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The visible/near infrared (Vis/NIR) spectrum will become distorted due to variations in sample color, thereby reducing the prediction accuracy of fruit composition. In this study, we aimed to develop a deep learning model with color correction capability to predict oranges soluble solids content (SSC) based on multi-source data fusion. Initially, a machine vision and Vis/NIR spectroscopy online acquisition device was designed to collect and analyze color images and transmission spectra. Subsequently, data fusion methods were proposed for color features and spectral data. Finally, color-correction one-dimensional convolutional neural network (1D-CNN) models base on multi-source data were constructed. The results showed that, the RMSEP of optimal color-correction model was decreased by 36.4 % and 16.1 % compared to partial least squares model and conventional 1D-CNN model, respectively. The multi-source data fusion of machine vision and Vis/NIR spectroscopy has the potential to improve the accuracy of food composition prediction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Lycopene, a biologically active phytochemical with health benefits, is a key quality indicator for cherry tomatoes. While ultraviolet/visible/near-infrared (UV/Vis/NIR) spectroscopy holds promise for large-scale online lycopene detection, capturing its characteristic signals is challenging due to the low lycopene concentration in cherry tomatoes. This study improved the prediction accuracy of lycopene by supplementing spectral data with image information through spectral feature enhancement and spectra-image fusion. The feasibility of using UV/Vis/NIR spectra and image features to predict lycopene content was validated. By enhancing spectral bands corresponding to colors correlated with lycopene, the performance of the spectral model was improved. Additionally, direct spectra-image fusion further enhanced the prediction accuracy, achieving R P 2 , RMSEP, and RPD as 0.95, 8.96 mg/kg, and 4.25, respectively. Overall, this research offers valuable insights into supplementing spectral data with image information to improve the accuracy of non-destructive lycopene detection, providing practical implications for online fruit quality prediction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

To explore the effect of signal acquisition way on screening ability, the multiplexing fiber optic laser induced fluorescence spectroscopy (LIFS) system with one-, two- and three-probe, were employed respectively to detect artificially aflatoxin B 1 (AFB 1 , 5, 10, 20, 30, and 50 ppb) contaminated 300 pistachio kernels in this study. Compared to one- and two-probe modes, highest accuracy (≥97.0%) by support vector machine (SVM) employing 390-660 nm were obtained using three-probe, which also showed the most attractive precision (root mean square error of prediction (RMSEP) < 4.5 ppb) for AFB 1 by stepwise multiple linear regression (SMLR) using 174-1100 nm. These suggested that the effective collection of spatial information could improve the performance of model, and the three-probe LIFS had a preliminary feasibility for discriminating pistachios contaminated with low concentration of AFB 1 . Further study on classifying naturally contaminated samples is needed to validate the applicability of this system., (Copyright © 2019 Elsevier Ltd. All rights reserved.)