Authors: Lijia Xu | Yanjun Chen | Xiaohui Wang | Heng Chen | Zuoliang Tang | Xiaoshi Shi | Xinyuan Chen | Yuchao Wang | Zhilang Kang | Zhiyong Zou | Peng Huang | Yong He | Ning Yang | Yongpeng Zhao
Source: Frontiers in Plant Science (article, 2023-01-18)
Field: Chemistry — Analytical Chemistry

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mtext>R</mml:mtext> <mml:mi>p</mml:mi> <mml:mn>2</mml:mn></mml:msubsup> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mtext>R</mml:mtext> <mml:mi>c</mml:mi> <mml:mn>2</mml:mn></mml:msubsup> </mml:mrow> </mml:math> and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mtext>R</mml:mtext> <mml:mi>p</mml:mi> <mml:mn>2</mml:mn></mml:msubsup> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mtext>R</mml:mtext> <mml:mi>c</mml:mi> <mml:mn>2</mml:mn></mml:msubsup> </mml:mrow> </mml:math> , and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality. Hyperspectral imaging ; Partial least squares regression ; Spectral imaging ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Computer science ; Particle swarm optimization ; Biological system ; Mathematics ; Remote sensing ; Algorithm ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies