Vis/NIR model development and robustness in prediction of potato dry matter content with influence of cultivar and season

• Published in: Postharvest biology and technology Vol. 197; p. 112202
• Main Authors: Wang, Shuai, Yan, Jinshan, Tian, Shijie, Tian, Hao, Xu, Huirong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2023
• Subjects: Dry matter content; Mahalanobis distance; Model updating; Potato; Variable selection; Potato; Model updating; Mahalanobis distance; Dry matter content; Variable selection
• DOI: 10.1016/j.postharvbio.2022.112202
• ISSN: 0925-5214

Visible/Near-infrared (Vis/NIR) spectroscopy is widely used in the detection of dry matter content (DMC) of potatoes. However, biological variability (e.g., cultivar and season) will affect the potato DMC and spectral features, and will further cause the DMC prediction model ineffective. This study aimed to develop robust Vis/NIR models for predicting potato DMC with influence of cultivar and season. The local and global models were developed to explore the influence of cultivar and season. The Mahalanobis distance and concentration gradient (MD-CG) method was developed to select representative samples, and the combinations of different variable selection methods (CARS, SPA and CSMW) and model updating methods (SBC and recalibration) were investigated for model enhancement. The results indicated that 10 new samples selected by MD-CG method, combined with variable selection and model updating, were sufficient to improve the performance of the local (RPDp>1.7) and global (RPDp>2) models. In the local models, for the datasets with different cultivars (EG-2021, XS-2021 and AT-2021), the optimal results were obtained using CSMW combined with recalibration, and the RMSEp was decreased from 4.18%, 1.14%, 2.54–1.05%, 0.72%, 0.79%, respectively. For the datasets with different seasons (FA-2022), the optimal result was obtained by using SPA combined with recalibration, and the RMSEp was decreased from 3.70% to 0.91%. For the global model, CSMW combined with recalibration and SPA combined with SBC obtained better results, with RMSEp decreasing from 0.83% to 0.52% and 0.51%, respectively. The MD-CG method and the combinations of variable selection and model updating proposed in this study are important to reduce the influence of external conditions and enhance the model robustness to biological variability. •Cultivar and seasonal factors affect spectral features and model robustness.•Local and global models were developed and externally predicted.•MD-CG method was proposed to select representative samples for model updating.•Variable selection combined with model updating were explored for model enhancement.•A minimum of 10 new samples improved model performance by more than 37%.