Mass modeling of kinnow mandarin based on some physical attributes

Mahawar, Manoj Kumar; Bibwe, Bhushan; Jalgaonkar, Kirti; Ghodki, Bhupendra M

doi:10.1111/jfpe.13079

Cited by 39 publications

(48 citation statements)

References 13 publications

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“…

D_{a} = \frac{L + W + T}{3}

D_{g} = {(L \times W \times T)}^{1 / 3}

D_{e} = {[\frac{\{L \times {((), W + T)}^{2}\}}{4}]}^{1 / 3}

where D a is arithmetic mean diameter, mm; D g is geometric mean diameter, mm; D e is equivalent mean diameter, mm; L is major dimension along the longest axis, mm; W is minor dimension along the longest axis perpendicular to L , mm; T is intermediate thickness dimension along the lowest axis perpendicular to both L and W . The projected areas perpendicular to the length (PLA), width (PIA), thickness (PTA), and criteria projected area (CPA) for ash gourd seed were calculated according to standard formulae given by Pathak, Pradhan, and Mishra (2019b), Mahawar, Bibwe, Jalgaonkar, and Ghodki (2019); and Shahbazi and Rahmati (2013), respectively.

PLA = \frac{πLW}{4}

PIA = \frac{π W^{2}}{4}

PTA = \frac{πTW}{4}

where PLA is projected area PLA (mm 2 ); PIA is projected area perpendicular to the intermediate axis (mm 2 ); PTA is projected area perpendicular to the transverse axis (mm 2 ); L is major dimension along the longest axis, mm; W is minor dimension along the longest axis perpendicular to L , mm; T is intermediate thickness dimension along the lowest axis perpendicular to both L and W .

CPA = \frac{PLA + PIA + PTA}{3}

where CPA is the criteria projected area (mm 2 ).…”

Section: Methodsmentioning

confidence: 99%

“…where D a is arithmetic mean diameter, mm; D g is geometric mean diameter, mm; D e is equivalent mean diameter, mm; L is major dimension along the longest axis, mm; W is minor dimension along the longest axis perpendicular to L, mm; T is intermediate thickness dimension along the lowest axis perpendicular to both L and W. The projected areas perpendicular to the length (PLA), width (PIA), thickness (PTA), and criteria projected area (CPA) for ash gourd seed were calculated according to standard formulae given by Pathak, Pradhan, and Mishra (2019b), Mahawar, Bibwe, Jalgaonkar, and Ghodki (2019);…”

Section: Determination Of Dimension Of Seedsmentioning

confidence: 99%

See 1 more Smart Citation

Engineering properties of dried ash gourd (Benincasa hispidaCogn) seeds: Mass modeling and its analysis

Gade

Meghwal

Prabhakar

2020

J Food Process Engineering

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The correlation of engineering properties and mass of ash gourd seeds was explored by using models like linear, quadratic, S-curve, power, and multivariate. The recorded range of values for seed groups (group of large mass, medium mass, and small mass) was 92.77 to 34.07 g for thousand seed mass, 28.06 to 24.46 for angle of repose, 5.64 to 4.90 m/s for terminal velocity, 213.73 to 227.12 N/mm for hardness. Coefficient of static friction was found the highest for rubber (0.488) and the lowest for aluminum (0.305) for all sizes of seeds. The recommended models based on dimensions were linear, quadratic, and power models with R 2 = 0.997 based on arithmetic mean diameter for large mass seeds, power model (R 2 = 0.999) based on geometric mean diameter for medium mass seeds, quadratic and power models with same R 2 = 0.999 based on arithmetic mean diameter for small mass seeds. The recommended area-based models for large mass, medium mass, and small mass seeds were quadratic model (R 2 = 0.997) based on projected areas perpendicular to length, power model (R 2 = 0.999) based on projected areas perpendicular to thickness, and quadratic model (R 2 = 0.999) based on projected area's perpendicular thickness, respectively. For surface area-based models, quadratic model (R 2 = 0.997) for group of large mass, quadratic and power models (R 2 = 0.999) for group of medium mass, and quadratic model (R 2 = 0.999) for group of small mass were found to be acceptable. Models based on ellipsoid volume of seed were recommended for all groups. The properties studied and models explored (best fitted) may be used for process and equipment design. Practical Applications Ash gourd seeds are discarded as waste by ash gourd processing food industries (mainly Petha industries in the northern part of India). Seeds are rich in proteins, minerals and oil, the ash gourd seed oil is high in therapeutic value, as it is rich in phytonutrients such as vitamins (A and E), squalene, terpenoids, and alkaloids. Seeds are also rich in antibacterial and antifungal properties. This makes it highly valuable for food, pharmaceutical, and cosmetic industries. The size of seed changes as the size of fruit changes and physical properties of seed depend on seed size. This study will give the knowledge of the properties of seeds, which help to design equipment for processing of seeds for various end products with the best quality. The explored models may be used for prediction of mass as per the size of seeds and help in understanding the processing operations.

