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Food quality and safety have been critical issues in the world. There is an urgent need for a fast, simple, selective, and inexpensive food detection method for the identification of the degree of food spoilage. As a molecular analysis tool, Raman spectroscopy has the advantages of high selectivity, accurate analysis, simple operation, and low sample consumption. This paper reports a novel remote fiber optic Raman sensor for real-time application in food spoilage detection. Eight volatile organic compounds (VOC) liquids that typically generated by corrupted food were under-tested. The proposed sensor successfully captures the back-scattered Raman spectra for all testing samples with various dilution levels. Multiple machine learning algorithms are also applied to further analyze the correlation between Raman spectra and molecules in spoiled foods by diluting chemical samples. As a result of combining with Raman spectroscopy and machine learning algorithm, the remote fiber optic Raman probe allows qualitative measurements of VOC samples at 100-fold dilution. In comparison with surface-enhanced Raman scattering (SERS), the remote fiber optic Raman sensor allows for direct Raman spectroscopy detection without sample and SERS substrate preparation, which opens a new chapter on the nondestructive and sensitive detection of food analytes.

Background Genomic variants of disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatment; however, population-level models, such as GWAS, may not capture all of the important, individualized factors well. In addition, GWAS typically requires a large sample size to detect association of low-frequency genomic variants with sufficient power. Here, we report an individualized Bayesian inference (IBI) algorithm for estimating the genomic variants that influence complex traits such as hypertension at the level of an individual (e.g., a patient). By modeling at the level of the individual, IBI seeks to find genomic variants observed in the individual's genome that provide a strong explanation of the phenotype observed in this individual. Results We applied the IBI algorithm to the data from the Framingham Heart Study to explore genomic influences of hypertension. Among the top-ranking variants identified by IBI and GWAS, there is a significant number of shared variants (intersection); the unique variants identified only by IBI tend to have relatively lower minor allele frequency than those identified by GWAS. In addition, we observed that IBI discovered more individualized and diverse variants that explain the hypertension patients better than did GWAS. Furthermore, IBI found several well-known low-frequency variants as well as genes related to blood pressure that were missed by GWAS in the same cohort. Finally, IBI identified top-ranked variants that predicted hypertension better than did GWAS, according to the area under the ROC curve. Conclusions The results provide support for IBI as a promising approach for complementing GWAS especially in detecting low-frequency genomic variants as well as learning personalized genomic variants of clinical traits and disease, such as the complex trait of hypertension, to help advance precision medicine.