Quantitative analysis of blended oils by matrix-assisted laser desorption/ionization mass spectrometry and partial least squares regression

Quick and reagent-free monitoring of edible oil saponification values using a handheld Raman device

Saponification value, the average molecular weight of fatty acids, is a crucial parameter for detecting adulteration of edible oils. Conventionally, it is determined in a laboratory setup through a time-consuming, laborious titration process using chemical reagents. Herein, the application of Raman spectroscopy for quick SV estimation of oils is demonstrated. It was hypothesized that the SV can be predicted from Raman spectra since the spectral patterns reflect the composition of fatty acid triglycerides. Two model oil systems were studied: coconut-gingelly oil and coconut-sunflower oil. Univariate models built from Raman spectra were successful only for the specific oil system; hence, PLS-Regression was executed across the two systems. The PLSR model on the validation set returned the average error, percentage error, and root mean square error of prediction as 2.1, 0.99 %, and 2.4, respectively. This method offers several advantages of portability, little reagent use, minimal sample preparation, and reduced analysis time.

Omega 3 Blends of Sunflower and Flaxseed Oil-Modeling Chemical Quality and Sensory Acceptability

Oil blending is increasingly utilized to improve and model the characteristics of enriched oils. This study aims to investigate the effect of blending refined sunflower oil (rich in essential omega 6 fatty acids) with cold-pressed flaxseed oil (a source of essential omega 3 fatty acids) on the fatty acid composition, quality, color, and sensory characteristics of the resulting oils. Principal component analysis (PCA) showed that the optimal fatty acid composition was achieved in the sample with 20% sunflower oil and 80% flaxseed oil (20S/80F). However, developing a new product is highly complex due to the importance of oil quality and sensory characteristics. Therefore, an Artificial Neural Network (ANN) was applied to optimize the proportions of flaxseed and sunflower oil to create an oil blend with improved nutritional, oxidative, and sensory characteristics compared to the individual oils. The ANN analysis determined the optimal composition of the oil blend to be 51.5% refined sunflower oil and 48.5% cold-pressed flaxseed oil. Sensory characteristics pose a particular challenge in optimization, as flaxseed oil, which increases essential omega 3 fatty acids, has a specific taste that is not widely favored by consumers. Nonetheless, by blending with refined sunflower oil, the resulting optimal blend (51.5% refined sunflower oil and 48.5% cold-pressed flaxseed oil) possesses pleasant sensory characteristics.

Quantitative Analysis of Blended Oils Based on Intensity Ratios of Marker Ions in MALDI-MS Spectra

Determination of quantitative compositions of blended oils is an essential but challenging step for the quality control and safety assurance of blended oils. We herein report a method for the quantitative analysis of blended oils based on the intensity ratio of triacylglycerol marker ions, which could be obtained from the highly reproducible spectra acquired by using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) to directly analyze blended oils in their oily states. We demonstrated that this method could provide good quantitative results to binary, ternary, and quaternary blended oils, with simultaneous quantitation of multiple compositions, and was applicable for quantitative analysis of commercial blended oil products. Moreover, the intensity ratio-based method could be used to rapidly measure the proportions of oil compositions in blended oils, only based on the spectra of the blended oils and related pure oils, making the method as a high-throughput approach to meet the sharply growing analytical demands of blended oils.

Kinetic study of thermal degradation of flaxseed oil and moringa oil blends with physico-chemical, oxidative stability index (OSI) and shelf-life prediction

The thermal degradation kinetics of flaxseed oil (FSO) and moringa oil (MO) blends with soyabean oil (SOY; 80%), rice bran oil (RBO; 80%), cotton seed oil (CSO; 80%) and sunflower oil (SFO; 80%) with Rancimat equipment. There was no significant (p ≤ 0.05) difference observed in the specific gravity (SG), density (D), and refractive index (RI) values of the MO and FSO blends, while the rancidity parameters showed the opposite variations. The FTIR spectra showed absorption bands at 966 cm-1, 1097 cm-1, 1160 cm-1, 1217 cm-1, 1377 cm-1, 1464 cm-1, 1743 cm-1, 2945 cm-1, 2852 cm-1 and 3008 cm-1. Oil blends' kinetic degradation (Ea, ΔH, ΔS, A) is represented by the semilogarithmic relationship between the oxidative stability index (OSI) and temperature. The activation energy (Ea) ranged from 77.1 ± 0.21 to 106.9 ± 0.03 kJ/mol and 73.2 ± 0.01 to 104.4 ± 0.02 kJ/mol for flaxseed oil (FSO) and moringa oil (MO) blends, respectively. The enthalpy (ΔH) and entropy (ΔS) ranged from 67.3 to 121.6 kJ/mol, and - 60.2 to - 8.4 J/mol, and 63.55 to 95.59 kJ/mol and - 20.66 to - 4.11 J/mol for FSO blends and MO blends, respectively.

Graphical Abstract

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.

