A multivariate regression model for detection of fumonisins content in maize from near infrared spectra

作者：Giacomo Della Riccia; Stefania Del Zotto^*

来源：Food Chemistry, 2013, 141(4): 4289-4294.

DOI：10.1016/j.foodchem.2013.07.021

• Fumonisins
• NIR spectroscopy
• Multivariate regression
• Statistical model
• PLS inference
• Calibration
• Full-cross validation

Fumonisins are mycotoxins produced by Fusarium species that commonly live in maize. Whereas fungi damage plants, fumonisins cause disease both to cattle breedings and human beings. Law limits set fumonisins tolerable daily intake with respect to several maize based feed and food. Chemical techniques assure the most reliable and accurate measurements, but they are expensive and time consuming. A method based on Near Infrared spectroscopy and multivariate statistical regression is described as a simpler, cheaper and faster alternative. We apply Partial Least Squares with full cross validation. Two models are described, having high correlation of calibration (0.995, 0.998) and of validation (0.908, 0.909), respectively. Description of observed phenomenon is accurate and overfitting is avoided. Screening of contaminated maize with respect to European legal limit of 4 mg kg(-1) should be assured.

• 出版日期2013-12-15