Visualization of Protein in Peanut Using Hyperspectral Image with Chemometrics.

The study aims to explore the potential of hyperspectral imaging (HSI) with chemometrics for rapidly and non-invasively visualizing the spatial distribution of protein content which can affect the quality of peanut products as a critical component of peanut. Spectral data contained in the region of interest (ROI) of the corrected hyperspectral images of peanut were extracted and protein contents were measured with conventional chemical method. By comparing different pretreatments and modeling algorithms, the second-order derivatives (2nd-der) on spectra is optimal pretreatment, and partial ceast square (PLS) is the best regression method. Based on the pretreatment spectra and the measured protein content model, a good performance model (RC=0.91, SEC=0.86; RP=0.86, SEP=0.69) was built with full wavelengths. The fourteen optimal wavelengths were carried out based on the regression coefficients (RC) of the established PLS model. Then, using optimal wavelengths built RC-PLS model which show resembling performance (RC=0.86, SEC=1.03; RP=0.80, SEP=0.77). At last, an imaging processing algorithm was developed to transfer each pixel in peanut to protein content with the 2nd-der-RC-PLS model. There was no significant difference between Kjeldahl and HSI method by the paired test. The result demonstrated the capacity of HSI in combination with chemometrics for fast and non- destructively determining protein content in peanut.