Abstract
Linear partial least square and non-linear support vector machine regression analysis with various preprocessing techniques and their combinations were used to determine the soluble solids content of hardy kiwi fruits by a handheld, portable near-infrared spectroscopy. Fruits of four species, namely Autumn sense (A), Chungsan (C), Daesung (D), and Green ball (Gb) were collected from five different areas of Gwangyang (G), Muju (M), Suwon (S), Wonju (Q), and Yeongwol (Y) in South Korea. The dataset for calibration and prediction was prepared based on each area, species, and in combination. Half of the dataset of each area, species, and combined dataset was used as calibrated data and the rest was used for model validation. The best prediction correlation coefficient ranges between 0.67 and 0.75, 0.61 and 0.77, and 0.68 for the area, species, combined dataset, respectively using partial least square regression (PLSR) method with different preprocessing techniques. On the other hand, the best correlation coefficient of predictions using the support vector machine regression (SVM-R) algorithm was 0.68 and 0.80, 0.62 and 0.79, and 0.74 for the area, species, and combined dataset, respectively. In most cases, the SVM-R algorithm produced better results with Autoscale preprocessing except G area and species Gb, whereas the PLS algorithm shows a significant difference in calibration and prediction models for different preprocessing techniques. Therefore, the SVM-R method was superior to the PLSR method in predicting soluble solids content of hardy kiwi fruits and non-linear models may be a better alternative to monitor soluble solids content of fruits. The finding of this research can be used as a reference for the prediction of hardy kiwi fruits soluble solids content as well as harvesting time with better prediction models.
Highlights
Fruits are manually or mechanically sorted based on size, color, shape, and surface defects, while internal quality parameters including soluble solids content (SSC), acidity, vitamins, and phytochemicals are generally determined by destructive approach [1]
The study was conducted to evaluate the application of a handheld portable Vis/NIR spectrometer for the SSC prediction using different preprocessing techniques with two analyzing methods
Hardy kiwi fruits were collected from five different areas of South Korea that include four different species
Summary
Fruits are manually or mechanically sorted based on size, color, shape, and surface defects, while internal quality parameters including soluble solids content (SSC), acidity (pH), vitamins, and phytochemicals are generally determined by destructive approach [1]. The improvement of non-destructive technology has gained much attention for the application in measuring food quality, which is easy in operation, fast, and reliable than traditional methods [2]. Visible/near-infrared (Vis/NIR) spectroscopy is one of the promising nondestructive. Several studies focused on the application of Vis/NIR as nondestructive quality measurement of apple, mango, citrus, and kiwi fruit. Reflectance spectra of 800–1600 nm were obtained to predict soluble solids content by normalizing the spectral reflectance with a standard error of predictions (SEP) [3]
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