
Citation: Haibo Li, Juncheng Tong. A novel clustering algorithm for time-series data based on precise correlation coefficient matching in the IoT[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 6654-6671. doi: 10.3934/mbe.2019331
[1] | Luh Suriati, I Made Supartha Utama, Bambang Admadi Harsojuwono, Ida Bagus Wayan Gunam . Incorporating additives for stability of Aloe gel potentially as an edible coating. AIMS Agriculture and Food, 2020, 5(3): 327-336. doi: 10.3934/agrfood.2020.3.327 |
[2] | Nafi Ananda Utama, Yulianti, Putrika Citta Pramesi . The effects of alginate-based edible coating enriched with green grass jelly and vanilla essential oils for controlling bacterial growth and shelf life of water apples. AIMS Agriculture and Food, 2020, 5(4): 756-768. doi: 10.3934/agrfood.2020.4.756 |
[3] | Ilenia Tinebra, Roberta Passafiume, Alessandra Culmone, Eristanna Palazzolo, Giuliana Garofalo, Vincenzo Naselli, Antonio Alfonzo, Raimondo Gaglio, Vittorio Farina . Boosting post-harvest quality of 'Coscia' pears: Antioxidant-enriched coating and MAP storage. AIMS Agriculture and Food, 2024, 9(4): 1151-1172. doi: 10.3934/agrfood.2024060 |
[4] | Hayriye Fatma Kibar, Ferhan K. Sabir . Chitosan coating for extending postharvest quality of tomatoes (Lycopersicon esculentum Mill.) maintained at different storage temperatures. AIMS Agriculture and Food, 2018, 3(2): 97-108. doi: 10.3934/agrfood.2018.2.97 |
[5] | Valentina M. Merlino, Danielle Borra, Aurora Bargetto, Simone Blanc, Stefano Massaglia . Innovation towards sustainable fresh-cut salad production: Are Italian consumers receptive?. AIMS Agriculture and Food, 2020, 5(3): 365-386. doi: 10.3934/agrfood.2020.3.365 |
[6] | Thi-Van Nguyen, Tom Ross, Hoang Van Chuyen . Evaluating the efficacy of three sanitizing agents for extending the shelf life of fresh-cut baby spinach: food safety and quality aspects. AIMS Agriculture and Food, 2019, 4(2): 320-339. doi: 10.3934/agrfood.2019.2.320 |
[7] | Jeffrey A. Clark, Hillary E. Norwood, Jack A. Neal, Sujata A. Sirsat . Quantification of pathogen cross-contamination during fresh and fresh-cut produce handling in a simulated foodservice environment. AIMS Agriculture and Food, 2018, 3(4): 467-480. doi: 10.3934/agrfood.2018.4.467 |
[8] | Alaika Kassim, Tilahun S. Workneh, Mark D. Laing . A review of the postharvest characteristics and pre-packaging treatments of citrus fruit. AIMS Agriculture and Food, 2020, 5(3): 337-364. doi: 10.3934/agrfood.2020.3.337 |
[9] | Soukaina Ouansafi, Fahde Abdelilah, Mostafa Kabine, Hind Maaghloud, Fatima Bellali, Karima El Bouqdaoui . The effects of soil proprieties on the yield and the growth of tomato plants and fruits irrigated by treated wastewater. AIMS Agriculture and Food, 2019, 4(4): 921-938. doi: 10.3934/agrfood.2019.4.921 |
[10] | Luana Muniz de Oliveira, Ágda Malany Forte de Oliveira, Railene Hérica Carlos Rocha Araújo, George Alves Dias, Albert Einstein Mathias de Medeiros Teodósio, José Franciraldo de Lima, Luana da Silva Barbosa, Wellinghton Alves Guedes . Spirulina platensis coating for the conservation of pomegranate. AIMS Agriculture and Food, 2020, 5(1): 76-85. doi: 10.3934/agrfood.2020.1.76 |
Mangosteen is a tropical fruit that is currently popular. The mangosteen fruit has a delicious taste and contains vitamins, minerals, and antioxidants that are beneficial for health [1,2]. Mangosteen is perishable. Although edible parts are still suitable for consumption, damage to nonedible parts, such as skin, determines consumer preferences. The number of nonedible parts of the mangosteen fruit (around 63%–75%) is relatively high and contributes to household waste [3,4], causing a growth in the sales of edible parts, i.e., fresh-cut fruits. The fresh-cut selection is also driven by consumer demand for quality products and a lack of preparation time [5,6,7].
Fresh cuts are products with minimal processing steps to get maximum quality. Processing includes stripping, cutting, slicing, pith removal, washing, and packaging [8]. Some of the advantages of minimally processed products include providing consumers with a variety of choices in one package, enabling consumers to get the required fresh quantity, easy assessment of quality, and reducing the volume and transport costs [4]. Fresh-cut fruit has the disadvantages of perishability and shorter shelf-life than the whole fruit [5,9]. Tissue injury causes the fruit to undergo physicochemical changes, which induce deterioration [10,11].
According to [5] and [12], the application of 1% CaCl2 combined with cold storage enhances the firmness of fresh-cut and prevents browning. The application of the edible coating helps maintain the freshness of fresh-cut products [13], as a barrier against mass transfer and gas exchange [14]. According to [15], edible coatings improve appearance (bright and shiny colors), retain moisture, prevent weight loss and protect against microorganisms. However, there has been no previous research on the application of the edible coating on fresh-cut mangosteen. Therefore, it is important to study and know the coating method and extend shelf life.
