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Total soluble solids, dry matter content prediction and maturity stage classification of durian fruit using long-wavelength NIR reflectance
Author(s)
Saenphon, Chirawan
Ditcharoen, Sirirak
Malai, Chayuttapong
Saengprachatanarug, Khwantri
Wongpichet, Seree
Phuphaphud, Arthit
Maraphum, Kanvisit
Posom, Jetsada
Date Issued
December 1, 2023
Type
Article
Abstract
The DM and TSS of durian pulp moving on a conveyor belt were measured for their rapid and non-destructive qualities based on quantitative and qualitative measurements. The calibration set and prediction set equaled 209 and 69 pulps, respectively. The quantitative test compared the performance of PLS regression for DM and TSS prediction developed from full wavelength (860–1754 nm) and a few significant variables using SPA, GA, and VIP methods. The qualitative test identified the possibility of maturity stage classification by comparing three supervised machine learning classifiers, namely SVM, random forest (RF), and LDA. Effective models for DM and TSS prediction were developed from second derivatives spectra combined with the GA method, exhibiting r2, SEP, and RPD of 0.85, 4.50%, and 2.64, respectively for DM, and 0.66, 5.15%, and 1.60, respectively, for TSS. The model classifying samples into two distinct groups, namely “reject” and “pass,” utilizing the LDA algorithm, exhibited an impressive accuracy rate of 94.20%, making it a suitable choice for quality assurance purposes. This result indicates that the few effective variables were more efficient than full wavelength and improved model accuracy with greater model stability. Enhancing the classification model could involve data sample balancing in each group, leading to further improvements.
Citation
Journal of Food Composition and Analysis, 124, 2023