The multiple linear regression approach typically used in near-infrared calibration yields equations in which any amount of reflectance at the analytical wavelengths leads to a corresponding composition value. As a result, when the sample contains a component not present in the training set, erroneous composition values can arise without any indication of error. The Quantile BEAST (<u>B</u>ootstrap <u>E</u>rror-<u>A</u>djusted <u>S</u>ingle-sample <u>T</u>echnique) is described here as a method of detecting one or more "false" samples. The BEAST constructs a multidimensional form in space using the reflectance values of each training-set sample at a number of wavelengths. New samples are then projected into this space, and a confidence test is executed to determine whether the new sample is part of the training-set form. The method is more robust than other procedures because it relies on few assumptions about the structure of the data; therefore, deviations from assumptions do not affect the results of the confidence test.

PDF Article

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
Login to access OSA Member Subscription