Aloe vera gel has potential as an edible coating (ecogel) because it consists of polysaccharides containing more than 75 functional chemical compounds, such as saponins, sterols, acemannan, vitamins, and folic acid [16]. The advantages of using ecogel are biodegradability, permeable oxygen, antioxidant activity, low toxicity, low cost, and ease of application [17]. The concentration of additives determines the consistency of the ecogel. The optimal concentration of citric acid, ascorbic acid, and potassium sorbate additives is 0.15% [18]. The obstacle in applying ecogel to fresh-cut fruit is the difficulty of adhesion on the surface of hydrophilic fruit slices [5].
The adhesion ability of the ecogel is influenced by the structure, size, and chemical constituents. The small size of the particle improves solubility, absorption of active compounds, and controlled release. The nano-ecogel is one of the applications of nanotechnology in the postharvest handling of fresh-cut fruit. There are many reports of aloe vera for coating fruits and vegetables, but the application of nano-ecogel is a new study. The advantages obtained from the use of nano-ecogel include a barrier, mechanical properties, emulsion system, and bioavailability [19]. The ecogel application is influenced by composition, time, method, and layer thickness. The use of nano-ecogel is an effort to maintain the physicochemical change in fresh-cut mangosteen [19,20]. However, to the best of the authors' knowledge, no information is available on the concentration of nano-ecogel and the best immersion time of fresh-cut mangosteen. Therefore, research is needed to determine the concentration of nano-ecogel and immersion time to maintain the physicochemical characteristics of fresh-cut mangosteen.
Preparation of nano-ecogel using the method invented by [18]. The first step of ecogel production is sorting 1-year-old aloe leaves (Aloe barbadense. Miller). Leaves were left for 24 h at room temperature to remove yellow mucus. Aloe leaves were washed with water to remove the yellow mucus residue and unpleasant odors that could reduce the quality of the gel. Tripping and filleting were to produce gel fillets by using a stainless knife. Gel filets were homogenized for 5 min and heated at 70 ± 1 ℃ for 5 min [21]. The gel was cooled for 1 h at 27 ℃ and filtered with the Rocker 300 vacuum pump, 5340FK1000R flash filter, and Whatman filter paper no. 42. Aloe gel was added with a mixture of citric acid, ascorbic acid, and potassium sorbate with concentrations of 0.15% (w/v). The agitation process used the sonicate masonic Q125 to obtain the nanostructures, with a 59-time delay pulse of 30 seconds for 50 min. The size of ecogel nanoparticles was determined using the UV–vis spectrophotometer. The maximum absorbance indicated a particle size of 20–110 nm [22].
Fresh and ripe mangosteen fruits aged 105 days since flowering were collected from a garden in Panji Village, Sukasada District, Buleleng Regency, Bali Province, Indonesia. The criteria of mangosteen fruit included greenish-yellow skin color with 50% pink spots spreading on the skin, round like a compressed ball, flesh consisting of 5–8 segments, fresh green petals, and fruit weight of 130–180 g. The mangosteen fruit was precooled by washing with water and stored in a clean tissue paper to drain excess water. The mangosteen fruit was peeled carefully to obtain a fruit without skin (fresh-cut fruit) and left with fused segments.
Peeled fresh-cut mangosteen was first dipped into 1% CaCl2 solution for 10 min and dried using a blower for 20 min. Fresh-cut mangosteen was applied with 100%, 75%, 50% and 25% nano-ecogel. A concentration of 100% nano-ecogel meant pure nano-ecogel, whereas 75% concentration indicated 75 mL nano-ecogel and 25 ml water. The immersion time of fresh-cut mangosteen in the nano-ecogel was 1, 2 and 3 min. This study was repeated three times. Fresh-cut fruits coated with nano-ecogel was drained and dried using a blower for 20 min. Furthermore, fresh-cut mangosteen was packaged in a 10 cm × 20 cm × 5 cm plastic box equipped with two holes with a diameter of 0.5 cm on the lid and stored at a cold temperature (7 ± 1 ℃). During storage for 3, 6 and 9 days, acidity [30], vitamin C [31], water content [34], total dissolved solids (TDS) [30], weight loss [23], texture [32], and browning index [33] were evaluated.
This study used a completely randomized design factorial pattern. Statistical analysis was performed using SPSS to measure the variance of all observed variables through analysis of variance. The significant value obtained using Duncan, p < 0.05 shows a noticeable difference.
Immature fruits contain some organic acids, which tend to degrade during ripening. A decrease in acidity changes the acidity of the fruit. The acidity of fresh-cut mangosteen after nano-ecogel application on day 3 is on average higher than before application which is 3.06. A high concentration of nano-ecogel results in an increased ability to cover the surface pores of the fruit, thereby inhibiting the process of converting sugar into organic acids [35]. The taste of fruits is mostly influenced by the contents of sugar, organic acids, phenolics, and volatile compounds.
