Changes in physicochemical properties of mulberry fruits ( Morus alba  L.) during ripening

Lee, Yongcheol; Hwang, Keum Taek

doi:10.1016/j.scienta.2017.01.042

Cited by 68 publications

(36 citation statements)

References 26 publications

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“…The total amount of amino acids in grass pea (Glycine, Alanine, Serine, Proline, Valine, Threonine, Cysteine, Isoleucine, Leucine, Aspargine, Lysine, Glutamine, Methionine, Histidine, Phenylalanine, Arginine, Trozin, Tryptophan and Cystine) were identified in the LC-MS/MS device by modifying the methods described by Lee and Hwang (2017), Chan and Matanjun (2017). According to this method, 0.2 g of the homogenized sample was weighed and 10 mL of 6 N HCl solution was added.…”

Section: Amino Acid Analysismentioning

confidence: 99%

Diversity for vitamin and amino acid content in grass pea (Lathyrus sativus L.)

Arslan

2017

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This study was conducted to determine vitamin A, B, C, -carotene and amino acid profile in 18 genotypes and four grass pea (Lathyrus sativus L.) varieties which have low -ODAP. Present results indicated that retinol, -carotene, thiamine, riboflavin, niacin, pantothene, pyridoxine, folic acid and ascorbic acid ranged from 25.6 to 44.1 µg/kg; 240.8 to 410.1 µg/ kg, 3.74 to 5.44; 1.86 to 2.76; 12.37 to 20.25; 14.43 to 22.41; 4.92 to 6.62; 4.04 to 6.77 and 33.4 to 58.2 mg/kg, respectively in seeds. In addition to, the amino acid profile of the genotypes differed significantly and total amino acid amounts were found to be 19.69 to 23.48 g/100 g seeds. A large and significant variation was observed among these genotypes with low -ODAP content in respect to the quality of the nutrient content. This variability will be useful to breeders for utilization in grass pea improvement.

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Section: Amino Acid Analysismentioning

confidence: 99%

Diversity for vitamin and amino acid content in grass pea (Lathyrus sativus L.)

Arslan

2017

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“…The popularity of mulberry fruits among consumers and processing companies increases due to their recently discovered nutritional and nutraceutical value [7], [20]. Since the nutritional and functional compounds decrease and/or increase during ripening, a proper classification approach would extend the range of applications of mulberries in food and pharmaceutical industries [4], [14], [21]. Due to the good performance of deep learning models in classifying agricultural products, in this paper we consider using the deep neural networks to classify the mulberry fruit according to their ripeness…”

Section: Introductionmentioning

confidence: 99%

Detection of Mulberry Ripeness Stages Using Deep Learning Models

et al. 2021

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Ripeness classification is one of the most challenging tasks in the postharvest management of mulberry fruit. The risks of microbial contamination and human error in manual sorting are significant; it may result in quality degradation and wasting of processed products. Due to advanced developments in computer vision and machine learning, automated sorting became possible. This study presents the results of developing and testing a computer vision-based application using convolutional neural networks (CNNs) for the classification of mulberry fruit ripening stages. To reduce the training cost and improve the accuracy of classification, transfer learning was used to fine-tune the CNN models. The CNN models in the test include DenseNet, Inception-v3, ResNet-18, ResNet-50, and AlexNet. Transfer learning was used to fine-tune the models and improve the accuracy of classification. The AlexNet and ResNet-18 networks exhibited the best performance with 98.32% and 98.65% overall accuracy for classifying the ripeness of white and black mulberries, respectively. Moreover, the performance of the models did not change when the data sets of both genotypes were mixed. The ResNet-18 was able to classify both genotype and ripeness from 600 fruit images in 2.36 min with an overall accuracy of 98.03%, which was superior to other architectures. It indicates that the model could be used for precise classification of the ripening stages of mulberries and other horticultural products, as a part of an automated sorting system.INDEX TERMS Convolutional neural network, computer vision, online detection, ripening classification, transfer learning.

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“…Several studies were conducted to investigate the effect of argon on enzyme activities and sensory characteristics of fruits and vegetables [52][53][54][55][56]. Various authors reported the effectiveness of Ar in limiting the growth of certain microorganisms, suppressing enzymatic activities, and controlling degradative chemical reactions in several perishable food products, such as mulberry fruits, which have been reported to show microbial activity, such as white spots of fungal hyphae growth, after just 1 or 2 days of shelf-life [39,57,58].…”

Section: Introductionmentioning

confidence: 99%

“…For these reasons, in this study, several gas compositions were explored in order to ensure and maintain the quality characteristics of freshly harvested mulberries. Particularly, the objective of this research was to evaluate the effect of different MAP treatments (on quality parameters of mulberry fruit, stored at 4°C up to 12 d. International Journal of Food Science a total soluble solid content of 13.0 (±0.4)°Brix [58]. Mulberry fruits that were over or under ripe and with slight injuries or defects were removed.…”

Section: Introductionmentioning

confidence: 99%

Effect of Different Modified Atmosphere Packaging on the Quality of Mulberry Fruit (Morus alba L. cv Kokuso 21)

Tinebra

Sortino

Inglese

et al. 2021

International Journal of Food Science

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The control of temperature and gas composition is essential to maintain the fresh flavor and quality of perishable fruits like mulberry. This study presented a modified atmosphere experiment (MAP) for fresh fruit showing the potential benefits of innovative gas mixing with argon. The effects of MAP were studied on the physicochemical and qualitative attributes of mulberry preserved at 4 ± 1 °C and 90 ± 5 % R.H. Fresh mulberries were packaged with different gas combinations: MAP1 (4%O2+6%CO2+90%N2), MAP2 (10%O2+5%CO2+85%Ar), CTR1 (20.9%O2+0.04%CO2), and CTR2 (10%O2+5%CO2+85%N2). Changes in quality parameters were evaluated after 0, 4, 8, and 12 days of storage. Mulberries packaged with MAP had a lower weight loss than CTR samples which lost more than 80% of their initial weight. Furthermore, the results showed that the argon treatment was the best in keeping the fruit juice content, preserving its structure. Despite not showing great differences with MAP1 treatment, Ar allowed to maintain high TSS up to 8 storage days, slowed CO2 production. The sensory profile of mulberry fruit was not significantly affected by storage in modified atmospheres, and the production of potential unpleasant odors in MAP2 could not be perceived. The results of this study confirm that this innovative approach, using MAP technology, has a potential use in maintaining mulberry fruit quality for a longer time.

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Changes in physicochemical properties of mulberry fruits ( Morus alba L.) during ripening

Cited by 68 publications

References 26 publications

Diversity for vitamin and amino acid content in grass pea (Lathyrus sativus L.)

Diversity for vitamin and amino acid content in grass pea (Lathyrus sativus L.)

Detection of Mulberry Ripeness Stages Using Deep Learning Models

Effect of Different Modified Atmosphere Packaging on the Quality of Mulberry Fruit (Morus alba L. cv Kokuso 21)

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