlation coefficient in cross validation (rcv) was >0.81 for 12 amino acids. The residual predictive deviation (RPD) value obtained was >1.5 in all amino acids except proline and arginine, and it was >2.0 in 6 amino acids. The results obtained in this study indicated that NIR spectroscopy could be used as an easy, rapid, and novel tool to quantitatively predict free amino acids in Chinese rice wine without sophisticated methods.