咖啡豆的风味，高光谱看得见？

当我们描述一杯咖啡带有花香、坚果或焦糖风味时，是否想过，这份独特的风味能否在不依赖杯测的情况下，于烘焙前就能被预测？

高光谱成像技术正在将这种构想变为可能。高光谱能无损地扫描咖啡生豆，获得其完整的光谱数据，研发人员从光谱中找出与特定风味物质（如糖分、caffeine）之间的定量关系，以此为基础，构建出可靠的预测模型。通过这些模型，我们得以科学地预见咖啡豆的风味轮廓。

在一项研究中，科研人员将单颗咖啡豆（生豆）放置在黑色样品台上，利用高光谱相机对其进行扫描。高光谱相机的覆盖短波红外区域（900-2500nm），咖啡豆内部的主要化学成分，如糖分、生物碱、油脂等，在此波段下会呈现出独特的“光谱指纹"。利用专业的软件分析和建立模型，定量推测豆子中各种物质的含量。

研究人员扫描了多个产区的数百颗样本，以检测蔗糖（甜味）、caffeine（苦味）和葫芦巴碱（烘焙香气）这三种关键风味前体。

通过结合高光谱数据与液相色谱质谱法的精密测量值，应用PLSR算法，研究团队构建了上述成分的定量预测模型。交叉验证结果表明，模型对caffeine和葫芦巴碱的预测精度很高（R² > 0.8），对蔗糖的预测也可用于初步筛选。

利用高光谱成像的特性，团队还生成了这些化学成分在豆子内部的空间分布图，直观显示了它们的不均匀分布，这为了解咖啡豆的生理结构提供了新视角。

通过高光谱成像（HSI）获取的纯品参比物质（caffeine、蔗糖和葫芦巴碱）以及磨碎生咖啡豆样品的平均光谱，同时展示了1400nm单一光谱波段的吸光度图像（右侧）

该研究团队还转向烘焙后咖啡豆，为应对更复杂的风味分析，采用了可同时预测多个响应变量的PLS2算法。研究人员不仅建立了针对醛类、吡嗪类等化学族群的预测模型，更进一步将化合物按其感官特征（如坚果香、甜香）分组，建立了针对整体风味属性的模型。结果表明，对醛类（甜香）和吡嗪类（烘烤香）等族群的预测效果尤为出色。

为了验证这一预测能力的实际价值，研究人员进行了一项实验：他们利用建立好的模型，对一批阿拉伯比卡咖啡豆进行扫描，并根据模型预测的吡嗪含量和坚果香气强度，手动筛选出预测值max和min的10%的豆子，分别组成新的批次。

对这两个批次豆子的化学分析证实，分选效果极其显著。被预测为“高吡嗪"的批次，其实际吡嗪类物质含量显著高于原始混合批次和“低吡嗪"批次。

这证明，高光谱成像技术结合预测模型，能够有效识别咖啡豆内部的特定风味物质差异。该研究为在产业线上实现非破坏性的精准风味分选提供了新思路，展现了其用于生产风味定制化咖啡产品的技术潜力，但其大规模稳定应用的效能仍有待进一步验证。

按A.预测吡嗪类化合物含量或B.分析预测 坚果味（粗体标注）的前面10%（高含量组，H）或后10%（低含量组，L）对咖啡豆进行分选后，分选试验对 4 组挥发性化合物（吡嗪类、醛类、酮类和杂环含氮化合物）相对丰度及分析预测的坚果味、果香味、酸味和烘焙味的影响。

从咖啡生豆的检测，到熟豆后天风味的预测，高光谱成像技术为我们提供了一条贯穿咖啡品质管控全程的强大纽带。它让“看豆识风味"成为可能，将咖啡的品质控制从传统依赖经验的“批量评估"，推向了一个数字化、可视化的“单颗精准管理"新时代。

案例来源：

Caporaso, N., Whitworth, M. B., Grebby, S., & Fisk, I. D. (2018). Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging. Food Research International, 106, 193–203.

Caporaso, N., Whitworth, M. B., & Fisk, I. D. (2022). Prediction of coffee aroma from single roasted coffee beans by hyperspectral imaging. Food Chemistry, 371, 131159.

