Discrimination of Two Kinds of Geographical Origin Protected Chinese Vinegars Using the Characteristics of Aroma Compounds and Multivariate Statistical Analysis

Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms

Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700-4350 cm-1 is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms.

Characterization and Comparison of Aroma Profiles and Aroma-Active Compounds between Traditional and Modern Sichuan Vinegars by Molecular Sensory Science

Aroma profiles and aroma-active compounds of Sichuan vinegar, which is one of the four famous vinegars in China, were systemically analyzed by solvent-assisted flavor evaporation-gas chromatography-mass spectrometry (SAFE-GC-MS) and gas chromatography-olfactometry (GC-O). In addition, descriptive profile analysis, aroma reconstitution, and omission test were used to evaluate and compare the Sichuan modern vinegar (SMV) and Sichuan traditional vinegar (STV). A total of 99 volatile compounds were tentatively identified from the neutral and acidic fractions of both samples. Among them, 77 compounds were positively identified after comparison with their corresponding standards. Forty-two aroma-active compounds were characterized with flavor dilution (FD) factors from 1 to 6561 by aroma extract dilution assay (AEDA)-GC-O, with the highest for 2-hydroxy-3-butanone, butyrolactone, furan-2-carbaldehyde, acetic acid, and 3-oxobutan-2-yl acetate in both STV and SMV samples. Among them, 10 were identified for the first time in vinegar. Moreover, 40 aroma-active compounds were quantitatively determined, and 26 compounds exhibited their odor activity values (OAVs) larger than 1. The reconstituted solutions showed similar aroma profiles to the original samples in their characteristic aromas in terms of fruity, sweet, roasty, spicy, and woody notes but had slight differences in nutty and herbal notes.