A Simple UV Spectrophotometric Method Based on Continuous Wavelet Transform and Multivariate Calibration Model for the Concurrent Analysis of Three Water-Soluble Vitamins in Fertility Supplements

BACKGROUND

Owing to the presence of overlapping spectra in pharmaceutical components, classical spectrophotometry is hard for concurrent determination. The advance of chemometrics along with UV-Vis spectrophotometry has contributed to solving this problem.

OBJECTIVE

In this study, a fast, easy, precise, accurate, low-cost, and eco-friendly spectrophotometric technique was introduced and validated for the simultaneous analysis of vitamin B6, vitamin B12, and vitamin C in fertility supplements for men and women using continuous wavelet transform (CWT) and partial least squares (PLS) techniques without using time-consuming extraction process and organic solvents.

METHOD

In the CWT method, the zero-crossing technique was applied to obtain the optimum points for plotting calibration curves for each component. The validation of both methods was evaluated by analyzing several mixtures with different concentrations. The efficiency of the proposed methods was also surveyed on commercial capsules.

RESULTS

Wavelet families, including Symlet (sym2) at 230, Biorthogonal (bior1.3) at 378 nm, and Daubechies (db2) at 261, were considered for vitamins B6, B12, and C, respectively. The linear range was found to be 8-20, 8-20, and 10-25 μg/mL with the coefficient of determination (R2) equal to 0.9982, 0.9978, and 0.9701 for B6, B12, and C, respectively. Low limit of detection (LOD) (<0.09 μg/mL) and limit of quantification (LOQ) <0.9 μg/mL were achieved. The mean recovery values in synthetic mixtures were from 98.38 to 98.89% and from 99.83 to 99.99%, where root-mean-square error (RMSE) of not more than 0.4 and 0.05 using the CWT and PLS methods, respectively.

CONCLUSIONS

The obtained results from the commercial capsules, applying the suggested techniques, were compared to those yielded by the high-performance liquid chromatography (HPLC) method using the analysis of variance (ANOVA) test. According to the results, there are no significant differences, and they were in good agreement. According to all the mentioned cases, the proposed approaches can replace the time-consuming and costly HPLC method in quality control laboratories.

HIGHLIGHTS

Green spectrophotometry coupling chemometrics methods were proposed. Simultaneous determination of three water-soluble vitamins in fertility supplements was done using these approaches. Rapidity, simplicity, low cost, and accuracy are the benefits of the proposed methods. A HPLC technique was used as a reference method to compare with the chemometrics methods.

Detection of the Adulteration of Motor Oil by Laser Induced Fluorescence Spectroscopy and Chemometric Techniques

Petroleum products are the target of fraudulent practices due to their high commercial value. The aim of this study is to provide a new analysis system to assess motor oil adulteration. For this purpose, Laser Induced Fluorescence (LIF) spectroscopy was exploited coupled with chemometric tools to detect motor oil adulteration by three types of cheap motor oils. Principal Component Analysis (PCA) was able to distinguish samples in three groups according to the type of adulterant. Besides, Partial Least Squares Regression (PLSR) was exploited to determine the percentage of adulteration. The best model was obtained with a regression coefficient of 0.96, Root Mean Square Error of Prediction (RMSEP) of 2.83, Standard Error of Prediction (SEP) of 2.83 and Bias of 0.40. The main results of this work provide new analysis system using the combination of LIF spectroscopy combined to PCA and PLS as an efficient and fast method for motor oil analysis.

Rapid quantification of goat milk adulteration with cow milk using Raman spectroscopy and chemometrics

As goat milk has a higher economic value compared to cow milk, the phenomenon of adulterating goat milk with cow milk appears in the market. In this study, the potential of Raman spectroscopy along with chemometrics was investigated for the authentication and quantitation of liquid goat milk adulterated with cow milk. First, the results of principal component analysis (PCA) showed that there were differences between the Raman spectra of cow and goat milk, which made quantitative experiments possible. For quantification, three different brands of cow milk and goat milk were selected randomly and adulterated goat milk with cow milk at the proportion of 5-95%. 342 samples were used for the construction of the partial least squares regression (PLSR) model with 80% for the calibration set and 20% for the prediction set. The PLSR model showed excellent performance in quantifying the level of adulteration, for the prediction set, with a coefficient of determination (R²) of 0.9781, root mean square error (RMSE) of 3.82%, and a ratio of prediction to deviation (RPD) of 6.8. The results demonstrated the potential of Raman spectroscopy as a rapid, low cost and non-destructive analytical tool for detecting adulteration in goat milk.

