Texture and rheological changes of Indian mango cultivars during ripening

Nambi, V. Eyarkai; Thangavel, K.; Rajeswari, Kotikalapudi; Manickavasagan, Annamalai; Geetha, V.

doi:10.1016/j.postharvbio.2016.02.009

Cited by 25 publications

(20 citation statements)

References 34 publications

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“…The textural characteristics of the mangoes were measured as proposed by Nambi et al . () using Texture Analyzer (TA‐HDi, Stable Micro Systems, UK). A 4 mm cylindrical probe (P/4) with flat bottom was used to obtain force displacement curve with 1 mm/s pre‐test speed, 1 mm/s test speed, 1 mm/s post‐test speed and 10 mm penetration depth.…”

Section: Methodsmentioning

confidence: 99%

“…Lab color coordinates were recorded on the surface of the mango using Hunter LAB color meter (Hunter Associates Laboratory, Inc. USA) as proposed by Nambi et al (2016b). The textural characteristics of the mangoes were measured as proposed by Nambi et al (2016a) using Texture Analyzer (TA-HDi, Stable Micro Systems, UK). A 4 mm cylindrical probe (P/4) with flat bottom was used to obtain force displacement curve with 1 mm/s pre-test speed, 1 mm/s test speed, 1 mm/s post-test speed and 10 mm penetration depth.…”

Section: Physico-chemical Color and Textural Propertiesmentioning

confidence: 99%

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Comparison of Various RGB Image Features for Nondestructive Prediction of Ripening Quality of “Alphonso” Mangoes for Easy Adoptability in Machine Vision Applications: A Multivariate Approach

Nambi

Thangavel

Shahir

et al. 2016

Journal of Food Quality

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A study was conducted to predict ripening quality in mangoes using RGB images. Mature “Alphonso” mangoes were selected for experiments. During ripening, random samples were chosen at 24‐h interval for imaging and quality analysis. Hierarchical clustering method was employed to classify the ripening period into five stages based on quality parameters (physico‐chemical, color and textural properties). From each image, 18 features were extracted and evaluated in the prediction of ripening stages. From linear discriminant analysis, group of normalized differential index (NDI) and area features obtained from RGB channels were found better in prediction with 14.9 and 12.3% of misclassification, respectively. While employing quadratic discriminant analysis, NDI and area features predicted the ripening quality effectively with lowest misclassification of 7.9 and 3.5%, respectively. Cross and external validation of selected models had also shown effective results (96.3% correct prediction) with these features. The proposed image features are easy to extract with simple algorithms and useful to predict the ripening quality of mangoes, particularly in machine vision applications. Practical Applications Mango is one of the important climacteric fruits, having great demand in the world market. India is the market leader, exporting the mango fruits in fresh as well as processed form. Since, level of ripeness plays a major role in the final product quality, mango pack houses and processing industries need an easy, rapid non‐destructive quality detection mechanism for predicting the ripeness level of mangoes in order to get the desire optimum quality of final products. This study explains about the adoptability of RGB based image processing method for prediction of ripeness level as a nondestructive method with various image features. This study will be helpful to great extend in the process of predicting the ripeness level of mango using the proposed image features with suitable for machine vision systems.

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Section: Methodsmentioning

confidence: 99%

Section: Physico-chemical Color and Textural Propertiesmentioning

confidence: 99%

Comparison of Various RGB Image Features for Nondestructive Prediction of Ripening Quality of “Alphonso” Mangoes for Easy Adoptability in Machine Vision Applications: A Multivariate Approach

Nambi

Thangavel

Shahir

et al. 2016

Journal of Food Quality

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“…Al observar lo reportado por Elshetawy et al (2016), se encuentra una similitud, ya que establece que el mango madurado presenta una firmeza entre 2,42 y 3,00kgf y, para el caso de esta investigación, el mango muestra una fuerza a la rotura entre 3,5 y 3,93kgf. La reducción en la firmeza de la fruta es probable, debido a la alteración en la estructura de la pared celular por degradación de enzimas (p. e. poligalacturonasa), también por degradación del almidón (Nambi et al 2016;Seymour et al 1993) y descomposición de almidón, celulosa y hemicelulosa (Salunkhe & Kadam, 1995).…”

