Application of Vis/NIR Spectroscopy in the Rapid and Non-Destructive Prediction of Soluble Solid Content in Milk Jujubes

Yinhai Yang, Shibang Ma (), Feiyang Qi, Feiyue Wang and Hubo Xu
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Yinhai Yang: School of Intelligent Manufacturing and Electrical Engineering, Nanyang Normal University, Nanyang 473061, China
Shibang Ma: School of Intelligent Manufacturing and Electrical Engineering, Nanyang Normal University, Nanyang 473061, China
Feiyang Qi: School of Intelligent Manufacturing and Electrical Engineering, Nanyang Normal University, Nanyang 473061, China
Feiyue Wang: School of Intelligent Manufacturing and Electrical Engineering, Nanyang Normal University, Nanyang 473061, China
Hubo Xu: National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing 100191, China

Agriculture, 2025, vol. 15, issue 13, 1-21

Abstract: Milk jujube has become an increasingly popular tropical fruit. The sugar content, which is commonly represented by the soluble solid content (SSC), is a key indicator of the flavor, internal quality, and market value of milk jujubes. Traditional methods for assessing SSC are time-consuming, labor-intensive, and destructive. These methods fail to meet the practical demands of the fruit market. A rapid, stable, and effective non-destructive detection method based on visible/near-infrared (Vis/NIR) spectroscopy is proposed here. A Vis/NIR reflectance spectroscopy system covering 340–1031 nm was constructed to detect SSC in milk jujubes. A structured spectral modeling framework was adopted, consisting of outlier elimination, dataset partitioning, spectral preprocessing, feature selection, and model construction. Comparative experiments were conducted at each step of the framework. Special emphasis was placed on the impact of outlier detection and dataset partitioning strategies on modeling accuracy. A data-augmentation-based unsupervised anomaly sample elimination (DAUASE) strategy was proposed to enhance the data validity. Multiple data partitioning strategies were evaluated, including random selection (RS), Kennard–Stone (KS), and SPXY methods. The KS method achieved the best preservation of the original data distribution, improving the model generalization. Several spectral preprocessing and feature selection methods were used to enhance the modeling performance. Regression models, including support vector regression (SVR), partial least squares regression (PLSR), multiple linear regression (MLR), and backpropagation neural network (BP), were compared. Based on a comprehensive analysis of the above results, the DAUASE + KS + SG + SNV + CARS + SVR model exhibited the highest prediction performance. Specifically, it achieved an average precision (AP p ) of 99.042% on the prediction set, a high coefficient of determination (R P 2 ) of 0.976, and a low root-mean-square error of prediction (RMSEP) of 0.153. These results indicate that Vis/NIR spectroscopy is highly effective and reliable for the rapid and non-destructive detection of SSC in milk jujubes, and it may also provide a theoretical basis for the practical application of rapid and non-destructive detection in milk jujubes and other jujube varieties.

Keywords: Vis/NIR spectroscopy; milk jujube; DAUASE; SSC; rapid and non-destructive detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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