摘要
针对镇江香醋固态发酵过程,利用在线智能检测模块实时检测发酵过程中温度和pH值的变化;结合近红外光谱技术,采用主成分分析（PCA）和K-最邻近法（K-nearest neighbors,KNN）对镇江香醋固态发酵过程进行分析,将样品的近红外光谱分别分成3、4阶段,然后均进行PCA,建立KNN识别模型。结果表明,根据温度的变化,将发酵过程分4个阶段（提热、过杓、露底和封醅阶段）分析;根据pH值的变化结果,发酵过程可分3个阶段——上升期、骤降期和平稳期,其中,发酵中、后期pH值维持在3.65~3.99;通过近红外光谱分析,将发酵过程分成4阶段（提热、过杓、露底和封醅阶段）;固态发酵过程分3阶段时,识别率是78.55%;分4阶段时,识别率达90.04%。对发酵温度进行全程跟踪,通过pH值和近红外光谱表征发酵过程的各个阶段,为进一步监控醋醅发酵过程和改善发酵工艺奠定基础。
In this study,the homemade intelligent acquisition module of thermal resistance was applied to real-time monitor the temperature in the fermentation process of Zhenjiang aromatic vinegar, and the pH value was being moni- tored and tracked to analyze the different fermentation stages. Besides, near-infrared spectroscopy （NIR）, combined with Principal component analysis （PCA） and K-nearest neighbors （KNN））, was applied to realize the vinegar culture. The main results are as follows： the fermentation process may be divided into four stages in accordance with the change of temperature. According to the curve of pH,the fermentation process can be divided into three stages（namely, rising peri- od, drop period and stable period）. In the middle ＆ later period of fermentation,the pH was maintained between 3.65 and 3.99. Analysis of near infrared spectroscopy （NIR） was applied to set identification model. When the fermentation process was divided into four stages, we obtained the optimal model and the highest recognition rate （90.04%）. Conclu- sion, the tracking of the whole process provided the foundation for further research of the vinegar culture.
出处
《中国食品学报》
EI
CAS
CSCD
北大核心
2014年第8期256-261,共6页
Journal of Chinese Institute Of Food Science and Technology
基金
国家自然科学基金项目(60901079)
全国优秀博士基金资助项目(200968)
国家863项目(2011AA100807)
江苏省农业自主创新计划资助项目(CX(11)2028)
关键词
固态发酵
不同阶段
温度
PH值
近红外光谱
solid-state fermentation
different stages
temperature
pH value
Near-infrared spectroscopy （NIRt