Section: Resultados Y Discusiónunclassified

“…El color es uno de los factores de calidad más importantes, ya que está estrechamente relacionado con las propiedades químicas y físicas de las frutas (Mim et al 2018). Las propiedades de la textura son factores clave, que influyen en la aceptabilidad de la fruta y el valor agregado de sus productos para el consumidor (Nambi et al 2016). Los principales cambios de textura que dan como resultado el ablandamiento de la fruta se debe a la alteración, mediada por enzimas en la estructura y en la composición de la pared celular, la solubilización o despolimerización parcial o completa de polisacáridos de pared celular, como pectinas y celulosas (Brummell & Harpster, 2001;Lohani et al 2004;Tucker & Grierson, 1987).…”

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Comportamiento de características fisicoquímicas durante la maduración del mango Tommy Atkins luego de cosechado

Villamizar-Vargas

Quiceno-Gómez

Giraldo

2019

Rev. U.D.C.A Act. & Div. Cient.

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“…The degree of ripeness or level of ripening of climacteric fruits is an important criterion for determining the optimal post-harvest strategies for handling and marketing of the fruits. Changes in biochemical, physiological composition, colour, textural and rheological properties associated with ripening have been reviewed and reported by many researchers (Bashir and Abu-Goukh, 2003;Brady, 1987;Charles and Tung, 1973;Chen and Ramaswamy, 2002;Lizada, 1993;Nambi et al, 2016aNambi et al, , 2016bSeymour et al, 1993;Stover and Simmonds, 1987) . Padda et al (2011) compared firmness, total soluble solids (TSS) and dry matter to assess the ripening in mangoes using principal component analysis (PCA) and reported that firmness test with penetrometer was suitable to assess changes.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive ripeness-index for prediction of ripening level in mangoes by multivariate modelling of ripening behaviour

Nambi

Thangavel

Manickavasagan

et al. 2017

International Agrophysics

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A b s t r a c t. Prediction of ripeness level in climacteric fruits is essential for post-harvest handling. An index capable of predicting ripening level with minimum inputs would be highly beneficial to the handlers, processors and researchers in fruit industry. A study was conducted with Indian mango cultivars to develop a ripeness index and associated model. Changes in physicochemical, colour and textural properties were measured throughout the ripening period and the period was classified into five stages (unripe, early ripe, partially ripe, ripe and over ripe). Multivariate regression techniques like partial least square regression, principal component regression and multi linear regression were compared and evaluated for its prediction. Multi linear regression model with 12 parameters was found more suitable in ripening prediction. Scientific variable reduction method was adopted to simplify the developed model. Better prediction was achieved with either 2 or 3 variables (total soluble solids, colour and acidity). Cross validation was done to increase the robustness and it was found that proposed ripening index was more effective in prediction of ripening stages. Three-variable model would be suitable for commercial applications where reasonable accuracies are sufficient. However, 12-variable model can be used to obtain more precise results in research and development applications.

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Texture and rheological changes of Indian mango cultivars during ripening

Cited by 25 publications

References 34 publications

Comparison of Various RGB Image Features for Nondestructive Prediction of Ripening Quality of “Alphonso” Mangoes for Easy Adoptability in Machine Vision Applications: A Multivariate Approach

Comparison of Various RGB Image Features for Nondestructive Prediction of Ripening Quality of “Alphonso” Mangoes for Easy Adoptability in Machine Vision Applications: A Multivariate Approach

Comportamiento de características fisicoquímicas durante la maduración del mango Tommy Atkins luego de cosechado

Comprehensive ripeness-index for prediction of ripening level in mangoes by multivariate modelling of ripening behaviour

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