High nano-ecogel concentrations caused the fresh-cut surface to close, thus delaying the conversion of sugar into organic acids. In line with the results of the study [35], that the application of nanoparticles chitosan inhibits the conversion of starch to sugar and sugar to organic acid, due to the ability of this coating as a barrier in the surface of fresh-cut. The nanostructured edible coating on minimally processed foods effectively controlled moisture loss and retained the color and extend shelf-life of apple slices [36]. Following the opinion of [23], aloe vera gel contains many functional components, and antimicrobials and antioxidants can inhibit postharvest damage.
Fresh-cut mangosteen stored for nine days and treated with 50% nano-ecogel has the highest vitamin C content. Figure 2 shows that the fresh-cut mangosteen fruit on day 3 was treated with 50% nano-ecogel, the highest vitamin C content (2.79 mg/100 g), 25% ecogel the lowest vitamin C content (2.27 mg/100 g). This means 50% is the most ideal concentration of nano-ecogel coating solution to cover the pores of the fresh-cut surface so that the vitamin C oxidation process can be avoided. The vitamin C content of fresh-cut mangosteen on days 6 and 9 ranges from 1.68 mg/100 g to 2.22 mg/100 g and from 1.55 mg/100 g to 1.89 mg/100 g, respectively. The mangosteen fruit contains several important nutrients, including xanthones and vitamin C [24,25], and their amounts are remarkably influenced by many factors, e. g. variety, environment, and maturity. Vitamin C levels in fresh-cut mangosteen are relatively stable, indicating that the application of nano-ecogel can maintain the vitamin C levels of fresh-cut mangosteen. The loss of vitamin C in the material is due to the oxidation process [26]. Aloe vera gel has antioxidant abilities that can inhibit postharvest damage [27].
The decrease in the water content of fresh-cut mangosteen is unavoidable. Mangosteen is a climacteric fruit that still undergoes respiration, i. e. carbohydrates are broken down into simple sugars, water, and energy [1]. Increasing the concentration of nano-ecogel can suppress water loss. A previous study [5] showed that the application of edible coating retains water and results in bright and shiny colors. Edible coatings on the fruit surface tissue aim to modify the environment, inhibit gas transfer, reduce water and aroma loss, change color and improve the appearance [28]. Fresh-cut mangosteen has the highest water content on the ninth day by a nano-ecogel concentration of 100% for 3 minutes, and the lowest on 25% nano-ecogel for 3 minutes. Water loss can be suppressed by increasing the concentration of nano-ecogel. [28] stated that edible coating on the surface of fresh-cut fruit aims to modify the atmosphere, inhibit gas transfer, lose water and aroma, delay color change, and improve appearance. Aloe vera gel consists of polysaccharides glucomannan and acemannan which have potential as edible coatings on fruits [18]
Table 1 shows no significant result in terms of different days at various treatments. The moisture contents of fresh-cut mangosteen fruit are 80.43%–84.45%, 80.82%–83.63%, and 80.47−87.10% on days 3, 6, and 9, respectively. The high concentration of nano-ecogel delays the loss of water of fresh-cut mangosteen. Aloe vera gel consists of glucomannan and acemannan polysaccharides, which have the potential as edible coatings and prevent water loss in fruits [27].
Concentration of Ecogel | Immersion time (minutes) | Day | ||
3 | 6 | 9 | ||
Control | 81.45 | 82.91 | 82.35 | |
100% | 1 | 82.73 b | 81.57 f | 81.93 g |
75% | 1 | 82.16 g | 81.10 i | 80.47 i |
50% | 1 | 81.72 i | 81.32 h | 82.02 f |
25% | 1 | 81.96 h | 81.73 e | 81.95 g |
100% | 2 | 84.45 a | 83.63 a | 85.15 b |
75% | 2 | 82.64 c | 81.80 d | 83.33 c |
50% | 2 | 82.33 f | 82.46 c | 81.15 h |
25% | 2 | 81.54 j | 81.57 f | 82.30 e |
100% | 3 | 82.46 d | 83.40 b | 87.10 a |
75% | 3 | 80.43 e | 81.60 f | 83.62 c |
50% | 3 | 82.64 c | 79.50 g | 82.41 d |
25% | 3 | 81.12 k | 80.82 i | 79.98 j |
Note: Different letters behind the average value in the same column showed a significant difference with Duncan's test 5% |
The application of nano-ecogel at a high concentration on day 3 leads to increased TDS of fresh-cut mangosteen because mangosteen is a climacteric fruit and still ripens during storage. Several types of sugar glucose, fructose, and sucrose in climacteric fruits, such as mangosteen, tend to increase during cell maturation [29]. The nano-ecogel concentration of 50% can maintain the TDS of fresh-cut mangosteen fruit on days 6 and 9. Table 2 shows that the highest total soluble solids of fresh-cut mangosteen fruit (24.82 °Brix) is obtained at immersion in 50% ecogel for 3 min. The nano-ecogel concentration of 50% produces a solution that is not too thick and also does not dilute. In line with the opinion [13] that a good coating solution is non-sticky and easily dry. TDS remain stable because the application of nanoparticles inhibits the conversion of sugar to organic acid, due to the ability as a barrier in the surface of fresh-cut [35]. The lowest TDS is 23.42 °Brix and observed at immersion in 25% ecogel for 1 min. Fresh-cut mangosteen fruit on day 6, a TDS value between 22.22 °Brix and 23.82 °Brix. On the ninth day of storage, fresh-cut mangosteen immersed at 50% ecogel for 3 min has the highest TDS (24.02 °Brix), and that immersed at 100% ecogel for 2 min has the lowest TDS (20.02 °Brix). The application of ecogel on the fruit surface has the advantage of several active ingredients that can be inserted into the polymer matrix for the maintenance of freshness and sensory attributes [30]. Results from a previous study [17], showed that aloe vera gel can be used to extend shelf life and maintain freshness at cold temperatures.
