Determination of invert syrup adulterated in acacia honey by terahertz spectroscopy with different spectral features.
J Sci Food Agric
; 100(5): 1913-1921, 2020 Mar 30.
Article
in En
| MEDLINE
| ID: mdl-31846080
BACKGROUND: Invert syrup is a common adulterant in honey falsification, thus generating risk for consumers. Most of the methods developed are tedious and time-consuming for manufactures and consumers. However, terahertz spectroscopy provides analytical information in a simple, rapid, and environmentally friendly manner. Subsequently, 3 kinds of terahertz spectroscopic characteristics data, the absorption coefficient, the slope of the absorption coefficient spectra, and the area of the absorption coefficient spectra, were employed for determination of acacia honey adulterated with invert syrup. RESULTS: Single linear regression (SLR) models with different terahertz spectroscopic features were adopted to predict the syrup adulterant proportion in acacia honey. The best SLR model used the area of the absorption coefficient, displaying an adjusted correlation coefficient of 0.985 and a root-mean-square error of 3.201. Meanwhile, multiple linear regression (MLR) models using a successive projections algorithm for variables selection were implemented. The MLR model considered the integral area of the absorption coefficient spectra, as the inputs yielded the best result with less variables selected, higher R c 2 and R p 2 , lower root-mean-square error of calibration and prediction, as well as higher residual predictive deviation. CONCLUSIONS: The results indicate terahertz spectroscopy combined with the integral area of the absorption coefficient spectra is reliable enough for invert syrup proportion quantification in acacia honey and is also a rapid and nondestructive determination method for other honey adulterants. © 2019 Society of Chemical Industry.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Food Contamination
/
Acacia
/
Terahertz Spectroscopy
/
Honey
Type of study:
Evaluation_studies
/
Prognostic_studies
Language:
En
Journal:
J Sci Food Agric
Year:
2020
Document type:
Article
Affiliation country:
Country of publication: