Abstract

Non-destructive detection of the pH value of kiwifruit has important practical significance for its quality classification. In this study, hyperspectral fluorescence imaging technology was proposed to quantitatively predict the pH value of kiwifruit non-destructively. Firstly, the SPXY algorithm was used to divide samples into training and prediction sets and three different algorithms were used to preprocess the raw spectral data. Secondly, algorithms such as the iteratively retaining information variables (IRIV), the variable iterative space shrinkage approach (VISSA), the model adaptive space shrinkage (MASS), the random frog (RF), and their combination (i.e., IRIV + VISSA + MASS + RF, IVMR) were used to extract effective variables from the preprocessed spectral data. Moreover, the second extractions, such as IRIV-VISSA and IRIV-MASS, and the third extraction (i.e., IVMR-VISSA-IRIV) were used to further reduce the redundant variables. Based on the effective variables, four regression models—random forest (RF), partial least square (PLSR), extreme learning machines (ELM), and multiple-kernel support vector regression (MK-SVR)—were built and compared for predicting. The results show that IVMR-VISSA-IRIV-MK-SVR had the best prediction results, with RP2, RC2 and RPD of 0.8512, 0.8580, and 2.66, respectively, which verifies that hyperspectral fluorescence imaging technology is reliable for predicting the pH value of kiwifruit non-destructively.

Highlights

  • Kiwifruit is rich in nutrients, minerals, and vitamins that can effectively promote digestion in human intestine [1], and is popular all over the world [2]

  • Ninety samples of “Red Sun” kiwifruit were sampled from Ya’an, Sichuan province, China. They were similar in size and showed no obvious damage on the surface

  • Three algorithms, namely de-trending (DT) [15,16], moving average (MA) [17], and Savitzky–Golay smoothing (S-G) [18], were selected to preprocess the raw fluorescence spectrum data. Both S-G and MA can improve the smoothness of spectral data

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Summary

Introduction

Kiwifruit is rich in nutrients, minerals, and vitamins that can effectively promote digestion in human intestine [1], and is popular all over the world [2]. Because of its great ornamental, therapeutic, and nutritional value, there are an increasing number of studies on the non-destructive testing of kiwifruit’s internal quality parameters, such as soluble solid content (SSC), pH value, moisture content, and vitamin content. The common detection method for the pH value of kiwifruit is to use a pH meter, which is time consuming and labor intensive. It is of great significance to detect the pH value of kiwifruit nondestructively. Hyperspectral imaging technology and visible-near infrared spectral imaging technology have the advantage of fast and non-destructive detection. In recent years, they have been successfully applied to detect the internal quality of fruits

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