Concentration of Ecogel | Immersion time (minutes) | Day | ||
3 | 6 | 9 | ||
Control | 23.50 | 22.70 | 22.60 | |
100% | 1 | 23.62 de | 22.32 d | 22.02 c |
75% | 1 | 24.12 cd | 23.12 b | 21.42 f |
50% | 1 | 24.12 cd | 23.12 b | 21.92 cd |
25% | 1 | 23.42 e | 22.42 cd | 21.72 e |
100% | 2 | 23.82 de | 22.22 e | 20.02 h |
75% | 2 | 24.82 a | 22.22 e | 21.82 de |
50% | 2 | 23.52 e | 21.32 d | 20.72 g |
25% | 2 | 23.82 de | 22.72 c | 21.82 de |
100% | 3 | 24.52 ab | 23.82 a | 21.82 de |
75% | 3 | 24.22 bc | 22.52 cd | 22.32 b |
50% | 3 | 24.62 ab | 23.82 a | 24.02 a |
25% | 3 | 24.32 bc | 22.42 cd | 21.82 de |
Note: Different letters behind the average value in the same column showed a significant difference with Duncan's test 5% |
A high concentration of nano-ecogel and long immersion on days 3, 6, and 9 results in a high possibility of closing the pores of the fresh-cut mangosteen surface, thereby suppressing transpiration and decreasing weight loss. Following the opinion of [31], edible coatings can retain moisture and prevent weight loss. The highest fresh-cut mangosteen weight loss at immersion in 25% nano-ecogel for 3 min is significantly different from those at other treatments. The lowest weight loss is observed at immersion in 100% nano-ecogel for 3 min. Figure 3 shows that the highest weight loss of fresh-cut mangosteen 3.83% (FW) is obtained at immersion in 25% ecogel for 3 min and that the lowest weight loss of fresh-cut mangosteen 0.51% (FW) is observed at immersion in 100% ecogel for 3 min. The fresh-cut mangosteen fruits on days 6 and 9 have weight loss values of 0.52%–6.54% and 0.52%–6.54% (FW), respectively. Aloe vera gel, as a protector against physical and chemical biological changes, is reported to form a thin layer, improve appearance, and retain moisture [17]. The weight loss of fresh-cut mangosteen increases until the end of storage, which is day 9. Given that fresh-cut mangosteen has a climacteric pattern, the respiration rate still increases during storage [4]. Fresh-cut mangosteen after the nano-ecogel application shows a lower weight loss than that before the nano-ecogel application.
The texture of fresh-cut mangosteen was measured using a texture analyzer at a speed: distance of 10:8. Texture changes during the storage period of fresh-cut mangosteen occur due to the ripening process, and the fruit that is stored for a long time softens because of the influence of pectolytic enzymes. The highest texture value of fresh-cut mangosteen until day 9 is obtained at immersion in 50% nano-ecogel for 1 min. The lowest texture is obtained at immersion in 25% nano-ecogel for 1 min. Table 3 shows that the texture values of fresh-cut mangosteen on days 3 and 6 are 1.58–3.56 and 1.51–3.07 N/m, respectively. The highest texture of fresh-cut mangosteen on day 9 (2.89 N/m) is obtained at immersion in 50% ecogel for 1 min. The lowest texture of 1.38 N/m is obtained at immersion in 25% ecogel for 1 min. This result shows that immersion in 50% nano-ecogel for 1 min can maintain the fresh-cut texture of mangosteen. [2] stated that fruit texture decreases during storage. The activity of pectinase during storage automatically causes loss of rigidity in the fruit tissue [14]. The nano-ecogel interacts with pectin polymers to form crosslinking networks that increase mechanical strength, which delays senescence and controls physiological damage of fresh-cut mangosteen.
