Papers
1
Total Citations
48
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1
About
Shuxiang Fan is a leading researcher in agricultural engineering and nondestructive food quality assessment, with a primary focus on visible and near-infrared (Vis/NIR) spectroscopy for fruit quality prediction. His most impactful work, "Optimization and comparison of models for prediction of soluble solids content in apple by online Vis/NIR transmission coupled with diameter correction method" (2020), has garnered 48 citations and represents a significant contribution to precision agriculture. In this study, Fan developed and refined machine learning models that integrate diameter correction with Vis/NIR transmission spectroscopy, dramatically improving the accuracy of real-time, noninvasive soluble solids content (SSC) prediction in apples. This advancement has practical implications for the fruit industry, enabling online sorting and quality control without damaging produce. Fan's research bridges the gap between spectroscopic sensing and agricultural automation, demonstrating how data-driven approaches can enhance food quality assessment. His work is widely cited by peers developing similar nondestructive techniques for fruits and vegetables, underscoring his influence in the field of postharvest technology and agricultural informatics.
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