Near infrared spectroscopy and hyperspectral imaging techniques have been widely used in agricultural and forestry products as well as food detection in recent years due to their advantages of high efficiency, nondestructive, and noncontact. These two techniques are applied to obtain spectral and imaging information of samples, and then chemometrics and machine learning modeling methods are combined to detect their quality and safety traits, adulteration issues, physical and chemical indicators, and origin traceability. Currently, they are both well recognized by people worldwide. However, the operating environment of optical instruments and the properties of the tested samples have their own limitations. The optical detection results are susceptible to various factors, which significantly affect the detection accuracy and should be eliminated or weakened. In this manuscript, the basic principles of near infrared spectroscopy and hyperspectral imaging are briefly described, and the influencing factors of these techniques at home and abroad are summarized. Considering the works of domestic and foreign researchers in related areas, the application of related correction methods regarding five aspects including temperature, illumination, moisture, curvature change and humidity is elaborated. Suggestions are finally presented for several existing problems to provide reference for researchers in this field.