Application and Analysis of Hyperspectral Technology in Cabbage Freshness Discrimination

食品新鮮度檢測是食品安全與品質控制中的重要環節，直接關系到消費者的健康體驗與營養攝入。對大眾而言，食品新鮮度不僅影響食材的口感和營養價值，更是保障飲食安全、減少食源性疾病風險的關鍵因素。

Food freshness detection is a critical aspect of food safety and quality control, directly impacting consumers' health experiences and nutritional intake. For the public, food freshness not only influences the taste and nutritional value of ingredients but also plays a key role in ensuring dietary safety and reducing the risk of foodborne illnesses.

本次測試以不同新鮮程度的白菜為研究對象，利用高光譜技術實現對白菜新鮮度的有效區分。

This study used cabbages of different freshness levels as research subjects and employed hyperspectral technology to achieve effective discrimination of cabbage freshness.

本次測試所采用的高光譜相機覆蓋400-1000nm的光譜范圍，具備優于2.8nm的光譜分辨率，高達300個光譜波段，F/2的大光圈設計提升光通量，480個空間像素確保空間細節表現，采用CMOS探測器并結合USB接口實現便捷高效的數據傳輸，12bits的有效位深保障了圖像數據的豐富層次與精度。該設備為農、林、食品檢測等應用提供了有力工具，歡迎大家進一步了解。

測試采用線性推掃成像方案，照明光源為鹵素光源。實驗在暗室環境中進行，樣品被放置于水平位移臺上以完成圖像采集。

The hyperspectral camera used in this test covers a spectral range of 400–1000 nm, with a spectral resolution better than 2.8 nm, up to 300 spectral bands, and an F/2 large aperture design that enhances light throughput. With 480 spatial pixels ensuring detailed spatial representation, it utilizes a CMOS detector combined with a USB interface for efficient data transmission. The 12-bit effective bit depth ensures rich image data hierarchy and precision. This device serves as a powerful tool for applications in agriculture, forestry, and food detection. Further inquiries are welcome.

The test adopted a linear push-broom imaging method, with a halogen light source for illumination. The experiment was conducted in a darkroom environment, and samples were placed on a horizontal translation stage for image acquisition.

測試樣品及測試環境 / Test Samples and Testing Environment

通過獲取不同新鮮度白菜在400-1000nm范圍內的光譜曲線，并分別選取完好的莖與葉區域以及干枯的莖與葉區域計算平均光譜，分析表明：

完好的葉片（紅色曲線）與干枯葉片（紫色曲線）在500-700nm和800-900nm波段的光譜響應存在明顯差異；

完好的莖（綠色曲線）與干枯的莖（黃色曲線）則在650-850nm范圍內表現出顯著光譜變化。

By obtaining spectral curves of cabbages with different freshness levels within the 400–1000 nm range and calculating average spectra from intact stem and leaf regions as well as dried stem and leaf regions, the analysis revealed:

Significant differences in spectral responses between intact leaves (red curve) and dried leaves (purple curve) in the 500–700 nm and 800–900 nm bands;

intact stems (green curve) and dried stems (yellow curve) exhibited notable spectral variations within the 650–850 nm range.

反射率測試 / Reflectance Testing

在數據處理階段，我們利用了兩種不同的算法：

During data processing, two different algorithms were applied:

算法一選取枯葉ROI區域作為分類標準，能夠有效識別部分白菜表面的干枯區域，但對莖部干枯區域的區分效果有限。

Algorithm 1 used dried leaf ROI regions as classification criteria, effectively identifying some dried areas on the cabbage surface but demonstrating limited ability to distinguish dried regions on stems.

算法一 / Algorithm 1

算法二通過對圖像進行特征提取，實現了對表面干枯區域的更有效識別，從而對不同新鮮度的白菜實現了良好區分。

Algorithm 2 employed feature extraction from images, achieving more effective identification of surface-dried areas and enabling better discrimination of cabbages with different freshness levels.

算法二 / Algorithm 2

實驗結果表明，基于400-1000nm波段的高光譜相機能夠檢測出不同新鮮程度白菜的光譜差異，且數據處理結果與實際狀態相符。

Experimental results indicate that the hyperspectral camera based on the 400–1000 nm band can detect spectral differences in cabbages of varying freshness levels, and the data processing outcomes align with actual conditions.

本實驗亦識別出若干實際測量中的難點：白菜表面覆蓋的保鮮膜易引起光線反射，對信號造成干擾；同時，白菜的弧形表面不僅影響光線反射特性，也對相機的對焦精度提出了挑戰。

針對這些問題，下一步計劃包括優化光源結構、引入多角度照明方案，建立反射率校正模型以消除弧面造成的光譜強度偏差，提升數據可比性。此外，還將擴大樣本數量，構建基于深度學習的新鮮度判別模型，以期實現更精確、可靠的白菜新鮮度分類能力，為實現更安全、更可靠的生鮮食品供應鏈提供了有效的技術保障。

This experiment also identified several challenges in practical measurements: The freshness-preserving film covering the cabbage surface easily causes light reflection, interfering with signals; meanwhile, the curved surface of the cabbage not only affects light reflection characteristics but also poses challenges to the camera’s focusing accuracy.

To address these issues, future plans include optimizing the light source structure, introducing multi-angle lighting schemes, and establishing a reflectance correction model to eliminate spectral intensity deviations caused by curved surfaces, thereby improving data comparability. Additionally, the sample size will be expanded to develop a deep learning-based freshness discrimination model, aiming to achieve more accurate and reliable cabbage freshness classification capabilities. This provides effective technical support for building a safer and more reliable fresh food supply chain.