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How Does Spectral Technology Make Fruit Quality Sorting Smarter?

水果的品质分选直接决定了供应链的经济价值，精准识别内部损伤不仅能显着降低优质果的损耗率，更能提供稳定可靠的高品质水果。然而，传统的人工分选和外部检测难以发现瘀伤、冻伤、水心病等内部缺陷，导致大量外表完好的水果因隐性损伤而被误判。

光谱与高光谱成像技术的出现，让水果内部品质的无损检测成为可能。这些技术通过解析水果内部水分、糖分及细胞结构的特征光信号，实现对瘀伤、冻伤、海绵组织病变等隐性缺陷的“透视"，从而大幅提升分选精度。

Fruit quality sorting directly determines the economic value of the supply chain. Accurately identifying internal damage not only significantly reduces the loss rate of premium fruits but also provides stable and reliable high-quality fruits for the premium market. However, traditional manual sorting and external inspection struggle to detect internal defects such as bruises, frost damage, and watercore, leading to misjudgment of large quantities of outwardly intact fruits due to hidden damage.

The emergence of spectral and hyperspectral imaging technologies has made non-destructive testing of internal fruit quality possible. These technologies analyze characteristic optical signals related to internal moisture, sugar content, and cellular structure, enabling "visualization" of hidden defects like bruises, frost damage, and spongy tissue disorders, thereby significantly improving sorting accuracy.

水果分选线，图片源自网络 / Fruit sorting line, image source: Internet

在国内外，科研团队已通过大量实验验证了光谱技术在水果分选中的潜力。例如，江苏大学团队利用高光谱成像系统检测苹果的轻微损伤，发现547苍尘波段的特征光谱能清晰反映皮下细胞损伤，通过主成分分析（笔颁础）提取该波段图像，结合二次差分算法消除果面亮度不均干扰，最终实现88.57%的损伤识别率。

Globally, research teams have validated the potential of spectral technology in fruit sorting through extensive experiments. For example, a team from Jiangsu University used a hyperspectral imaging system to detect slight damage in apples. They found that the characteristic spectrum at the 547nm wavelength clearly reflects subcutaneous cellular damage. By extracting images of this wavelength through principal component analysis (PCA) and combining it with a second-order difference algorithm to eliminate interference from uneven surface brightness, they achieved an 88.57% damage recognition rate.

苹果的轻微损伤和正常区域的光谱曲线

Refectance spectra from the subtle bruise and normal region on the apple

类似地，国外研究团队在芒果海绵组织检测中，通过优化特定波段的贵颈蝉丑别谤特征选择算法，使分类准确率达到84.5%，且预测缺陷位置与实际损伤的误差小于1尘尘。

Similarly, a foreign research team optimized the Fisher feature selection algorithm for specific wavelengths in mango spongy tissue detection, achieving a classification accuracy of 84.5% with prediction errors of defect locations within 1mm of actual damage.

缺陷样本与健康样本的光谱图，(a) 波长范围673nm–1100nm，(b) 波长范围1100nm–1900nm

A plot of defective and healthy samples  (a) Wavelength range 673 nm–1100 nm. (b) Wavelength range 1100 nm–1900 nm.

这些研究成果为实际产线应用奠定了基础。基于光谱的水果分析系统通常由光源模块、光谱仪/高光谱成像仪、传送带等核心硬件组成，其工作流程包括样品采集、光谱数据预处理、化学分析方法测定水果样品成分的准确含量、模型构建与验证、优化模型等关键步骤。而在实际分选场景中，这个流程如何高效运行？关键在于自动化和光谱检测的紧密结合：

1. 动态触发：传送带水果抵达检测位，光电传感器触发光源

2. 光谱采集：光源发射出光，光谱仪获反射/透射光谱

3. 数据处理：光谱仪分解特征峰，分析模型实时输出糖度/损伤值

4. 分拣执行：触发品质分级

These research findings lay the foundation for practical production line applications. Spectral-based fruit analysis systems typically consist of core hardware such as a light source module, spectrometer/hyperspectral imager, and conveyor belt. The workflow includes key steps such as sample collection, spectral data preprocessing, chemical analysis to determine the accurate content of fruit sample components, model construction and validation, and model optimization. In actual sorting scenarios, the efficiency of this process hinges on the seamless integration of automation and spectral detection:

1. Dynamic Triggering: Photoelectric sensors activate the light source when fruit reaches the detection position on the conveyor belt.

2. Spectral Acquisition: The light source emits light, and the spectrometer captures the reflected/transmitted spectra.

3. Data Processing: The spectrometer decomposes characteristic peaks, and the analysis model outputs real-time brix/damage values.

4. Sorting Execution: Quality grading is triggered for sorting.

高光谱系统的示意图（由于水果的尺寸大小、果肉薄厚，糖酸度高有低，且分布不均的情况，光谱采集时光源摆放有多种方式）

Schematic diagram of a hyperspectral system (Due to variations in fruit size, flesh thickness, and uneven distribution of sugar/acid content, multiple light source configurations are used during spectral acquisition)

目前，光纤光谱仪因其成本低、结构紧凑等优势，仍是水果分选的主流设备。但对于圣女果、樱桃等小尺寸水果，光纤光谱仪的检测效率可能受限，而高光谱成像仪凭借其空间与光谱信息的同步获取能力，理论上能实现更高效的分选。然而，高光谱设备的成本和数据处理复杂度仍是实际应用中的挑战。

未来，随着硬件优化和算法的持续升级，光谱技术有望在更多水果品类中实现高效、经济的分选方案，推动水果供应链向更智能、更精准的方向发展。

Currently, fiber optic spectrometers remain the mainstream equipment for fruit sorting due to their low cost and compact structure. However, for small-sized fruits like cherry tomatoes and cherries, the detection efficiency of fiber optic spectrometers may be limited. In contrast, hyperspectral imagers, with their ability to simultaneously capture spatial and spectral information, theoretically enable more efficient sorting. Nevertheless, the cost of hyperspectral equipment and the complexity of data processing remain challenges in practical applications.

Looking ahead, with continuous hardware optimization and algorithm advancements, spectral technology is expected to deliver efficient and cost-effective sorting solutions for more fruit varieties, driving the fruit supply chain toward smarter and more precise development.

案例来源 / Source：

1. Zhao, J.-W., Liu, J.-H., Chen, Q.-S., & Vittayapadung, S. (2008). 利用高光谱图像技术检测水果轻微损伤 [Detection of slight fruit bruises using hyperspectral imaging technology]. Transactions of the Chinese Society for Agricultural Machinery, 39(1), 106-109.

2. Raghavendra, A., Guru, D. S., & Rao, M. K. (2021). Mango internal defect detection based on optimal wavelength selection method using NIR spectroscopy. Artificial Intelligence in Agriculture, 5, 43-51.