Biomolecular Profiling by MALDI-TOF Mass Spectrometry in Food and Beverage Analyses

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has frequently been applied to the analysis of biomolecules. Its strength resides not only in compound identification but particularly in acquiring molecular profiles providing a high discriminating power. The main advantages include its speed, simplicity, versatility, minimum sample preparation needs, and a relatively high tolerance to salts. Other benefits are represented by the possibility of automation, high throughput, sensitivity, accuracy, and good reproducibility, allowing quantitative studies. This review deals with the prominent use of MALDI-TOF MS profiling in food and beverage analysis ranging from the simple detection of sample constituents to quantifications of marker compounds, quality control, and assessment of product authenticity. This review summarizes relevant discoveries that have been obtained with milk and milk products, edible oils, wine, beer, flour, meat, honey, and other alimentary products. Marker molecules are specified: proteins and peptides for milk, cheeses, flour, meat, wine and beer; triacylglycerols and phospholipids for oils; and low-molecular-weight metabolites for wine, beer and chocolate. Special attention is paid to sample preparation techniques and the combination of spectral profiling and statistical evaluation methods, which is powerful for the differentiation of samples and the sensitive detection of frauds and adulterations.

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.

Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil

In this study, near infrared (NIR) spectroscopy combined with chemometrics was used for the quantitative analysis of corn oil in binary to hexanary edible blend oil. Sesame oil, soybean oil, rice oil, sunflower oil and peanut oil were mixed with corn oil subsequently to form binary, ternary, quaternary, quinary and hexanary blend oil datasets. NIR spectra for the five order blend oil datasets were measured in a transmittance mode in the range of 12000-4000 cm^-1. Partial least square (PLS) was used to build models for the five datasets. Six spectral preprocessing methods and their combinations were investigated to improve the prediction performance. Furthermore, the optimal preprocessing-PLS models were further optimized by uninformative variable elimination (UVE), Monte Carlo uninformative variable elimination (MCUVE) and randomization test (RT) variable selection methods. The optimal models acquire root mean square error of prediction (RMSEP) of 1.7299, 2.2089, 2.3742, 2.5608 and 2.6858 for binary, ternary, quaternary, quinary and hexanary blend oil datasets, respectively. The determination coefficients of prediction set (R²_P) and residual predictive deviations (RPDs) for the five datasets are all above 0.93 and 3. Results show that the prediction accuracy is gradually decreased with the increasing of mixture order of blend oil. However, with proper spectral preprocessing and variable selection, the optimal models present good prediction accuracy even for the higher order blend oil. It demonstrates that NIR technology is feasible for determining the pure oil contents in binary to hexanary blend oil.

A new update of MALDI-TOF mass spectrometry in lipid research

Matrix-assisted laser desorption and ionization (MALDI) mass spectrometry (MS) is an indispensable tool in modern lipid research since it is fast, sensitive, tolerates sample impurities and provides spectra without major analyte fragmentation. We will discuss some methodological aspects, the related ion-forming processes and the MALDI MS characteristics of the different lipid classes (with the focus on glycerophospholipids) and the progress, which was achieved during the last ten years. Particular attention will be given to quantitative aspects of MALDI MS since this is widely considered as the most serious drawback of the method. Although the detailed role of the matrix is not yet completely understood, it will be explicitly shown that the careful choice of the matrix is crucial (besides the careful evaluation of the positive and negative ion mass spectra) in order to be able to detect all lipid classes of interest. Two developments will be highlighted: spatially resolved Imaging MS is nowadays well established and the distribution of lipids in tissues merits increasing interest because lipids are readily detectable and represent ubiquitous compounds. It will also be shown that a combination of MALDI MS with thin-layer chromatography (TLC) enables a fast spatially resolved screening of an entire TLC plate which makes the method competitive with LC/MS.

Reflectance spectroscopy with operator difference for determination of behenic acid in edible vegetable oils by using convolutional neural network and polynomial correction

A novel polynomial correction method, order-adaptive polynomial correction (OAPC), was proposed to correct reflectance spectra with operator differences, and convolutional neural network (CNN) was used to develop analysis model to predict behenic acid in edible oils. With application of OAPC, CNN performed well with coefficient of determination of correction (R²_cor) of 0.8843 and root mean square error of correction (RMSE_cor) of 0.1182, outperforming partial least squares regression, support vector regression and random forest with OAPC, as well as the cases without OAPC. Based on 16 effective wavelengths selected by combination of bootstrapping soft shrinkage, random frog and Pearson's correlation, CNN and OAPC exhibited excellent performance with R²_cor of 0.9560 and RMSE_cor of 0.0730. Meanwhile, only 5% correction samples were selected by Kennard-Stone for OAPC. Overall, the proposed method could alleviate the impact of operator differences on spectral analysis, thereby providing potential to correct differences from measurement instruments or environments.

Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review

In this review, we present the applications of chemometric techniques for green and sustainable chemistry. The techniques, such as cluster analysis, principal component analysis, artificial neural networks, and multivariate ranking techniques, are applied for dealing with missing data, grouping or classification purposes, selection of green material, or processes. The areas of application are mainly finding sustainable solutions in terms of solvents, reagents, processes, or conditions of processes. Another important area is filling the data gaps in datasets to more fully characterize sustainable options. It is significant as many experiments are avoided, and the results are obtained with good approximation. Multivariate statistics are tools that support the application of quantitative structure–property relationships, a widely applied technique in green chemistry.