A Novel FTIR-Based Chemometric Solution for the Assessment of Saffron Adulteration with Non-Fresh Stigmas

A non-invasive, sensor-based approach to exploit the autofluorescence of saffron (Crocus sativus L.) for on-site evaluation of aging

So far, compliance with ISO 3632 standard specifications for top-quality saffron guarantees good agricultural and post-harvest production practices. Tracking early-stage oxidation remains challenging. Our study aims to address this issue by exploring the visible, fluorescence, and near-infrared spectra of category I saffron. Using a multi-spectral sensor, we tested fresh and artificially aged saffron in powder form. High autofluorescence intensities at 600-700 nm allowed calibration for the 'content of aged saffron'. Samples with minimum coloring strength (200-220 units) were classified as 70% aged, while those exceeding maximum aroma strength (50 units) as 100% aged. Consistent patterns across origin, age, and processing history indicated potential for objectively assessing early-oxidation markers. Further analyses uncovered multiple contributing fluorophores, including cis-apocarotenoids, correlated with FTIR-based aging markers. Our findings underscore that sensing autofluorescence of traded saffron presents an innovative quality diagnostic approach, paving new research pathways for assessing the remaining shelf-life along its supply chain.

Research on the pharmacognostic characteristics, physicochemical properties and in vitro antioxidant potency of Rosa laxa Retz. flos

Rosa laxa Retz. is an unexplored Rosaceae plant in Xinjiang, China, and its flower is traditionally used in Kazak to treat the common cold, fever, and epileptic seizures and lessen the effects of aging. In the present study, the pharmacognostic profiles, physicochemical properties, phytochemical characteristics, and in vitro antioxidant potency of Rosa laxa Retz. flos (RLF) were presented. In the pharmacognostic evaluation of RLF, organoleptic characteristics, internal structures, and powder information were observed, and physicochemical parameters, including moisture content, ash, pH value, swelling degree, and extractives were examined. The quantitative analysis of the chemical composition of four different polar extracts of RLF showed that the aqueous part had the highest total triterpene acid, flavonoid, and polyphenol content (4.50 ± 0.04 mg/g, 50.56 ± 0.03 mg/g, and 60.20 ± 0.09 mg/g, respectively). A high-performance liquid chromatography (HPLC)-diode array detector (DAD) method was established and the contents of gallic acid, ellagic acid, astragalin, and tiliroside in RLF were determined simultaneously. In the set concentration range, the linear relationship among the four components was good (r > 0.999), the average recoveries were 97.36%-100.54%. The contents of gallic acid, ellagic acid, astragalin, and tiliroside in RLF samples were (9.46 ± 2.31) mg/g, (10.60 ±0.75) mg/g, (1.13 ± 2.50) mg/g, and (1.11 ± 2.65) mg/g, respectively. The types of its secondary metabolites were determined by fluorescence, color reaction by chemical solvent method, and ultraviolet-visible (UV-Vis) spectroscopy. The functional groups of its secondary metabolites were determined by Fourier transform infrared (FTIR) spectroscopy. Results showed that RLF contains a variety of secondary metabolic products, including flavonoids, phenolic acid, glycoside, and organic acid. TLC identification showed it contains ursolic acid, β-sitosterol, tiliroside, astragalin, isoquercitrin, kaempferol-3-O-rutinoside, gallic acid, and ellagic acid. The in vitro antioxidant activity of different polar parts of RLF was investigated by DPPH, ABTS, and reduction performance experiments. The aqueous extract had the strongest antioxidant capacity, consistent with the high content of triterpene acids, flavonoids, and polyphenolic compounds. These findings will provide critical information for the study of quality standards and medicinal value of RLF and its extracts, justify its usage in traditional medicinal systems, and encourage the use of this plant in disease prevention and treatment. Its phytochemical composition and pharmacological studies need to be explored in future. RESEARCH HIGHLIGHTS: Optical microscope and scanning electron microscope (SEM) were used to observe the morphology, and microstructure of Rosa laxa Retz. flos (RLF). The physicochemical properties, fluorescence and phytochemical composition of four different polar extracts of RLF were analyzed by UV-Vis and FTIR. Determination of total triterpenic acid, total flavonoids, and total polyphenols in four different polar extracts of RLF by UV spectrophotometry. A high-performance liquid chromatography (HPLC)-diode array detector (DAD) method was established and the contents of gallic acid, ellagic acid, astragalin, and tiliroside in RLF were determined simultaneously. TLC confirmed that RLF contains ursolic acid, β-sitosterol, tiliroside, astragalin, isoquercitrin, kaempferol 3-rutinoside, gallic acid, and ellagic acid. The in vitro antioxidant activity of RLF was studied by DPPH, ABTS, and reducing ability experiments.

