The quantity of biomass, glucose concentration and ethanol concentration are important parameters in ethanol fermentation. Traditional methods are usually based on samples for off-line measurement, which not only requires multiple instruments for test and analysis but also consumes notable time and effort, and therefore is inconvenient for real-time process control and optimization. In this study, an in-situ detection method based on the near-infrared (NIR) spectroscopy is proposed for measuring the above process parameters in real time. The in-situ measurement is carried out by using an immersion type NIR spectroscopy. A multi-output prediction model for simultaneously estimating the quantity of glucose, biomass and ethanol is established based on a multi-output least-squares support vector regression algorithm. The experimental results show that the proposed method can precisely measure the quantity of glucose, biomass and ethanol during the ethanol fermentation process. Compared to the existing partial-least-squares method for modeling and prediction of individual components, the proposed method could evidently improve the measurement accuracy and reliability.