Development of non-contact strawberry quality evaluation system using visible-near infrared spectroscopy: optimization of texture qualities prediction model

Strawberries are a high-value fruit with distinctive characteristics, including having a bright red color and juicy texture. The importance of their texture qualities requires the development of non-destructive analytical methods. This study focuses on the use of silicon-based visible-near infrared (Vis-NIR) spectroscopy to predict the texture qualities of strawberries. The highest correlation values (r) of prediction of firmness were 0.81 (transmittance) and 0.78 (reflectance), while those of brittleness were 0.78 (transmittance) and 0.77 (reflectance). It was found that transmittance mode can predict the texture qualities of strawberries better than reflectance mode. Savitzky-Golay filtering improved the prediction accuracy for most characteristics. The results showed that Vis-NIR spectroscopy, combined with partial least square regression analysis and Savitzky-Golay smoothing, can predict the texture qualities of strawberries at moderate to high accuracy. Further studies are needed to reduce the effects of individual sample sizes and improve prediction accuracy.