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The usefulness of digital image analysis in estimating sensory attributes of grape seeds in relation to maturation level was evaluated for the first time. Seeds from Syrah grapes harvested throughout the ripening period were grouped according to maturity using the DigiEye® system. The discriminant ability, homogeneity, repeatability, and uniformity of a sensory panel were assessed after training on grape seeds. The aim was to evaluate the use of digital image techniques in order to accurately establish the maturity level of grape seeds, based on sensory and textural features. All sensory attributes (color, hardness, cracking, vegetal, bitterness and astringency) showed significant (p &lt, 0.05) correlations with the chemical maturity stage. Color and vegetal (sensory attributes), together with deformation energy (instrumental texture parameter) (De), allowed for the classification of the seeds into four real maturity stages, hence their usefulness as grape seed ripening indicators. Significant (p &lt, 0.05) and high-correlation factors were also found between instrumental and sensory attributes. Therefore, digital analysis can be a useful tool to better define the maturity stage in the vineyard, and to dispose of grape seeds with well-defined sensory profiles for specific oenological applications. This could help to determine the optimal harvest date to manage winemaking, in order to produce high quality wines in warm climates.

Phenolic composition, colour and morphology variables were monitored during ripening in grape seeds of Vitis vinifera L. cv. Tempranillo in 2010. The aim of the study was to determine the effect of limitation induced by early leaf removal (ELR) vs. non-defoliated (ND) control vines. The ultimate goal of this research was to assess whether phenolic composition could be predicted based on variables obtained by image analysis. ELR advanced grape maturity also had lower phenolic concentration and smaller and darker seeds. (+)-Catechin and total cinnamic acid contents as well as L* and aspect ratio were the most significant parameters for distinguishing treatments. Furthermore, area, length, width, L*, a*, b* and heterogeneity predict the phenolic composition in grape seeds. Although it is not yet a substitute for chemical analysis, it could become a quick way to estimate the phenolic composition of grape seeds during maturation. The methodology proposed in this work could be a powerful tool for winemakers.

Bee pollen is a natural matrix widely studied in its nutritional and bioactive compounds, including carotenoids. That composition is usually identified by Rapid Resolution Liquid Chromatography (RRLC) coupled to UV–Vis spectrophotometry, an expensive method that requires complex sample preparation and long analysis time. In this work, a correlation between colorimetric coordinates and carotenoid composition was evaluated. Through Digital Image Analysis (DIA) by DigiEye, the color characteristics were determined, and carotenoids profile was done by RRLC. The correlations were made by multiple linear regression (MLR). From 12 carotenoids found in the samples, six had a coefficient R2 > 0.75 between reference and predict value. Heterogeneous mixtures of bee pollen samples were analyzed, and the suitability of the mathematical models could be corroborated because the relative error for most of the compounds was less than 20%. It has been demonstrated that union of Tristimulus Colorimetry and Image Analysis represent an effective tool to estimate the chemical composition in food industry.

Background and Aims Winemaking generates a large quantity of grape marc that causes environmental and economic problems, which could be minimised by exploiting and adding value to these by-products. Marc is composed of seeds, skins and stems that are an important source of phenolic substances, which have antioxidant properties and potential benefits to human health. It is necessary to develop novel methods for assessing the composition of marc because conventional analyses are destructive and require lengthy preparation procedures and chemical reagents. Methods and Results Near-infrared hyperspectral imaging has been used for evaluating 27 individual phenolic substances in the components of grape marc after freeze-drying, the only sample preparation step. By applying partial least squares regression to the spectral data, values of R2 up to 0.98 were obtained even for estimating some minor compounds. Conclusions This methodology arises as an attractive alternative to analyse the phenolic composition of grape marc because of its speed and simplicity. Significance of the Study The use of this technique will allow wine producers to quickly determine the phenolic composition and decide the destination of this by-product.

BACKGROUND:Anthocyanins are the main compounds responsible for the colour of red wines and therefore itmay be important to evaluate the contentof the aforesaid secondary metabolites duringgraperipeningdueto the crucial importance to determine wine colour. Nowadays, there is a growing demand of rapid and non-destructive analytical tools for analysing grapes, such as the emerging hyperspectral analysis. RESULTS: The hyperspectral images of intact grapes (Vitis vinifera L. cv. Tempranillo, Graciano, Garnacha and Mazuelo red grape from vineyards located in the D.O.Ca. Rioja at two different developmental stages) were recorded using a near infrared hyperspectral imaging device (900–1700 nm). Reference values of anthocyaninswere obtained by HPLC-DAD. Calibrationswere performed by modified partial least squares regression and present a good potential (coefficient of determination of 0.72 and standard error of cross-validation values of 0.78 and 0.70mg per grape for total and non-acylated anthocyanins respectively). CONCLUSION: The procedure reported here presents a good potential for a fast and reasonably inexpensive screening of these compounds in intact single berries. Moreover, the heterogeneity of anthocyanins within the same ripeness stage could be evaluated using this non-detructive tool.

Three independent methodologies were investigated to achieve the differentiation of red grapes from different grape varieties (Garnacha, Graciano, Mazuelo and Tempranillo) collected from five vineyards located in the D.O.Ca. Rioja. Anthocyanin chromatographic analysis, color image analysis and near infrared hyperspectral imaging were carried out for the grapes. Then, a Stepwise Linear Discriminant Analysis (SLDA) was developed for each data set in order to discriminate grapes according to their grape variety. As a result, using anthocyanin profile, color image analysis and near infrared hyperspectral imaging respectively, 88%, 54% and 100% of the samples were correctly classified in the internal validation process and 86%, 52% and 86% were correctly classified in the leave-one-out cross-validation process

The effect of salt stress (200 mM NaCl for 28 days) on physiological characteristics of Hibiscus rosa-sinensis, such as abscisic acid (ABA) content, electrolyte leakage, and photochemical efficiency in leaves, and its influence on biomass production, anthocyanin composition, and color expression of flowers were evaluated. Salinity significantly increased electrolyte leakage and ABA content in leaves and reduced the flower fresh weight. Chlorophyll fluorescence parameters were lower in salt stress condition, compared to control. Moreover, salt stress negatively affected the content of anthocyanins (mainly cyanidin-3-sophoroside), which resulted in a visually perceptible loss of color. The detailed anthocyanin composition monitored by HPLC-DAD-MS and the color variations by digital image analysis due to salt stress showed that the effect was more noticeable at the basal portion of petals. A forward stepwise multiple regression was performed for predicting the content of anthocyanins from appearance characteristics obtained by image analysis, reaching R-square values up to 0.90.

Graphical abstractDisplay Omitted The method for evaluating heterogeneous colours in foodstuffs has been improved.An algorithm that adjusts point clouds to ellipsoids in CIELAB colour space has been introduced.The methodology presented can be useful in studies that use 'Chemical Imaging'. The use of computer vision for estimating composition in food products has become wide spread in recent years, especially for products where by measuring colour or other spectral features, we are able to estimate the composition, variety, or ripeness. On the other hand, the appearance is one of the most worrying issues for producers due to its influence on quality and consumer preferences. Computer vision is the best option to satisfy the need of measuring colour and heterogeneity in these products. Previous studies have expressed the heterogeneity with the standard deviation or other magnitudes that do not explain accurately the distribution of colour in CIELAB colour space. Graphing the scatterplot of the a*b* values belonging to the pixels of the image helps to explain the shape of the point cloud, but currently there is not an objective procedure to quantify these point clouds. This work has established a method for improving the illustration of colour measurements by image analysis. The proposed algorithm classified the point clouds by clustering methods and established the methodology for fitting the resulting clusters into ellipsoids. Their geometric features described the shape of the clouds in a quantitatively manner and they could be useful in multivariate statistical techniques for classifying and predicting chemical properties.

