# Non-Destructive Detection of Internal Quality of Sanhua Plum Based on Multi-Source Information Fusion

## TL;DR

This study introduces a non-destructive method using near-infrared spectroscopy and images to assess the internal quality of Sanhua Plums, improving accuracy and automation.

## Contribution

A novel multi-source information fusion model combining spectroscopy and images for enhanced non-destructive quality assessment of Sanhua Plums.

## Key findings

- The fusion model achieved an R2 of 0.8871 and RMSE of 0.4141, outperforming individual models.
- Spectral data preprocessing with SG and SNV improved model accuracy.
- The method supports automated quality evaluation in narrow terrains.

## Abstract

This research addresses the limitations of traditional assembly line equipment, which is costly and impractical for narrow terrains, as well as the challenges of portable devices in large-scale detection. We propose a non-destructive testing method for assessing the internal quality of Sanhua Plums using a free-fall approach that integrates near-infrared spectroscopy and images. Through analysis of models created from spectral data collected under optimal conditions (motor speed: 6.6 r/min, integration time: 14 ms, spot diameter: 20 mm), we processed near-infrared data from 120 plums. The spectral data underwent preprocessing with polynomial smoothing (SG) and Standard Normal Variate (SNV) calibration, followed by feature extraction using Competitive Adaptive Reweighted Sampling (CARS), resulting in a prediction model for soluble solid content with R2 of 0.8374 and RMSE of 0.5014. Simultaneously, a prediction model based solely on visual image data achieved an R2 of 0.3341 and RMSE of 1.0115. We developed a multi-source information fusion model that incorporated Z-score normalization, linear weighted fusion, and Partial Least Squares Regression (PLSR), resulting in an R2 of 0.8871 and RMSE of 0.4141 for the test set. This model outperformed individual spectroscopy and visual models, supporting the development of an automated non-destructive system for evaluating Sanhua Plum’s internal quality.