Evidence map›Paper›PMID 31741515›Full record

ArticleJournal of food science and technology2019

Effect of ultrasound and chemical pretreatment on drying characteristics and quality attributes of hot air dried pineapple slices.

Poonam Rani, P P Tripathy

Abstract read
In one paragraph

Article in Journal of food science and technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Poonam RaniAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302 India.
P P TripathyAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302 India.ORCID 0000-0002-7726-6451

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drying of food materials is a time consuming activity making the process cost and energy intensive and hence, several pretreatments are used to improve the drying rate. The present study aims to study the effect of potassium metabisulphite (KMS solution, 0.25% w/v) and ultrasound (20 and 30 min) pretreatment on hot air drying characteristics and quality of pineapple slices. The results indicated that pretreated samples provided higher drying rate, enhanced moisture diffusivity, brighter color and lower hardness than that of untreated dried sample. It was observed that KMS and ultrasound pretreatment for 20 and 30 min reduced the drying time by 23.8%, 19% and 14.3%, respectively. Further, ten thin layer drying models were applied to the experimental drying data and logarithmic model was best fitted to explain the drying behavior of pretreated and untreated samples. Additionally, the effect of shrinkage on moisture transfer mechanism was also studied. Results highlighted that instantaneous moisture diffusivity was increased during drying while shrinkage was not accounted. However, shrinkage consideration reduced the average moisture diffusivity values by 72-83%. Overall color change (13.95 ± 0.92) and browning index (36.02 ± 2.45) were found to be lowest in ultrasound (30 min) pretreated dried sample, highlighting better color stability. Scanning electron microscopy presented noticeable effects of pretreatment on alterations of microstructure of pineapple slices. It can be interpreted that KMS pretreatment was found to be more effective for improvement of drying characteristics of pineapple slices as compared to ultrasound pretreatment.

Indexed as

DryingHardnessMoisture diffusivityScanning electron microscopyShrinkageUltrasound

Identifiers

PMID31741515
PMCPMC6828901

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.