Near-infrared(NIR) spectroscopy is a green,fast analytical technology,and thus has been widely used in scientific research,industrial production and routine detection.The application of chemometric algorithms plays an important role in the development of NIR spectroscopy.Chemometrics focuses on exploring the relation between the measured variables,modeling the differences among samples in a qualitative or quantitative way,finding out the underlying trend of intrinsic sample changes,and predicting unknown samples reasonably and accurately.This is also the thumb of the “big data” strategy.This review discusses the issues commonly encountered in NIR spectroscopy,concerning the weakness of spectral signals,the serious overlapping of NIR bands,the interference from background,noise,non informative variables or environmental factors,etc.,which could either mislead to an incorrect qualitative or quantitative analysis model relating the NIR spectroscopic measurements to target compositions of samples or worsen the model in terms of prediction capacity and accuracy.Furthermore,it also describes new chemometric methods with respect to spectral preprocessing,variable selection,multivariate calibration and calibration transfer.These methods have been proposed or developed in recent years to improve the reliability,accuracy and applicability of the chemometric NIR spectral models.