A Comparative Study of PLSR and SVM-R with Various Preprocessing Techniques for the Quantitative Determination of Soluble Solids Content of Hardy Kiwi Fruit by a Portable Vis/NIR Spectrometer

• Published in: Foods 2020-08, Vol.9 (8), p.1078
• Main Authors: Sarkar, Shagor, Basak, Jayanta Kumar, Moon, Byeong Eun, Kim, Hyeon Tae
• Format: Article
• Language: English
• Subjects: Algorithms; Calibration; Comparative studies; Correlation coefficient; Correlation coefficients; Datasets; Food science; Fruits; hardy kiwi; Infrared spectra; Infrared spectroscopy; Kiwifruit; Least squares; Near infrared radiation; near-infrared spectroscopy; non-destructive measurement; partial least square; Prediction models; Preprocessing; Principal components analysis; Quality; Regression analysis; Solids; soluble solids content; Species; Spectrum analysis; support vector machine; Support vector machines
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods9081078

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.