• Identification of early bruisi...

Treffer: Identification of early bruising degrees in blueberries using visible and near-infrared spectroscopy coupled with deep learning.

Title:
Identification of early bruising degrees in blueberries using visible and near-infrared spectroscopy coupled with deep learning.
Authors:
Huang Y; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China. Electronic address: huangyuping@njfu.edu.cn., Bian Z; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China., Jin H; State Key Laboratory of Flexible Electronics & Institute of Advanced Materials, School of Flexible Electronics (Future Technologies), Nanjing Tech University, Nanjing 211816, China., Zheng G; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China., Zhang Q; School of Agricultural Engineering, Jiangsu University; Zhenjiang 212013, China., Hu D; College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China., Xie W; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China., Fan C; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Source:
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy [Spectrochim Acta A Mol Biomol Spectrosc] 2026 Mar 05; Vol. 348 (Pt 1), pp. 127200. Date of Electronic Publication: 2025 Nov 16.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Country of Publication: England NLM ID: 9602533 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3557 (Electronic) Linking ISSN: 13861425 NLM ISO Abbreviation: Spectrochim Acta A Mol Biomol Spectrosc Subsets: MEDLINE
Imprint Name(s):
Publication: : Amsterdam : Elsevier
Original Publication: [Kidlington, Oxford, U.K. ; Tarrytown, NY] : Pergamon, c1994-
MeSH Terms:
Blueberry Plants*/chemistry , Fruit*/chemistry , Deep Learning*, Spectroscopy, Near-Infrared/methods ; Least-Squares Analysis ; Neural Networks, Computer ; Discriminant Analysis
Contributed Indexing:
Keywords: Blueberry; Bruising; Deep learning; New designed spectroscopic system; Scanning electron microscope
Entry Date(s):
Date Created: 20251122 Date Completed: 20251212 Latest Revision: 20251212
Update Code:
20260130
DOI:
10.1016/j.saa.2025.127200
PMID:
41273859
Database:
MEDLINE

Weitere Informationen

The early detections of internal bruises in blueberries caused by external impacts after harvest play a crucial role in enhancing their economics. The aim of this study was to identify early bruise blueberries at different impact energies based on Partial Least Squares Discriminant Analysis (PLSDA), Convolutional Neural Network (CNN) and Tabular Transformer (TabTransformer) models using a new developed spectroscopic system to collect blueberry reflectance spectra with high signal-to-noise ratio covering 400-1000 nm and 1350-2200 nm. The microscopic structures of the peel and pulp between healthy and bruised blueberries were observed to determine the relationship between bruise degrees and cell structures. The results showed two deep learning models exhibited better detection performance than PLSDA model with highest accuracies for discriminating bruise degrees of 96.9 % for CNN model and 91.8 % for TabTransformer model. The binary classification of each bruise degree demonstrated slightly higher results for CNN model with accuracies over 95 % for almost each bruise degree. The lower classification accuracy observed in the 1350-2200 nm was likely due to some cell disruption and release of free water, which influenced the detection of strong water absorption bands beyond 1400 nm. These findings presented the potential to recognize the bruise degrees using developed optical system coupled with deep learning models.
(Copyright © 2025 Elsevier B.V. All rights reserved.)

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.