Analytical (Descriptive) Testing:利用小规模的专家或目标用户小组进行深入评估。 highly trained judges客观识别、描述并量化产品的具体感官特性（如甜度强度、苦味程度、奶油感程度）。它回答的问题是： What are the sensory characteristics of this product?
Affective (Hedonic) Testing:采用庞大的样本群体进行测试，确保数据的代表性与可靠性。 untrained consumers代表目标市场，用以衡量他们对产品的主观感受。它回答的问题是： How much do consumers like this product?
Hedonic evaluation focuses entirely on the latter. It is the bridge between a flavor's chemical composition (our expertise) and its ultimate consumer acceptance (market reality). The most critical metrics measured are Acceptance （喜欢的程度）以及 Preference （在两个或多个样品之间的选择）
该量表最初由20世纪50年代的研究者们开发。 David Peryam and colleagues at the Quartermaster Food and Container Institute of the U.S. Armed Forces系统性测量士兵的食物偏好，这是提升部队士气与表现的关键任务目标【引用1：Peryam, D. R.，Pilgrim, F. J. (1957). 食物偏好的享乐尺度测量方法。】 Food Technology].其结构为 bipolar, balanced category scale配备九个口头锚点：
评分
言语锚点
情感状态
9
极度喜欢
高度接受
8
非常喜欢
7
中度喜欢
6
略微喜欢
5
既不喜欢也不讨厌
中立
4
轻微不喜欢
3
中度不喜欢
2
非常不喜欢
1
极度不喜欢
高度拒绝
该量表的奇数点数保证了中心位置的存在。 neutral point (5)这对于区分冷漠与明显反感至关重要。分析的核心假设是相邻口头标示之间的心理距离大致相等，从而支持对整数评分采用参数统计方法（如方差分析和t检验）。
B. 关于区间数据的争论
While widely used and effective, the mathematical treatment of the 9-point scale data is a point of ongoing technical discussion within sensory science. Critics argue that human perception may not treat the intervals equally; for example, the psychological distance between "Like Extremely" (9) and "Like Very Much" (8) may not be the same as the distance between "Neither Like nor Dislike" (5) and "Dislike Slightly" (4).因此， researchers often treat data obtained with the 9-point scale as if the numbers assigned to the categories were points on a continuum尽管其类别结构在技术上产生的是顺序或最多为区间数据[引文2：Stone, H. & Sidel, J. L. (2004). 感官评估实践（第3版）] Academic Press].这一争论强调了拥有庞大且具有代表性样本的重要性，以减小个体差异，支持进行参数分析，从而得出有力的统计结论。
C. 量表的变体
为满足不同研究需求与限制，传统九点量表亦衍生出多种变体。
Labeled Affective Magnitude (LAM) Scale:A sophisticated, non-category scale designed to yield ratio-level data, offering greater resistance to "ceiling effects" (where subjects run out of positive points).
Facial Hedonic Scales:Utilizes a series of "smiley faces" to measure liking, making it highly effective for non-verbal populations, such as young children or in certain cross-cultural studies where translation may be ambiguous.
Numeric and Visual Scales:用数字或连续线取代或补充口头标尺，以简化评分流程，并进一步支持连续数据的处理。
这 Just-About-Right (JAR) Scale味道优化中不可或缺的工具。它取代了传统的询问消费者对某一属性的偏好程度： likes 某一具体属性（如甜度），它会询问关于该属性的 intensity相对于他们理想状态的该属性：$$\text{Too Little (1) - Just Right (3) - Too Much (5)}$$When a product receives a high OLL score but a specific attribute (like the vanilla note or the level of sourness) is rated as "Too Little" or "Too Much" by a significant portion of the consumers, it signals a direct path for formulation adjustment [Citation 3: Civille, G. V., & Heymann, H. (2017). Sensory Evaluation Techniques (5th ed.). CRC Press].Practical Application for Flavorists: JAR数据使我们能够精准控制风味成分的强度。当一种新型草莓奇异果饮料获得高OLL评分，但有30%的消费者评价为 kiwi flavor intensity as "Too Little," our flavorists know exactly where to focus—not on changing the fundamental flavor profile, but on modulating the intensity of the kiwi character. The goal is to maximize the "Just Right" percentage.
