Rapid Detecting Total Acid Content and Classifying Different Types of Vinegar based on Near Infrared Spectroscopy and Ant Colony Optimization Partial Least-Squares Analysis


  • Zhu Yao-Di Jiangsu university
  • Zou Xiao-Bo Key Laboratory of Modern Agricultural Equipment and Technology
  • Huang Xiao-Wei Jiangsu university
  • Shi Ji-Yong Jiangsu university
  • Zhao Jie-Wen Jiangsu university
  • Li Yanxiao Jiangsu university
  • Hao Limin The Research Center of China Hemp Materials
  • Zhang Jianchun The Research Center of China Hemp Materials




Near infrared spectroscopy, Ant colony optimization, Vinegar, Total acid content, Principle component analysis, Partial least-square


Abstract: More than 3.2 million litres of vinegar is consumed every day in China. Traditional Chinese vinegars are prepared through solid-state fermentation (SFF) and made from different sorts of cereals. Chinese vinegars have specific local features. Every region has its own manufacturers, who produce vinegar in specific processes, using particular raw materials. How to control the quality of vinegar is problem. Near infrared spectroscopy (NIR) transmission technique was applied to achieve this purpose. 46 traditional vinegar samples were collected. They were classified into Sanxi vinegar, Zhenjiang vinegar, Micu vinegar, and Baonin vinegar according to their origin. Micu vinegar and Baonin vinegar were separated from the other categories in the two-dimension principal component space of NIR after principle component analysis (PCA). Ant colony optimization partial least-squares analysis (ACO-PLS) was firstly applied to identify the four categories vinegar. The accuracies of identification were more than 85%. As total acid content (TAC) is highly connecting with the quality of vinegar, NIR was used to predicate the TAC of samples. ACO-PLS was applied to building the TAC prediction model based on spectral transmission rate. Compared with full spectral partial least-square (PLS) model, ACO-PLS model gave better precision and accuracy in predicting TAC. The determination coefficient for prediction (Rp) of the ACO-PLS model was 0.921 and root mean square error for prediction (RMSEP) was 0.3031. This work demonstrated that near infrared spectroscopy technique coupled with ACO-PLS could be used as a quality control method for vinegar.

Author Biographies

Zhu Yao-Di, Jiangsu university

School of Food and Biological Engineering

Huang Xiao-Wei, Jiangsu university

School of Food and Biological Engineering

Shi Ji-Yong, Jiangsu university

School of Food and Biological Engineering

Zhao Jie-Wen, Jiangsu university

School of Food and Biological Engineering

Li Yanxiao, Jiangsu university

School of Food and Biological Engineering


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