Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

Abstract

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 Rp2, Rc2 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 Rp2, Rc2, 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.

Prediction of Soil Water Content Based on Hyperspectral Reflectance Combined with Competitive Adaptive Reweighted Sampling and Random Frog Feature Extraction and the Back-Propagation Artificial Neural Network Method

“…Firstly, CARS was used for the initial screening of the feature variables. However, the CARS extraction results were random [20], and the feature variables extracted by only one method are numerous, making the model too complex to model.…”

“…Data preprocessing and feature extraction algorithms can reduce noise, remove interfering variables, and improve model prediction [19]. However, when only one method is used to extract feature variables, the stability might be poor, and too many variables may be located, which would make the prediction model too complex [20]. To address the deficiency with feature band extraction methods, different variable extraction methods were used, for example, uninformative variable elimination plus the successive projections algorithm (UVE-SPA).…”

Non-destructive assessment of soluble solids content in kiwifruit using hyperspectral imaging coupled with feature engineering

“…During the spectral acquisition step, various disturbances, such as sample differences, environmental noise, and baseline drift, can affect the final captured spectral image ( Xu et al., 2023 ). To mitigate these variations in spectral reflectance and emphasize the features related to SSC, a spectral pre-processing procedure is conducted.…”

“…This technique enables the measurement of spectral reflectance across a broad range of wavelengths, providing detailed insights into the chemical and physical properties of samples. In the case of kiwifruit, the visible near-infrared (Vis-NIR) spectral range contains valuable information related to the absorption of O–H, N–H, and C–H vibrations ( Guo et al., 2017 ; Xu et al., 2023 ). These vibrational modes facilitate the identification and quantification of key chemical constituents associated with SSC, such as sugars and other organic compounds.…”

Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms

“…KNN, SVM, LDA, and decision tree were used to establish maize seed varieties. IRIV has good feature extraction ability for high-dimensional data, and its use has not yet been reported for maize seed variety identification ( Xu et al, 2021 ; Sun et al, 2018b ; Yun et al, 2014 ; Sun et al, 2018a ). Based on different pre-processing and characteristic band extraction methods, maize seed variety identification models based on the base learner are quite different.…”