Characterization of polyphenol oxidase from Cape gooseberry (Physalis peruviana L.) fruit

Bravo, Karent; Osorio, Edison

doi:10.1016/j.foodchem.2015.10.126

Cited by 85 publications

(63 citation statements)

References 28 publications

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“…Physalis (Physalis L) is an exotic fruit which presents various compounds of nutritional and pharmacological interest (Bravo et al, 2015;Bravo & Osorio, 2016;Moura et al, 2016a). Among the physalis species whose fruits are rich in compounds beneficial to human health and have potential as a food stand out Physalis peruviana L., Physalis pubescens L., Physalis angulata L., Physalis mínimos L. and Physalis ixocarpa Brot.…”

Section: Introductionmentioning

confidence: 99%

“…Consumption of physalis in fresh form is restricted due to limited post-harvest life because it has high enzyme activity, which promotes its rapid darkening, especially after mechanical damage during transport and storage (Bravo & Osorio, 2016). Thus, physalis processing, in the form of products such as jellies, would be an excellent way to increase the use of fruit.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of different native american physalis species and evaluation of their processing potential as jelly in combination with brie-type cheese

et al. 2017

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Faced with the need for greater knowledge of the different physalis species, the aim of this study was to characterize different Native American physalis species (Physalis peruviana L., Physalis pubescens L., Physalis angulata L., Physalis mínimos L. and Physalis ixocarpa Brot) as to their physicochemical characteristics, bioactive compounds and antioxidant activity. Besides that, in order to increase their use and add even more value to this fruit, we also evaluate the influence of these different species on the physicochemical, rheological and sensory characteristics of physalis jelly. In addition, this study evaluated the sensory acceptance of the combination of physalis jellies obtained from different species with brie-type cheese. The Peruviana, Pubences and Angulata, are highlighted for being the nutritionally richest species, with the highest levels of phenolic compounds, vitamin C and antioxidant. Moreover, they stand out for originating the most widely sensory accepted jellies, either in pure form or in combination with brie-type cheese.Keywords: Physalis L.; different species; characterization; jelly processing; harmonization. Practical Aplications: It was possible to verify which physalis are the nutritionally richest species and which species are more suitable for jelly processing.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterization of different native american physalis species and evaluation of their processing potential as jelly in combination with brie-type cheese

et al. 2017

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“…The K i value was accordingly calculated as 33.1 ± 8 μM, similar to the IC50 obtained above. Comparing laccase to PPO, Na 2 SO 3 was one of the most efficient inhibitors of a Cape gooseberry PPO when using 4‐methylcatechol and chlorogenic acid as substrates, where IC50 values were 0.150 and 0.157 mM, respectively (Bravo & Osorio, ), thus showing the PPO to be less sensitive to SO 2 than the laccase in the present study.…”

Section: Resultsmentioning

confidence: 54%

Enzymatic characterization of a laccase from lychee pericarp in relation to browning reveals the mechanisms for fruit color protection

Zhang

Fang

et al. 2017

J Food Process Preserv

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An anthocyanin degradation‐related laccase (LcADE/LAC) from lychee fruit was found to be responsible for the pericarp browning. In this study, LcADE/LAC was purified and characterized for better control of laccase‐based browning. The optimum pH for LcADE/LAC activities was 5.0–6.0, with less than 20% of the activity retained at pH 3.0. The optimum temperature is 45 °C, and the activity is thermostable at this temperature. The enzyme was inhibited by 1 mM Fe2+, but induced by 0.1 mM Fe3+. Na2SO3 and l‐cysteine highly inhibited the activity at 0.1 mM, while kojic and phytic acids inhibited 40 and 43%, respectively, at 1 mM. Na2SO3 inhibited the activity in a noncompetitive model with a Ki value of 33.1 μM, and the inhibition increased when pH decreased to 3.5. In postharvest fruit, the enzyme activity and LcADE/LAC gene expression were strongly inhibited by SO2‐HCl treatment. Practical applications The present study systematically characterizes a pericarp browning related laccase (LcADE/LAC) from lychee. The high sensitivity of the enzyme to low pH and several inhibitors, such as SO2, l‐cysteine, and kojic acid and phytic acids, will help in the development of more efficient strategies for browning control of lychee pericarp or other laccase‐based food browning.

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“…Fruit images were classified into seven different levels according to the ripening stage, similar to the method used by Bravo and Osorio Bravo and Osorio (2016). The surface color was used for the classification as indicated by the NTC 4580 (Colombian Technical Normative) standard for Cape gooseberry, and the visual scale proposed by Fischer et al Fischer et al (2005) was employed (see Figure 2).…”

Section: /19mentioning

confidence: 99%

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

Castro

Oblitas

De-la-Torre

et al. 2019

Preprint

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The classification of fresh fruits according to their visual ripeness is typically a subjective and tedious task; consequently, there is growing interest in the use of non-contact techniques to automate this process. Machine learning techniques such as artificial neural networks, support vector machines, decision trees, and K-nearest neighbor algorithms have been successfully applied for classification problems in the literature, particularly for images of fruit. However, the particularities of each classification problem make it difficult, if not impossible, to select a general technique that is applicable to all types of fruit. In this paper, combinations of four machine learning techniques and three color spaces (RGB, HSV, and L*a*b*) were evaluated with regard to their ability to classify Cape gooseberry fruits. To this end, 925 Cape gooseberry fruit samples were collected, and each fruit was manually classified into one of seven different classes according to its level of ripeness. The color values of each fruit image in the three color spaces and their corresponding ripening stages were organized for training and validation following a 5-fold cross-validation strategy in an iterative process repeated 100 times. According to the results, the classification of Cape gooseberry fruits by their ripeness level was sensitive to both the color space and the classification technique used. The models based on the L*a*b* color space and the SVM classifier showed the highest f-measure regardless of the color space, and the principal component analysis combination of color spaces improved the performance of the models at the expense of an increased complexity.

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Characterization of polyphenol oxidase from Cape gooseberry (Physalis peruviana L.) fruit

Cited by 85 publications

References 28 publications

Characterization of different native american physalis species and evaluation of their processing potential as jelly in combination with brie-type cheese

Characterization of different native american physalis species and evaluation of their processing potential as jelly in combination with brie-type cheese

Enzymatic characterization of a laccase from lychee pericarp in relation to browning reveals the mechanisms for fruit color protection

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

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