Rapid and non‐invasive multivariate approach for the quality control of raw milk from mountain areas based on proton transfer reaction mass spectrometry data

Asaduzzaman, Md; Kerschbaumer, Martin; Scampicchio, Matteo

doi:10.1002/rcm.8146

Cited by 2 publications

(1 citation statement)

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“…14 Further approaches comprise the analysis of the volatile organic compounds profile. 15 Multi-method approaches have been also reported. 16 However, their implementation in farms is limited since they are expensive, timeconsuming and require trained personnel.…”

Section: Introductionmentioning

confidence: 99%

Short-wave near infrared spectroscopy for the quality control of milk

Asaduzzaman

Kerschbaumer

Bodner

et al. 2019

Journal of Near Infrared Spectroscopy

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The present study aims to demonstrate the potential use of short-wave near infrared spectroscopy for the quality control of raw cow milk samples, collected from high mountain areas. The sampling plan comprised three farms, all located within the same Alpine region (South Tyrol, Italy), but located at different altitudes (1900 m, 1050 m and 950 m a.s.l). Each farm used a similar extensive grassland-based farming system. For comparison, raw milk samples were also collected from a farm located in the valley (Milan, Italy), at 200 m a.s.l. and subjected to an intensive farming system. From each location, the samples were collected 10 times within one month of production. All the milk samples were analysed by diffuse trans-reflectance in the wavelength range from 850 to 1350 nm. Principal component analysis of the spectra revealed that the short-wave near infrared bands, respectively, 847, 1084, and 1095 nm, were the most important to distinguish milk between farms. The signal intensities of these wavelengths were used to build a multivariate control chart based on the Hotelling T2 statistic. The results showed that short-wave near infrared spectroscopy can be successfully used to monitor milk products in a fast, simple and on-line way.

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Section: Introductionmentioning

confidence: 99%