Concentration of Ecogel |
Immersion time (minutes) |
Day | ||
3 | 6 | 9 | ||
Control | 1.20 | 0.83 | 0.60 | |
100% | 1 | 3.01 a | 2.76 a | 2.65 ab |
75% | 1 | 2.44 a | 2.15 a | 1.66 cd |
50% | 1 | 3.08 a | 3.00 a | 2.89 a |
25% | 1 | 3.25 a | 3.07 a | 1.38 d |
100% | 2 | 3.56 a | 1.94 a | 1.83 cd |
75% | 2 | 2.49 a | 2.80 a | 2.79 a |
50% | 2 | 2.53 a | 1.95 a | 2.19 cd |
25% | 2 | 1.59 a | 1.88 a | 1.86 cd |
100% | 3 | 2.15 a | 1.51 a | 1.42 d |
75% | 3 | 2.60 a | 2.78 a | 2.36 bc |
50% | 3 | 1.84 a | 1.62 a | 1.77 cd |
25% | 3 | 1.58 a | 1.70 a | 1.78 cd |
Note: Different letters behind the average value in the same column showed a significant difference with Duncan's test 5% |
The lowest browning index of fresh-cut mangosteen on day 3 of 1.99 is observed at immersion in 100% nano-ecogel for 3 min. Figure 4 shows that the highest browning index of fresh-cut mangosteen on day 3 is 8.18, which is obtained at immersion in 25% ecogel for 3 min. The highest browning index on day 6 ranges from 1.01 to 8.34. Observations on day 9 show that the highest browning index is 32.62 and observed at immersion in 100% ecogel for 3 min and that the lowest browning index is 9.88 and observed at immersion in 50% ecogel for 1 min. This result indicates that increasing the concentration of nano-ecogel at the same immersion time results in reduced browning of fresh-cut mangosteen. Nano-ecogel at the right concentration and immersion time will produce a layer that can cover the fresh-cut surface perfectly and contact with oxygen was avoided. Thus, the browning reaction can be prevented. If the fresh-cut surface is tightly closed, the addition of immersion has no effect. [28] stated that edible coating on the surface of fresh-cut fruit aims to modify the atmosphere, inhibit gas transfer, delay color change, and improve appearance.
The enzymatic oxidation of monophenols produces o-diphenols, which are converted into quinones. Nonenzymatic polymerization forms the brown or melanin color [9]. The ability of nano-ecogel as an antioxidant can inhibit the oxidation process of phenol compounds and postharvest damage [27]. Day 6 also shows the same results as day 3, the immersion of fresh-cut mangosteen in 50% nano-ecogel for 1 min has provided a low browning index of 4.87 than control of 8.30. On day 9, the treatment of 50% nano-ecogel for 1 min has provided a low browning index of 27.83 than control 29.10. Several researchers applied aloe vera gel with concentrations ranging from 50% to 100% as an edible coating to preserve whole fruits, such as table grapes [32], mango [33,37], blueberries [34], and apricot [35,38].
The effects of nano-ecogel concentration, immersion time, and their interaction on the degree of acidity were investigated. In addition, the water content, TDS, weight loss, and browning index of fresh-cut mangosteen were studied. In conclusion, immersion in 50% nano-ecogel for 1 min maintained the freshness of fresh-cut mangosteen. Compared with the initial fruit, the fresh-cut mangosteen after nano-ecogel application was more attractive, whiter, juicy, and shiny until the ninth day of storage.
The author would like to thank the rector of the University of Warmadewa and the chairman of the KORPRI Welfare Foundation for their support in this research and all colleagues who helped with this project.
[1] | M. Weiser, R. Gold and J. S. Brown, The origins of ubiquitous computing research at PARC in the late 1980s, IBM Syst. J., 38 (1999), 693–696. |
[2] | V. A. Memos, K. E. Psannis, Y. Ishibashi, et al., An efficient algorithm for media-based surveillance system (EAMSuS) in IoT smart city framework, Future Gener. Comp. Syst., 83 (2018), 619–628. |
[3] | S. Tang, D. R. Shelden, C. M. Eastman, et al., A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends, Automat. Const., 101 (2019), 127–139. |
[4] | T. Baker, A. Taleb-Bendiab, M. Randles, et al., Understanding elasticity of cloud services compositions, In 2012 IEEE Fifth International Conference on Utility and Cloud Computing, Chicago(USA), IEEE, (2012), 231–232. |
[5] | A. Jula, E. Sundararajan and Z. Othman, Cloud computing service composition: A systematic literature review, Expert Syst. Appl., 41 (2014), 3809–3824. |
[6] | Q. Wu, G. Ding, Y. Xu, et al., Cognitive internet of things: a new paradigm beyond connection, IEEE Int. Things J., 1 (2014), 129–143. |
