Rapid Screening of Phenolic Compounds from Wild Lycium ruthenicum Murr. Using Portable near-Infrared (NIR) Spectroscopy Coupled Multivariate Analysis

Insights on the Extraction and Analysis of Phenolic Compounds from Citrus Fruits: Green Perspectives and Current Status

Citrus fruits (CF) are highly consumed worldwide, fresh, processed, or prepared as juices and pies. To illustrate the high economic importance of CF, the global production of these commodities in 2021 was around 98 million tons. CF's composition is considered an excellent source of phenolic compounds (PC) as they have a large amount and variety. Since ancient times, PC has been highlighted to promote several benefits related to oxidative stress disorders, such as chronic diseases and cancer. Recent studies suggest that consuming citrus fruits can prevent some of these diseases. However, due to the complexity of citrus matrices, extracting compounds of interest from these types of samples, and identifying and quantifying them effectively, is not a simple task. In this context, several extractive and analytical proposals have been used. This review discusses current research involving CF, focusing mainly on PC extraction and analysis methods, regarding advantages and disadvantages from the perspective of Green Chemistry.

Near-infrared spectroscopy bioprobe estimation of metabolites' responses to Pb2+ in Cladophora rupestris

Near-infrared (NIR) spectroscopy was used as a bioprobe to estimation chlorophyll, total sugar, proline, and metallothionein (MT) response to Pb2+ (0, 1, 2.5, 5, 7.5, 10, and 20 mg/l) in Cladophora rupestris. Spectra between 3775.94 and 12315.19 cm-1 were analysed using a partial least-squares regression (PLS). Principal component analysis (PCA) was performed by evaluating the distance between the C. rupestris samples treated with different concentrations of Pb2+. Results showed that 4000-7500 cm-1 of raw NIR spectra from C. rupestris was linked to carbohydrates and stretching of N-H, and the O-H overtone was associated with proline and MT. NIR spectroscopy technique coupled with PLS quantitative analysis model has high potential. Multiplicative scatter correction and standard normal variate combining first-order derivative with S-G smoothing filter are superior to the other models. The root mean square error of cross of chlorophyll, total sugar, proline, and MT was 0-1, indicating the accuracy of the model is high. The Rc2 values of chlorophyll, total soluble sugar, proline, and MT between measured and predicted values were higher than 0.99. PCA analysis indicate clear boundaries in the spatial distributions of seven Pb2+ concentrations, indicating that NIR spectroscopy has high potential as a tool assisting bioprobes for estimating metabolites' responses to Pb2+ in Cladophora rupestris.