Highly efficient authentication of edible oils by FTIR spectroscopy coupled 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.

Reverse design of haptens based on antigen spatial conformation to prepare anti-capsaicinoids&gingerols antibodies for monitoring of gutter cooking oil

Rapid simultaneous detection of multi-component adulteration markers can improve the accuracy of identification of gutter cooking oil in edible oil, which is made possible by broad-spectrum antibody (bs-mAb). This study used capsaicinoids (CPCs) and gingerol derivatives (GDs) as adulteration markers, and two broad-spectrum haptens (bs-haptens) were designed and synthesized based on a reverse design strategy of molecular docking. Electrostatic potential (ESP) and monoclonal antibodies (mAbs) preparation verified the strategy's feasibility. To further investigate the recognition mechanism, five other reported antigens and mAbs were also used. Finally, the optimal combination (Hapten 5-OVA/1-F12) and key functional groups (f-groups) were determined. The half maximal inhibitory concentration (IC50) for CPCs-GDs was between 88.13 and 499.16 ng/mL. Meanwhile, a preliminary lateral flow immunoassay (LFIA) study made practical monitoring possible. The study provided a theoretical basis for the virtual screening of bs-haptens and simultaneous immunoassay of multiple exogenous markers to monitor gutter oil rapidly and accurately.

Gelatin-nanocellulose stabilized emulsion-filled hydrogel beads loaded with curcumin: Preparation, encapsulation and release behavior

In this study, the curcumin was firstly encapsulated in gelatin (GLT) and/or cellulose nanocrystals (CNC) stabilized emulsions, then further mixed with sodium alginate (SA) to form emulsion-filled hydrogel beads loaded with curcumin (Cur). The Cur-loaded emulsions showed a droplet size of 20.3-24.4 μm with a uniform distribution. Introducing CNC and/or SA increased the viscosity of emulsions accompanied by viscoelastic transition, while the modulus was reduced due to destruction of GLT gel. Cur was doubly immobilized in the hydrogel beads with >90 % of encapsulation efficiency. The results of simulated gastrointestinal tract experiments revealed that the beads possessed a good pH sensitivity and controlled release behavior to prolong the retention of Cur in the gastrointestinal tract. After 6 h of UV irradiation, the Cur-loaded emulsion-filled hydrogel beads showed a higher antioxidant activity than that of pure Cur, effectively delaying the photodegradation of Cur. In addition, the beads had better stability in aqueous and acidic environments, which was favorable for prolonging the release of Cur. These results suggest that the emulsion-filled hydrogel beads have great potential for the delivery of lipophilic bioactive molecules.

Rapid quantitative authentication and analysis of camellia oil adulterated with edible oils by electronic nose and FTIR spectroscopy

Camellia oil, recognized as a high-quality edible oil endorsed by the Food and Agriculture Organization, is confronted with authenticity issues arising from fraudulent adulteration practices. These practices not only pose health risks but also lead to economic losses. This study proposes a novel machine learning framework, referred to as a transformer encoder backbone with a support vector machine regressor (TES), coupled with an electronic nose (E-nose), for detecting varying adulteration levels in camellia oil. Experimental results indicate that the proposed TES model exhibits the best performance in identifying the adulterated concentration of camellia oi, compared with five other machine learning models (the support vector machine, random forest, XGBoost, K-nearest neighbors, and backpropagation neural network). The results obtained by E-nose detection are verified by complementary Fourier transform infrared (FTIR) spectroscopy analysis for identifying functional groups, ensuring accuracy and providing a comprehensive assessment of the types of adulterants. The proposed TES model combined with E-nose offers a rapid, effective, and practical tool for detecting camellia oil adulteration. This technique not only safeguards consumer health and economic interests but also promotes the application of E-nose in market supervision.

Revealing the adulteration of sesame oil products by portable Raman spectrometer and 1D CNN vector regression: A comparative study with chemometrics and colorimetry

Identification and quantification of sesame oil products are crucial due to the existing problems of adulteration with lower-priced oils and false labeling of sesame proportions. In this study, 1D CNN models were established to achieve discrimination of oil types and multiple quantification of adulteration using portable Raman spectrometer. An improved data augmentation method involving discarding transformations that alter peak positions was proposed, and synchronously injecting noise during geometric transformations. Furthermore, a novel neural network structure was introduced incorporating vector regression to accurately predict each component simultaneously. The proposed method has achieved higher accuracy in detecting multi-component adulteration compared with chemometrics (100 % accuracy in classifying different oils; R2 over 0.99 and RMSE within 2 % in predicting unknown adulterated samples). Finally, commercially available sesame oil products were tested and compared with gas chromatography and colorimetric methods, demonstrating the effectiveness of our proposed model in achieving higher detection accuracy at low-concentration adulteration.

Raman spectroscopy combined with multiple one-dimensional deep learning models for simultaneous quantification of multiple components in blended olive oil

Blended vegetable oils are highly prized by consumers for their comprehensive nutritional profile. Therefore, there is an urgent need for a rapid and accurate method to identify the true content of blended oils. This study combined Raman spectroscopy with three deep learning models (CNN-LSTM, improved AlexNet, and ResNet) to simultaneously quantify extra virgin olive oil (EVOO), soybean oil, and sunflower oil in olive blended oil. The results demonstrate that all three deep learning models exhibited superior predictive ability compared to traditional chemometric methods. Specifically, the CNN-LSTM model achieved a coefficient of determination (R2p) of over 0.995 for each oil in the quantitative analysis of three-component blended oils, with a mean square error of prediction (RMSEP) of less than 2%. This study presents a novel approach for the simultaneous quantitative analysis of multi-component blended oils, providing a rapid and accurate method for the identification of falsely labeled blended oils.

Rapid quantification of royal jelly quality by mid-infrared spectroscopy coupled with backpropagation neural network

Royal jelly is rich in nutrients but its quality is greatly affected by storage conditions. To determine the quality of royal jelly accurately and quickly, a qualitative discrimination model was established based on the fusion of conventional parameters and mid-infrared spectrum, using support vector machine. The prediction models for three representative quality parameters were developed by the backpropagation neural network with various algorithms. The results demonstrated that the recognition rate of the multi-source information fusion model was increased to 100% when compared with that of the spectral data preprocessed by Savitzky-golay smoothing (95.83%). The mean square errors of the constructed model for moisture, water-soluble protein, and total sugar were 0.0032, 0.0058, and 0.0069, respectively. The constructed model had an ensured accuracy for the calibration set, with the correlation coefficient of prediction maintained at 0.9353, 0.9533, and 0.9563, which could meet the requirement of non-destructive rapid detection of royal jelly quality.

High sensitivity electrochemical detection of ultra-trace imidacloprid in fruits and vegetables using a Fe-rich FeCoNi-MOF

High sensitivity and ultra-trace detection of imidacloprid are important and challenging in the field of food. In this study, we prepared a Fe-rich FeCoNi-MOF in-situ modified nickel foam working electrode by one-step hydrothermal method, and achieved a highly sensitive detection of the imidacloprid. The characterization techniques confirmed that Fe-rich FeCoNi-MOF had excellent crystallinity, tighter structure, and exposed rich active sites. The detection results showed that Fe-rich FeCoNi-MOF electrochemical sensor had a minimum detection limit of 0.04 pmol/L (100 times lower than that of the bioelectrochemical sensors), a wide response range (1 pmol/L-120 μmol/L), and high sensitivity (124 μA pmol/L^-1 cm^-2). These advantages of the electrochemical sensor were revealed theoretically by the valence change of active metal and the first principle calculation. Lastly, the Fe-rich FeCoNi-MOF electrochemical sensor was applied to detect imidacloprid in apple, fresh tea leaves, tomato, cucumber, and had an excellent recovery of 98-102.8 %.

Hogweed Seed Oil: Physico-Chemical Characterization, LC-MS Profile, and Neuroprotective Activity of Heracleum dissectum Nanosuspension

The seeds of dissected hogweed (Heracleum dissectum Ledeb., Apiaceae) are the source of hogweed oil (HSO), which is still underexplored and requires careful chemical and biological studies. The performed physico-chemical analysis of HSO elucidated basic physical characteristics and revealed the presence of fatty acids, essential oil components, pigments, and coumarins. High-performance liquid chromatography with photodiode array detection and electrospray ionization triple quadrupole mass spectrometric detection (HPLC-PDA-ESI-tQ-MS/MS) identified 38 coumarins that were characterized and quantified. Various furanocoumarins were the major components of HSO polyphenolics, including imperatorin, phellopterin, and isoimperatorin, and the total coumarin content in HSO varied from 181.14 to 238.42 mg/mL. The analysis of storage stability of the selected compounds in HSO indicated their good preservation after 3-year storage at cold and freezing temperatures. The application of the CO₂-assisted effervescence method allowed the production of an HSO nanosuspension, which was used in a brain ischemia model of rats. The HSO nanosuspension enhanced cerebral hemodynamics and decreased the frequency of necrotic processes in the brain tissue. Thus, H. dissectum seeds are a good source of coumarins, and HSO nanosuspension promotes neuroprotection of the brain after lesions, which supports earlier ethnopharmacological data.

Rapid determination of chemical components and antioxidant activity of the fruit of Crataegus pinnatifida Bunge by NIRS and chemometrics

OBJECTIVE

To establish a method for quality evaluation of the fruit of Crataegus pinnatifida Bunge, also known as Shanzha, by near-infrared spectroscopy combined with chemometrics.

METHOD

Seventy-two batches of Shanzha samples were collected, and the content of total components (flavonoids, phenols and organic acids), monomer components (chlorogenic acid, hyperoside and isoquercitrin), as well as the antioxidant activity of 60% ethanol extract were determined by usual methods. Then, all measured values were correlated with the near infrared spectra of Shanzha, and the partial least squares regression models were established. As to improve the model performance, various methods for spectra pretreatment and wavelength selection were investigated.

RESULTS

After optimization, the models obtained the coefficients of determination in both calibration and prediction >0.9, and the residual prediction deviations >3, indicating that the models had good prediction abilities.

CONCLUSION

The present method can serve as an alternative to the methods for comprehensive and rapid quality evaluation of Shanzha.

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.