Abstract The use of instrumental methods to support sensory panels in the routine quality control of crispiness remains challenging. Texture analysis is often insufficient to accurately classify this complex sensory attribute. Herein, 70 different food properties were combined via machine learning algorithms to mimic multisensory integration. Force and sound were measured during crushing of puffed snacks equilibrated at different humidity levels. Sensory panels then ranked crispiness-related freshness and preference based on the recorded sounds. Selected feature combinations were used to train machine learning models to recognize the freshness levels at different humidity levels. The classification accuracy was improved compared with traditional texture analysis techniques; an accuracy of up to 92% could be achieved with quadratic support vector machine or artificial neural network algorithms. Moreover, third-octave frequency bands, characterizing breakage frequencies and sound pitches, were determined to be main descriptors to be taken into account during the research and development of puffed snacks.

中文翻译：

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基于脆度相关机械和声学特性的膨化零食新鲜度分类

摘要 在常规脆度质量控制中使用仪器方法支持感官小组仍然具有挑战性。纹理分析通常不足以准确分类这种复杂的感官属性。在这里，通过机器学习算法组合了 70 种不同的食物特性，以模拟多感官整合。在压碎在不同湿度水平下平衡的膨化小吃期间测量力和声音。然后感官小组根据记录的声音对脆度相关的新鲜度和偏好进行排名。选定的特征组合用于训练机器学习模型，以识别不同湿度水平下的新鲜度。与传统的纹理分析技术相比，分类精度有所提高；使用二次支持向量机或人工神经网络算法可以实现高达 92% 的准确率。此外，表征破裂频率和音高的第三个八度频带被确定为膨化零食研究和开发过程中要考虑的主要描述符。