Assessment of technological maturity parameters and anthocyanins in berries of cv. Sangiovese ( Vitis vinifera L.) by a portable vis/NIR device

Ribera-Fonseca, Alejandra; Noferini, M.; Jorquera-Fontena, Emilio; Rombolà, Adamo Domenico

doi:10.1016/j.scienta.2016.06.004

Cited by 27 publications

(14 citation statements)

References 34 publications

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“…Some methods using proximal remote sensing within the near-infrared (NIR) light spectrum reflectivity have been developed to assess quality traits from berries in a non-destructive way. Some applications have been implemented to assess the sugar content in berries [26,27], berry pigments [28,29], phenolic compounds [30,31], and grape maturity in general [28,[32][33][34]. However, since these techniques are still manual, they cannot account for the natural intra-bunch and vineyard spatial variability, requiring a huge number of measurements and modeling strategies to obtain meaningful results.…”

Section: Introductionmentioning

confidence: 99%

Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence

Fuentes

Tongson

Torrico

et al. 2019

Foods

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Wine aroma profiles are determinant for the specific style and quality characteristics of final wines. These are dependent on the seasonality, mainly weather conditions, such as solar exposure and temperatures and water management strategies from veraison to harvest. This paper presents machine learning modeling strategies using weather and water management information from a Pinot noir vineyard from 2008 to 2016 vintages as inputs and aroma profiles from wines from the same vintages assessed using gas chromatography and chemometric analyses of wines as targets. The results showed that artificial neural network (ANN) models rendered the high accuracy in the prediction of aroma profiles (Model 1; R = 0.99) and chemometric wine parameters (Model 2; R = 0.94) with no indication of overfitting. These models could offer powerful tools to winemakers to assess the aroma profiles of wines before winemaking, which could help adjust some techniques to maintain/increase the quality of wines or wine styles that are characteristic of specific vineyards or regions. These models can be modified for different cultivars and regions by including more data from vertical vintages to implement artificial intelligence in winemaking.

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Section: Introductionmentioning

confidence: 99%

Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence

Fuentes

Tongson

Torrico

et al. 2019

Foods

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“…However, the relationship between the maturity of polyphenols and the harvest time has not been fully elucidated in Northwest China. Therefore, we concluded that not only the basic indexes should be considered (SSC, reducing sugar and sugar/TA), but also other factors, such as phenolic compounds that affect the quality of wines (Ribera-Fonseca et al, 2016), when determining the most appropriate harvest time.…”

Section: Resultsmentioning

confidence: 99%

“…Total soluble solids (°Brix) and titratable acidity, apart from phenolic content, are the best parameters to use in in the evaluation of grape quality (Conde et al, 2006;Kontoudakis et al, 2011;Ribera-Fonseca et al, 2016;Urraca et al, 2015). However, the content of reduced sugar could not guarantee the best quality of grapes and wines (Conde et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Effects of Different Harvest Times on the Maturity of Polyphenols in Two Red Wine Grape Cultivars (Vitis vinifera L.) in Qingtongxia (China)

Zhou

Yin

et al. 2018

SAJEV

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Due to the special climate conditions in the Qingtongxia region, grapes are high in sugar and low in titratable acidity from the stages of ripening. Therefore, the common methods used for determining the maturity of grapes, which depend on the ratio of sugar and titratable acidity in other regions, are inappropriate in Qingtongxia. This research was done in order to seek for a simple and convenient method of determining the optimal harvest time of grapes, further providing some theoretical basis for improving the quality of wine in Qingtongxia. Phenolic contents and some basic physico-chemical parameters of Merlot and Pinot Noir were evaluated during different ripening stages. The results showed that a different harvest time significantly affects the phenolic contents and physico-chemical parameters of Merlot and Pinot Noir. The total contents of anthocyanins in skins and total contents of phenolic in seeds was screen out as two important indexes to evaluate the maturity of polyphenols, in order to better improve the quality of grape and wine.

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“…Also, with portable vis-NIR equipment, the state of maturation of the grape variety Sangiovese could be determined in situ, by calculating the index of absorbance difference (IAD) from the values at wavelengths of 560 and 640 nm. These values correlate with TSS as well as with parameters such as titratable acidity (TA), firmness (DI), and anthocyanins-which allows one to know quickly and accurately the date on which the fruit should be harvested [69].…”

Section: Total Soluble Solidsmentioning

confidence: 99%

Using Near-Infrared Spectroscopy in Agricultural Systems

Garcı́a-Sánchez¹,

Galvez-Sola²,

Nicolás³

et al. 2017

Developments in Near-Infrared Spectroscopy

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This chapter provides a review on the state of art of the use of the visible near-infrared (vis-NIR) spectroscopy technique to determine mineral nutrients, organic compounds, and other physical and chemical characteristics in samples from agricultural systemssuch as plant tissues, soils, fruits, cocomposted sewage sludge and wastes, cereals, and forage and silage. Currently, all this information is needed to be able to carry out the appropriate fertilization of crops, to handle agricultural soils, determine the organoleptic characteristics of fruit and vegetable products, discover the characteristics of the various substrates obtained in composting processes, and characterize byproducts from the industrial sector. All this needs a large number of samples that must be analyzed; this is a time-consuming work, leading to high economic costs and, obviously, having a negative environmental impact owing to the production of noxious chemicals during the analyses. Therefore, the development of a fast, environmentally friendly, and cheaper method of analysis like vis-NIR is highly desirable. Our intention here is to introduce the main fundamentals of infrared reflectance spectroscopy, and to show that procedures like calibration and validation of data from vis-NIR spectra must be performed, and describe the parameters most commonly measured in the agricultural sector.

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Assessment of technological maturity parameters and anthocyanins in berries of cv. Sangiovese ( Vitis vinifera L.) by a portable vis/NIR device

Cited by 27 publications

References 34 publications

Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence

Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence

Effects of Different Harvest Times on the Maturity of Polyphenols in Two Red Wine Grape Cultivars (Vitis vinifera L.) in Qingtongxia (China)

Using Near-Infrared Spectroscopy in Agricultural Systems

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