Authentication of Eucommia ulmoides Seed Oil Using Fourier Transform Infrared and Synchronous Fluorescence Spectroscopy Combined with Chemometrics

Trends in authentication of edible oils using vibrational spectroscopic techniques

The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.

Identification of the interference spectra of edible oil samples based on neighborhood rough set attribute reduction

Due to numerous edible oil safety problems in China, an automatic oil quality detection technique is urgently needed. In this study, rough set theory and Fourier transform spectrum are combined for proposing a digital identification method for edible oil. First, the Fourier transform spectra of three different types of edible oil samples, including colza oil, waste oil, and peanut oil, are measured. After the input spectra are differentially and smoothly processed, the characteristic wavelength bands are selected with neighborhood rough set attribution reduction (NRSAR). Moreover, the classification models are established based on random forest (RF) and extreme learning machine (ELM) algorithms. Finally, confusion matrix, classification accuracy, sensitivity, specificity, and the distribution of judgment are calculated for evaluating the classification performances of different models and determining the optimal oil identification model. The results show that by using the third-order difference pre-processing method, 193 wavelength bands in the visible range can be reduced to 10 characteristic wavelengths, with a compression ratio of over 88.61%. Using the established NRS-RF and NRS-ELM models, the total identification accuracies are 91.67% and 93.33%, respectively. In particular, the identification accuracy of peanut oil using the NRS-ELM model reaches up to 100%, whereas the identification accuracies obtained using the principal component analysis (PCA)-based models that are commonly used in information processing (PCA-RF and PCA-ELM) are 81.67% and 90.00%, respectively. As compared with feature extraction methods, the proposed NRSAR shows directive advantages in terms of precision, sensitivity, specificity, and the distribution of judgment. In addition, the execution time is also reduced by approximately 1/3. Conclusively, the NRSAR method and NRS-ELM the model in the spectral identification of edible oil show favorable performance. They are expected to bring forth insightful oil identification techniques.

Identification of the effective α-amylase inhibitors from Dalbergia odorifera: Virtual screening, spectroscopy, molecular docking, and molecular dynamic simulation

Inhibiting the activity of α-amylase has been considered as one efficient way to prevent and treat type 2 diabetes recently. Dalbergia odorifera, a kind of Leguminosae plant, has a positive therapeutic effect on type 2 diabetes, possibly contributing by some constituents that can inhibit the activity of α-amylase. In this study, we found that eriodictyol was one potential constituent through virtual screening. The interaction mode between eriodictyol and α-amylase was elucidated by molecular docking, multi-spectroscopic analysis, and molecular dynamic simulation. The results revealed that eriodictyol quenched the intrinsic fluorescence of α-amylase, and the quenching mode was static quenching. Eriodictyol could spontaneously interact with α-amylase, mostly stabilized and influenced by the hydrophobic interaction, while the binding sites (n) was 1.13 ± 0.07 and binding constant (K_b) was (1.43 ± 0.14) × 10⁵ at 310 K, respectively. In addition, FT-IR and CD had been applied to identify that eriodictyol can trigger the conformational change of α-amylase. Taken together, the results provided some experimental data for developing new α-amylase inhibitors from Dalbergia odorifera, which may further prevent and treat diabetes and diabetes complications.

A Review of Advanced Methods for the Quantitative Analysis of Single Component Oil in Edible Oil Blends

Edible oil blends are composed of two or more edible oils in varying proportions, which can ensure nutritional balance compared to oils comprising a single component oil. In view of their economical and nutritional benefits, quantitative analysis of the component oils in edible oil blends is necessary to ensure the rights and interests of consumers and maintain fairness in the edible oil market. Chemometrics combined with modern analytical instruments has become a main analytical technology for the quantitative analysis of edible oil blends. This review summarizes the different oil blend design methods, instrumental techniques and chemometric methods for conducting single component oil quantification in edible oil blends. The aim is to classify and compare the existing analytical techniques to highlight suitable and promising determination methods in this field.