Can Hyperspectral Imaging Detect the Flavor of Coffee Beans?

When describing a cup of coffee as having floral, nutty, or caramel notes, have you ever wondered whether these unique flavors could be predicted before roasting—without relying on cupping?

Hyperspectral imaging technology is turning this concept into a reality. By scanning green coffee beans non-destructively, hyperspectral systems capture complete spectral data. Researchers identify quantitative relationships between the spectral signatures and specific flavor compounds—such as sugars and caffeine—and use these to build reliable predictive models. Through these models, the flavor profile of coffee beans can be scientifically anticipated.

In one study, researchers placed individual green coffee beans on a black sample stage and scanned them using a hyperspectral camera. The camera covered the short-wave infrared range (900–2500 nm), where key chemical components inside the beans—such as sugars, alkaloids, and oils—exhibit distinct "spectral fingerprints." Using specialized software for analysis and modeling, the content of various compounds in the beans was quantitatively estimated.

Hundreds of samples from multiple global growing regions were scanned to detect three key flavor precursors: sucrose (sweetness), caffeine (bitterness), and trigonelline (roasty aroma).

By combining hyperspectral data with precise measurements from liquid chromatography–mass spectrometry, the research team applied PLSR (Partial Least Squares Regression) to develop quantitative prediction models for these components. Cross-validation results showed high prediction accuracy for caffeine and trigonelline (R² > 0.8), and sucrose predictions were suitable for preliminary screening.

Leveraging the capabilities of hyperspectral imaging, the team also generated spatial distribution maps of these chemical compounds inside the beans. These visualizations clearly revealed their uneven distribution, offering new insights into the physiological structure of coffee beans.

The average spectra of pure reference substances (caffeine, sucrose, and trigonelline) and ground green coffee bean samples obtained through hyperspectral imaging (HSI), along with the absorbance image at a single spectral band of 1400 nm (shown on the right).

The research team also examined roasted coffee beans. To address more complex flavor analysis, they employed the PLS2 algorithm, which can predict multiple response variables simultaneously. The researchers not only built prediction models for chemical groups such as aldehydes and pyrazines, but also grouped compounds by sensory attributes—such as nutty aroma and sweet aroma—to develop models targeting overall flavor characteristics. Results indicated particularly strong predictive performance for groups like aldehydes (sweet aroma) and pyrazines (roasty aroma).

To test the practical value of this predictive capability, the researchers conducted an experiment: using the established model, they scanned a batch of Arabica coffee beans. Based on the predicted pyrazine content and nutty aroma intensity, they manually selected the top 10% and bottom 10% of beans to form two new batches.

Chemical analysis of these two batches confirmed highly significant sorting results. The batch predicted as "high pyrazine" showed substantially higher actual pyrazine content compared to the original mixed batch and the "low pyrazine" batch.

This demonstrates that hyperspectral imaging combined with predictive models can effectively identify differences in specific flavor compounds within coffee beans. The study offers a new approach for non-destructive, precise flavor sorting on industrial production lines, highlighting the technology’s potential for producing customized coffee products. However, the efficiency and stability of large-scale application still require further validation.

After sorting coffee beans into either the top 10% (high-content group, H) or the bottom 10% (low-content group, L) based on A. predicted pyrazine content or B. predicted "nutty flavor" (indicated in bold), the sorting experiment’s impact on the relative abundance of four groups of volatile compounds (pyrazines, aldehydes, ketones, and nitrogen-containing heterocycles) and the predicted intensities of nutty flavor, fruity flavor, acidity, and roasty flavor.

From detecting green coffee beans to predicting the developed flavors of roasted beans, hyperspectral imaging provides a powerful link throughout the entire coffee quality control process. It makes it possible to "see flavor in the bean," shifting coffee quality control from traditional, experience-based "batch assessment" toward a new era of digital, visual, and "single-bean precision management."

Sources：

Caporaso, N., Whitworth, M. B., Grebby, S., & Fisk, I. D. (2018). Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging. Food Research International, 106, 193–203.

Caporaso, N., Whitworth, M. B., & Fisk, I. D. (2022). Prediction of coffee aroma from single roasted coffee beans by hyperspectral imaging. Food Chemistry, 371, 131159.