S. Saallah, J. Roslan, Nurul Nadjwa Zakaria, W. Pindi, S. Siddiquee, M. Misson, Clarence M. Ongkudon, N. H. Jamil, W. Lenggoro
In the present study, facile one-pot production of nanocellulose from ripe and unripe Saba’ banana (Musa acuminata x balbisiana) peel was conducted by utilizing hydrogen peroxide (H2O2) as an oxidizing agent prior to hydrolysis with sulfuric acid (H2SO4) at different concentrations (8%, 24% and 40%). Proximate and chemical compositions of the ripe and unripe banana peel (BP) powder were analyzed, followed by physicochemical characterizations of the resulting nanocellulose by using Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR) spectroscopy and Dynamic Light Scattering (DLS). FTIR analysis has confirmed the successful removal of non-cellulosic components from the BP through the distinguishable spectra of both the ripe and unripe BP powder with the H2O2/H2SO4- treated samples. SEM analysis revealed morphological changes of the BP powder from an irregular structure with a presence of starch granules to lamellar and fibrous structures after the H2O2/H2SO4 treatment and freeze-drying. The size of the nanocellulose is strongly influenced by the concentration of sulfuric acid used. Nanocellulose from ripe BP produced by using the 40% H2SO4 has the smallest size with D50 < 80 nm. These findings suggest the potential of banana peel, an abundant agricultural waste to be valorized into value-added materials with significant economic potentials.
在本研究中，以成熟和未成熟的Saba’banana (Musa acuminata x balbisiana)果皮为原料，先用过氧化氢(H2O2)作为氧化剂，再用不同浓度(8%、24%和40%)的硫酸(H2SO4)水解，进行一锅制得纳米纤维素。利用扫描电镜(SEM)、傅立叶变换红外光谱(FTIR)和动态光散射(DLS)对成熟和未成熟香蕉皮(BP)粉末的化学成分和近似成分进行了分析，并对制备的纳米纤维素进行了理化表征。FTIR分析证实，通过H2O2/H2SO4处理样品的成熟和未成熟BP粉末的可区分光谱，BP中非纤维素成分被成功去除。扫描电镜分析表明，经过H2O2/H2SO4处理和冷冻干燥后，BP粉的形貌发生了变化，由含有淀粉颗粒的不规则结构转变为片状和纤维状结构。纳米纤维素的大小受所用硫酸浓度的影响很大。采用40% H2SO4法制备的成熟BP纳米纤维素粒径最小，D50 < 80 nm。这些发现表明，香蕉皮这一丰富的农业废弃物有潜力转化为具有巨大经济潜力的增值材料。
{"title":"Isolation of nanocellulose from Saba’ (Musa acuminata x balbisiana) banana peel by one-pot oxidation-hydrolysis system","authors":"S. Saallah, J. Roslan, Nurul Nadjwa Zakaria, W. Pindi, S. Siddiquee, M. Misson, Clarence M. Ongkudon, N. H. Jamil, W. Lenggoro","doi":"10.36877/AAFRJ.A0000096","DOIUrl":"https://doi.org/10.36877/AAFRJ.A0000096","url":null,"abstract":"In the present study, facile one-pot production of nanocellulose from ripe and unripe Saba’ banana (Musa acuminata x balbisiana) peel was conducted by utilizing hydrogen peroxide (H2O2) as an oxidizing agent prior to hydrolysis with sulfuric acid (H2SO4) at different concentrations (8%, 24% and 40%). Proximate and chemical compositions of the ripe and unripe banana peel (BP) powder were analyzed, followed by physicochemical characterizations of the resulting nanocellulose by using Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR) spectroscopy and Dynamic Light Scattering (DLS). FTIR analysis has confirmed the successful removal of non-cellulosic components from the BP through the distinguishable spectra of both the ripe and unripe BP powder with the H2O2/H2SO4- treated samples. SEM analysis revealed morphological changes of the BP powder from an irregular structure with a presence of starch granules to lamellar and fibrous structures after the H2O2/H2SO4 treatment and freeze-drying. The size of the nanocellulose is strongly influenced by the concentration of sulfuric acid used. Nanocellulose from ripe BP produced by using the 40% H2SO4 has the smallest size with D50 < 80 nm. These findings suggest the potential of banana peel, an abundant agricultural waste to be valorized into value-added materials with significant economic potentials. ","PeriodicalId":420247,"journal":{"name":"Advances in Agricultural and Food Research Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130446577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. N. Ahmad, A. R. Mohamed Shariff, I. Aris, Izhal Abdul Halin, Ramle Moslim
