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.