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

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 (R p ) 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.