Application of Feature Selection Algorithms to Select Elements that Distinguish Regional Differences in Chinese Grown Winter Jujube Fruit (Zizyphus jujuba Mill. cv. Huanghua Dongzao)

Glycine betaine inhibits postharvest softening and quality decline of winter jujube fruit by regulating energy and antioxidant metabolism

Winter jujube fruit easily softens after harvest. To investigate the effects of glycine betaine (N,N,N-trimethylglycine; GB) treatment on the quality of postharvest jujubes, fresh winter jujubes (Zizyphus jujuba Mill. cv. Dongzao) were immersed in 20 mmol·L^-1 GB for 20 min. The results showed that GB application can effectively maintain cell wall component content by restraining gene expression and enzyme activities, including PG, CX, PME and β-Glu. Meanwhile, the activities of antioxidant enzymes (APX, CAT, SOD, POD) and the contents of nonenzymatic antioxidants (MDA, H₂O₂, ASA, GSH) were enhanced in treated jujubes, thereby reducing the content of ROS. In addition, energy metabolism enzyme activities (H⁺-ATPase, Ca²⁺-ATPase, SDH and CCO) and gene expression were also significantly increased, thus maintaining higher energy levels (ATP, ADP, AMP and EC). In summary, GB enhances ATP biosynthesis by increasing energy metabolism. It offers essential energy for the antioxidant metabolism, thus retarding the softening of postharvest jujubes.

Integration of Non-targeted Proteomics Mass Spectrometry with Machine Learning for Screening Cooked Beef Adulterated Samples

The degradation of ingredients in heat-processed meat products makes their authentication challenging. In this study, protein profiles of raw beef, chicken, duck, pork, and binary simulated adulterated beef samples (chicken-beef, duck-beef, and pork-beef) and their heat-processed samples were obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Heat-stable characteristic proteins were found by screening the overlapping characteristic protein ion peaks of the raw and corresponding heat-processed samples, which were discovered by partial least-squares discriminant analysis. Based on the 36 heat-stable characteristic proteins, qualitative classification for the raw and heat-processed meats was achieved by extreme gradient boosting. Moreover, quantitative analysis via partial least squares regression was applied to determine the adulteration ratio of the simulated adulterated beef samples. The validity of the approach was confirmed by a blind test with the mean accuracy of 97.4%. The limit of detection and limit of quantification of this method were determined to be 5 and 8%, respectively, showing its practical aspect for the beef authentication.

Fingerprint Approaches Coupled with Chemometrics to Discriminate Geographic Origin of Imported Salmon in China's Consumer Market

Of the salmon sold in China’s consumer market, 92% was labelled as Norwegian salmon, but was in fact was mainly imported from Chile. The aim of this study was to establish an effective method for discriminating the geographic origin of imported salmon using two fingerprint approaches, Near-infrared (NIR) spectroscopy and mineral element fingerprint (MEF). In total, 80 salmon (40 from Norway and 40 from Chile) were tested, and data generated by NIR and MEF were analysed via various chemometrics. Four spectral preprocessing methods, including vector normalization (VN), Savitzky Golay (SG) smoothing, first derivative (FD) and second derivative (SD), were employed on the raw NIR data, and a partial least squares (PLS) model based on the FD + SG9 pretreatment could successfully differentiate Norwegian salmons from Chilean salmons, with a R² value of 98.5%. Analysis of variance (ANOVA) and multiple comparative analysis were employed on the contents of 16 mineral elements including Pb, Fe, Cu, Zn, Al, Sr, Ni, As, Cr, V, Se, Mn, K, Ca, Na and Mg. The results showed that Fe, Zn, Al, Ni, As, Cr, V, Se, Ca and Na could be used as characteristic elements to discriminate the geographical origin of the imported salmon, and the discrimination rate of the linear discriminant analysis (LDA) model, trained on the above 10 elements, could reach up to 98.8%. The results demonstrate that both NIR and MEF could be effective tools for the rapid discrimination of geographic origin of imported salmon in China’s consumer market.