Abstract

Food quality and safety, as well as bacteria identification, have become very important issues. Establishing fast and non-destructive methods for control is of a great importance. The performance and safety of processed milk and milk products are influenced by the quality of the raw milk used and animal health. Cell count in udder quarter milk has been established as the main criteria by which milk abnormality is evaluated and, in addition to electrical conductivity and pathogen identification, it is also a principle means for the diagnosis of the inflammatory udder disease known as mastitis. Near infrared (NIR) spectroscopy has proved to be a fast non-destructive method for the analysis of food and agricultural products, including non-homogenised milk. In this study, we have analysed NIR spectra of udder quarter milk samples collected continuously from individual cows, at various farms, at different times of the year. We report that NIR spectra of cows' udder quarter milk, when subjected to multivariate data analysis, provides information about milk abnormality and health disorders in cows. We have developed spectroscopic models for the simultaneous measurement of somatic cell count and electrical conductivity, as well as for identification of the main mastitis-causing bacterial pathogens in cow's udder quarter milk. These findings present NIR spectroscopy as a powerful technology for in vivo health monitoring, disease diagnosis at molecular level and bacteria identification.

© 2006 NIR Publications

Deep learning-based cell identification and disease diagnosis using spatio-temporal cellular dynamics in compact digital holographic microscopy

Timothy O’Connor, Arun Anand, Biree Andemariam, and Bahram Javidi
Biomed. Opt. Express 11(8) 4491-4508 (2020)

Towards the clinical application of laser-induced breakdown spectroscopy for rapid pathogen diagnosis: the effect of mixed cultures and sample dilution on bacterial identification

Steven J. Rehse, Qassem I. Mohaidat, and Sunil Palchaudhuri
Appl. Opt. 49(13) C27-C35 (2010)

Disease pattern recognition in infrared spectra of human sera with diabetes mellitus as an example

Wolfgang Petrich, Brion Dolenko, Johanna Früh, Manfred Ganz, Helmut Greger, Stephan Jacob, Franz Keller, Alexander E. Nikulin, Matthias Otto, Ortrud Quarder, Ray L. Somorjai, Arnulf Staib, Gerhard Werner, and Hans Wielinger
Appl. Opt. 39(19) 3372-3379 (2000)

Optical properties of human milk

Colin Veenstra, Anki Lenferink, Wilma Petersen, Wiendelt Steenbergen, and Nienke Bosschaart
Biomed. Opt. Express 10(8) 4059-4074 (2019)

Rapid identification of the variety of maize seeds based on near-infrared spectroscopy coupled with locally linear embedding

Shu Liu, Zhengguang Chen, and Feng Jiao
Appl. Opt. 61(7) 1704-1710 (2022)