Comparative infrared drying of Lucilia sericata larvae under different infrared conditions: drying kinetics, RSM optimization, and ANFIS-based modeling
International Journal of Tropical Insect Science, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s42690-026-01980-7
- Dergi Adı: International Journal of Tropical Insect Science
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, Zoological Record, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Earth, Atmospheric, & Aquatic Science Collection (ProQuest), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: ANFIS, Drying, Infrared dryer, Lucilia sericata, RSM
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
The utilization of insects as alternative protein sources has attracted increasing attention in the context of global food security and sustainable food production. Drying is a critical preservation process that extends shelf life, reduces microbial activity, facilitates storage, and improves the handling properties of insect-derived materials. In this study, the effects of different infrared lamp types (IRA, IRB, and IRC) on major drying characteristics, including moisture content, effective diffusion coefficient, shrinkage, and color change, were investigated during the drying of Lucilia sericata larvae. Drying behavior and process optimization were evaluated using Response Surface Methodology (RSM), while response variables were optimized through the desirability function and modeled using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The results demonstrated that the IRA lamp provided the most rapid and efficient drying performance at a drying duration of 240 min, whereas the IRB lamp promoted slower drying and greater moisture reduction. The optimum drying conditions for the IRA lamp were determined as 28.18 °C and 42.95 min, with a desirability value of 0.59. In addition, the ANFIS model showed strong predictive capability with an R² value of 0.99. These findings demonstrate the applicability of infrared drying technologies for the processing of insect-based biomaterials and protein sources. Furthermore, the integration of mathematical optimization and intelligent prediction systems may contribute to the development of energy-efficient, scalable, and quality-preserving drying strategies for sustainable insect processing industries and future functional food applications. To the best of our knowledge, this study is among the first to integrate infrared drying optimization, desirability-based statistical analysis, and ANFIS predictive modeling for Lucilia sericata larvae processing.