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

Near-infrared spectroscopy: application in ensuring food quality and safety

In recent years, the demand for intelligent control of food quality during processing has been increasing in the food industry. As a practical analytical tool, near-infrared (NIR) spectroscopy has become a common detection method to ensure food quality and safety because of its advantages of continuous, rapid on-line determination and strong analytical performance. In the past 20 years, many attempts and research studies have been conducted on the applications of NIR spectroscopy. Based on this, this review focuses on the specific application of near-infrared technology in the field of food, highlighting its breakthrough and applicability. NIR spectroscopy is widely used for online quantitative analysis of beneficial food components to the human body, which include proteins, polysaccharides, and polyphenols. Additionally, this technology is applied to food microbiological analysis, food safety detection (such as food adulteration), and food origin prediction. This review discusses the existing challenges, future development directions, and opportunities for NIR spectroscopy technology.

Rapid quantification of single component oil in perilla oil blends by ultraviolet-visible spectroscopy combined with chemometrics

As a unconventional oil, perilla oil is much more expensive than conventional oils since it has the highest content of α-linolenic acid among vegetable oils. Thus the adulteration of perilla oil is serious, which needs to be solved. In this study, the single component oil in perilla oil blends were first quantitatively analyzed by ultraviolet-visible (UV-vis) spectroscopy combined with chemometric methods. Soybean oil and palm oil were added into perilla oil to form binary and ternary perilla oil blends. Partial least squares (PLS), back propagation-artificial neural network (BP-ANN), support vector regression (SVR) and extreme learning machine (ELM) were compared and the best model was selected for calibration. In order to improve the prediction performance of the calibration model, ten preprocessing methods and five variable selection methods were investigated. Results show that PLS was the best calibration method for binary and ternary perilla oil blends. For binary perilla oil blends, the correlation coefficients of prediction (Rp) obtained by PLS were both above 0.99, which does not need preprocessing and variable selection. For ternary perilla oil blends, after the best continuous wavelet transform (CWT) preprocessing and discretized whale optimization algorithm (WOA) variable selection, the Rp values obtained by the best model CWT-WOA-PLS were all above 0.97. This research provides a common framework for calibration of perilla oil blends, which maybe a promising method for quality control of perilla oil in industry.

Quantitative detection of heavy metal Cd in vegetable oils: A nondestructive method based on Raman spectroscopy combined with chemometrics

Heavy metal contaminants in vegetable oils can cause irreversible damage to human health. In this study, the quantitative detection of Cd in vegetable oils was investigated based on Raman spectroscopy combined with chemometric methods. The necessary preprocessing of the Raman signal was performed using baseline calibration and the Savitzky-Golay method. Three variable optimization methods were applied to the preprocessed Raman spectra. Namely, bootstrap soft shrinkage, multiple feature spaces ensemble strategy with least absolute shrinkage and selection operator, and competitive adaptive reweighted sampling (CARS), respectively. Partial least squares regression (PLSR) modeling for the determination of Cd in vegetable oils. The results show that three variable optimization algorithms improved the predictive performance of the model. Among them, the CARS-PLSR model has strong generalization performance and robustness. Its prediction coefficient of determination (R P 2 $R_{\mathrm{P}}^2$ ) was 0.9995, the root mean square error of prediction was 0.3533 mg/kg, and the relative prediction deviation was 44.3748, respectively. In summary, rapid quantitative analysis of Cd contamination in vegetable oils can be realized based on Raman spectroscopy combined with chemometrics.

Accurate prediction of hyaluronic acid concentration under temperature perturbations using near-infrared spectroscopy and deep learning

Accurate prediction of the concentration of a large number of hyaluronic acid (HA) samples under temperature perturbations can facilitate the rapid determination of HA's appropriate applications. Near-infrared (NIR) spectroscopy analysis combined with deep learning presents an effective solution to this challenge, with current research in this area being scarce. Initially, we introduced a novel feature fusion method based on an intersection strategy and used two-dimensional correlation spectroscopy (2DCOS) and Aquaphotomics to interpret the interaction information in HA solutions reflected by the fused features. Subsequently, we created an innovative, multi-strategy improved Walrus Optimization Algorithm (MIWaOA) for parameter optimization of the deep extreme learning machine (DELM). The final constructed MIWaOA-DELM model demonstrated superior performance compared to partial least squares (PLS), extreme learning machine (ELM), DELM, and WaOA-DELM models. The results of this study can provide a reference for the quantitative analysis of biomacromolecules in complex systems.

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.

Infrared Spectroscopy for Rapid Triage of Cancer Using Blood Derivatives: A Reality Check

Infrared (IR) spectroscopy of serum/plasma represents an alluring molecular diagnostic tool, especially for cancer, as it can provide a molecular fingerprint of clinical samples based on vibrational modes of chemical bonds. However, despite the superior performance, the routine adoption of this technique for clinical settings has remained elusive. This is due to the potential confounding factors that are often overlooked and pose a significant barrier to clinical translation. In this Perspective, we summarize the concerns associated with various confounding factors, such as fluid sampling, optical effects, hemolysis, abnormal cardiovascular and/or hepatic functions, infections, alcoholism, diet style, age, and gender of a patient or normal control cohort, and improper selection of numerical methods that ultimately would lead to improper spectral diagnosis. We also propose some precautionary measures to overcome the challenges associated with these confounding factors.

Near infrared spectroscopic variable selection by a novel swarm intelligence algorithm for rapid quantification of high order edible blend oil

The quantification of single oil in high order edible blend oil is a challenging task. In this research, a novel swarm intelligence algorithm, discretized whale optimization algorithm (WOA), was first developed for reducing irrelevant variables and improving prediction accuracy of hexanary edible blend oil samples. The WOA is inspired by hunting strategy of humpback whales, which mainly includes three behaviors, i.e., encircling prey, bubble-net attacking and searching for prey. In discretized WOA, positions of whales were updated and then discretized by arctangent function. The whale population performance, iteration number and whale number of WOA were investigated. To validate the performance of selected variables, partial least squares (PLS) was used to build model and predict single oil contents in hexanary blend oil. Results show that WOA-PLS can provide the best prediction accuracy compared with full-spectrum PLS, continuous wavelet transform-PLS (CWT-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). Furthermore, CWT-WOA-PLS can further produce better results with fewer variables compared with WOA-PLS.