Fourier-transform infrared (FTIR) offers the advantages of rapid analysis with minimal sample preparation. FTIR in combination with multivariate approach, particularly partial least squares regression (PLSR), has been widely used for adulterant analysis. Limited study has been done to compare PLSR with other regression strategies. In this paper, we apply simple linear regression (SLR), multiple linear regression (MLR), and PLSR for prediction of lard in palm olein oil. Pure palm olein oil was adulterated with lard at different concentrations and subjected to analysis with FTIR. The marker bands distinguishing lard and palm olein oil were determined using Fisher’s weights. The marker regions were then subjected to regression analysis with the models verified based on 100 training/test sets. The prediction performance was measured based on the percentage root mean square error (%RMSE). The absorption bands at 3006 cm−1, 2852 cm−1, 1117 cm−1, 1236 cm−1, and 1159 cm−1 were identified as the marker bands. The bands at 3006 and 1117 cm−1 were found with satisfactory predictive ability, with PLSR demonstrating better prediction yielding %RMSE of 16.03 and 13.26%, respectively.

Short-chain fatty acids (SCFAs) are small molecules ubiquitous in nature. In mammalian guts, SCFAs are mostly produced by anaerobic intestinal microbiota through the fermentation of dietary fiber. Levels of microbe-derived SCFAs are closely relevant to human health status and indicative to gut microbiota dysbiosis. However, the quantification of SCFA using conventional chromatographic approaches is often time consuming, thus limiting high-throughput screening tests. Herein, we established a novel method to quantify SCFAs by coupling amidation derivatization of SCFAs with paper-loaded direct analysis in real time mass spectrometry (pDART-MS). Remarkably, SCFAs of a biological sample were quantitatively determined within a minute using the pDART-MS platform, which showed a limit of detection at the μM level. This platform was applied to quantify SCFAs in various biological samples, including feces from stressed rats, sera of patients with kidney disease, and fermentation products of metabolically engineered cyanobacteria. Significant differences in SCFA levels between different groups of biological practices were promptly revealed and evaluated. As there is a burgeoning demand for the analysis of SCFAs due to an increasing academic interest of gut microbiota and its metabolism, this newly developed platform will be of great potential in biological and clinical sciences as well as in industrial quality control.

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