Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods.

Fan Wang,Guijun Yang,Chunjiang Zhao

doi:10.3390/foods9121778

Abstract

Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible−near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650–1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient (R2v) of 0.93 and root mean square error (RMSEv) of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears.

Highlights

Pear is one of the most important fruits in the global fresh produce market
The results demonstrate that VIS/NIR coupled with linear regression correction combined with spectral ratio (LRC-spectral ratio (SR)) method can be a suitable strategy for the quick assessment of juiciness for pears
Different preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR) were used to eliminate the additive effect and multiplicative effect in the raw spectrum [38,39,40,41]

Summary

Introduction

Pear is one of the most important fruits in the global fresh produce market. The global production of pears has been steadily increasing (2.26 million tons in 2010 and 2.37 million tons in 2018). Pears are classified manually or automatically according to their external quality attributes. The visual attributes can only affect the initial purchase, while repeated sales are based on eating quality of the pear [1]. The control of the internal quality of pear greatly affects sales and the profit space of the fruit industry [2]. Previous studies have determined that the juiciness is the indispensable quality attribute of pear [3,4,5,6]. Jaeger et al [7] evaluated 10 samples from 6 genotypes through a consumer survey that most consumers describe the “ideal” pear as “sweet and juicy”, and Turner et al [8] obtained a similar conclusion that the most important quality factors of pear recognized by consumers were texture, Foods 2020, 9, 1778; doi:10.3390/foods9121778 www.mdpi.com/journal/foods

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