Infrared assisted hot air dryer for turmeric slices:Effect on drying rate and quality parameters

Jeevarathinam, G.; Pandiselvam, R.; Pandiarajan, T.; Preetha, P.; Balakrishnan, Malini; Thirupathi, V.; Kothakota, Anjineyulu

doi:10.1016/j.lwt.2021.111258

Cited by 64 publications

(55 citation statements)

References 34 publications

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“…In the past, solar energy was used to dry a variety of agricultural products and food, but due to inappropriate changes in food quality, lack of enough control during the drying process, long drying time, and unsanitary, the product has many problems in using this method, so it is necessary to use new technology in the drying process [7].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Sharabiani

Kaveh²,

Taghinezhad

et al. 2022

Applied Sciences

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Drying as an effective method for preservation of crop products is affected by various conditions and to obtain optimum drying conditions it is needed to be evaluated using modeling techniques. In this study, an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR) was used for modeling the infrared-hot air (IR-HA) drying kinetics of parboiled hull. The ANFIS, ANN, and SVR were fed with 3 inputs of drying time (0–80 min), drying temperature (40, 50, and 60 °C), and two levels of IR power (0.32 and 0.49 W/cm2) for the prediction of moisture ratio (MR). After applying different models, several performance prediction indices, i.e., correlation coefficient (R2), mean square error index (MSE), and mean absolute error (MAE) were examined to select the best prediction and evaluation model. The results disclosed that higher inlet air temperature and IR power reduced the drying time. MSE values for the ANN, ANFIS tests, and SVR training were 0.0059, 0.0036, and 0.0004, respectively. These results indicate the high-performance capacity of machine learning methods and artificial intelligence to predict the MR in the drying process. According to the results obtained from the comparison of the three models, the SVR method showed better performance than the ANN and ANFIS methods due to its higher R2 and lower MSE.

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Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Sharabiani

Kaveh²,

Taghinezhad

et al. 2022

Applied Sciences

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“…This is the main reason for the faster drying rate where the drying time is greatly minimized. For instance, the infrared drying time of turmeric slices was able to reduce about 2 hr as compared to hot air drying (Jeevarathinam et al, 2021). Another study by Miraei Ashtiani et al (2017) reported a similar finding on the drying of peppermint leaves (Miraei Ashtiani et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Infrared drying of Clinacanthus nutans leaves

Abdullah

Yusof

Rukunudin

et al. 2022

Food Processing Preservation

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Clinacanthus nutans (C. nutans) herb is one of the medicinal herbal species from Acanthaceae family. This plant is native to tropical and subtropical Asian regions. It is mostly cultivated in Malaysia, Thailand, and Indonesia. In Thailand, C. nutans was initially invented as an antiviral cream and clinically proven to treat herpes simplex virus (HSV) and varicella-zoster virus (VZS) infections (Charuwichitrat et al., 1996;Jayavasu et al., 1992;Sangkitporn et al., 1995). The cream also exhibited anti-dengue property. Another vital medicinal benefit of the C. nutans herb is as an anticancer. Numerous studies have revealed the cytotoxicity and antiproliferative effects of C. nutans extract against various human cancer cell lines such as

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“…As a traditional drying technology, hot air drying has some limitations, such as slow drying rate, poor product quality, serious nutrients loss, and obvious surface hardening (Yamakage et al, 2021; Zhou, Feng, et al, 2021). Infrared radiation drying is able to heat the materials quickly, while the texture cannot meet the requirements of crispness (Jeevarathinam et al, 2021). Vacuum freeze‐drying can maintain the color and appearance quality of papaya slices with crispy structure and small shrinkage (Duarte‐Correa et al, 2020), but its equipment investment is high and the drying time is relatively long.…”

Section: Introductionmentioning

confidence: 99%

Drying characteristics and quality optimization of papaya crisp slices based on microwave vacuum drying

Dai

et al. 2022

Food Processing Preservation

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In order to improve the drying quality and obtain a crispy texture of papaya slices, microwave vacuum drying (MVD) was studied in this research. The effects of different slice thickness (3, 6, 9, 12 mm), microwave power density (6, 8, 10, 12 W/g), drying temperature (50, 55, 60, 65, 70°C), and vacuum degree in relative pressure (−75, −80, −85, −90 kPa) on the drying characteristics and comprehensive quality of papaya slice were discussed. The Box-Behnken central combination experiments were designed

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Infrared assisted hot air dryer for turmeric slices:Effect on drying rate and quality parameters

Cited by 64 publications

References 34 publications

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Infrared drying of Clinacanthus nutans leaves

Drying characteristics and quality optimization of papaya crisp slices based on microwave vacuum drying

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