Modeling of color values for nondestructive evaluation of maturity of mango

Jha, Shyam Narayan; Chopra, Sangeeta; Kingsly, A.R.P.

doi:10.1016/j.jfoodeng.2005.08.048

Cited by 103 publications

(55 citation statements)

References 22 publications

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“…The judges were instructed to put their sensory score only under four categories viz. 'liked extremely", 'liked moderately', 'disliked moderately' and 'disliked extremely' and percentage of respondent was computed to check that whether the trends of acceptability level and overall quality index to be formulated were in line or not as previously done for maturity index determination of mango by Jha et al (2007).…”

Section: Measurement Of Colourmentioning

confidence: 99%

“…Expressions were formulated taking ideas from the increasing or decreasing trends of physico-chemical quality parameters based on previous experience (Jha et al 2007). The I q values were thereafter computed at each storage interval and matched with the likeness patterns of the sensory panel for selecting the best expression.…”

Section: Formulation Of Quality Index (I Q )mentioning

confidence: 99%

“…Determination of various quality characteristics of fruits and vegetables using nondestructive techniques have been reported (Lesage and Destain 1996;Kato 1997;Nussinovitch et al 1996;Jha et al 2005Jha et al , 2006Jha et al , 2007, but each of them predicts only a single or in some cases double parameters such as acid to Brix ratio (Jha and Matsuoka 2004). A few reports on determination of maturity index (Sirinnapa et al 2004;Lakshmi et al 2006;Jha et al 2007), freshness index (Jha and Matsuoka 2002;McGlone et al 2002) and physical appearances using machine vision systems (Blasco et al 2003) are also available. These techniques determine single quality parameter using relatively costlier techniques and needed to employ as many times as the number of parameters required to be estimated.…”

Section: Introductionmentioning

confidence: 99%

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Physico-chemical quality parameters and overall quality index of apple during storage

Jha

Shrama

2011

J Food Sci Technol

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Physico-chemical quality parameters of apple were measured during storage using standard techniques and fitted to model expressions for developing an overall quality index (I q ). Predicted I q was validated with the trends of sensory scores. Total Soluble Solids (TSS) and acidity varied from 13.2 to 12.3 ºBrix and 0.161 to 0.079%, respectively whereas, Hunter colour values L, a, b and yellowness index were 48.7-56.1, 11.0-19.4, 18.8-20.2 and 84.6-98.2, respectively. The gloss at 45 and 60º incidence angles, density and I q varied from 7.5 to 4.3 and 6.7 to 2.6 GU, 1.01 to 0.96 kg m −3 and 0.26 to 1.02, respectively. The variation in sensory overall quality scores with storage period was found to be in line with computed overall quality index. The I q thus, could be defined as the ratio of product of acidity and TSS to the mode of product of a and b Hunter colour values. The polynomial regression equations for I q with TSS, acidity, a, b, and storage period yielded the correlation coefficients of 0.8443, 0.9838, 0.7130, 0.7183 and 0.9665, respectively; which indicated that overall quality index could be predicted nondestructively using any one of these parameters during storage.

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Section: Measurement Of Colourmentioning

confidence: 99%

Section: Formulation Of Quality Index (I Q )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Physico-chemical quality parameters and overall quality index of apple during storage

Jha

Shrama

2011

J Food Sci Technol

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“…Jha et al (2006a) have identifi ed the quality parameters of mango to be used for their determination using visual and/or NIR spectroscopy. Using visual spectroscopy and colour modelling, they have predicted sweetness, fi rmness, yellowness index and acidity of 'Dasheri' mango non-destructively (Jha et al 2005(Jha et al , 2006a(Jha et al , 2007 with reasonable amount of accuracy. Eating quality of some ripe mangoes has also been determined in Japan with good accuracy (multiple correlation coeffi cient 0.92) (Saranwong et al 2004).…”

Section: Nuclear Magnetic Resonance (Nmr) Techniquementioning

confidence: 99%

Quality parameters of mango and potential of non-destructive techniques for their measurement — a review

et al. 2010

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The king of fruits "Mango" (Mangifera indica L.) is very nutritious and rich in carotenes. India produces about 50% of the total world's mango. Many researchers have reported the maturity indices and quality parameters for determination of harvesting time and eating quality. The methods currently used for determination of quality of mango are mostly based on the biochemical analysis, which leads to destruction of the fruits. Numerous works are being carried out to explore some non-destructive methods such as Near Infrared (NIR), Nuclear Magnetic Resonance (NMR), X-ray and Computed Tomography (CT), electronic nose, machine vision and ultrasound for quality determination of fruits. This paper deals with some recent work reported on quality parameters, harvesting and post-harvest treatments in relation to quality of mango fruits and reviews on some of the potential non-destructive techniques that can be explored for quality determination of mango cultivars.

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“…Modelling based on single FFV quality attributes has also been used by others in an attempt to predict shelf life under different storage conditions. For example, colour has been used to predict the batch-keeping quality of cucumbers based on the chlorophyll degradation patterns [33], to predict the maturity of mangoes [34] and for the assessment of safe storage based on changes in the whiteness of mushrooms under MAP [35]. Firmness, another important quality attribute, has been used to predict nectarine shelf life [36,37].…”

Section: (C) Single Quality Factormentioning

confidence: 99%

Improvement in fresh fruit and vegetable logistics quality: berry logistics field studies

Nunes

Nicometo

Émond

et al. 2014

Phil. Trans. R. Soc. A.

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Shelf life of fresh fruits and vegetables is greatly influenced by environmental conditions. Increasing temperature usually results in accelerated loss of quality and shelf-life reduction, which is not physically visible until too late in the supply chain to adjust logistics to match shelf life. A blackberry study showed that temperatures inside pallets varied significantly and 57% of the berries arriving at the packinghouse did not have enough remaining shelf life for the longest supply routes. Yet, the advanced shelf-life loss was not physically visible. Some of those pallets would be sent on longer supply routes than necessary, creating avoidable waste. Other studies showed that variable pre-cooling at the centre of pallets resulted in physically invisible uneven shelf life. We have shown that using simple temperature measurements much waste can be avoided using ‘first expiring first out’. Results from our studies showed that shelf-life prediction should not be based on a single quality factor as, depending on the temperature history, the quality attribute that limits shelf life may vary. Finally, methods to use air temperature to predict product temperature for highest shelf-life prediction accuracy in the absence of individual sensors for each monitored product have been developed. Our results show a significant reduction of up to 98% in the root-mean-square-error difference between the product temperature and air temperature when advanced estimation methods are used.

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Modeling of color values for nondestructive evaluation of maturity of mango

Cited by 103 publications

References 22 publications

Physico-chemical quality parameters and overall quality index of apple during storage

Physico-chemical quality parameters and overall quality index of apple during storage

Quality parameters of mango and potential of non-destructive techniques for their measurement — a review

Improvement in fresh fruit and vegetable logistics quality: berry logistics field studies

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