Rapid Determination of Amino Acids in Chinese Wolfberry (Lycium bararum L.) Fruit by Using Fourier Transform Infrared Spectroscopy and Partial Least Square Regression

Terahertz spectroscopy for quantitatively elucidating the crystal transformation of chiral histidine enantiomers to racemic compounds

d-Histidine (d-His), l-Histidine (l-His), and their racemic compound dl-Histidine (dl-His) have different stereo chirality, making them intrinsic diverse functionalities to the living system. Identifying the configuration and crystal structures of enantiomers and the racemic compound is always the foremost requirement in processing protein foods. Although these features can be analyzed by spectroscopic technologies individually, it remains challenging to incorporate these complex methods into a facile analytical strategy. Herein, we propose a terahertz spectroscopy with solid-state density functional theory to both distinguish the configurational difference and quantify the crystal transformation from l-His and d-His to dl-His. By comparison with X-ray diffraction analysis, the validity of the crystal transformation evaluation based on terahertz spectroscopy is verified. A normalized fitting line regarding the terahertz absorption frequency and intensity is calculated to quantitatively elucidate the crystal transformation from enantiomers to dl-His. Our findings provide a new analytical approach to the research on food chemistry.

Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices

Nowadays, coffee, cocoa, and spices have broad applications in the food and pharmaceutical industries due to their organoleptic and nutraceutical properties, which have turned them into products of great commercial demand. Consequently, these products are susceptible to fraud and adulteration, especially those sold at high prices, such as saffron, vanilla, and turmeric. This situation represents a major problem for industries and consumers’ health. Implementing analytical techniques, i.e., Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis, can ensure the authenticity and quality of these products since these provide unique information on food matrices. The present review addresses FT-MIR spectroscopy and multivariate analysis application on coffee, cocoa, and spices authentication and quality control, revealing their potential use and elucidating areas of opportunity for future research.

Nutritional components characterization of Goji berries from different regions in China

Goji berries are used as functional food for hundreds of years in Asia, Europe, North America and Austria, and are popular for nutritive properties in global. Commercial Goji berries are mainly produced in Ningxia, Xinjiang, Gansu, Qinghai and Inner Mongolia of China. However, the Goji berries produced in these regions exhibited different appearance and taste. Thus, characterization of the nutritional components in Goji berries produced in these regions could provide the guidance for application of them. In this study, 94 samples were collected, and a total of 20 amino acids, 17 nucleosides and nucleobases, 4 sugars and protein were determined by UHPLC-MS/MS, HPLC-ELSD or UV, and the variation was illustrated through heatmap clustering analysis, PCA and PLS-DA. The results showed that Goji berries from Xinjiang were rich in protein than the samples from other regions; those from Gansu and Ningxia were rich in amino acids, nucleosides and nucleobases; and those from Jiuquan of Gansu and Qinghai were rich in sugars. Heatmap clustering and PCA analysis results showed that all the samples exhibited a significant spatial aggregation, and the producing regions located along the Yellow River (belonging to the Hetao plain) produced Goji berries with the similar chemical profile. Additionally, PLS-DA analysis results showed that fructose and glucose were the predominant markers to distinguish Goji berries from different producing regions.

Near infrared system coupled chemometric algorithms for the variable selection and prediction of baicalin in three different processes

Characteristic variables are essential and necessary basis in model construction, and are related to the prediction result closely in near infrared spectroscopy (NIRS) analysis. However, the same compound usually has different characteristic variables for different analysis and it would be lower correlation between variables and structure in many researches. So, the accuracy and reliability are expected to improve by exploring characteristic variables in different spectrum analysis. In this study, competitive adaptive weighted resampling method (CARS) was applied to select characteristic variables related to baicalin from NIRS analysis data, which were applied to analysis of baicalin in three different processes including the herb, extraction process and concentration process of Scutellaria baicalensis. After application of CARS method, 70, 50 and 50 variables were selected respectively from three processes above. The selected variables were firstly analyzed by statistical methods that they were found to be consistent and correlated among three different processes after one-way analysis of variance test and Kendall's W. Partial least-squares (PLS) regression and extreme learning machine (ELM) models were constructed based on optimized data. Models after variable selection were less complicated and had better prediction results than global models. After comparison, CARS-PLS was suitable for the prediction of extraction process, while for the concentration process and herb, CARS-ELM performed better. The Rc value of the herb, extraction and concentration model were 0.9469, 0.9841 and 0.9675, respectively. The RSEP values were 4.54%, 6.96% and 8.37%, respectively. The results help to frame a theoretical basis for characteristic variables of baicalin.