Abstract
The quality of potato tubers is dependent on several attributes been maintained at appropriate levels during storage. One of these attributes is sprouting activity that is initiated from meristematic regions of the tubers (eyes). Sprouting activity is a major problem that contributes to reduced shelf life and elevated sugar content, which affects the marketability of seed tubers as well as fried products. This study compared the capabilities of three different optical systems (1: visible/near-infrared (Vis/NIR) interactance spectroscopy, 2: Vis/NIR hyperspectral imaging, 3: NIR transmittance) and machine learning methods to detect sprouting activity in potatoes based on the primordial leaf count (LC). The study was conducted on Frito Lay 1879 and Russet Norkotah cultivars stored at different temperatures and classification models were developed that considered both cultivars combined and classified the tubers as having either high or low sprouting activity. Measurements were performed on whole tubers and sliced samples to see the effect this would have on identifying sprouting activity. Sequential forward selection was applied for wavelength selection and the classification was carried out using K-nearest neighbor, partial least squares discriminant analysis, and soft independent modeling class analogy. The highest classification accuracy values obtained by the hyperspectral imaging system and was 87.5% and 90% for sliced and whole samples, respectively. Data fusion did not show classification improvement for whole tubers, whereas a 7.5% classification accuracy increase was illustrated for sliced samples. By investigating different optical techniques and machine learning methods, this study provides a first step toward developing a handheld optical device for early detection of sprouting activity, enabling advanced aid potato storage management.
Highlights
Effective storage of potato tubers is important to maintain quality attributes in fresh tubers
This study investigated the capability of three different spectroscopic systems (Transmittance, reflectance and hyperspectral) and machine learning methods for classifying high or low levels of spouting activity on whole tubers and sliced sampled for two different cultivars of potatoes
The results showed that applying sequential forward selection followed by K-nearest Neighbor (Knn) or Partial Least Squares Discriminant Analysis (PLS-DA) on hyperspectral data resulted in a classification accuracy of 90% for whole tubers with slightly lower values for the sliced samples (87.5%)
Summary
Effective storage of potato tubers is important to maintain quality attributes in fresh tubers. These attributes include specific gravity, carbohydrate content, glycoalkaloids content, flesh and skin color, and the absence of internal and external defects such as bruises, physiological disorders and sprouting [1]. Such attributes have significant effects on the final value of the tubers and the quality of any final potato products. Purchasers of fresh potatoes always look for tubers with no visual signs of defects.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.