当四川微辣=广东暴辣…高光谱相机给辣度“上秤“

When "Mild Spicy" in Sichuan Equals "Extremely Spicy" in Guangdong... Hyperspectral Camera Measures Chili Heat Levels

每个人对辣度的接受程度都不一样，火锅底料的辣度如何科学量化？本次实验利用高光谱相机，对6种不同辣度的火锅底料进行测试，探索光谱数据与辣度的关联性。

People's tolerance for spiciness varies widely, but how can the heat level of hot pot base be scientifically quantified? This experiment utilized a hyperspectral camera to test six hot pot bases with different spiciness levels, exploring the correlation between spectral data and chili heat intensity.

「样品介绍 / Samples」

测试6种不同辣度的火锅底料，辣度分别为：12°、36°、45°、52°、65°、75°

Six hot pot base samples with varying heat levels were tested: 12°, 36°, 45°, 52°, 65°, and 75°.

「数据采集 / Data Acquisition」

高光谱相机：覆盖400~1700nm波段（可见光+短波红外）

成像方式：线性推扫，确保数据精准

光源与环境：卤素灯均匀照明，暗室环境减少干扰

样品摆放：水平位移台固定，保证成像稳定

Hyperspectral Camera: Covered 400–1700 nm (visible light + short-wave infrared).

Imaging Method: Linear push-broom scanning for precise data capture.

Lighting & Environment: Halogen lamp for uniform illumination, darkroom to minimize interference.

Sample Setup: Fixed on a horizontal displacement platform for stable imaging.

400-1000nm

900-1700nm

「分析方法 / Analysis Method」

高光谱成像不仅能拍出照片，还能记录每个像素点的光谱“指纹"。 

实验过程中，首先使用400-1000nm可见近红外和900-1700nm短波红外两台高光谱相机采集6种火锅底料样品的光谱数据。

在数据预处理阶段，通过专业的高光谱分析软件对原始数据进行降噪处理和反射率计算，同时消除背景光谱干扰，确保获得纯净的目标物体光谱信息，这一过程通常在数据采集时同步完成。

随后从处理后的高光谱数据中提取关键特征，包括光谱反射率、吸收峰位置及光谱形态特征等，并运用主成分分析等降维方法筛选出具有代表性的特征参数。

在分类识别环节，利用不同物质对特定波段反射率的差异特性，分别采用监督学习和无监督学习两种方法：前者通过标记数据集训练光谱角制图或卷积神经网络等分类模型，后者则运用K均值或层次聚类等算法实现数据自动分类。

最终将分析结果以伪彩色图像形式直观呈现，展示不同物质的空间分布情况，并基于光谱特征开展定量和定性分析，计算得出各类物质的浓度或类别参数。

Hyperspectral imaging not only captures photos but also records the spectral "fingerprint" of each pixel.

During the experiment, two hyperspectral cameras (400–1000 nm visible-NIR and 900–1700 nm SWIR) were used to collect spectral data from the six samples.

In the preprocessing stage, raw data underwent noise reduction and reflectance calibration via specialized software, while background interference was eliminated to ensure clean spectral data. This process was synchronized with data acquisition.

Key features were then extracted from the processed data, including spectral reflectance, absorption peak positions, and spectral shape characteristics. Dimensionality reduction methods like PCA were applied to identify the most representative parameters.

For classification, both supervised and unsupervised learning were employed:

Supervised methods (e.g., spectral angle mapper or CNN) used labeled datasets to train models.

Unsupervised methods (e.g., K-means or hierarchical clustering) automated data grouping based on reflectance differences in specific bands.

Results were visualized as pseudo-color images to display spatial distributions of materials, followed by quantitative/qualitative analysis to calculate concentrations or categories.

「光谱曲线 / Spectral Curves」

在400-1700nm波长范围内，六种不同辣度的火锅底料样本在a面和b面的反射率曲线呈现出相似的光谱波形，但反射率数值随辣度变化而存在显著差异。具体表现为辣度越高，反射率越低，这一趋势在a面和b面均保持一致。

值得注意的是，在b面的860-930nm波段范围内，反射率曲线对辣度的区分效果尤为明显，能够更清晰地反映辣度差异。

Within 400–1700 nm, reflectance curves of the six samples (A-side and B-side) showed similar waveforms but significant reflectance variations correlated with spiciness. Higher heat levels consistently exhibited lower reflectance on both sides.

Notably, the 860–930 nm range on the B-side provided the clearest distinction between heat levels.

a面反射率(400-1000nm)

a面反射率(900-1700nm)

b面反射率(400-1000nm)

「建立CNN模型 / CNN Modeling」

为了进一步分析辣度分类的可行性，研究采用卷积神经网络（CNN）对高光谱数据进行建模。

To further assess classification feasibility, a CNN model was applied to hyperspectral data.

建立CNN模型（400-1000nm  a/b面） / CNN Model (400–1000 nm, A/B-sides)

在400-1000nm波段的a面数据分类中，模型整体准确率介于75%-85%之间，其中辣度45°和75°的分类效果较好，而辣度12°和52°由于数据采集时受容器遮挡影响，部分区域出现误判。此外，辣度36°因样品表面凹陷导致数据质量下降，而辣度65°的部分区域被错误归类为75°。

相比之下，b面的分类表现更为稳定，整体准确率约为85%，仅辣度45°的少量区域被误判为36°。

A-side: Overall accuracy ranged 75%–85%. Samples at 45° and 75° were classified best, while 12° and 52° suffered partial misclassification due to container obstruction during imaging. The 36° sample had uneven surfaces, and 65° was occasionally mislabeled as 75°.

B-side: Performance was more stable (~85% accuracy), with only minor misclassification (45° vs. 36°).

a面结果 (400-1000nm)

a面结果 (400-1000nm)

建立CNN模型（900-1700nm  a/b面） / CNN Model (900–1700 nm, A/B-sides)

在900-1700nm波段的分析中，a面数据的分类准确率在70%-80%之间，其中辣度12°和36°因表面凹凸不平或凹陷导致数据质量较差，误判率较高，而辣度45°、52°、65°和75°的分类效果较好。

相比之下，b面数据由于表面更平滑，且无干辣椒等固体遮挡，分类表现显著优于a面，整体准确率超过90%，仅有少量区域出现误判。

这一结果表明，900-1700nm波段可能更适合用于火锅底料辣度的精准检测，尤其是结合b面数据时，分类效果更佳。

A-side: Accuracy was 70%–80%. Samples at 12° and 36° showed higher misclassification due to surface irregularities, while 45°–75° performed better.

B-side: Superior accuracy (>90%) was achieved thanks to smoother surfaces and absence of solid obstructions (e.g., dried chilies).

These results suggest that 900–1700 nm SWIR, especially with B-side data, is more suitable for precise heat-level detection.

a面结果 (900-1700nm)

b面结果 (900-1700nm)

「总结 / Conclusion」

基于高光谱视觉技术的研究表明，通过对六种不同辣度的火锅底料样品进行高光谱数据采集，并经过数据预处理和算法分析，能够有效区分样品的辣度等级。

实验数据显示，虽然样品a面和b面的光谱曲线均能反映辣度变化，但b面的区分效果更为显著。在光谱波段选择方面，相比400-1000nm的可见近红外谱段，900-1700nm的短波红外谱段展现出更高的识别准确率和检测精度。

为进一步提升研究结果的可靠性，后续工作将重点扩大样本数据量，通过增加样本多样性来持续优化识别准确率。

Hyperspectral imaging effectively differentiated the six heat levels of hot pot base samples after data preprocessing and algorithmic analysis.

While both A-side and B-side spectral curves reflected spiciness trends, the B-side provided clearer distinctions. Compared to 400–1000 nm visible-NIR, the 900–1700 nm SWIR band demonstrated higher accuracy and precision.

To enhance reliability, future work will expand sample diversity and dataset size for further optimization.