The bagworm species of Metisa plana, is one of the major species of leaf-eating insect pest that attack oil palm in Peninsular Malaysia. Without any treatment, this situation may cause 43% yield loss from a moderate attack. In 2020, the economic loss due to bagworm attacks was recorded at around RM 180 million. Based on this scenario, it is necessary to closely monitor the bagworm outbreak at infested areas. Accuracy and precise data collection is debatable, due to human errors. . Hence, the objective of this study is to design and develop a specific machine vision that incorporates an image processing algorithm according to its functional modes. In this regard, a device, the Automated Bagworm Counter or Oto-BaCTM is the first in the world to be developed with an embedded software that is based on the use of a graphic processing unit computation and a TensorFlow/Teano library setup for the trained dataset. The technology is based on the developed deep learning with Faster Regions with Convolutional Neural Networks technique towards real time object detection. The Oto-BaCTM uses an ordinary camera. By using self-developed deep learning algorithms, a motion-tracking and false colour analysis were applied to detect and count number of living and dead larvae and pupae population per frond, respectively, corresponding to three major groups or sizes classification. Initially, in the first trial, the Oto-BaCTM has resulted in low percentages of detection accuracy for the living and dead G1 larvae (47.0% & 71.7%), G2 larvae (39.1 & 50.0%) and G3 pupae (30.1% & 20.9%). After some improvements on the training dataset, the percentages increased in the next field trial, with amounts of 40.5% and 7.0% for the living and dead G1 larvae, 40.1% and 29.2% for the living and dead G2 larvae and 47.7% and 54.6% for the living and dead pupae. The development of the ground-based device is the pioneer in the oil palm industry, in which it reduces human errors when conducting census while promoting precision agriculture practice.
{"title":"Oto-BaCTM: An Automated Artificial Intelligence (AI) Detector and Counter for Bagworm (Lepidoptera: Psychidae) Census","authors":"M. N. Ahmad, A. R. Mohamed Shariff, I. Aris, Izhal Abdul Halin, Ramle Moslim","doi":"10.36877/aafrj.a0000218","DOIUrl":"https://doi.org/10.36877/aafrj.a0000218","url":null,"abstract":"The bagworm species of Metisa plana, is one of the major species of leaf-eating insect pest that attack oil palm in Peninsular Malaysia. Without any treatment, this situation may cause 43% yield loss from a moderate attack. In 2020, the economic loss due to bagworm attacks was recorded at around RM 180 million. Based on this scenario, it is necessary to closely monitor the bagworm outbreak at infested areas. Accuracy and precise data collection is debatable, due to human errors. . Hence, the objective of this study is to design and develop a specific machine vision that incorporates an image processing algorithm according to its functional modes. In this regard, a device, the Automated Bagworm Counter or Oto-BaCTM is the first in the world to be developed with an embedded software that is based on the use of a graphic processing unit computation and a TensorFlow/Teano library setup for the trained dataset. The technology is based on the developed deep learning with Faster Regions with Convolutional Neural Networks technique towards real time object detection. The Oto-BaCTM uses an ordinary camera. By using self-developed deep learning algorithms, a motion-tracking and false colour analysis were applied to detect and count number of living and dead larvae and pupae population per frond, respectively, corresponding to three major groups or sizes classification. Initially, in the first trial, the Oto-BaCTM has resulted in low percentages of detection accuracy for the living and dead G1 larvae (47.0% & 71.7%), G2 larvae (39.1 & 50.0%) and G3 pupae (30.1% & 20.9%). After some improvements on the training dataset, the percentages increased in the next field trial, with amounts of 40.5% and 7.0% for the living and dead G1 larvae, 40.1% and 29.2% for the living and dead G2 larvae and 47.7% and 54.6% for the living and dead pupae. The development of the ground-based device is the pioneer in the oil palm industry, in which it reduces human errors when conducting census while promoting precision agriculture practice.","PeriodicalId":420247,"journal":{"name":"Advances in Agricultural and Food Research Journal","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123324611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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