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“…

D_{a} = \frac{L + W + T}{3}

D_{g} = {(L \times W \times T)}^{1 / 3}

D_{e} = {[\frac{\{L \times {((), W + T)}^{2}\}}{4}]}^{1 / 3}

PLA = \frac{πLW}{4}

PIA = \frac{π W^{2}}{4}

PTA = \frac{πTW}{4}

CPA = \frac{PLA + PIA + PTA}{3}

where CPA is the criteria projected area (mm 2 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Determination Of Dimension Of Seedsmentioning

confidence: 99%

Engineering properties of dried ash gourd (Benincasa hispidaCogn) seeds: Mass modeling and its analysis

Gade

Meghwal

Prabhakar

2020

J Food Process Engineering

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“…It is significantly grown in northern region of India, especially in Punjab, Haryana, Rajasthan and Himachal Pradesh (Sharma et al., 2007). The total production of orange group, such as kinnow mandarins contributes around 4.75 million tonnes grown over 0.43 million hectares' area in India (Mahawar et al., 2019). Due to poor post‐harvest infrastructure facilities and its availability in large quantities over a short period, efficient marketing and utilization got affected.…”

Section: Introductionmentioning

confidence: 99%

Enzymatic debittering of Citrus reticulata (Kinnow) pulp residue and its utilization for the preparation of vermicelli

Singla

Panesar

Sangwan

et al. 2020

J. Food Process. Preserv.

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This study involves the debittering of Citrus reticulata (kinnow) pulp residue and its value addition through incorporation for the preparation of vermicelli. The optimization of debittering has been monitored through conversion of naringin (bitterness causing compound) to naringenin (nonbitter compound) through enzymatic method. The enzyme (naringinase) concentration has been varied from 0.5 to 2.5 U/mg with temperature range of 25–65°C at pH 4.5 with incubation period of 2–6 hr. The maximum reduction (~66.19%) naringin was observed in the kinnow pulp residue, with increase in the naringenin concentration (nonbitter) of 52.38% under optimized conditions, that is, enzyme concentration (1 U/mg), temperature (50°C), and treatment time (4 hr) at natural pH (4.5). The debittered kinnow pulp residue was characterized for compositional analysis and morphological studies using SEM and FTIR. The obtained debittered kinnow pulp residue was further successfully utilized in the preparation of nutrient and antioxidant‐rich vermicelli. Practical applications Food waste has been generated in abundance by fruits processing industries which are rich in mineral, antioxidants, and polyphenols. The bitterness in citrus waste (kinnow pulp, peel, and seeds), has posed a major issue for their utilization in different food products due to the problem of consumer acceptability. In present investigation, reduction in the content of naringin below the threshold limit resulted in debittering of kinnow pulp residue. Furthermore, this debittered pulp residue has been explored for its utilization for the preparation of antioxidant rich vermicelli. Results of this work depicted that such technology can be used for debittering of kinnow industry by‐products and can be subsequently utilized by the food industry in the preparation of antioxidant rich food products. This process is not only an effective utilization of agro‐industrial by‐products/wastes but also solution for environmental waste caused by kinnow juice industry.

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“…L had the highest R 2 (0.980) and the lowest SEE (0.007) values for ripe pepper berries when compared to the others as given in the Quadratic model equation (Equation (16)).…”

Section: Models Based On Dimensionsmentioning

confidence: 99%

“…Projected area values are fundamental information in the design and development of machine vision-based grading systems [16]. Projected area is also useful for estimating the respiration rate, maturity index, and gas permeability to predict optimum harvest time, water loss, and heat and mass transfer during drying and cooling [7,16]. However, some ellipsoidal shapes of ripe pepper berries were observed among the samples.…”

Section: Physical Properties Of Pepper Berriesmentioning

confidence: 99%

Some Physical Properties and Mass Modelling of Pepper Berries (Piper nigrum L.), Variety Kuching, at Different Maturity Levels

et al. 2020

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Pepper berry (Piper nigrum L.) is known as the king of spices and has sharp, pungent flavour and aroma. In this study, the physical properties (weight, dimensions, sphericity, volume, surface area, and projected area) were measured, and the mass of pepper berries of the Kuching variety at different maturity levels (immature, mature, and ripe) was predicted using four models: linear, quadratic, s-curve, and power. When the models were based on volume and projected area, the mass could be predicted with maximum precision. The Quadratic model was best fitted for mass prediction at all mass maturity levels (immature, mature, and ripe). The results showed that mass modelling based on the actual volume of pepper berries was more applicable compared to other properties with the highest determination coefficient, 0.995, at the 1% probability level. From an economical point of view, mass prediction based on actual volume in the Quadratic form, M= 0.828 − 0.015 V + 7.376 ×10−5V2, is recommended. The findings of physical properties and mass modelling of the berries would be useful to the scientific knowledge base, which may help in developing grading, handling, and packaging systems.

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Mass modeling of kinnow mandarin based on some physical attributes

Cited by 39 publications

References 13 publications

Engineering properties of dried ash gourd (Benincasa hispidaCogn) seeds: Mass modeling and its analysis

Engineering properties of dried ash gourd (Benincasa hispidaCogn) seeds: Mass modeling and its analysis

Enzymatic debittering of Citrus reticulata (Kinnow) pulp residue and its utilization for the preparation of vermicelli

Some Physical Properties and Mass Modelling of Pepper Berries (Piper nigrum L.), Variety Kuching, at Different Maturity Levels

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