An overview of recent advances and applications of FT-IR spectroscopy for quality, authenticity, and adulteration detection in edible oils

A methodological approach to preprocessing FTIR spectra of adulterated sesame oil

Fourier transform infrared (FTIR) spectroscopy is established as an effective and fast method for the confirmation of the authenticity of food and among other, edible oils. However, no standard procedure is available for applying preprocessing as a vital step in obtaining accurate results from spectra. This study proposes a methodological approach to preprocessing FTIR spectra of sesame oil adulterated with vegetable oils (canola oil, corn oil, and sunflower oil). The primary preprocessing methods investigated are orthogonal signal correction (OSC), standard normal variate transformation (SNV), and extended multiplicative scatter correction (EMSC). Other preprocessing methods are used both as standalone methods and in combination with the primary preprocessing methods. The preprocessing results are compared using partial least squares regression (PLSR). OSC alone or with detrending were the most accurate in predicting the adulteration level of sesame oil, with a maximum coefficient of prediction (R²_p) range of 0.910 to 0.971 for different adulterants.

Rapid and Low-Cost Quantification of Adulteration Content in Camellia Oil Utilizing UV-Vis-NIR Spectroscopy Combined with Feature Selection Methods

This study aims to explore the potential use of low-cost ultraviolet-visible-near infrared (UV-Vis-NIR) spectroscopy to quantify adulteration content of soybean, rapeseed, corn and peanut oils in Camellia oil. To attain this aim, test oil samples were firstly prepared with different adulterant ratios ranging from 1% to 90% at varying intervals, and their spectra were collected by an in-house built experimental platform. Next, the spectra were preprocessed using Savitzky-Golay (SG)-Continuous Wavelet Transform (CWT) and the feature wavelengths were extracted using four different algorithms. Finally, Support Vector Regression (SVR) and Random Forest (RF) models were developed to rapidly predict adulteration content. The results indicated that SG-CWT with decomposition scale of 25 and the Iterative Variable Subset Optimization (IVSO) algorithm can effectively improve the accuracy of the models. Furthermore, the SVR model performed best for predicting adulteration of camellia oil with soybean oil, while the RF models were optimal for camellia oil adulterated with rapeseed, corn, or peanut oil. Additionally, we verified the models' robustness by examining the correlation between the absorbance and adulteration content at certain feature wavelengths screened by IVSO. This study demonstrates the feasibility of using low-cost UV-Vis-NIR spectroscopy for the authentication of Camellia oil.

Biomimetic, biodegradable and osteoinductive treated dentin matrix/α-calcium sulphate hemihydrate composite material for bone tissue engineering

It is still a huge challenge for bone regenerative biomaterial to balance its mechanical, biological and biodegradable properties. In the present study, a new composite material including treated dentin matrix (TDM) and α-calcium sulphate hemihydrate (α-CSH) was prepared. The optimal composition ratio between TDM and α-CSH was explored. The results indicate that both components were physically mixed and structurally stable. Its compressive strength reaches up to 5.027 ± 0.035 MPa for 50%TDM/α-CSH group, similar to human cancellous bone tissues. Biological experiments results show that TDM/α-CSH composite exhibits excellent biocompatibility and the expression of osteogenic related genes and proteins (ALP, RUNX2, OPN) is significantly increased. In vivo experiments suggest that the addition of TDM for each group (10%, 30%, 50%) effectively promotes cell proliferation and osteomalacia. In addition, 50% of the TDM/α-CSH combination displays optimal osteoconductivity. The novel TDM/α-CSH composite is a good candidate for certain applications in bone tissue engineering.

A comparative study on classification of edible vegetable oils by infrared, near infrared and fluorescence spectroscopy combined with chemometrics

