预测羊羔shashliks的VOC含量和烤方法,使用深度学习与化学测量和感官评估相结合
Che Shen1, Yun Cai1, Meiqi Ding1
1College of Food Science and Technology, Bohai University, Jinzhou 121013, China.
Food chemistry: X
|June 30, 2023
概括
这项研究将传统的木炭烤羊肉沙什利克与空气炸和电烤等现代方法进行比较,发现先进的分析技术和机器学习模型可以有效地区分烤技术和预测挥发性有机化合物.
科学领域:
- 食品科学与技术 食品科学与技术
- 分析化学 分析化学
- 感官科学 感官科学
背景情况:
- 传统的木炭烧烤是准备羔羊沙什利克的流行的方法.
- 探索可能更有效或可控的替代加热方法是值得关注的.
- 了解不同加热方法对食物感官和化学特征的影响至关重要.
研究的目的:
- 为了比较使用传统木炭烧烤制备的羊肉沙什利克的感觉和化学特性,与四种替代方法进行比较:电烤箱,电烤,微波炉和空气炸.
- 评估各种分析技术 (E-鼻子,E-舌头,QDA,HS-GC-IMS,HS-SPME-GC-MS) 在区分这些方法中的有效性.
- 开发和评估机器学习模型,用于预测挥发性有机化合物 (VOC) 含量和确定烤制方法.
主要方法:
- 用五种不同的加热方法准备羊羔shashliks:传统 (T),电烤箱 (D),电烤 (L),微波炉 (W) 和空气炸 (K).
- 感官分析是使用定量描述性分析 (QDA) 进行的.
- 化学分析涉及电子鼻子 (E-nose),电子舌头 (E-tongue),头空气色谱-离子运动谱 (HS-GC-IMS) 和头空固相微提取气色谱-质谱 (HS-SPME-GC-MS).
- 机器学习模型,包括CNN-SVM,用于预测和分类任务.
主要成果:
- 量化描述性分析 (QDA),E-nose和E-tongue成功地根据它们的烤方法区分了羊羔shashliks.
- 总共使用HS-GC-IMS和HS-SPME-GC-MS分别确定了43个和79个挥发性有机化合物 (VOC).
- 使用空气炸 (K) 和电烤 (L) 方法准备的样本显示不和化物,和的普遍性更高.
- 与其他测试模型相比,CNN-SVM模型在预测VOC含量 (准确度> 0.95) 和识别烤制方法 (准确度> 0.92) 方面表现优异.
结论:
- 像E-nose,E-tongue和QDA这样的现代分析工具在区分通过不同的加热技术准备的羊肉shashliks方面是有效的.
- 与传统方法相比,空气炸和电烧烤产生了不同的挥发性化合物配置.
- 在CNN-SVM模型显示了重要的潜力,准确预测化学成分和分类的制备方法羔羊shashliks.
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