Vibrational spectroscopy in practice: Detection of mineral oil in sunflower oil with near- and mid-infrared spectroscopy

NIR news ◽  
2018 ◽  
Vol 29 (3) ◽  
pp. 6-11 ◽  
Author(s):  
Michael K-H Pfister ◽  
Bettina Horn ◽  
Janet Riedl ◽  
Susanne Esslinger ◽  
Carsten Fauhl-Hassek

Fourier transform infrared spectroscopy becomes increasingly important for detecting adulterations in food due to a minimal sample preparation and a fast nondestructive measurement. Sunflower oil is a popular food ingredient, which might be contaminated or even adulterated by compounds with health concerns such as mineral oil. In this context a feasibility study was performed to compare the suitability of near- and mid-infrared spectroscopy for detecting mineral oil in sunflower oil. For this purpose, sunflower oils spiked with mineral oil in the concentration range of 0.001–1.0% w/w were analyzed by Fourier transform near- and mid-infrared spectroscopy, respectively, and spectra data were preprocessed prior to partial least squares regression. Hereby, the data preparation was optimized for each technique to account for model performance influences. The model performance was fairly similar for both approaches with a slightly better precision and thus limit of detection (near infrared 0.12% w/w, mid infrared 0.16% w/w) for the near-infrared-based model compared to the mid-infrared model. Consequently, both techniques are considered suitable for the determination of mineral oil in sunflower oil in the context of food authentication.

Molecules ◽  
2020 ◽  
Vol 25 (21) ◽  
pp. 4987
Author(s):  
Hongyan Zhu ◽  
Jun-Li Xu

Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700–4350 cm−1 is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms.


2018 ◽  
Vol 99 (4) ◽  
pp. 1946-1953 ◽  
Author(s):  
Annalisa De Girolamo ◽  
Salvatore Cervellieri ◽  
Marina Cortese ◽  
Anna Chiara Raffaella Porricelli ◽  
Michelangelo Pascale ◽  
...  

2014 ◽  
Vol 5 ◽  
Author(s):  
Asier Largo-Gosens ◽  
Mabel Hernández-Altamirano ◽  
Laura García-Calvo ◽  
Ana Alonso-Simón ◽  
Jesús Álvarez ◽  
...  

1995 ◽  
Vol 78 (6) ◽  
pp. 1537-1541 ◽  
Author(s):  
Angela Fehrmann ◽  
Monika Franz ◽  
Andreas Hoffmann ◽  
Lutz Rudzik ◽  
Eberhard Wüst

Abstract Identification of microorganisms by traditional microbiological methods is time consuming. The German Federal Health Office has developed a method using mid-infrared spectroscopy to identify microorganisms rapidly. This method has been modified for application to microorganisms important in the dairy industry. Mid- and near-infrared spectroscopies are well-established methods for quantitative measurements of fat, protein, lactose, and solid content in a variety of products. A disadvantage of both methods is the huge absorption due to water; extraction of other components is complicated and can be achived only statistically. With Raman spectroscopy, water causes less absorption. We investigated the use of Raman spectroscopy as a quantitative method for milk powder.


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