Quality Assessment of Red Wine Grapes through NIR Spectroscopy

Abstract

Red wine grapes require a constant follow-up through analytical chemistry to assure the greatest wine quality. Wet chemical procedures are time-consuming and produce residues that are hard to eliminate. NIR (near infrared radiation) spectroscopy has been referred as an accurate, rapid, and cost-efficient technique to evaluate quality in many fruit species, both in field and in industry. The main objective of this study was to develop predictive models using NIR spectroscopy to quantify important quality attributes in wine grapes. Soluble solids content (SSC), titratable acidity (TA), total phenolic content, total flavonoids, total anthocyanins, and total tannins were quantified in four red wine grape varieties, ‘Aragonês’, ‘Trincadeira’, ‘Touriga Nacional’, and ‘Syrah’. Samples were collected during 2017 and 2018 along véraison. Prediction models were developed using a near-infrared portable device (Brimrose, Luminar 5030), and spectra were collected from entire grapes under near field conditions. Models were built using a partial least square regression (PLSR) algorithm and SSC, TA, total anthocyanins, and total tannins exhibited a determination coefficient of 0.89, 0.90, 0.87, and 0.88, respectively. The Residual Prediction Deviation (RPD) values of these models were higher than 2.3. The prediction models for SSC, TA, total anthocyanins, and total tannins have considerable potential to quantify these attributes in wine grapes. Total flavonoids and total phenolic content were predicted with a slightly lower capacity, with R2 = 0.72 and 0.71, respectively, and both with a RPD of 1.6, indicating a very low to borderline potential for quantitative predictions in flavonoids and phenols models.

Accurate varietal classification and quantification of key quality compounds of grape extracts using the absorbance-transmittance fluorescence excitation emission matrix (A-TEEM) method and machine learning

“…As with the absorbance and colour profiles in Figure 1, the EEM contours in Figure 2 Pinot noir exhibits a unique phenolic composition, being notably enriched in flavan-3-ols, including catechin and epicatechin, and with generally reduced total anthocyanins (Cliff et al, 2007;Rouxinol et al, 2022;Urcan et al, 2016).…”

“…Whereas High-Performance Liquid Chromatography (HPLC) is recognised as the primary reference method for wine phenolic analysis, it may be too expensive, time-consuming and complex for effective implementation scale-up. Hence secondary methods, including Fourier Transform Infrared (FTIR), Near and Mid-Infrared (NIR and MIR), Nuclear Magnetic Resonance (NMR) and UV-VIS spectroscopies, have been examined with respect to their ability to be calibrated against HPLC and other reference chemistry methods (Aleixandre-Tudo et al, 2017;Harbertson et al, 2003;Harbertson and Spayd, 2006;Miramont et al, 2020;Rouxinol et al, 2022;Sen et al, 2016). The consensus is that while these secondary spectroscopic techniques provide generalised compound class information, they do not exhibit highly correlated molecular specificity for the key phenolic compounds generally regarded as quality parameters.…”

“…Interestingly reports of successful data fusion for UV-VIS and FTIR for effective wine classification have also been reported (Geană et al, 2019;Sen et al, 2016). HPLC has been used for arguably the most highly effective varietal classification, albeit as mentioned above, the method is relatively slow and laborious compared to spectroscopic methods (Peng et al, 2002;de Villiers et al, 2005;Rouxinol et al, 2022;Urcan et al, 2016).…”

Destructive and non‐destructive early detection of postharvest noble rot (Botrytis cinerea) in wine grapes aimed at producing high‐quality wines

“…11 In the same way, NIR spectroscopy has been increasingly adopted for evaluation of the internal quality in intact fruit and food crops, 12 even for performing a quality assessment of wine grapes and wines. 13 NIR technology has also been tested for featuring fungal infections and Botrytis presence in several horticultural crops. 12 Both E-nose and NIR spectroscopy, as nondestructive technologies, in their use and application must be supported by multivariate data analysis and chemometrics.…”

Near-infrared spectroscopy analysis of wines through bottles to assess quality traits and provenance

“…Near-infrared spectroscopy can offer a complete chemical fingerprinting profile of wines depending on the spectral range and sensitivity of different instruments. This instrument has been used for various applications, from general quality assessment [2] to predicting aroma profiles in beverages [3]. Furthermore, the use of digital technologies and sensors, such as NIR, electronic noses, and tongues, allows the implementation of Artificial Intelligence (AI) and ML modeling for wine quality prediction [4], to the specific prediction of phenolic compounds in red wines and geographical provenance [5], sugar content [5] astringency [6], and wine authenticity [7].…”