Driven by economic benefits like any other foods, vegetable oil has long been plagued by mislabeling and adulteration. Many studies have addressed the field of classification and identification of vegetable oils by various analysis techniques, especially spectral analysis. A comparative study was performed using Fourier transform infrared spectroscopy (FTIR), visible near-infrared spectroscopy (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics to distinguish different types of edible vegetable oils. FTIR, Vis-NIR and EEMs datasets of 147 samples of five vegetable oils from different brands were analyzed. Two types of pattern recognition methods, principal component analysis (PCA)/multi-way principal component analysis (M-PCA) and partial least squares discriminant analysis (PLS-DA)/multilinear partial least squares discriminant analysis (N-PLS-DA), were used to resolve these data and distinguish vegetable oil types, respectively. PCA/M-PCA analysis exhibited that three spectral data of five vegetable oils showed a clustering trend. The total correct recognition rate of the training set and prediction set of FTIR spectra of vegetable oil based on PLS-DA method are 100%. The total recognition rate of Vis-NIR based on PLS-DA are 100% and 97.96%. However, the total correct recognition rate of training set and prediction set of EEMs data based on N-PLS-DA method is 69.39% and 75.51%, respectively. The comparative study showed that FTIR and Vis-NIR combined with chemometrics were more suitable for vegetable oil species identification than EEMs technique. The reason may be concluded that almost all chemical components in vegetable oil can produce FTIR and NIR absorption, while only a small amount of fluorophores can produce fluorescence. That is, FTIR and NIR can provide more spectral information than EEMs. Analysis of EEMs data using self-weighted alternating trilinear decomposition (SWATLD) also showed that fluorophores were a few and irregularly distributed in vegetable oils.

Application of Analytical Technologies in the Discrimination and Authentication of Herbs from Fritillaria: A Review

Medicinal plants of Fritillaria are widely distributed in numerous countries around the world and possess excellent antitussive and expectorant effects. In particular, Fritillariae Bulbus (FB) as a precious traditional medicine has thousands of years of medical history in China. Herbs of Fritillaria have a high market value and demand while limited by harsh growing circumstances and scarce wild resources. As a consequence, fraudulent behaviors are regularly engaged by the unscrupulous merchants in an attempt to reap greater profits. It is of an urgent need to evaluate the quality of Fritillaria herbs and their products using various analytical instruments and techniques. This review has scrutinized approximately 160 articles from 1995 to 2022 published on the investigation of Fritillaria herbs and related herbal products. The botanical classification of genus Fritillaria, types of counterfeits, technologies applied for differentiating Fritillaria species were comprehensively summarized and discussed in the current review. Molecular and chromatographic identification were the dominant technologies in the authentication of Fritillaria herbs. Additionally, we brought some potential and promising technologies and analytical strategies into attention, which are worthy attempting in the future researches. This review could conduce to excellent reference value for further investigations of the authenticity assessment of Fritillaria species.

Rapid Identification of Wild Gentiana Genus in Different Geographical Locations Based on FT-IR and an Improved Neural Network Structure Double-Net

Gentiana Genus, a herb mainly distributed in Asia and Europe, has been used to treat the damp heat disease of the liver for over 2000 years in China. Previous studies have shown significant differences in the compositional contents of wild Gentiana Genus samples from different geographical origins. Therefore, the traceable geographic locations of the wild Gentiana Genus samples are essential to ensure practical medicinal value. Over the last few years, the developments in chemometrics have facilitated the analysis of the composition of medicinal herbs via spectroscopy. Notably, FT-IR spectroscopy is widely used because of its benefit of allowing rapid, nondestructive measurements. In this paper, we collected wild Gentiana Genus samples from seven different provinces (222 samples in total). Twenty-one different FT-IR spectral pre-processing methods that were used in our experiments. Meanwhile, we also designed a neural network, Double-Net, to predict the geographical locations of wild Gentiana Genus plants via FT-IR spectroscopy. The experiments showed that the accuracy of the neural network structure Double-Net we designed can reach 100%, and the F1_score can reach 1.0.

Fraud Detection in Meat Using Hyperspectral Imaging

Fraud detection in meat is a challenging task for researchers, consumers, industries, and regulatory agencies. Traditional approaches for fraud detection are time-consuming, complicated, laborious, and expensive; they require technical skills. Therefore, much effort has been devoted in academia and industry to developing rapid and nondestructive optical techniques for fraud detection in meat. Among them, hyperspectral imaging has gained enormous attention and curiosity throughout the world. Hyperspectral imaging is an emerging analytical technique that combines spectroscopy and imaging in one system to acquire spectra and spatial information from an object simultaneously. Hyperspectral imaging is the only analytical technology that answers commonly asked analytical questions such as what chemical species are in the samples, how much, and most importantly, where they are located. Therefore, the technology will undoubtedly play indispensable roles in research and industry for fraud detection in the coming days.

Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods

The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction (RP2) was 0.9504 and the root mean square error of prediction (RMSEP) was 0.8181. For the stearic acid, RP2 and RMSEP were 0.9636 and 0.2965. In the prediction of arachidic acid, RP2 and RMSEP were 0.9576 and 0.0577. In the prediction of behenic acid, the RP2 and RMSEP were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils.