Hyperspectral Imaging Technology: Distinguishing and Detecting New and Aged Tea

「背景 / Background」

在茶葉品質(zhì)鑒別領(lǐng)域，如何準確區(qū)分新的茶葉與老茶葉一直是個技術(shù)難題。傳統(tǒng)方法主要依靠感官評定和經(jīng)驗判斷，存在主觀性強、標準不統(tǒng)一等問題。而隨著高光譜成像技術(shù)的發(fā)展，這一難題正迎來全新的解決方案。

新的茶葉與老茶在感官特征上存在明顯差異。新鮮茶葉色澤翠綠鮮亮，葉面富有光澤，形態(tài)完整飽滿，特別是綠茶和白茶這類未經(jīng)發(fā)酵的茶葉，其新鮮特征更為突出。相比之下，老茶由于存放時間較長，色澤會逐漸轉(zhuǎn)暗，光澤度降低，葉片可能出現(xiàn)碎裂或變形。

從香氣特征來看，新的散發(fā)著清新怡人的花草果香，而老茶則呈現(xiàn)出沉穩(wěn)的陳香、藥香或木質(zhì)香調(diào)，尤其像普洱茶、白茶這類適合長期存放的茶葉，其陳化特征更為顯著。

這些感官差異的本質(zhì)在于茶葉內(nèi)部化學成分的變化。新的茶葉中茶多酚、咖啡堿、氨基酸等活性物質(zhì)含量較高，而隨著時間推移，這些成分會逐漸氧化分解，同時產(chǎn)生新的次級代謝產(chǎn)物。

高光譜成像技術(shù)正是通過捕捉這些細微的化學變化，實現(xiàn)對茶葉新老的精準鑒別。該技術(shù)能夠檢測茶葉在不同波長下的光譜特征，通過分析反射或透射光譜的變化，揭示茶葉內(nèi)部的化學成分差異。這種方法可用于茶葉品質(zhì)的無損檢測，輔助茶葉的分類、分級和市場交易。

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沉穩(wěn) (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.

「設備介紹 / Equipment Introduction」

在本次實驗中，我們采用400-1000nm波段的國產(chǎn)高光譜相機進行數(shù)據(jù)采集。

•光譜范圍：400-1000nm

•光譜分辨率：優(yōu)于2.5nm

•探測器：CMOS

•空間維有效像元數(shù)：1920

•波段數(shù)：300

•視場角（FOV）：32°@f=17mm

•幀頻：128fps

配套的專業(yè)分析軟件具備強大的數(shù)據(jù)處理能力，包括反射率校正、輻射校正、濾波、降噪等。

軟件內(nèi)置高光譜數(shù)據(jù)裁切與拼接算法；具有光譜角、監(jiān)督分類，非監(jiān)督分類等常用算法，支持用戶自定義波段進行運算，內(nèi)置NDVI、NDWI等25種以上常見植被指數(shù)分析，為數(shù)據(jù)解析提供多維度支持。

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.

「反射率光譜曲線 / Reflectance Spectral Curve」

使用50%反射率板標定后，選取新葉與老葉的特征區(qū)域進行ROI分析，計算得出平均反射率曲線,可以看到老茶葉的整體反射率整體低于新的茶葉。

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.

「不同算法的茶葉區(qū)分 / Tea Differentiation Using Different Algorithms」

本實驗測試了歸一化差值植被指數(shù)（NDVI）和監(jiān)督分類兩種方法。

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

NDVI通過分析紅光和近紅外波段的反射特征，能夠有效反映茶葉的生理狀態(tài)變化。

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

監(jiān)督分類則基于統(tǒng)計識別原理，通過典型樣本訓練建立分類模型。實驗結(jié)果顯示，區(qū)分準確率均達到80%以上，雖然葉片邊緣和莖部區(qū)域還存在少量誤判，但整體效果令人滿意。

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」

展望未來，將從三個方面持續(xù)優(yōu)化技術(shù)方案：

首先，收集更多標記清晰的樣品數(shù)據(jù)，擴充樣本庫規(guī)模，優(yōu)化算法參數(shù)設置；其次，改進光照環(huán)境設計，采用專用線光源提升信噪比，降低環(huán)境光干擾；隨著定性分析達的準確率逐步提高后，可開展茶葉陳化程度的定量反演研究。

這套技術(shù)方案不僅適用于茶葉新老鑒別，還可拓展應用于茶葉分級、品質(zhì)檢測等多個領(lǐng)域，為茶葉產(chǎn)業(yè)高質(zhì)量發(fā)展提供有力的技術(shù)支撐。

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.