Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Current refinement(s):

Records 1 - 2 / 2

  • help
  • print

    Print search results

  • export

    Export search results

  • alert
    We will mail you new results for this query: keywords==Near-infrared spectroscopy
Check title to add to marked list
Predicting soil microplastic concentration using vis-NIR spectroscopy
Corradini, Fabio ; Bartholomeus, Harm ; Huerta Lwanga, Esperanza ; Gertsen, Hennie ; Geissen, Violette - \ 2019
Science of the Total Environment 650 (2019). - ISSN 0048-9697 - p. 922 - 932.
Microplastics - Near-infrared spectroscopy - Soil pollution - Spectroradiometer - Vis-NIR

Microplastic accumulation in soil may have a detrimental impact on soil biota. The lack of standardized methods to identify and quantify microplastics in soils is an obstacle to research. Existing techniques are time-consuming and field data are seldom collected. To tackle the problem, we explored the possibilities of using a portable spectroradiometer working in the near infrared range (350–2500 nm) to rapidly assess microplastic concentrations in soils without extraction. Four sets of artificially polluted soil samples were prepared. Three sets had only one polymer polluting the soil (low-density polyethylene (LDPE), polyethylene terephthalate (PET), or polyvinyl chloride (PVC)). The fourth set contained random amounts of the three polymers (Mix). The concentrations of microplastics were regressed on the reflectance observed for each of the 2150 wavelengths registered by the instrument, using a Bayesian approach. For a measurement range between 1 and 100 g kg−1, results showed a root-mean-squared-deviation (RMSD) of 8, 18, and 10 g kg−1 for LDPE, PET, and PVC. The Mix treatment presented an RMSD of 8, 10, and 5 g kg−1 for LDPE, PET, and PVC. The repeatability of the proposed method was 0.2–8.4, 0.1–5.1, and 0.1–9.0 g kg−1 for LDPE, PET, and PVC, respectively. Overall, our results suggest that vis-NIR techniques are suitable to identify and quantify LDPE, PET, and PVC microplastics in soil samples, with a 10 g kg−1 accuracy and a detection limit ≈ 15 g kg−1. The method proposed is different than other approaches since it is faster because it avoids extraction steps and can directly quantify the amount of plastic in a sample. Nevertheless, it seems to be useful only for pollution hotspots.

Targeted and Untargeted Detection of Skim Milk Powder Adulteration by Near-Infrared Spectroscopy
Capuano, Edoardo ; Boerrigter-Eenling, Rita ; Koot, Alex ; Ruth, S.M. van - \ 2015
Food Analytical Methods 8 (2015)8. - ISSN 1936-9751 - p. 2125 - 2134.
Acid whey - Adulteration - Class modelling - Near-infrared spectroscopy - Skim milk powder - Starch

In the present study, near-infrared spectroscopy (NIRS) was explored as a fast and reliable screening method for the detection of adulteration of skim milk powder (SMP). Sixty genuine SMP were adulterated with acid whey (1–25 % w/w), starch (2 and 5 %) and maltodextrin (2 and 5 %) for a total of 348 adulterated samples. Two chemometric approaches were employed. In the first approach, an untargeted one class model for genuine skim milk powder was developed by Soft Independent Modelling of Class Analogy. In the second approach, adulterant-specific regression models were developed to assess the amount of each adulterant by partial least square regression and principal component regression. The class modelling approach had the advantage that several adulterants could be detected with the same chemometric model, including situations where multiple adulterants are present in the test sample or where yet unknown adulterants are present. Regression models showed a better sensitivity with genuine SMP samples completely discriminated from samples adulterated with 5 % acid whey and 2 % of starch or maltodextrin. NIRS proved to be a useful tool for the rapid and cost-efficient untargeted and/or targeted detection of adulterations in SMP.

Check title to add to marked list

Show 20 50 100 records per page

 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.