New Implications of Metabolites and Free Fatty Acids in Quality Control of Crossbred Wagyu Beef during Wet Aging Cold Storage.

Efficient cold-chain delivery is essential for maintaining a sustainable global food supply. This study used metabolomic analysis to examine meat quality changes during the "wet aging" of crossbred Wagyu beef during cold storage. The longissimus thoracic (Loin) and adductor muscles (Round) of hybrid Wagyu beef, a cross between the Japanese Black and Holstein-Friesian breeds, were packaged in vacuum film and refrigerated for up to 40 days. Sensory evaluation indicated an increase in the umami and kokumi taste owing to wet aging. Comprehensive analysis using gas chromatography-mass spectrometry identified metabolite changes during wet aging. In the Loin, 94 metabolites increased, and 24 decreased; in the Round, 91 increased and 18 decreased. Metabolites contributing to the umami taste of the meat showed different profiles during wet aging. Glutamic acid increased in a cold storage-dependent manner, whereas creatinine and inosinic acid degraded rapidly even during cold storage. In terms of lipids, wet aging led to an increase in free fatty acids. In particular, linoleic acid, a polyunsaturated fatty acid, increased significantly among the free fatty acids. These results provide new insight into the effects of wet aging on Wagyu-type beef, emphasizing the role of free amino acids, organic acids, and free fatty acids generated during cold storage.

The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee.

This study aimed to establish a rapid and practical method for monitoring and predicting volatile compounds during coffee roasting using near-infrared (NIR) spectroscopy coupled with chemometrics. Washed Arabica coffee beans from Ethiopia and Congo were roasted to industry-validated light, medium, and dark degrees. Concurrent analysis of the samples was performed using gas chromatography-mass spectrometry (GC-MS) and NIR spectroscopy, generating datasets for partial least squares (PLS) regression analysis. The results showed that NIR spectroscopy successfully differentiated the differently roasted samples, similar to the discrimination achieved by GC-MS. This finding highlights the potential of NIR spectroscopy as a rapid tool for monitoring and standardizing the degree of coffee roasting in the industry. A PLS regression model was developed using Ethiopian samples to explore the feasibility of NIR spectroscopy to indirectly measure the volatiles that are important in classifying the roast degree. For PLSR, the data underwent autoscaling as a preprocessing step, and the optimal number of latent variables (LVs) was determined through cross-validation, utilizing the root mean squared error (RMSE). The model was further validated using Congo samples and successfully predicted (with R2 values > 0.75 and low error) over 20 volatile compounds, including furans, ketones, phenols, and pyridines. Overall, this study demonstrates the potential of NIR spectroscopy as a practical and rapid method to complement current techniques for monitoring and predicting volatile compounds during the coffee roasting process.