Determination of anthocyanin concentration in whole grape skins using hyperspectral imaging and adaptive boosting neural networks

Fernandes, Armando; Oliveira, Paula; Moura, João Paulo; Oliveira, Ana Alexandra; Falco, Virgílio; Correia, Maria José; Melo‐Pinto, Pedro

doi:10.1016/j.jfoodeng.2011.02.018

Cited by 70 publications

(39 citation statements)

References 35 publications

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“…In scientific literature there were only a few works that measured enological parameters for a small number of whole grape berries using spectroscopic techniques in reflectance mode combined with machine learning methods, see Table 1, they were Arana et al (2005), Cao et al (2010) and Fernandes et al (2011). These three works measured single berries, but none of them has determined simultaneously pH, sugars and anthocyanins concentration.…”

Section: Reflectance Mode Spectroscopy For Small Number Of Berriesmentioning

confidence: 99%

Brix, pH and anthocyanin content determination in whole Port wine grape berries by hyperspectral imaging and neural networks

Fernandes

Franco

Mendes‐Ferreira

et al. 2015

Computers and Electronics in Agriculture

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Section: Reflectance Mode Spectroscopy For Small Number Of Berriesmentioning

confidence: 99%

Brix, pH and anthocyanin content determination in whole Port wine grape berries by hyperspectral imaging and neural networks

Fernandes

Franco

Mendes‐Ferreira

et al. 2015

Computers and Electronics in Agriculture

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“…Moreover, for classifying strawberries according to the stage of their ripeness, a texture analysis was conducted on the images based on greylevel co-occurrence matrix (GLCM) parameters, a maximum classification accuracy of 90% being achieved. Fernandes et al (2011) reported a system based on neural networks for the estimation of grape anthocyanin concentration using hyperspectral images (400-1000 nm). They used a method based on NN called AdaBoost.…”

Section: Estimation Of Internal Quality Parametersmentioning

confidence: 99%

Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

Lorente

Aleixos

Gómez-Sanchís

et al. 2011

Food Bioprocess Technol

433

185

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Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time consuming for such high dimensional data sets and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, nondestructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task.

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“…One other study evaluated the feasibility of using hyperspectral imaging in the Vis-NIR range for the prediction of anthocyanins, polyphenols, sugars and density (González-Caballero et al, 2012). Other works also predicted phenolic content including flavonols (Ferrer-Gallego et al, 2011) and anthocyanins (Fernandes et al, 2011) of wine grapes. Moreover fructose and glucose concentration, pH value, TA and glycerol, gluconic acid and acetic acid were also predicted with on-line near Vis-NIR spectrometer upon grape reception at wineries (Porep et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Spectra evolution over on-vine holding of Italia table grapes: prediction of maturity and discrimination for harvest times using a Vis-NIR hyperspectral device

Piazzolla

Amodio

Colelli

2017

J Agricult Engineer

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Measurement of certain grape quality parameters (sugars, acidity, and pH-value) is essential for the determination of the optimum harvest time. Non-destructive analytical techniques, including near infrared (NIR) spectroscopy, can be valid alternatives to traditional analytical methods for the determination of maturity indexes, enabling the possibility of on-field applications. This work aims to study the reliability to monitor spectra changes related with ripening of table grapes and to select optimal wavelengths for the discrimination of bunches from different harvests, in addition to the prediction of total soluble solids, pH, titratable acidity, phenols and antioxidant activity of table grapes. Grapes were harvested four times from the same plants at day 0 (I HT), and after 11 (II HT), 27 (III HT) and 48 (IV HT) days. Spectra were acquired from the images obtained using a spectral scanner Vis-NIR (ver 1.4.; DV Srl, Padova, Italy), with a detector in the region between 400-1000 nm principal component analysis was used to remove outliers followed by spectra pre-treatment. The best prediction model was achieved for soluble solids with the regression coefficient values of 0.91 for calibration and 0.88 for validation followed by titratable acidity (0.71 and 0.78) and antioxidant activity (0.68 and 0.62). In addition an excellent correlation was observed between spectra and days before harvest (R 2 of 0.98 for calibration and prediction models) indicating that is possible to relate spectra changes with ripening, leading also to the effective discrimination of the fruits from the different harvest times. The results showed that this technique may be a valid support to select the optimal harvest time also based on the prediction of the maturity related constituents.

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Determination of anthocyanin concentration in whole grape skins using hyperspectral imaging and adaptive boosting neural networks

Cited by 70 publications

References 35 publications

Brix, pH and anthocyanin content determination in whole Port wine grape berries by hyperspectral imaging and neural networks

Brix, pH and anthocyanin content determination in whole Port wine grape berries by hyperspectral imaging and neural networks

Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

Spectra evolution over on-vine holding of Italia table grapes: prediction of maturity and discrimination for harvest times using a Vis-NIR hyperspectral device

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