Abstract
Near-infrared reflectance (NIR) spectroscopy is well established as a rapid and nondestructive analytical technique in many agro-food industries. Most published applications have been concerned with the use of NIR for quantitative analyses of technologically important chemical constituents such as water and protein in grain, alcohol in wine, oil in mayonnaise, etc., although successes have been reported with the prediction of less precisely defined but nonetheless functionally important sample attributes (e.g., wheat hardness). A number of mathematical techniques have been used to develop accurate and stable prediction equations, among which may be mentioned stepwise multiple linear regression, principal component regression, partial least-squares regression, and derivative quotient mathematics.
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