An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange

Sai Xu,Huazhong Lu,Qianqian Zhang,Christopher Ference

doi:10.3390/electronics10010080

Abstract

The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky–Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of “Luogang” orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits.

Highlights

The “Luogang” orange [1] is a national geographical indication product of China (No DB44/T1258-2013), and is famous for its high total soluble solid content (TSSC) and water content
The reason for this decline is due to the uneven color and rough texture of “Luogang” orange pericarp, which leads the consumer to mistakenly believe that the product is of poor quality inside
The internal quality nondestructive detection effect of fruit like “Luogang” orange is usually unsatisfied due to the uneven appearance would affect the stability of detection signal

Summary

Introduction

The “Luogang” orange [1] is a national geographical indication product of China (No DB44/T1258-2013), and is famous for its high total soluble solid content (TSSC) and water content. The cultivated area and yield for this type of orange has decreased from a previous high of 1067 hectares and 20,000 tons to current levels of 607 hectares and 2500 tons. The reason for this decline is due to the uneven color and rough texture of “Luogang” orange pericarp, which leads the consumer to mistakenly believe that the product is of poor quality inside. With efficient nondestructive detection technology, the TSSC and water content of “Luogang” orange can be quantified to highlight its advantages, and defective fruit can be eliminated to guarantee uniform fruit quality at the market. The internal quality nondestructive detection effect of fruit like “Luogang” orange is usually unsatisfied due to the uneven appearance would affect the stability of detection signal

Objectives

Results

Conclusion