[7] | C. Gomez, S. Chessa, A. Fleury, et al., Internet of Things for enabling smart environments: A technology-centric perspective, J. Ambient Int. Smart Environ., 11 (2019), 23–43. |
[8] | L. Atzori, A. Iera and G. Morabito, The internet of things: A survey, Comput. Netw., 54 (2010), 2787–2805. |
[9] | F. Chen, P. Deng, J. F. Wan, et al., Data Mining for the Internet of Things. Literature Review and Challenges, Int. J. Distrib. Sensor Netw., 11 (2015), P431047. |
[10] | H. Li, Z. Zhang, X. Wang, et al., Electricity consumption behaviour analysis based on time sequence clustering, In 2018 International Conference on Computer Information Engineering and Bioinformatics, Guangzhou(China), IOP Publishing, (2018), 032011. |
[11] | S. Pravilovic, M. Bilancia, A. Appice, et al., Using multiple time series analysis for geosensor data forecasting, Inf. Sci., 380 (2017), 31–52. |
[12] | J. Liu, W. Li, J. Wu, et al., Visualizing the intercity correlation of PM2. 5 time series in the Beijing-Tianjin-Hebei region using ground-based air quality monitoring data, PloS One, 13 (2018), e0192614. |
[13] | J. Soares, P. A. Makar, Y. Aklilu, et al., The use of hierarchical clustering for the design of optimized monitoring networks, Atmos. Chem. Phys., 18 (2018), 6543–6566. |
[14] | A. Zaslavsky, C. Perera and D. Georgakopoulos, Sensing as a service and big data, In International Conference on Advances in Cloud Computing (ACC-2012), Bangalore(India), Eprint Arxiv, (2012), 21–29. |
[15] | C. Chang and C. Li, Algebraic secret sharing using privacy homomorphisms for IoT-based healthcare systems, Math. Biosci. Eng., 16 (2019), 3367–3381. |
[16] | Y. Ren, Y. Leng, Y Cheng, et al., Secure data storage based on blockchain and coding in edge computing, Math. Biosci. Eng., 16 (2019), 1874–1892. |
[17] | C. Li and B. Palanisamy, Privacy in internet of things: From principles to technologies, IEEE Int. Things J., 6 (2019), 488–505. |
[18] | A. P. Plageras, K. E. Psannis, C. Stergiou, et al., Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings, Future Gener. Comp. Syst., 82 (2018), 349–357. |
[19] | K. P. Kibiwott, Y. Zhao, J. Kogo, et al., Verifiable fully outsourced attribute-based signcryption system for IoT eHealth big data in cloud computing, Math. Biosci. Eng., 16 (2019), 3561–3594. |
[20] | S. K. Jensen, T. B. Pedersen and C. Thomsen, Time series management systems: A survey, IEEE T. Knowledge Data Eng., 29 (2017), 2581–2600. |
[21] | C. Stergiou, K. E. Psannis, A. P. Plageras, et al., Algorithms for efficient digital media transmission over IoT and cloud networking, J. Multimedia Inf. Syst., 5 (2018), 27–34. |
[22] | K. E. Psannis, C. Stergiou and B. B. Gupta, Advanced media-based smart big data on intelligent cloud systems, IEEE T. Sustain. Comput., 4 (2018), 77–87. |
[23] | W. Ejaz, M. Naeem, A. Shahid, et al., Efficient energy management for the internet of things in smart cities, IEEE Commun. Mag., 55 (2017), 84–91. |
[24] | A. F. Mohammad and V. Korosh, Energy management-as-a-service over fog computing platform, IEEE Int. Things J., 3 (2015), 161–169. |
[25] | F. Adenugba, S. Misra, R. Maskeliūnas, et al., Smart irrigation system for environmental sustainability in Africa: An Internet of Everything (IoE) approach, Math. Biosci. Eng., 16 (2019), 5490–5503. |
[26] | M. Izal, D. Morató, E. Magaña, et al., Computation of traffic time series for large populations of IoT devices, Sensors, 19 (2019), 78. |
[27] | Ş. Kolozali, D. Puschmann, M. Bermudez-Edo, et al., On the effect of adaptive and nonadaptive analysis of time-series sensory data, IEEE Int. Things J., 3 (2016), 1084–1098. |
[28] | R. Salles, P. Mattos, A. M. D. Iorgulescu, et al., Evaluating temporal aggregation for predicting the sea surface temperature of the Atlantic Ocean. Ecol. Inform., 36 (2016), 94–105. |
[29] | J. Roberts, M. Curran, S. Poynter, et al., Correlation confidence limits for unevenly sampled data, Comput. Geosci., 104 (2017), 120–124. |
[30] | I. Ozken, D. Eroglu, S. F. Breitenbach, et al., Recurrence plot analysis of irregularly sampled data, Phys. Rev. E., 98 (2018), 052215. |
[31] | H. Li, K. C. C. Chan, M. Liang, et al., Composition of resource-service chain for cloud manufacturing, IEEE T. Ind. Informat., 12 (2016), 211–219. |