An overview of analytical methods employed for quality assessment of Crocus sativus (saffron)

This paper reviews qualitative and quantitative analytical methodologies used for the appraisal of saffron quality, as the most expensive spice. Due to the chemical diversity of biologically active compounds of the Crocus genus, analytical methods with different features are required for their complete analysis. However, screening of the main components, such as carotenoids and flavonoids, appears to be sufficient for quality control, a more precise examination needs evaluation of minor compounds, including anthocyanins and fatty acids. High-performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), ultraviolet-visible spectroscopy (UV), nuclear magnetic resonance spectroscopy (NMR), and thin-layer chromatography (TLC), are elementary and applicable methods in quality control analysis, whereas HPLC provides metabolite fingerprint and monitoring multi-compound instances at preparative and analytical levels. Combination approaches like metabolomics using different methods could classify saffron types, identify its adulterations, contaminants and provide a comprehensive metabolite map for quality control of selected compounds.

Rapid Indentification of Auramine O Dyeing Adulteration in Dendrobium officinale, Saffron and Curcuma by SERS Raman Spectroscopy Combined with SSA-BP Neural Networks Model

(1) Background: Rapid and accurate determination of the content of the chemical dye Auramine O(AO) in traditional Chinese medicines (TCMs) is critical for controlling the quality of TCMs. (2) Methods: Firstly, various models were developed to detect AO content in Dendrobium officinale (D. officinale). Then, the detection of AO content in Saffron and Curcuma using the D. officinale training set as a calibration model. Finally, Saffron and Curcuma samples were added to the training set of D. officinale to predict the AO content in Saffron and Curcuma using secondary wavelength screening. (3) Results: The results show that the sparrow search algorithm (SSA)-backpropagation (BP) neural network (SSA-BP) model can accurately predict AO content in D. officinale, with Rp2 = 0.962, and RMSEP = 0.080 mg/mL. Some Curcuma samples and Saffron samples were added to the training set and after the secondary feature wavelength screening: The Support Vector Machines (SVM) quantitative model predicted Rp2 fluctuated in the range of 0.780 ± 0.035 for the content of AO in Saffron when 579, 781, 1195, 1363, 1440, 1553 and 1657 cm-1 were selected as characteristic wavelengths; the Partial Least Squares Regression (PLSR) model predicted Rp2 fluctuated in the range of 0.500 ± 0.035 for the content of AO in Curcuma when 579, 811, 1195, 1353, 1440, 1553 and 1635 cm-1 were selected as the characteristic wavelengths. The robustness and generalization performance of the model were improved. (4) Conclusion: In this study, it has been discovered that the combination of surface-enhanced Raman spectroscopy (SERS) and machine learning algorithms can effectively and promptly detect the content of AO in various types of TCMs.

Deep Learning-Based Quantitative Assessment of Melamine and Cyanuric Acid in Pet Food Using Fourier Transform Infrared Spectroscopy

Melamine and its derivative, cyanuric acid, are occasionally added to pet meals because of their nitrogen-rich qualities, leading to the development of several health-related issues. A nondestructive sensing technique that offers effective detection must be developed to address this problem. In conjunction with machine learning and deep learning technique, Fourier transform infrared (FT-IR) spectroscopy was employed in this investigation for the nondestructive quantitative measurement of eight different concentrations of melamine and cyanuric acid added to pet food. The effectiveness of the one-dimensional convolutional neural network (1D CNN) technique was compared with that of partial least squares regression (PLSR), principal component regression (PCR), and a net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA/GO). The 1D CNN model developed for the FT-IR spectra attained correlation coefficients of 0.995 and 0.994 and root mean square error of prediction values of 0.090% and 0.110% for the prediction datasets on the melamine- and cyanuric acid-contaminated pet food samples, respectively, which were superior to those of the PLSR and PCR models. Therefore, when FT-IR spectroscopy is employed in conjunction with a 1D CNN model, it serves as a potentially rapid and nondestructive method for identifying toxic chemicals added to pet food.