Location: Environmental Microbial & Food Safety LaboratoryTitle: Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging
|MO, CHANGYEUN - Korean Rural Development Administration|
|KIM, GIYOUNG - Korean Rural Development Administration|
|LIM, JONGGUK - Korean Rural Development Administration|
|Delwiche, Stephen - Steve|
|Chao, Kuanglin - Kevin Chao|
|LEE, HOONSOO - Us Forest Service (FS)|
|CHO, BYOUNG-KWAN - Chungnam National University|
Submitted to: Biosystems Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/31/2017
Publication Date: 5/27/2017
Citation: Mo, C., Kim, M.S., Kim, G., Lim, J., Delwiche, S.R., Chao, K., Lee, H., Cho, B. 2017. Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging. Biosystems Engineering. 159:10-21.
Interpretive Summary: One of the most important quality properties of apples is sweetness. The traditional method for sweetness assessment is one that measures the level of dissolved sugars in the juice expelled from lightly pressed apple tissue. Also known as soluble solids, the procedure for its measurement is typically performed on randomly selected apples at the time of harvest or from a wholesale lot. Soluble solids, reported in units known as degrees Brix, are typically represented as one value for an apple, without regard to how sweetness may vary throughout the apple. In this research, we successfully developed an alternate procedure that can measure soluble solids in localized regions of half-apple slices. Based on a combination of digital imaging and visible/near-infrared spectrometry, this “hyperspectral imaging” technique may be used in postharvest operations to better characterize ripeness and eating quality. Beneficiaries of this research include apple growers, packing house operators, and researchers of apple ripening.
Technical Abstract: A partial least squares regression (PLSR) model to map internal soluble solids content (SSC) of apples using visible/near-infrared (VNIR) hyperspectral imaging was developed. The reflectance spectra of sliced apples were extracted from hyperspectral absorbance images obtained in the 400e1000 nm range. Prediction models for SSC mapping were developed for three different measurement/sampling designs that varied in the number and size of the regions of interest (ROIs) used for apple SSC measurement and spectral averaging. Case 1 used 29 small ROIs per apple, Case II used 9 moderate-size ROIs per apple, and Case III used 5 large ROIs per apple. The optimal pre-treatment of the spectra extracted from the hyperspectral images was investigated to enhance the performance of the prediction models. The coefficients of determination and root mean square errors of the best-performing models were, respectively, 0.802 and ±0.674 Brix for Case I, 0.871 and ±0.524 Brix for Case II, and 0.876 and ±0.514 Brix for Case III. The accuracy of the PLSR models was enhanced by using the spectra and SSC measured/averaged from the fewer but larger areas of the apples rather than from more numerous but smaller areas. PLS images of SSC showed the predicted internal distribution of SSC within the apples. The overall results demonstrate that hyperspectral absorbance imaging techniques may be useful for mapping internal soluble solids content of apples.