Postharvest Handling of Horticultural Crops
Research Field
Margo Sulistio is an Assistant Professor in the Department of Horticulture at National Ilan University, Taiwan. His academic and research expertise lies in postharvest physiology, fruit ripening, and preservation technologies for tropical fruits. He earned his Ph.D. from National Taiwan University, where he specialized in the molecular and physiological regulation of ethylene biosynthesis in guava. He has published several papers in international peer-reviewed journals, including Postharvest Biology and Technology, and has co-authored book chapters related to fruit quality and postharvest handling. His notable contributions include identifying key genes associated with ripening behavior and developing quality indicators for guava fruit under different storage conditions. Beyond his own research projects, he has also contributed to numerous collaborative studies focused on postharvest technology, including the evaluation of heat treatments, controlled atmosphere, modified atmosphere packaging, and applications of 1-MCP to extend the storability of fresh produce. Currently serving as the principal investigator of an NSTC-funded project on guava aroma profile and storability, he brings a combination of scientific rigor and practical insight.
The Postharvest Handling of Horticultural Crops Laboratory at the Department of Horticulture, National Ilan University, primarily focuses on studying horticultural crop physiology and postharvest technology to maintain quality, reduce postharvest loss of horticultural crops, and increase the storability of horticultural crops.
- Relationship among maturity, storability, and quality of horticultural crops after harvest;
- Characterization of physiological and biochemical changes during postharvest storage of horticultural crops;
- Application of postharvest technologies (low-temperature storage, heat treatments, modified atmosphere packaging, 1-MCP treatment, coatings, and other technologies) to extend the storability and preserve the postharvest quality of horticultural crops;
- Application of molecular biology methods to elucidate the molecular mechanisms and physiological characteristics of horticultural crops during postharvest storage.
- Development of nondestructive fruit quality evaluation techniques using image processing, near-infrared spectroscopy, and machine learning
2025 National Ilan University Teaching Excellence Award
2022 Taiwan Horticultural Society Outstanding Doctoral Thesis - Characterization of the genotypes of PgACS1, a key System 2 1-Aminocyclopropane-1-Carboxylate synthase gene in guava, from cultivars with different ripening behaviors
- Ph.D. – Graduate Institute of Horticulture, National Taiwan University
- Master of Biotechnology (M. Biotech) – Universitas Gadjah Mada, Indonesia
Job Description
Development of Non-Destructive Fruit Quality Evaluation Using Image Processing, Near-Infrared Spectroscopy (NIRS), and Machine Learning
The responsibility of this position encompasses:
- Establish a database of internal physicochemical properties of fruit samples: collect quantitative data on fruit firmness, sugar content, and acidity using standard destructive methods as reference values.
- Construct a comprehensive image database of fruit samples: acquire high-resolution, full-color images from multiple angles to capture surface features such as size, shape, and color, which can be correlated with internal quality indices.
- Establish a near-infrared spectral database: record absorption and reflectance spectra of fruit samples using NIRS to capture nonvisible quality indicators associated with sweetness, acidity, and firmness.
- Develop and validate machine learning models: analyze correlations among physicochemical parameters, image features, and NIRS spectra. Train and validate predictive ML models to estimate internal quality attributes.
Preferred Intern Educational Level
- Senior Bachelor students
- Master students
Skill sets or Qualities
Priority will be given to candidates with the following skills and qualifications:
- Basic knowledge in postharvest physiology and technology, and machine learning.
- Research experiences, measurement of fruit qualities (e.g., color changes, firmness, total soluble solids, titratable acidity)
- Experience with relevant software/tools (e.g., Excel, R package, Phyton, and other statistical software) in data analyses and visualization.
- Systematic literature review and data analysis experience, and manuscript or poster preparation.
- Active learner, curious, self-motivated, and interested in research and development.