[32] | H. Li, M. Liang and T. Liang, Optimizing the composition of a resource service chain with inter-organizational collaboration, IEEE T. Ind. Informat., 13 (2017), 1152–1161. |
[33] | H. Li and T. He, Selecting key feature sequence of resource services in industrial internet of things, IEEE Access, 6 (2018), 72152–72162. |
[34] | L. Wen, L. Gao, Y. Dong, et al., A negative correlation ensemble transfer learning method for fault diagnosis based on convolutional neural network, Math. Biosci. Eng., 16 (2019), 3311–3330. |
[35] | Z. Zhang, L. Liu, S. Zhang, et al., A service-based method for multiple sensor streams aggregation in fog computing, Wireless Commun. Mobile Comput., 1 (2018), 1–11. |
[36] | M. Mehdizadeh, R. Ghazi and M. Ghayeni, Power system security assessment with high wind penetration using the farms models based on their correlation, IET Renew. Power Gener., 12 (2018), 893–900. |
[37] | Z. Chen, Z. Xue, L. Zhang, et al., Analyzing the correlation and predictability of wind speed series based on mutual information, IEEE T. Electr. Electr. Eng., 13 (2018), 1829–1830. |
[38] | J. Olauson and M. Bergkvist, Correlation between wind power generation in the European countries, Energy, 114 (2016), 663–670. |
[39] | F. Wang, A novel coefficient for detecting and quantifying asymmetry of California electricity market based on asymmetric detrended cross-correlation analysis, Chaos Interdiscipl. J. Nonlinear Sci., 26 (2016), 063109. |
[40] | T. Cui, F. Caravelli and C. Ududec, Correlations and clustering in wholesale electricity markets, Physica A., 492 (2018), 1507–1522. |
[41] | R. Lin, B Wu and Y Su, An adaptive weighted pearson similarity measurement method for load curve clustering, Energies, 11 (2018), 1–17. |
[42] | A. Mueen, H. Hamooni and T. Estrada, Time series join on subsequence correlation, In 2014 IEEE International Conference on Data Mining, Shenzhen(China), IEEE Computer Society Press, (2014), 450–459. |
[43] | Z. Ye, S. Mistry, A. Bouguettaya, et al., Long-term QoS-aware cloud service composition using multivariate time series analysis, IEEE T. Services Comput., 9 (2014), 382–393. |
[44] | M. Disegna, P. D'Urso and F. Durante, Copula-based fuzzy clustering of spatial time series, Spat. Stat., 21 (2017), 209–225. |
[45] | J. C. Dunn, Well-separated clusters and optimal fuzzy partitions, J. Cybernetics, 4 (1974), 95–104. |
[46] | M. Halkidi, Y. Batistakis and M Vazirgiannis, On clustering validation techniques, J. Intell. Inf. Syst., 17 (2001), 107–145. |
1. | Nabeela Haneef, Yvan Garièpy, Vijaya Raghavan, Jiby Kudakasseril Kurian, Najma Hanif, Tahira Hanif, Effects of Aloe-pectin coatings and osmotic dehydration on storage stability of mango slices, 2022, 25, 1981-6723, 10.1590/1981-6723.02822 | |
2. | Luh Suriati, I. Made Supartha Utama, Bambang Admadi Harsojuwono, Ida Bagus Wayan Gunam, Effect of Additives on Surface Tension, Viscosity, Transparency and Morphology Structure of Aloe vera Gel-Based Coating, 2022, 6, 2571-581X, 10.3389/fsufs.2022.831671 | |
3. | Luh Suriati, Nano Coating of Aloe-Gel Incorporation Additives to Maintain the Quality of Freshly Cut Fruits, 2022, 6, 2571-581X, 10.3389/fsufs.2022.914254 | |
4. | A. Soltani, K. Benfreha, K. Hamraoui, Analysis of Physico-chemical Properties, and Antimicrobial Activity of Aloe vera (Aloe barbadensis Miller), 2022, 1624-8597, 10.3166/phyto-2022-0349 | |
5. | Luh Suriati, Nanocoating-konjac application as postharvest handling to extend the shelf life of Siamese oranges, 2023, 7, 2571-581X, 10.3389/fsufs.2023.1104498 | |
6. | Luh Suriati, Ni Made Ayu Suardani Singapurwa, Aida Firdaus Muhamad Nurul Azmi, Rovina Kobun, I Wayan Widiantara Putra, Putu Ajus Raditya Putra, I.B.W. Gunam, N.S. Antara, A.K. Anal, P.J. Batt, T. Sone, I N.K. Putra, G.P.G. Putra, P. Hariyadi, A.B. Sitanggang, K.A. Nocianitri, I D.G.M. Permana, I W.R. Widarta, N.N. Puspawati, I.B.A. Yogeswara, I D.P.K. Pratiwa, The Effect of Porang Coating Application and Storage Time on The Characteristics of Kintamani Siamese Oranges, 2024, 98, 2117-4458, 06011, 10.1051/bioconf/20249806011 | |
7. | Luh Suriati, I Gede Pasek Mangku, Luh Kade Datrini, Hanilyn A. Hidalgo, Josephine Red, Serviana Wunda, Anak Agung Sagung Manik Cindrawat, Ni Luh Putu Sulis Dewi Damayanti, The effect of maltodextrin and drying temperature on the characteristics of Aloe-bignay instant drink, 2023, 3, 27725022, 100359, 10.1016/j.afres.2023.100359 |
