Linking ATR-FTIR and Raman Features to Phenolic Extractability and Other Attributes in Grape Skin

The importance of wine phenolics on the sensory characteristic of red wines is well-known. Therefore, it is necessary to control the extractability of phenolic compounds from grape skins, which depends significantly on grape ripeness and hence, on cell wall degradation. In the present study, attenuated total reflectance Fourier transform infrared (ATR-FTIR) and Raman spectra of grape skin have been recorded. Then, these spectral matrices have been studied and the main spectral features have been linked to extractabilities of phenolic compounds (anthocyanins, flavanols and total phenols). Moreover, spectral differences between external and internal grape skin surfaces also have been studied. It has been confirmed that the amount of polysaccharides and the degree of esterification of pectins have significant influence on the phenolic extractability levels of grape skin tissue

Trying to set up the flavanolic phases during grape seed ripening: A spectral and chemical approach

[EN] Grape seeds were collected in ten different dates and classified in seven groups according to their individual hyperspectral imaging characteristics. Proanthocyanidin composition was studied using HPLC-MS for oligomers and acid catalyzed cleavage for polymers characterization. The combination of both analysis provided a complete description of the flavanols. Chemometric analysis was performed to summarize the analytical results. None of the considered variables presented statistical differences among all groups. From one to five groups were found for each variable, while three was the most frequent value, consequently three putative stages might be considered the real number of different analytical stages since it is the number of statistically significant groups for the majority of the compounds. This classification could be considered as the first step to optimize the use of seeds in winemaking to minimize the gap between sugar and phenolic maturities, consequence of the global climate change, mainly observed in warm climate

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Presentació

Predicting the Evolution of Pasture Quality by NIRS: Perspectives for Real-Time Pasture and Grazing Management

Pasture quality monitoring is a key element in the decision making process of the farm manager. Laboratory reference methods for assessing pasture quality parameters such as crude protein (CP) or neutral detergent fibre (NDF) require cutting, collection and analytical procedures involving technicians, time and reagents, making them laborious and expensive. The objective of this study was to evaluate the potential of near infrared reflectance spectroscopy (NIRS) combined with multivariate data analysis as a rapid method to predict and monitor the evolution of pasture quality parameters (CP, NDF and a pasture quality index, PQI=CP/NDF). During the 2018 and 2019 growing seasons a total of 398 composite pasture samples were collected from 9 biodiverse pastures, representing a wide range of botanical composition and phenological states. These samples were scanned with a FT-NIR spectrometer: 315 (collected in 2018) were used in the calibration phase and 83 (collected in 2019) were used during the validation phase. Calibration and validation models were developed and regression equations between predicted and laboratory reference values of CP, NDF and PQI were established. Were used as evaluation parameters the coefficient of determination (R2 ), the residual predictive deviation (RPD) and the root mean square errors (RMSE). The best results obtained were: (i) for CP prediction model (R2 =0.844; RPD=4.0; RMSE=1.622); (ii) for NDF prediction model (R2 =0.826; RPD=2.4; RMSE=4.200); and (iii), for PQI prediction model (R2 =0.808; RPD=3.2; RMSE=0.066). The results show the practical interest of portable spectrometry, associated with GNSS, as expeditious tools for monitoring pasture quality. Good prospects and opportunities open up for technology-based service providers to develop remote sensing-based computer applications from satellite imagery that enable dynamic management of animal grazing

Real-Time Quantification of Crude Protein and Neutral Detergent Fibre in Pastures Under Montado Ecosystem Using the Portable NIR Spectrometer

The Montado is a Mediterranean agro–forestry–pastoral ecosystem. Knowledge of pastures' nutritional value is critical for farm managers’ decision-making. Laboratory determinations are very expensive, destructive and costly, in terms of time and labour. The objective of this experimental work was to calibrate and validate a portable near-infrared spectrometer (micro-NIR) to predict the nutritive value (neutral detergent fibre, NDF and crude protein, CP) of pastures in the peak of spring 2021. Thus, a total of 87 pasture samples were collected at eight experimental fields located in the Alentejo, Southern region of Portugal. The results show good correlations between in-situ micro-NIR measurements and pasture NDF reference values (R2 of 0.73 and 0.69 for calibration and validation models, respectively), and a moderate correlation between micro-NIR measurements and pasture CP reference values (R2 of 0.51 and 0.36 for calibration and validation models, respectively). These results show the potential of this tool for the quick evaluation of pasture quality and constitute a starting point for future work, which should include the monitoring of temporal variability (throughout the entire vegetative cycle of the pasture) and spatial (with geo-referenced information) diversity of pastures characteristic of the Montado ecosystem in the Mediterranean region

Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem

Pasture quality monitoring is a key element in the decision making process of a farm manager. Laboratory reference methods for assessing quality parameters such as crude protein (CP) or fibers (neutral detergent fiber: NDF) require collection and analytical procedures involving technicians, time, and reagents, making them laborious and expensive. The objective of this work was to evaluate two technological and expeditious approaches for estimating and monitoring the evolution of the quality parameters in biodiverse Mediterranean pastures: (i) near infrared spectroscopy (NIRS) combined with multivariate data analysis and (ii) remote sensing (RS) based on Sentinel-2 imagery to calculate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). Between February 2018 and March 2019, 21 sampling processes were carried out in nine fields, totaling 398 pasture samples, of which 315 were used during the calibration phase and 83 were used during the validation phase of the NIRS approach. The average reference values of pasture moisture content (PMC), CP, and NDF, obtained in 24 tests carried out between January and May 2019 in eight fields, were used to evaluate the RS accuracy. The results of this study showed significant correlation between NIRS calibration models or spectral indices obtained by remote sensing (NDVIRS and NDWIRS) and reference methods for quantifying pasture quality parameters, both of which open up good prospects for technological-based service providers to develop applications that enable the dynamic management of animal grazing

Evaluation of Near Infrared Spectroscopy (NIRS) for Estimating Soil Organic Matter and Phosphorus in Mediterranean Montado Ecosystem

The Montado is an agro-silvo-pastoral ecosystem characteristic of the Mediterranean region. Pasture productivity and, consequently, the possibilities for intensifying livestock production depend on soil fertility. Soil organic matter (SOM) and phosphorus (P2O5) are two indicators of the evolution of soil fertility in this ecosystem. However, their conventional analytical determination by reference laboratory methods is costly, time consuming, and laborious and, thus, does not meet the needs of current production systems. The aim of this study was to evaluate an alternative approach to estimate SOM and soil P2O5 based on near infrared spectroscopy (NIRS) combined with multivariate data analysis. For this purpose, 242 topsoil samples were collected in 2019 in eleven fields. These samples were subjected to reference laboratory analysis and NIRS analysis. For NIRS, 165 samples were used during the calibration phase and 77 samples were used during the external validation phase. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantification of these soil parameters. The coefficient of determination (R2, 0.85 for SOM and 0.76 for P2O5) and the residual predictive deviation (RPD, 2.7 for SOM and 2.2 for P2O5 ) obtained in external validation indicated the potential of NIRS to estimate SOM and P2O5 , which can facilitate farm managers’ decision making in terms of dynamic management of animal grazing and differential fertilizer application