高光谱成像技术：新老茶叶的区分检测

Hyperspectral Imaging Technology: Distinguishing and Detecting New and Aged Tea

「背景 / Background」

在茶叶品质鉴别领域，如何准确区分新的茶叶与老茶叶一直是个技术难题。传统方法主要依靠感官评定和经验判断，存在主观性强、标准不统一等问题。而随着高光谱成像技术的发展，这一难题正迎来全新的解决方案。

新的茶叶与老茶在感官特征上存在明显差异。新鲜茶叶色泽翠绿鲜亮，叶面富有光泽，形态完整饱满，特别是绿茶和白茶这类未经发酵的茶叶，其新鲜特征更为突出。相比之下，老茶由于存放时间较长，色泽会逐渐转暗，光泽度降低，叶片可能出现碎裂或变形。

从香气特征来看，新的散发着清新怡人的花草果香，而老茶则呈现出沉稳的陈香、药香或木质香调，尤其像普洱茶、白茶这类适合长期存放的茶叶，其陈化特征更为显著。

这些感官差异的本质在于茶叶内部化学成分的变化。新的茶叶中茶多酚、咖啡碱、氨基酸等活性物质含量较高，而随着时间推移，这些成分会逐渐氧化分解，同时产生新的次级代谢产物。

高光谱成像技术正是通过捕捉这些细微的化学变化，实现对茶叶新老的精准鉴别。该技术能够检测茶叶在不同波长下的光谱特征，通过分析反射或透射光谱的变化，揭示茶叶内部的化学成分差异。这种方法可用于茶叶品质的无损检测，辅助茶叶的分类、分级和市场交易。

In the field of tea quality identification, accurately distinguishing new tea from aged tea has always been a technical challenge. Traditional methods primarily rely on sensory evaluation and empirical judgment, which suffer from strong subjectivity and inconsistent standards. With the development of hyperspectral imaging technology, this challenge is now being addressed with a novel solution.

New and aged teas exhibit distinct sensory characteristics. Fresh tea leaves are vibrant green in color, with glossy surfaces and intact, plump shapes—especially in unfermented teas like green tea and white tea, where these fresh features are more pronounced. In contrast, aged tea, due to prolonged storage, gradually darkens in color, loses glossiness, and may exhibit leaf fragmentation or deformation.

In terms of aroma, new tea emits a fresh and pleasant floral or fruity fragrance, while aged tea presents a more沉稳 (mellow) aged aroma, medicinal or woody notes. This is particularly evident in teas suitable for long-term storage, such as pu-erh and white tea, where aging characteristics are more pronounced.

The essence of these sensory differences lies in changes in the tea's internal chemical composition. New tea contains higher levels of active substances like polyphenols, caffeine, and amino acids. Over time, these components gradually oxidize and decompose, while new secondary metabolites are produced.

Hyperspectral imaging technology captures these subtle chemical changes to achieve precise identification of new and aged tea. By detecting the spectral characteristics of tea leaves at different wavelengths and analyzing variations in reflectance or transmittance spectra, it reveals differences in internal chemical composition. This method enables non-destructive testing of tea quality, assisting in classification, grading, and market transactions.

「设备介绍 / Equipment Introduction」

在本次实验中，我们采用400-1000nm波段的国产高光谱相机进行数据采集。

•光谱范围：400-1000nm

•光谱分辨率：优于2.5nm

•探测器：CMOS

•空间维有效像元数：1920

•波段数：300

•视场角（FOV）：32°@f=17mm

•帧频：128fps

配套的专业分析软件具备强大的数据处理能力，包括反射率校正、辐射校正、滤波、降噪等。

软件内置高光谱数据裁切与拼接算法；具有光谱角、监督分类，非监督分类等常用算法，支持用户自定义波段进行运算，内置NDVI、NDWI等25种以上常见植被指数分析，为数据解析提供多维度支持。

In this experiment, a domestically produced hyperspectral camera with a 400–1000 nm wavelength range was used for data acquisition.

•Spectral range: 400–1000 nm

•Spectral resolution: Better than 2.5 nm

•Detector: CMOS

•Spatial dimension effective pixels: 1920

•Number of bands: 300

•Field of view (FOV): 32°@f=17 mm

•Frame rate: 128 fps

The accompanying professional analysis software features robust data processing capabilities, including reflectance correction, radiometric correction, filtering, and noise reduction.

The software also incorporates built-in algorithms for hyperspectral data cropping and stitching, spectral angle mapping, supervised and unsupervised classification, and supports user-defined band operations. It includes over 25 common vegetation indices (e.g., NDVI, NDWI) for multidimensional data analysis.

「反射率光谱曲线 / Reflectance Spectral Curve」

使用50%反射率板标定后，选取新叶与老叶的特征区域进行ROI分析，计算得出平均反射率曲线,可以看到老茶叶的整体反射率整体低于新的茶叶。

After calibration with a 50% reflectance panel, regions of interest (ROIs) were selected from characteristic areas of new and aged leaves to calculate average reflectance curves. The results show that aged tea exhibits overall lower reflectance compared to new tea.

「不同算法的茶叶区分 / Tea Differentiation Using Different Algorithms」

本实验测试了归一化差值植被指数（NDVI）和监督分类两种方法。

This experiment tested two methods: the normalized difference vegetation index (NDVI) and supervised classification.

NDVI通过分析红光和近红外波段的反射特征，能够有效反映茶叶的生理状态变化。

NDVI effectively reflects changes in the physiological state of tea leaves by analyzing reflectance characteristics in the red and near-infrared bands.

监督分类则基于统计识别原理，通过典型样本训练建立分类模型。实验结果显示，区分准确率均达到80%以上，虽然叶片边缘和茎部区域还存在少量误判，但整体效果令人满意。

Supervised classification is based on statistical recognition principles, establishing classification models through training with typical samples. Experimental results show that both methods achieved accuracy rates above 80%, with minor misjudgments remaining at leaf edges and stem regions. Overall, the performance was satisfactory.

「展望 / Outlook」

展望未来，将从三个方面持续优化技术方案：

首先，收集更多标记清晰的样品数据，扩充样本库规模，优化算法参数设置；其次，改进光照环境设计，采用专用线光源提升信噪比，降低环境光干扰；随着定性分析达的准确率逐步提高后，可开展茶叶陈化程度的定量反演研究。

这套技术方案不仅适用于茶叶新老鉴别，还可拓展应用于茶叶分级、品质检测等多个领域，为茶叶产业高质量发展提供有力的技术支撑。

Looking ahead, the technical solution will be optimized in three aspects:

1) Collect more clearly labeled sample data to expand the sample library and optimize algorithm parameters.

2) Improve lighting environment design by adopting dedicated line光源 (light sources) to enhance signal-to-noise ratio and reduce ambient light interference.

3) As qualitative analysis accuracy improves, quantitative inversion research on tea aging degree can be conducted.

This technical solution is not only applicable to distinguishing new and aged tea but can also be extended to tea grading, quality testing, and other fields, providing robust support for the high-quality development of the tea industry.