Academic Journal of Engineering and Technology Science, 2024, 7(3); doi: 10.25236/AJETS.2024.070312.
Fengkang Qiao, Tingting Liu, Zhipeng Li, Yanan Xue
School of Electronic Information, Xijing University, Xi'an, Shaanxi, China
With food safety issues gaining widespread attention, monitoring the quality of fruits has become an essential part of ensuring consumer health. Apples, being a significant component of daily diets, have their safety and quality highly prioritized by consumers. This study employs Near-Infrared Spectroscopy (NIR) to develop two classification models: Partial Least Squares Discriminant Analysis (PLS-DA) and One-Dimensional Convolutional Neural Network (1D CNN), aimed at performing non-destructive detection of apple quality and precise grading of sweetness. Through the analysis of experimental data, especially after preprocessing with SG smoothing and partitioning using the SPXY algorithm, the 1D CNN model achieved an accuracy rate of 0.8856 in apple quality classification, demonstrating its significant potential for application in apple sweetness grading. This study validates the efficiency and reliability of Near-Infrared Spectroscopy in the assessment and grading of agricultural product quality, providing substantial support for improving agricultural product quality and ensuring consumer food safety and health.
Food Safety, Non-Destructive Testing, Near-Infrared Spectroscopy, 1D-CNN
Fengkang Qiao, Tingting Liu, Zhipeng Li, Yanan Xue. Exploration of an Apple Grading Model Based on Near-Infrared Spectroscopy. Academic Journal of Engineering and Technology Science (2024) Vol. 7, Issue 3: 84-88. https://doi.org/10.25236/AJETS.2024.070312.
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