Effect of ethanol concentration on the infrared spectroscopic characteristics of silk beads

Kim, Sungkuk; Jo, You‐Young; Lee, Kwang-Gill; Kim, Kee‐Young; Kim, Hyun-Bok; Kweon, HaeYong

doi:10.7852/jses.2015.53.2.118

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…The sericulture industry currently exists in 25 countries worldwide and is economically significant in most developing countries [21], [22]. Silk production is the primary objective of the industry and the production of high-quality silk cocoons has become more significant with the development of high-value-added applications such as healthy functional foods [23][ [24], functional products [25][ [26], and medical materials based on sericulture byproducts [27][28] [29]. Dead cocoons have no commercial value and cause the cross-contamination of normal cocoons and therefore detection and removal of these dead cocoons is crucial.…”

Section: Introductionmentioning

confidence: 99%

Classification of Dead Cocoons Using Convolutional Neural Networks and Machine Learning Methods

Lee,

Kim,

Hong

et al. 2023

IEEE Access

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Image recognition methods classify or categorize objects by extracting significant properties from digital images of the objects and are used in the field of agriculture for quality determination. With the development of artificial intelligence technology, deep-learning techniques and tools such as convolutional neural networks (CNNs) have been used in image recognition. Existing discrimination studies have tended to extract features from images and classify them using multivariate analysis; however, deep learning algorithms have the self-learning ability to extract the feature points themselves for each neural layer. In this study, we developed models for discriminating dead cocoons using various discriminant analysis methods, including deep learning options, to establish an automation technology for the sericulture industry. A 100 W halogen light source was used for direct irradiation onto the cocoons, and a camera was positioned at the bottom of the cocoons (of which 43.9% were dead) to obtain RGB images. We conducted discrimination analyses based on the color space using four discrimination algorithms, namely, k-nearest neighbor, support vector machine, linear discriminant analysis, and partial least squares-discriminant analysis, within deep learning models (a proposed lightweight CNN model, VGG16, ResNet50, EfficientNetB0, MobileNet, ShuffleNet, GhostNet, and ConvNext). The proposed lightweight CNN model, which consisted of six convolutional layers and two fully connected layers, showed the highest discrimination accuracy (97.66%) in the Lab color space. It was thus confirmed that it is possible to automate the discrimination of dead cocoons using digital images and deep learning techniques.

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Section: Introductionmentioning

confidence: 99%

Classification of Dead Cocoons Using Convolutional Neural Networks and Machine Learning Methods

Lee,

Kim,

Hong

et al. 2023

IEEE Access

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“…concentration, temperature, mechanical strength, chemical treatment, ultrasound sonication, and composition (Kim et al, 2015;Samal et al, 2013;Wang et al, 2008;Kweon et al, 2000;Kang et al, 2000;Ayub et al, 1994). Among them, sonication induced rapid gelation by reduction of molecular weight and β-sheet conformation (Samal et al, 2013).…”

Section: Gelation Of Sf Depends On a Number Of Parameters Includingmentioning

confidence: 99%

Structural and thermal characteristics of photocrosslinked silk fibroin - PEG hydrogel

Jo¹,

Lee²,

Bragg³

et al. 2016

International Journal of Industrial Entomology

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Hydrogels are crosslinked hydrophilic matrices for a variety of biomedical applications. Silk fibroin (SF), one of typical natural biomaterials, has been explored as base material for hydrogel. Photocrosslinked SF hydrogel containing poly(ethylene glycol) (PEG) was formulated through visible light initiated thiol-acrylate photopolymerization. The morphological, structural and thermal properties of SF -PEG hydrogel was investigated through scanning electron microscopy, X-ray diffractometry, thermogravimetry, and differential scanning calorimetry. The morphology of SF hydrogel showed dot and uneven surface with network cross-section. X-ray diffraction curves showed that the specific diffraction peaks of PEG were not changed by the intensity of the peaks were affected by sonication. Thermo-degradation behavior of SF -PEG hydrogel sonicated was significantly affected and became complex pattern compared to unsonicated ones. However, the melting endothermic temperature of SF -PEG hydrogel was not changed but the crystalline enthalpy was decreased by gelation and sonication. IntroductionHydrogels are crosslinked hydrophilic matrices suitable for a variety of biomedical applications, including tissue engineering matrix, contact lenses, drug delivery carriers and cell therapy Lin and Anseth, 2009;Kweon et al., 2001;Hanawa et al., 1995). A variety of natural and synthetic polymers have been explored as the base materials for hydrogel crosslinking. Silk fibroin (SF) produced by Bombyx mori silkworm can be formulated into physical hydrogels or be modified to display reactive motifs (e.g., methacrylate) for chemical crosslinking (Yecel et al., 2014;Kim et al., 2014;Kasoju and Bora, 2012;Kweon et al., 2001;Kweon et al., 2000).SF can also form β-sheets structure, which can self-assemble into higher order structures with enhanced stability. On the other hand, hydrogels prepared from derivatives of poly(ethylene glycol) (PEG) chemically crosslinked with limited degradability (Lin, 2015;Lin and Anseth, 2009;Lutolf and Hubbell, 2005).

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Development of a Discrimination Model for Dead Cocoons Using Near-Infrared Transmission Spectra Analyses

Lee,

Kim,

Kim

et al. 2022

SSRN Journal

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Effect of ethanol concentration on the infrared spectroscopic characteristics of silk beads

Cited by 3 publications

References 15 publications

Classification of Dead Cocoons Using Convolutional Neural Networks and Machine Learning Methods

Classification of Dead Cocoons Using Convolutional Neural Networks and Machine Learning Methods

Structural and thermal characteristics of photocrosslinked silk fibroin - PEG hydrogel

Development of a Discrimination Model for Dead Cocoons Using Near-Infrared Transmission Spectra Analyses

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