Digital imaging has become a powerful tool for the characterization and quality control of foodstuff. Because of the need to automate processes, faster tools are needed and Computer Vision is a good alternative to chemical analysis of many products in quality control. Appearance of grape seeds and grape berries change during the ripeness. These changes are closely related to the chemical composition, especially phenolics, which are very important compounds due to their implications on the intensity and stability of red wine colour. In this study, a complete characterization of grape seeds and grape berries by digital image analysis is described. The size of grapes and the veraison has been determined by image analysis and it has been also established an objective Browning Index of seeds. Morphological differences between varieties were studied by applying discriminant analysis models which allowed us to classify the grape seeds with high accuracy.

Solanum lycopersicum L. (tomato) is a very important fruit vegetable with high economic importance and nutritional impact on the consumers worldwide. Moreover, tomato fruits are an important source of nutraceutical compounds. This work describes the physiological diversity affecting the ripening process that yields variation in fruit pigmentation with regard to anthocyanins compounds for one non-anthocyanin-accumulating (Ailsa Craig) and two anthocyanin-accumulating tomato genotypes (anthocyanin fruit type, low pigment accumulation, and Sun Black, high pigment accumulation). Using tomato fruits obtained by traditional breeding the present study reported modified hormone equilibrium at different ripening stages. This phenomenon can be considered as a consequence of the different pattern in the anthocyanins accumulation in fruits. Moreover, the fruit genotype showing the highest pigment accumulation appear more firm at the commercial stage. Overall, these results showed the considerable potential of exploiting natural genetic diversity to obtain tomatoes with higher levels of anthocyanins, and different quality traits such as colour and firmness.

[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.

The phenolic composition (by HPLC-DAD-MS) and color characteristics (by Imaging Tristimulus Colorimetry) of four strawberry cultivars that have shown good climate adaptation to subtropical area (Nikte, Zamorana, Jacona and Pakal) have been assessed. 24 monomeric phenolics were identified, including 15 anthocyanins, 5 phenolic acids, 1 flavanol and 4 flavonols. Nikte and Zamorana showed the highest phenolic potential mainly due to their higher content of anthocyanins, while Pakal was richer in phenolic acids. Regarding color, Nikte and Zamorana were the more similar cultivars having the lowest values of lightness and hue. On the contrary, the color of Pakal was quite different from all the rest, due to the specific distribution between pelargonidin and cyanidin. The inclusion of both phenolic and colorimetric information in the Linear Discriminant Analysis allowed reaching very good discriminations among cultivars.

Quality of wine depends fundamentally on the condition of the components of grapes at the moment of harvest. In this respect, their seeds directly affect to structure, astringency, stability, and indirectly to colour of wine. In this work, the grape seeds have been evaluated by techniques of image analysis. In all cases, the methodology was developed trying to entail the minimum sample preparation and maximum quickness of the method, and as far as possible, substitute the chemical analysis that usually involves long and tedious processes that hamper monitoring of ripening in a rapid way. The proposed methodology for measuring and evaluating colour and near infrared spectrum has yielded satisfactory results for estimating the ripening of grape seeds, and it has been useful for establishing stages of ripeness from their optical features. La calidad del vino depende en gran medida del estado en que se encuentren los diferentes componentes de la uva en el momento de la recolección y, en este sentido, las semillas afectan directamente a su astringencia, así como a la estructura y estabilidad del color del vino. En el presente trabajo se han evaluado semillas de uva de diferentes variedades aplicando distintas técnicas de análisis de imagen. En todos los casos se han desarrollado metodologías que implican la mínima preparación de muestra y la máxima rapidez del método, con el objeto de poder sustituir, en la medida de la posible, el análisis químico, que suele suponer largos y tediosos procesos que impiden un seguimiento rápido de la maduración. La metodología propuesta para la medición y evaluación del color y el espectro infrarrojo cercano ha dado resultados satisfactorios en la estimación del estado de madurez de las semillas de uva, y ha sido útil para establecer etapas de maduración a partir de sus propiedades ópticas. Ministerio de Economía y Competitividad AGL2008-05569-C02-02, AGL2011-30254-C02-02 y AGL2014-58486-C2-2-R