Authors: Chih‐Jung Chen | Yung‐Jhe Yan | Chi-Cho Huang | Jen‐Tzung Chien | Chang-Ting Chu | Je-Wei Jang | Tzung-Cheng Chen | Shiou-Gwo Lin | Ruei-Siang Shih | Mang Ou‐Yang
Source: Scientific Reports (article, 2022-02-17)
Field: Chemistry — Analytical Chemistry

Sugariness prediction of Syzygium samarangense using convolutional learning of hyperspectral images Sugariness is one of the most important indicators to measure the quality of Syzygium samarangense, which is also known as the wax apple. In general, farmers used to measure sugariness by testing the extracted juice of the wax apple products. Such a destructive way to measure sugariness is not only labor-consuming but also wasting products. Therefore, non-destructive and quick techniques for measuring sugariness would be significant for wax apple supply chains. Traditionally, the non-destructive method to predict the sugariness or the other indicators of the fruits was based on the reflectance spectra or Hyperspectral Images (HSIs) using linear regression such as Multi-Linear Regression (MLR), Principal Component Regression (PCR), and Partial Least Square Regression (PLSR), etc. However, these regression methods are usually too simple to precisely estimate the complicated mapping between the reflectance spectra or HSIs and the sugariness. This study presents the deep learning methods for sugariness prediction using the reflectance spectra or HSIs from the bottom of the wax apple. A non-destructive imaging system fabricated with two spectrum sensors and light sources is implemented to acquire the visible and infrared lights with a range of wavelengths. In particular, a specialized Convolutional Neural Network (CNN) with hyperspectral imaging is proposed by investigating the effect of different wavelength bands for sugariness prediction. Rather than extracting spatial features, the proposed CNN model was designed to extract spectral features of HSIs. In the experiments, the ground-truth value of sugariness is obtained from a commercial refractometer. The experimental results show that using the whole band range between 400 and 1700 nm achieves the best performance in terms of °Brix error. CNN models attain the °Brix error of ± 0.552, smaller than ± 0.597 using Feedforward Neural Network (FNN). Significantly, the CNN's test results show that the minor error in the interval 0 to 10°Brix and 10 to 11°Brix are ± 0.551 and ± 0.408, these results indicate that the model would have the capability to predict if sugariness is below 10°Brix or not, which would be similar to the human tongue. These results are much better than ± 1.441 and ± 1.379 by using PCR and PLSR, respectively. Moreover, this study provides the test error in each °Brix interval within one Brix, and the results show that the test error is varied considerably within different °Brix intervals, especially on PCR and PLSR. On the other hand, FNN and CNN obtain robust results in terms of test error. Hyperspectral imaging ; Partial least squares regression ; Artificial intelligence ; Pattern recognition (psychology) ; Wax ; Convolutional neural network ; Principal component analysis ; Computer science ; Linear regression ; Biological system ; Measure (data warehouse) ; Regression ; Simple linear regression ; Remote sensing ; Mathematics ; Materials science ; Statistics ; Machine learning ; Biology ; Data mining ; Geology Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Smart Agriculture and AI