Concentration of Ecogel | Immersion time (minutes) | Day | ||
3 | 6 | 9 | ||
Control | 81.45 | 82.91 | 82.35 | |
100% | 1 | 82.73 b | 81.57 f | 81.93 g |
75% | 1 | 82.16 g | 81.10 i | 80.47 i |
50% | 1 | 81.72 i | 81.32 h | 82.02 f |
25% | 1 | 81.96 h | 81.73 e | 81.95 g |
100% | 2 | 84.45 a | 83.63 a | 85.15 b |
75% | 2 | 82.64 c | 81.80 d | 83.33 c |
50% | 2 | 82.33 f | 82.46 c | 81.15 h |
25% | 2 | 81.54 j | 81.57 f | 82.30 e |
100% | 3 | 82.46 d | 83.40 b | 87.10 a |
75% | 3 | 80.43 e | 81.60 f | 83.62 c |
50% | 3 | 82.64 c | 79.50 g | 82.41 d |
25% | 3 | 81.12 k | 80.82 i | 79.98 j |
Note: Different letters behind the average value in the same column showed a significant difference with Duncan's test 5% |
Concentration of Ecogel | Immersion time (minutes) | Day | ||
3 | 6 | 9 | ||
Control | 23.50 | 22.70 | 22.60 | |
100% | 1 | 23.62 de | 22.32 d | 22.02 c |
75% | 1 | 24.12 cd | 23.12 b | 21.42 f |
50% | 1 | 24.12 cd | 23.12 b | 21.92 cd |
25% | 1 | 23.42 e | 22.42 cd | 21.72 e |
100% | 2 | 23.82 de | 22.22 e | 20.02 h |
75% | 2 | 24.82 a | 22.22 e | 21.82 de |
50% | 2 | 23.52 e | 21.32 d | 20.72 g |
25% | 2 | 23.82 de | 22.72 c | 21.82 de |
100% | 3 | 24.52 ab | 23.82 a | 21.82 de |
75% | 3 | 24.22 bc | 22.52 cd | 22.32 b |
50% | 3 | 24.62 ab | 23.82 a | 24.02 a |
25% | 3 | 24.32 bc | 22.42 cd | 21.82 de |
Note: Different letters behind the average value in the same column showed a significant difference with Duncan's test 5% |
Concentration of Ecogel |
Immersion time (minutes) |
Day | ||
3 | 6 | 9 | ||
Control | 1.20 | 0.83 | 0.60 | |
100% | 1 | 3.01 a | 2.76 a | 2.65 ab |
75% | 1 | 2.44 a | 2.15 a | 1.66 cd |
50% | 1 | 3.08 a | 3.00 a | 2.89 a |
25% | 1 | 3.25 a | 3.07 a | 1.38 d |
100% | 2 | 3.56 a | 1.94 a | 1.83 cd |
75% | 2 | 2.49 a | 2.80 a | 2.79 a |
50% | 2 | 2.53 a | 1.95 a | 2.19 cd |
25% | 2 | 1.59 a | 1.88 a | 1.86 cd |
100% | 3 | 2.15 a | 1.51 a | 1.42 d |
75% | 3 | 2.60 a | 2.78 a | 2.36 bc |
50% | 3 | 1.84 a | 1.62 a | 1.77 cd |
25% | 3 | 1.58 a | 1.70 a | 1.78 cd |
Note: Different letters behind the average value in the same column showed a significant difference with Duncan's test 5% |
Concentration of Ecogel | Immersion time (minutes) | Day | ||
3 | 6 | 9 | ||
Control | 81.45 | 82.91 | 82.35 | |
100% | 1 | 82.73 b | 81.57 f | 81.93 g |
75% | 1 | 82.16 g | 81.10 i | 80.47 i |
50% | 1 | 81.72 i | 81.32 h | 82.02 f |
25% | 1 | 81.96 h | 81.73 e | 81.95 g |
100% | 2 | 84.45 a | 83.63 a | 85.15 b |
75% | 2 | 82.64 c | 81.80 d | 83.33 c |
50% | 2 | 82.33 f | 82.46 c | 81.15 h |
25% | 2 | 81.54 j | 81.57 f | 82.30 e |
100% | 3 | 82.46 d | 83.40 b | 87.10 a |
75% | 3 | 80.43 e | 81.60 f | 83.62 c |
50% | 3 | 82.64 c | 79.50 g | 82.41 d |
25% | 3 | 81.12 k | 80.82 i | 79.98 j |
Note: Different letters behind the average value in the same column showed a significant difference with Duncan's test 5% |
Concentration of Ecogel | Immersion time (minutes) | Day | ||
3 | 6 | 9 | ||
Control | 23.50 | 22.70 | 22.60 | |
100% | 1 | 23.62 de | 22.32 d | 22.02 c |
75% | 1 | 24.12 cd | 23.12 b | 21.42 f |
50% | 1 | 24.12 cd | 23.12 b | 21.92 cd |
25% | 1 | 23.42 e | 22.42 cd | 21.72 e |
100% | 2 | 23.82 de | 22.22 e | 20.02 h |
75% | 2 | 24.82 a | 22.22 e | 21.82 de |
50% | 2 | 23.52 e | 21.32 d | 20.72 g |
25% | 2 | 23.82 de | 22.72 c | 21.82 de |
100% | 3 | 24.52 ab | 23.82 a | 21.82 de |
75% | 3 | 24.22 bc | 22.52 cd | 22.32 b |
50% | 3 | 24.62 ab | 23.82 a | 24.02 a |
25% | 3 | 24.32 bc | 22.42 cd | 21.82 de |
Note: Different letters behind the average value in the same column showed a significant difference with Duncan's test 5% |
Concentration of Ecogel |
Immersion time (minutes) |
Day | ||
3 | 6 | 9 | ||
Control | 1.20 | 0.83 | 0.60 | |
100% | 1 | 3.01 a | 2.76 a | 2.65 ab |
75% | 1 | 2.44 a | 2.15 a | 1.66 cd |
50% | 1 | 3.08 a | 3.00 a | 2.89 a |
25% | 1 | 3.25 a | 3.07 a | 1.38 d |
100% | 2 | 3.56 a | 1.94 a | 1.83 cd |
75% | 2 | 2.49 a | 2.80 a | 2.79 a |
50% | 2 | 2.53 a | 1.95 a | 2.19 cd |
25% | 2 | 1.59 a | 1.88 a | 1.86 cd |
100% | 3 | 2.15 a | 1.51 a | 1.42 d |
75% | 3 | 2.60 a | 2.78 a | 2.36 bc |
50% | 3 | 1.84 a | 1.62 a | 1.77 cd |
25% | 3 | 1.58 a | 1.70 a | 1.78 cd |
Note: Different letters behind the average value in the same column showed a significant difference with Duncan's test 5% |