Muhammad Achirul Nanda, S. Rosalinda, Reinaldy, Rahmat Budiarto, Inna Novianty, Taufik Ibnu Salim, Pradeka Brilyan Purwandoko, Dimas Firmanda Al Riza
Citrus fruit quality, particularly taste, plays a crucial role in consumer preference and marketability. Conventional taste tests, such as sensory panel assessments and chemical analysis, are time-consuming and destructive, underscoring the need for rapid and non-destructive evaluation methods. Therefore, this study aimed to design laser-light backscattering imaging (LLBI) system as a novel approach for evaluating citrus taste. A total of 150 Siamese citrus samples were collected from Cisurupan Orchards. Sensory evaluation was performed using Quantitative Descriptive Analysis by 20 trained panelists to classify citrus taste into two categories namely sour and sweet. Moreover, the LLBI system was developed using laser diodes at three wavelengths (450, 532, and 648 nm) to capture backscattering images. A ResNet50-based deep learning model was implemented to classify citrus samples, with the performance evaluated using accuracy and the area under the receiver operating characteristic curve (AUC). The results showed that the 648 nm wavelength yielded the highest classification performance, achieving accuracies of 98.968 % for training, 96.898 % for validation, and 96.759 % for testing. The corresponding AUC values were 0.9996, 0.9967, and 0.9961, respectively, confirming the model excellent predictive capability. LLBI demonstrates significant potential as a non-destructive, rapid, and objective technique for evaluating citrus sensory quality. © 2025 Elsevier Inc.
Department of Agricultural and Biosystem Engineering, Faculty of Agro-Industrial Technology, Universitas Padjadjaran, Sumedang, 45363, Indonesia; Undergraduate Student of Agriculture Engineering Study Program, Department of Agricultural and Biosystem Engineering, Faculty of Agro-Industrial Technology, Universitas Padjadjaran, Sumedang, 45363, Indonesia; Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, West Java, Sumedang, 45363, Indonesia; Computer Engineering Technology Study Program, College of Vocational Studies, IPB University, Bogor, 16680, Indonesia; Research Center for Smart Mechatronics, National Research and Innovation Agency, Bandung, 40135, Indonesia; Research Center for Appropriate Technology, National Research and Innovation Agency, Subang, 41213, Indonesia; Department of Biosystems Engineering, Faculty of Agricultural Technology, Universitas Brawijaya, Malang, 65145, Indonesia
