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.

Using a random laser to measure the content of protein in skim milk

Suely dos Santos Araújo, Marcus V. A. Prado, Luis M. G. Abegão, Alessandra A. C. Pagani, José Joatan Rodrigues, Sérgio C. Zílio, and Márcio A. R. C. Alencar
Appl. Opt. 62(8) C53-C58 (2023)

Terahertz spectroscopy detection of zinc citrate in flour and milk powder mixtures

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Quantitative analysis of bayberry juice acidity based on visible and near-infrared spectroscopy

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Laser-induced breakdown spectroscopy for the classification of unknown powders

Emily Gibb Snyder, Chase A. Munson, Jennifer L. Gottfried, Frank C. De Lucia, Jr., Brian Gullett, and Andrzej Miziolek
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Optical system for tablet variety discrimination using visible/near-infrared spectroscopy

Yongni Shao, Yong He, and Xingyue Hu
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