B. 购买意愿与行动标准
一种风味若要实现商业化，喜好必须转化为购买行为。享乐测试几乎总是与 Purchase Intent (PI) Scales.Action Standards are pre-defined thresholds that determine a flavor's readiness for market. A common standard requires:
Mean OLL Score:最低平均得分
Top Box Score:A minimum percentage of consumers rating the product in the top two categories ("Like Extremely" or "Like Very Much")
Purchase Intent Top Box:A minimum percentage of consumers rating "Definitely Will Buy" or "Probably Will Buy" .
达到这些统计阈值，为推进高成本的市场发布或大规模生产提供了必要的信心。
JAR评分与OLL关系
四、整合享乐与描述性数据的战略力量
The most profound insights in flavor development come from combining the "What" (Descriptive) with the "How Much" (Hedonic). This is the foundation of Preference Mapping.
A. 内部与外部偏好映射
偏好映射是一套统计技术，直观展现一组样品（风味轮廓）感官特性与消费者喜好评分之间的关系。
External Preference Mapping:利用已有的描述性数据（如感官属性的测量强度）进行分析。 Caramel Note, Saltiness, and Viscosity) and plots consumer preference scores onto this sensory map. It reveals the "ideal" product coordinates on the map, allowing R&D to formulate a product that hits that sweet spot.
这种整合方法回答了核心问题： Which specific sensory attributes drive consumer liking or rejection? If a new "Bold Citrus" flavor is rejected, preference mapping might reveal that the rejection is not due to the citrus character itself, but to a perceived "Astringency" note that is too intense. This steers the flavorist away from modifying the core flavor and toward using a masking or modulating agent.
B. 测量情感与认知反应
先进的享乐评价已融合认知与情感指标，认识到偏好不仅仅是感官输入的总和。
Check-All-That-Apply (CATA):消费者从预设的感官与情感词汇表中，选择所有适用于某产品的描述（例如： Refreshing, Comforting, Too Sweet, Artificial, Authentic它为定量喜好评分增添了丰富的质性层次。
此外，一项关于新颖食物组合成功率的研究发现， consumer evaluations are influenced by a combination of perceptual (balance of intensity), conceptual (norms), and affective (surprise) pairing principles强调认知因素与期望在口味接受中的作用[引文4：Zampini, M. & Spence, C. (2020). 食物与风味的契合之道：消费者对（非）寻常产品组合的感知] Food Quality and Preference].这表明我们的工作不仅限于化学层面，还包括 story 与 concept味道的。
Environment Control:Maintain consistent temperature, lighting (often white or red light to mask appearance differences if testing 'blind'), and ensure the area is odor-neutral.
样品呈现：
Randomization:样品须以三位随机编号（如852、296）呈现，且采用平衡或随机的陈列顺序（例如： Latin Square Design以控制顺序及遗留效应（前一样品残留的味道）对结果的影响。
Analysis of Variance (ANOVA):用于检验两个或多个风味变体的平均喜好评分是否存在统计学上的显著差异。
Top Box/Bottom Box Analysis:Examining the percentage of scores in the top (9, 8) and bottom (1, 2) categories is often more indicative of market success/failure than the mean alone. A product with a high mean might still have a concerningly high percentage of "Dislike Extremely" scores, indicating a polarizing flavor that needs reformulation.
Driver of Liking Analysis:终极统计应用，通过线性回归或更先进的方法，将描述性与享乐性数据结合，建立模型以预测消费者偏好，依据感官属性的强度。这精确告诉我们，某一感官指标每提升一分，偏好将发生何种变化。 Saltiness对最终结果产生深远影响 OLL
In global markets, hedonic testing is crucial for localization. What is "Like Extremely" in one region (e.g., intense sourness) may be "Dislike Moderately" in another. Hedonic scales are often adapted, using localized language or non-verbal scales, to ensure accurate measurement of acceptance across diverse cultural palates.
4. 成本优化与配方调整
When ingredient costs rise or health regulations demand a change (e.g., sodium reduction, sugar removal), hedonic testing provides the objective proof that the reformulated flavor remains "parity" (equally liked) or "preferred" over the original or the competitive benchmark. This ensures cost savings or formulation compliance without sacrificing consumer acceptance.
Ready to quantify consumer delight and de-risk your next product launch? Partner with our flavor scientists to utilize our state-of-the-art sensory analysis facilities. Contact our technical exchange team today for a detailed consultation, or request a free sample of a custom-developed flavor profile tailored to exceed your target market's hedonic standards.📧 邮箱：[信息@cuiguai.com]🌐 网站：[www.cuiguai.cn]📱 WhatsApp：[+86 189 2926 7983]☎ 电话：[+86 0769 8838 0789]
