Identification of adulterated milk based on auto-correlation spectra

A qualitative analysis of melamine-adulterated milk was proposed based on two-trace two-dimensional (2T2D) auto-correlation spectra. The concentration of melamine was used as external perturbation, and 40 adulterated samples of each brand with different concentrations of melamine (0.01 g/L to 1 g/L) were configured. Four brands of milk were used to configure experimental samples, including Guangming brand, Mengniu brand, Sanyuan brand and Wandashan brand. Spectroscopic data of pure milk and melamine-adulterated milk were measured by infrared (IR) (80-4000 cm^-1) spectrophotometer. 2T2D auto-correlation spectral technology combined with least squares support vector machine (LS-SVM) method was used for qualitative analysis. The two strongest auto-correlation peaks in the auto-correlation spectra were selected for modeling. For Guangming brand, the intensities of auto-correlation at two wave numbers 2898 cm^-1 and 2972 cm^-1 were selected as independent variables. For Mengniu brand, the intensities of auto-correlation at two wave numbers 2852 cm^-1 and 2920 cm^-1 were selected. For Sanyuan brand, the intensities of auto-correlation at two wave numbers 2900 cm^-1 and 2974 cm^-1 were selected. For Wandashan brand, the intensities of auto-correlation at two wave numbers 2900 cm^-1 and 2974 cm^-1 were selected. For four brands fused together, the intensities of auto-correlation at two wave numbers 2900 cm^-1 and 2974 cm^-1 were selected. For each brand, the accuracy of qualitative analysis was 100 %. For four brands fused together, the accuracy of qualitative analysis was 99.05 %. In this way, it greatly reduced the amount of data to be processed. This study showed that 2T2D auto-correlation spectral technology combined with LS-SVM method was perfect for the discrimination of melamine-adulterated milk.

Development of predictive models for total phenolics and free p-coumaric acid contents in barley grain by near-infrared spectroscopy

Barley grains are rich in phenolic compounds, which are associated with reduced risk of chronic diseases. Development of barley cultivars with high phenolic acid content has become one of the main objectives in breeding programs. A rapid and accurate method for measuring phenolic compounds would be helpful for crop breeding. We developed predictive models for both total phenolics (TPC) and p-coumaric acid (PA), based on near-infrared spectroscopy (NIRS) analysis. Regressions of partial least squares (PLS) and least squares support vector machine (LS-SVM) were compared for improving the models, and Monte Carlo-Uninformative Variable Elimination (MC-UVE) was applied to select informative wavelengths. The optimal calibration models generated high coefficients of correlation (r_pre) and ratio performance deviation (RPD) for TPC and PA. These results indicated the models are suitable for rapid determination of phenolic compounds in barley grains.