Effects of Orientations and Regions on Performance of Online Soluble Solids Content Prediction Models Based on Near-Infrared Spectroscopy for Peaches

Abstract

Predicting the soluble solid content (SSC) of peaches based on visible/near infrared spectroscopy has attracted widespread attention. Due to the anisotropic structure of peach fruit, spectra collected from different orientations and regions of peach fruit will bring variations in the performance of SSC prediction models. In this study, the effects of spectra collection orientations and regions on online SSC prediction models for peaches were investigated. Full transmittance spectra were collected in two orientations: stem-calyx axis vertical (Orientation1) and stem-calyx axis horizontal (Orientation2). A partial least squares (PLS) method was used to evaluate the spectra collected in the two orientations. Then, each peach fruit was divided into three parts. PLS was used to evaluate the corresponding spectra of combinations of these three parts. Finally, effective wavelengths were selected using the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS). Both orientations were ideal for spectra acquisition. Regions without peach pit were ideal for modeling, and the effective wavelengths selected by the SPA led to better performance. The correlation coefficient and root mean square error of validation of the optimal models were 0.90 and 0.65%, respectively, indicating that the optimal model has potential for online prediction of peach SSC.

NIRS Methodology for Quantifying Dry Matter Variability in Hass Avocado Fruit

“…The results are in agreement with those of other works that have used NIR spectroscopy to predict fruit quality and composition. Specifically, some studies have focused on the effect of different fruit orientations, such as the stem-calyx axis and equator, on the quality of acquired spectra [24][25][26][27]. The results of the research show that measurement orientation on spectra greatly affects the prediction accuracy of lignin, soluble solids, and acidity content in pears, peaches, kiwifruit, and apples, respectively.…”

Prediction of chemical indicators for quality of Zanthoxylum spices from multi-regions using hyperspectral imaging combined with chemometrics

“…The regression model performance was assessed by the following statistical indices: the determination coefficient of the calibration set (R 2 c ) and the prediction set (R 2 p ), the root mean square error of the calibration set (RMSEC) and the prediction set (RMSEP), and relative percent deviation (RPD) of the prediction set (Liu et al, 2022). R 2 values from 0.60 to 0.80 and RPD values between 2.0 and 2.50 indicate that the model can be used for prediction.…”

Enhancing Transferability of Near-Infrared Spectral Models for Soluble Solids Content Prediction across Different Fruits

“…Fruit quality evaluation is one of the most important and challenging tasks for NIR spectroscopy, as fruits are complex biological systems that contain a diverse range of components, such as sugars, acids, vitamins, minerals, water content, and firmness [11][12][13]. The total amount of solid substances that can be dissolved in water, comprising sugars, acids, vitamins, minerals, and other components, is referred to as soluble solids content (SSC) [14][15][16]. Fruit quality is a crucially important factor that is indicated by the SSC through the process of photosynthesis, plants convert sunlight, carbon dioxide, and water into sugar substances such as glucose and fructose which are subsequently stored in the fruit.…”