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Volume 15
Issue 17
10.3390/foods15173166
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Tri-Band Vis–NIR Spectroscopy with Color Residual-Variance Gated Attention Fusion for Rapid Assessment of Hongmeiren (Citrus reticulata) Soluble Solids Content

by
Anan Tao
1,†,
Longfei Ye
2,†,
Chaoxu Yu
1,
Liuye Cao
2,
Tiantian Pan
2 and
Fei Liu
2,3,*
1
Green Development Center, Bureau of Agriculture and Rural Affairs of Xiangshan County, Ningbo 315799, China
2
Zhejiang Key Laboratory of Agricultural Remote Sensing and Information Technology, College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
3
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Foods 2026, 15(17), 3166; https://doi.org/10.3390/foods15173166
Submission received: 1 July 2026 / Revised: 24 August 2026 / Accepted: 4 September 2026 / Published: 7 September 2026
(This article belongs to the Special Issue Spectroscopy and Intelligent Data Processing for Food Quality, Safety and Authenticity)
Versions Notes

Abstract

Rapid assessment of internal fruit quality is essential for fruit grading, postharvest management, and consumer-oriented quality evaluation. Among the quality attributes, soluble solids content (SSC) is a key indicator of citrus sweetness and maturity. Visible and near-infrared (Vis–NIR) spectroscopy provides an effective approach for rapid SSC detection in fruit. However, most existing studies rely on single full-spectrum models or simple band stacking strategies, which limits their ability to fully exploit complementary information among different spectral sub-bands. To address this limitation, a color residual-variance gated attention fusion network (CR-VGAFNet) is proposed for efficient SSC assessment in Hongmeiren. On the independent prediction set, CR-VGAFNet achieved a prediction correlation coefficient (RP) of 0.7744, a root mean square error of prediction (RMSEP) of 0.6530 °Brix, and a mean absolute percentage error of prediction (MAPEP) of 4.83%. These findings suggest the potential of the framework for multi-band spectral fusion. This study provides a new technical perspective for multi-band spectral fusion and rapid fruit quality assessment.
Keywords: citrus; soluble solids content detection; Vis–NIR spectroscopy; data fusion; deep learning citrus; soluble solids content detection; Vis–NIR spectroscopy; data fusion; deep learning

Share and Cite

MDPI and ACS Style

Tao, A.; Ye, L.; Yu, C.; Cao, L.; Pan, T.; Liu, F. Tri-Band Vis–NIR Spectroscopy with Color Residual-Variance Gated Attention Fusion for Rapid Assessment of Hongmeiren (Citrus reticulata) Soluble Solids Content. Foods 2026, 15, 3166. https://doi.org/10.3390/foods15173166

AMA Style

Tao A, Ye L, Yu C, Cao L, Pan T, Liu F. Tri-Band Vis–NIR Spectroscopy with Color Residual-Variance Gated Attention Fusion for Rapid Assessment of Hongmeiren (Citrus reticulata) Soluble Solids Content. Foods. 2026; 15(17):3166. https://doi.org/10.3390/foods15173166

Chicago/Turabian Style

Tao, Anan, Longfei Ye, Chaoxu Yu, Liuye Cao, Tiantian Pan, and Fei Liu. 2026. "Tri-Band Vis–NIR Spectroscopy with Color Residual-Variance Gated Attention Fusion for Rapid Assessment of Hongmeiren (Citrus reticulata) Soluble Solids Content" Foods 15, no. 17: 3166. https://doi.org/10.3390/foods15173166

APA Style

Tao, A., Ye, L., Yu, C., Cao, L., Pan, T., & Liu, F. (2026). Tri-Band Vis–NIR Spectroscopy with Color Residual-Variance Gated Attention Fusion for Rapid Assessment of Hongmeiren (Citrus reticulata) Soluble Solids Content. Foods, 15(17), 3166. https://doi.org/10.3390/foods15173166

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