Infrared spectroscopy technique for quantification of compounds in plant-based medicine and supplement

A technique based on infrared spectroscopy for determining sulfanilamide levels sustainably: Progress and comparisons of greenness and whiteness using ComplexGAPI, AGREE, and RGB

The study aimed to determine the potential of the infrared (IR) spectrophotometric technique for measuring the content of sulphanilamide with the sulfonamide group. The study aimed to obtain the IR spectra of sulfanilamide and use the -SO2 band at 1114.37 for the quantitative assay, determining its area under the curve (AUC). The study gives an alternative approach to existing analytical techniques that require vast amounts of organic solvents, which are costly and can be toxic, thus impacting the environment and increasing the analysis cost. The study evaluated the method's whiteness and greenness by utilizing the Complex green analytical procedure index, analytical GREEness calculator and Red Green Blue algorithm tool. The linierity was found to be 5 to 30 µg/ml. The present study has developed an infrared (IR) spectroscopic method that employs a straightforward sample preparation technique in methanol. The IR spectroscopic method's linearity range was determined to be 5-30 µg/ml. The p-value was 0.001 at 95 % confidence level assuring better recovery. This method is evaluated according to the Q2R1 ICH guideline. It is applicable to routine quality control analysis without pre-extraction using green IR spectroscopy. In conclusion, the study demonstrated that IR spectrophotometric techniques can quantify sulfanilamide while reducing the use of organic solvents, contributing to the green-and-white analytical chemistry approach. The developed methods are reliable, accurate, and cost-effective and have the potential to be implemented in routine analysis of sulfanilamide.

Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy

Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.

Statistical FT-IR Spectroscopy for the Characterization of 17 Vegetable Oils

Vegetable oils have been utilized for centuries in the food, cosmetic, and pharmaceutical industries, and they contribute beneficially to overall human health, to active skincare, and to effective treatments. Monitoring of the vegetable oils is carried out by the methods described in the European Pharmacopeia, which is time-consuming, has poor repeatability, and involves the use of toxic organic chemicals and expensive laboratory equipment. Many successful studies using IR spectroscopy have been carried out for the detection of geographical origin and adulteration as well as quantification of oxidation parameters. The aim of our research was to explore FT-IR spectroscopy for assessing the quality parameters and fatty acid composition of cranberry, elderberry, borage, blackcurrant, raspberry, black mustard, walnut, sea buckthorn, evening primrose, rosehip, chia, perilla, black cumin, sacha inchi, kiwi, hemp, and linseed oil. Very good models were obtained for the α-linolenic acid and linoleic acid contents, with R2 = 1.00; Rv2 values of 0.98, 0.92, 0.89, and 0.84 were obtained for iodine value prediction, stearic acid content, palmitic acid content, and unsaponifiable matter content, respectively. However, we were not able to obtain good models for all parameters, and the use of the same process for variable selection was found to be not suitable for all cases.