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All Journal Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)
Riris Waladatun Nafi’ah
Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Engineering Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering

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Enzyme dosage detection to degrade feathers in edible bird’s nests: A comparative convolutional neural networks study Verianti Liana; Rizal Arifiandika; Bagas Rohmatulloh; Riris Waladatun Nafi’ah; Arif Hidayat; Yusuf Hendrawan; Dimas Firmanda Al-Riza; Tunjung Mahatmanto; Hermawan Nugroho
Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Vol 6, No 4 (2023)
Publisher : Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.afssaae.2023.006.04.6

Abstract

Edible Bird’s Nest (EBN), a costly food product made from swiftlet’s saliva, has encountered a longstanding problem of plucking the swiftlet’s feather from the nests. The destructive and inefficient manual process of plucking the feathers can be substituted with a serine protease enzyme alternative. Accurate detection of enzyme dosage is crucial for ensuring efficient feather degradation with cost-effective enzyme usage. This research employed the transfer learning method using pretrained Convolutional Neural Networks (Pt-CNN) to detect enzyme dosage based on EBN’s images. This study aimed to compare the image classification mechanisms, architectures, and performance of three Pt-CNN: Resnet50, InceptionResnetV2, and EfficientNetV2S. InceptionResnetV2, using parallel convolutions and residual networks, significantly contributes to learning rich informative features. Consequently, the InceptionResnetV2 model achieved the highest accuracy of 96.18%, while Resnet50 and EfficientNetV2S attained only 30.44% and 17.82%, respectively. The differences in architecture complexity, parameter count, dataset characteristics, and image resolution also play a role in the performance disparities among the models. The study’s findings aid future researchers in streamlining model selection when facing limited datasets by understanding the reasons for the model’s performance and contributing to a non-destructive and quick solution for EBN’s cleaning process.  
Bioconversion of black soldier fly (Hermetia illucens) on agricultural waste: Potential source of protein and lipid, the application (A mini-review) Nur Hidayat; Sakunda Anggarini; Nimas Mayang Sabrina Sunyoto; Loeki Enggar Fitri; Sri Suhartini; Novita Ainur Rohma; Elviliana Elviliana; Sang Aji Arif Setyawan; Indah Fitriana Subekti; Anggi Alya Namira; Riris Waladatun Nafi’ah; Firdiani Nur Afifah; Andhika Putra Agus Pratama
Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Vol 7, No 1 (2024)
Publisher : Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.afssaae.2024.007.01.8

Abstract

Hermetia illucens, well-known as black soldier fly (BSF), is an insect easily found in subtropical and tropical regions. It contains high protein and lipids. BSF is known as one of the biological agents consuming organic components, thus having a high potential to overcome organic waste problems. BSF has promising advantages due to its long development time in the larval stage (compared to other flies) and its ability to self-separate from organic waste. BSF with large protein and lipid content can substitute the commonly used protein source in aquaculture, poultry and livestock compound diet formulation, which can be an option to overcome limited sources of future food and feed insecurity. This review analyses the latest study of bioconversion using BSF from the viewpoint of nutrient composition, degradation rate and biomass results from different feed treatments. Various feed and growth mediums have been studied to obtain high protein and lipid biomass. Hopefully, the information will provide new research directions and solutions for converting agro-industrial waste using bioconversion with BSF.
Co-Authors Andhika Putra Agus Pratama Anggi Alya Namira Arif Hidayat Bagas Rohmatulloh Dimas Firmanda Al-Riza Elviliana Elviliana Firdiani Nur Afifah Hermawan Nugroho Indah Fitriana Subekti Loeki Enggar Fitri Nimas Mayang Sabrina Sunyoto Novita Ainur Rohma Nur Hidayat Rizal Arifiandika Sakunda Anggarini Sang Aji Arif Setyawan Sri Suhartini, PhD Tunjung Mahatmanto Verianti Liana Yusuf Hendrawan