Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing

Abstract

The consumption of pears has increased, thanks not only to their delicious and juicy flavor, but also their rich nutritional value. Traditional methods of detecting internal qualities (e.g., soluble solid content (SSC), titratable acidity (TA), and taste index (TI)) of pears are reliable, but they are destructive, time-consuming, and polluting. It is necessary to detect internal qualities of pears rapidly and nondestructively by using near-infrared (NIR) spectroscopy. In this study, we used a self-made NIR spectrum detector with an improved variable selection algorithm, named the variable stability and cluster analysis algorithm (VSCAA), to establish a partial least squares regression (PLSR) model to detect SSC content in snow pears. VSCAA is a variable selection method based on the combination of variable stability and cluster analysis to select the infrared spectrum variables. To reflect the advantages of VSCAA, we compared the classical variable selection methods (synergy interval partial least squares (SiPLS), genetic algorithm (GA), successive projections algorithm (SPA), and bootstrapping soft shrinkage (BOSS)) to extract useful wavelengths. The PLSR model, based on the useful variables selected by SiPLS-VSCAA, was optimal for measuring SSC in pears, and the correlation coefficient of calibration (Rc), root mean square error of cross validation (RMSECV), correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were 0.942, 0.198%, 0.936, 0.222%, and 2.857, respectively. Then, we applied these variable selection methods to select the characteristic wavelengths for measuring the TA content and TI value in snow pears. The prediction PLSR models, based on the variables selected by GA-BOSS to measure TA and that by GA-VSCAA to detect TI, were the best models, and the Rc, RMSECV, Rp and RPD were 0.931, 0.124%, 0.912, 0.151%, and 2.434 and 0.968, 0.080%, 0.968, 0.089%, and 3.775, respectively. The results showed that the self-made NIR-spectrum detector based on a portable NIR spectrometer with multivariate data processing was a good tool for rapid and nondestructive analysis of internal quality in pears.

Portable NIR spectroscopy: the route to green analytical chemistry

“… Yang et al (2022) and ( Yu et al, 2021b ) determined soluble solids content in pears based on VIS-NIR and model analysis and variable selection. Wu et al (2021) . settled up a non-destructive characterization of pears by a home-made NIR and a chemometric prediction model.…”

Effect of spectrum measurement position on detection of Klason lignin content of snow pears by a portable NIR spectrometer

“…Sheng et al (Sheng et al, 2020) used NIR spectroscopy and the uninformative variable elimination algorithm to nondestructively detect the LC in Korla fragrant pear. Wu et al (Wu, Li, & He, 2021) concluded that NIR diffuse reflectance spectroscopy technology combined with frequency and regression coefficient of variables bootstrapping soft shrinkage (FRC‐BOSS) was an accurate and useful tool for the non‐destructive and rapid determination of snow pear LC.…”

Quality Characteristics, Sensory Profiles and Ethylene Production of Stored ‘Abate Fetel’ Pears Sorted at Harvest by Time-Resolved Reflectance Spectroscopy

“…Visible and near-infrared (Vis-NIR) spectroscopy is the most advanced nondestructive technique for fruit quality assessment due to its high repeatability, ease of operation, lack of pollution and measurement stability [13]. NIR, Vis-NIR spectroscopy and hyperspectral imaging have been used to assess soluble solid content, titratable acidity, dry matter and firmness in pears with less or more successful results [14]. Spectroscopic techniques were also used to detect internal browning [15][16][17], to classify pears in different ripening degrees [18] or to predict postharvest shelf-storage time [19].…”