Analysis of Peanut Using Near-Infrared Spectroscopy and Gas Chromatography–Mass Spectrometry: Correlation of Chemical Components and Volatile Compounds

Peanut contains protein, oil, oleic acid, and linoleic acid its flavor is largely determined by pyrazine and aldehyde compounds. Both nutritional value and flavor are standards for measuring peanut quality. In this report, the contents of protein, oil, oleic acid, and linoleic acid were determined using near-infrared reflectance spectroscopy, and flavor compounds were identified using headspace solid-phase microextraction combined with gas chromatography–mass spectrometry in 12 different peanut cultivars. Our results showed that the content of oleic acid in raw peanut ranged from 35.69 to 82.79 g/100 g oil and the linoleic acid content ranged from 2.92 to 44.19 g/100 g oil, with high coefficients of variation. The coefficients of variation of protein and oil were lower, with content of 26.97–33.07 g/100 g raw materials and 45.53–55.53 g/100 g raw materials, respectively. Overall, 14 volatile components were isolated and identified, among which pyrazine and aldehyde compounds were the major aroma components in 12 different peanut cultivars.. Based on these results, peanuts with high protein content have high linoleic oil levels but low oleic oil levels, and roasted peanuts have a high content of pyrazines but low content of aldehydes. The results of this study will enable manufacturers to develop simple tests that predict the flavor of roasted peanuts based on their composition.     

Variable Selection in Visible and Near-Infrared Spectral Analysis for Noninvasive Determination of Soluble Solids Content of ‘Ya’ Pear

Informative variable selection or wavelength selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra because the modern spectroscopy instrumentations usually have a high resolution and the obtained spectral data sets may have thousands of variables and hundreds or thousands of samples. In this study, a new combination of Monte Carlo–uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA; MC-UVE-SPA) was proposed to select the most effective variables. MC-UVE was firstly used to eliminate the uninformative variables in the raw spectra data. Then, SPA was applied to determine the variables with the least collinearity. A case study was done based on the NIR spectroscopy for the non-destructive determination of soluble solids content (SSC) in ‘Ya’ pear. A total of 160 samples were prepared for the calibration (n?=?120) and prediction (n?=?40) sets. Three calibration algorithms including linear regressions of partial least square regression (PLS) and multiple linear regression (MLR), and nonlinear regression of least-square support vector machine (LS-SVM) were used for model establishment by using the selected variables by SPA, UVE, MC-UVE, UVE-SPA, and MC-UVE-SPA, respectively. The results indicated that linear models such as PLS and MLR were more effective than nonlinear model such as LS-SVM in the prediction of SSC of ‘Ya’ pear. In terms of linear models, different variable selection methods can obtain a similar result with the RMSEP values range from 0.2437 to 0.2830. However, combination of MC-UVE and SPA was helpful for obtaining a more parsimonious and efficient model for predicting the SSC values in ‘Ya’ pear. Twenty-two effective variables selected by MC-UVE-SPA achieved the optimal linear MC-UVE-SPA-MLR model compared with other all developed models by balancing between model accuracy and model complexity. The coefficients of determination (r ²), root mean square error of prediction, and residual predictive deviation by MC-UVE-SPA-MLR were 0.9271, 0.2522, and 3.7037, respectively.