Qin Ouyang has completed her PhD from Jiangsu University and currently working at Jiangsu University. She has published more than 20 papers in reputed journals.


Chinese rice wine sensory attributes including color, aroma, taste and style play a major role in product acceptability by directly influencing the success of a product in the marketplace. The estimation of the sensory attributes is generally done by human sensory analysis. However, the use of human for sensory evaluation entails several drawbacks, such as fatigue, stress and inconsistence etc. This study aimed to assess the feasibility of using near infrared (NIR) spectroscopy technique as an instrumental evaluation technique for predicting the sensory attributes (i.e., color, aroma, taste and style) in Chinese rice wine. Synergy interval partial least squares (si-PLS) was used to select the optimal spectral intervals and construct models between the NIR spectra and the reference scores of each sensory attribute, respectively. The performance of model was evaluated by the correlation coefficient (Rp) and the root mean square error (RMSEP) in the prediction set. The optimal performance by the si-PLS model for color was Rp=0.8735, RMSEP=0.274; the aroma was Rp=0.8316, RMSEP=0.893; the taste was Rp=0.8791, RMSEP=1.22; and the style was Rp=0.7784, RMSEP=0.557. This work demonstrated that the instrumental test using NIR spectroscopy technique has a high potential in predicting the sensory attributes in Chinese rice wine; it can overcome the drawbacks of human sensory analysis and achieve the objective measurements in a short time in a consistent and cost-effective manner.