Hyperspectral Imaging: An Inside View of Food Quality

舌尖上的中國，早已告別“吃飽就行”的時代。如今，我們追求的是更高品質、更安全放心的食品。高光譜成像技術作為一種新興的技術，它能像“火眼金睛”一樣，穿透食品的表象，檢測其內在的成分、品質和安全狀況。下面，讓我們一起走進高光譜成像技術在食品行業的應用巡禮。

In China, the era of being satisfied with merely "having enough to eat" is long gone. Today, we pursue higher quality, safer, and more reliable food. As an emerging technology, hyperspectral imaging can penetrate the surface of food products—much like having "eyes that see beneath the surface"—to detect their internal composition, quality, and safety conditions. Below, let’s explore the applications of hyperspectral imaging technology in the food industry.

農產品的品質檢測 / Quality Inspection of Agricultural Products

高光譜成像在農產品分選中，尤其擅長進行無損檢測，發現肉眼難以察覺的內部損傷。例如，它可以檢測蘋果外表可能看不見的瘀傷或內部損傷，實現更精準的分級，提升產品價值。這種非破壞性的檢測方式，能夠幫助生產者剔除存在潛在質量問題的產品，提升整體的產品質量和市場競爭力。

Hyperspectral imaging excels in the non-destructive inspection of agricultural products, particularly in sorting operations. It can detect internal damage that is difficult to see with the naked eye. For example, it can identify bruises or internal defects in apples that are not visible externally, enabling more accurate grading and enhancing product value. This non-destructive testing method helps producers remove items with potential quality issues, thereby improving overall product quality and market competitiveness.

基于高光譜的蘋果分選 / Hyperspectral-Based Apple Sorting

農產品的感官特征評價 / Evaluation of Sensory Characteristics in Agricultural Products

感官特征，如糖度、酸度、硬度和成熟度，直接影響消費者對農產品的喜好。高光譜成像技術在食品感官評價中展現出巨大潛力，能夠無損地預測這些關鍵指標。例如，通過分析柿子在特定波長的反射率，可以建立澀味預測模型；通過分析葡萄在近紅外波段的光譜吸收特征，可以預測其糖度和酸度。與傳統感官評價方法相比，高光譜成像具有快速、客觀、無損的優點。

Sensory attributes such as sugar content, acidity, firmness, and ripeness directly influence consumer preference for agricultural products. Hyperspectral imaging shows great potential in the sensory evaluation of food, as it can non-destructively predict these key indicators. For instance, by analyzing the reflectance of persimmons at specific wavelengths, a prediction model for astringency can be developed. Similarly, by examining the spectral absorption features of grapes in the near-infrared range, their sugar and acid levels can be predicted. Compared with traditional sensory evaluation methods, hyperspectral imaging offers the advantages of speed, objectivity, and non-destructiveness.

使用高光譜技術實時在線檢測葡萄糖度 / Real-Time Online Detection of Grape Sugar Content Using Hyperspectral Technology

茶葉品質評估 / Tea Quality Assessment

通過分析茶葉的光譜信息，可以無損、精確地分析茶葉中的生物活性成分，如茶多酚、咖啡堿和氨基酸等，這些成分對茶葉的風味和品質至關重要。某團隊利用高光譜數據建立了模型，可以準確估計茶葉中的茶多酚含量。另一團隊利用無人機搭載的高光譜相機，預測茶多酚和氨基酸含量，并評估茶多酚與氨基酸比值。

By analyzing the spectral information of tea leaves, key bioactive components such as tea polyphenols, caffeine, and amino acids—which are crucial to the flavor and quality of tea—can be assessed accurately and non-destructively. One research team developed a model based on hyperspectral data to accurately estimate the content of tea polyphenols. Another team used a drone-mounted hyperspectral camera to predict the contents of tea polyphenols and amino acids, and further evaluated the ratio between the two.

肉品質量評估 / Meat Quality Assessment

高光譜成像可準確測量脂肪和蛋白質含量，評估營養價值和風味。例如，分析牛肉大理石花紋預測嫩度和多汁性，或評估魚類蛋白質降解程度判斷新鮮度，區分正常胸肉和“木質胸肉”，它不僅能優化利用率，還能檢測骨碎片等異物，確保食品安全。

Hyperspectral imaging can accurately measure fat and protein content, helping to evaluate nutritional value and flavor profiles. For example, it can analyze the marbling of beef to predict tenderness and juiciness, or assess the degree of protein degradation in fish to determine freshness. It is also capable of distinguishing between normal chicken breast and woody breast, and can detect foreign materials such as bone fragments—enhancing utilization efficiency while ensuring food safety.

利用高光譜技術區分雞胸肉肉質 / Differentiating Chicken Breast Meat Quality Using Hyperspectral Technology

高光譜成像技術作為新興的食品檢測手段，潛力巨大。盡管數據處理、設備成本和應用場景復雜性帶來挑戰，但人工智能和機器學習的快速發展，為高光譜成像技術帶來了新的機遇。通過結合大數據分析與建模，有望進一步提高食品品質預測的準確性，實現農產品等產業鏈的智能化升級，從生產到加工再到銷售，帶來更高效、更有品質保障的生產模式。

As an emerging tool in food inspection, hyperspectral imaging holds significant potential. Although challenges remain in data processing, equipment costs, and the complexity of application scenarios, the rapid development of artificial intelligence and machine learning presents new opportunities for the technology. By integrating big data analysis and modeling, hyperspectral imaging is expected to further improve the accuracy of food quality prediction and enable intelligent upgrades across agricultural and food production chains—from production and processing to sales—paving the way for more efficient and quality-assured production models.