scholarly journals Short communication: Fourier-transform mid-infrared spectroscopy to predict coagulation and acidity traits of sheep bulk milk

2019 ◽  
Vol 102 (3) ◽  
pp. 1927-1932 ◽  
Author(s):  
C.L. Manuelian ◽  
M. Penasa ◽  
G. Giangolini ◽  
C. Boselli ◽  
S. Currò ◽  
...  
2014 ◽  
Vol 5 ◽  
Author(s):  
Asier Largo-Gosens ◽  
Mabel Hernández-Altamirano ◽  
Laura García-Calvo ◽  
Ana Alonso-Simón ◽  
Jesús Álvarez ◽  
...  

2020 ◽  
Vol 309 ◽  
pp. 125585 ◽  
Author(s):  
Chithra Karunakaran ◽  
Perumal Vijayan ◽  
Jarvis Stobbs ◽  
Ramandeep Kaur Bamrah ◽  
Gene Arganosa ◽  
...  

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.


2018 ◽  
Vol 266 ◽  
pp. 254-261 ◽  
Author(s):  
Carolina Sheng Whei Miaw ◽  
Marcelo Martins Sena ◽  
Scheilla Vitorino Carvalho de Souza ◽  
Maria Pilar Callao ◽  
Itziar Ruisanchez

2018 ◽  
Vol 85 (1) ◽  
pp. 83-86 ◽  
Author(s):  
Massimo Malacarne ◽  
Giulio Visentin ◽  
Andrea Summer ◽  
Martino Cassandro ◽  
Mauro Penasa ◽  
...  

This Research Communication investigated the potential of mid-infrared spectroscopy to predict detailed mineral composition of bovine milk. A total of 153 bulk milk samples were analysed for contents of Ca, Cl, Cu, Fe, K, Mg, Na, P and Zn. Also, soluble and colloidal fractions of Ca, Mg and P were quantified. For each milk sample the mid-infrared spectrum was captured and stored. Prediction models were developed using partial least squares regression and the accuracy of prediction was evaluated using both cross- and external validation. The proportion of variance explained by the prediction models in cross-validation ranged from 34% (Na) to 77% (total P), and it ranged from 13% (soluble Mg) to 54% (Cl−) in external validation. The ratio of the standard deviation of each trait to the standard error of prediction in external validation, which is an indicator of the practical utility of the prediction model, was low and never greater than 2. Results from the current study supported the limited usefulness of mid-infrared spectroscopy to predict minerals present in low concentration in bulk milk. For major mineral components, results from the present research did not match previous findings demonstrating the need for further studies using larger reference datasets.


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