FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils

A modified sequential wavenumber selection-discriminant analysis with Bayesian optimization strategy for detection and identification of chia seed oil adulteration using Raman spectroscopy

The detection of oil fraud can be accomplished through the use of Raman spectroscopy, which is a potent analytical technique for identifying the adulteration of edible oils with inferior or less expensive oils. However, appropriate data reduction and classification methods are required to achieve high accuracy and reliability in the analysis of Raman spectra. In this study, data reduction algorithms such as principal component analysis (PCA) and modified sequential wavenumber selection (MSWS) were applied, along with discriminant analysis (DA) as a classifier for detecting oil fraud. The parameters of DA, such as the discriminant type, the amount of regularization, and the linear coefficient threshold, were optimized using Bayesian optimization. The methods were tested on a dataset of chia oil mixed with 5-40 % sunflower oil, which is a common form of fraud in the market. The results showed that MSWS-DA achieved 100 % classification accuracy, while PCA-DA achieved 91.3 % accuracy. Therefore, it was demonstrated that Raman spectroscopy combined with MSWS-DA and Bayesian optimization can effectively detect oil fraud with high accuracy and robustness.

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.

Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data

We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the "PCA-merge" method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space-gas chromatograph/mass spectrometry while drying the paint films for 1-48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications.

Evening primrose oil: a comprehensive review of its bioactives, extraction, analysis, oil quality, therapeutic merits, and safety

Oil crops have become increasingly farmed worldwide because of their numerous functions in foods and health. In particular, oil derived from the seeds of evening primrose (Oenothera biennis) (EPO) comprises essential fatty acids of the omega-6 (ω-6) series. It is well recognized to promote immune cells with a healthy balance and management of female ailments. The nutrients of interest in this oil are linoleic acid (LA, 70-74%) and γ-linolenic acid (GLA, 8-10%), which are polyunsaturated fatty acids (PUFA) that account for EPO's popularity as a dietary supplement. Various other chemicals in EPO function together to supply the body with PUFA, elevate normal ω-6 essential fatty acid levels, and support general health and well-being. The inclusive EPO biochemical analysis further succeeded in identifying several other components, i.e., triterpenes, phenolic acids, tocopherols, and phytosterols of potential health benefits. This comprehensive review capitalizes on EPO, the superior product of O. biennis, highlighting the interrelationship between various methods of cultivation, extraction, holistic chemical composition, sensory characters, and medicinal value. Besides the literature review, this study restates the numerous health advantages of primrose oil and possible drug-EPO interactions since a wide spectrum of drugs are administered concomitantly with EPO. Modern techniques to evaluate EPO chemical composition are addressed with emphasis on the missing gaps and future perspectives to ensure best oil quality and nutraceutical benefits.

Extraction method impact on the physicochemical characteristics of lipids from chia nutlets applicable to long-term storage studies

Lipids are relevant during the seed aging process, for which it is pertinent to choose an extraction method that does not alter their nature. Thus, three methods were applied to extract lipids from chia seeds: one used as reference (Soxhlet) and two at room temperature using hexane/ethanol (COBio) and hexane/isopropanol (COHar). The fatty acid composition and the tocopherol content of the oils were analyzed. Also, their oxidative status through the peroxide index, conjugated dienes and trienes, and malondialdehyde were determined. Besides, biophysical techniques, such as DSC and FT-IR, were applied. The extraction yield was not affected by the extraction method, while the fatty acid composition presented slight differences. Despite the high content of PUFAs, the oxidation level was low in all cases, especially in COBio, associated with the high content of α-tocopherol. DSC and FT-IR outcomes coincided with those obtained by conventional studies, resulting in efficient and fast characterization tools.

Advanced process analytical tools for identification of adulterants in edible oils - A review

This review summarizes the use of spectroscopic processes-based analytical tools coupled with chemometric techniques for the identification of adulterants in edible oil. Investigational approaches of process analytical tools such asspectroscopy techniques, nuclear magnetic resonance (NMR), hyperspectral imaging (HSI), e-tongue and e-nose combined with chemometrics were used to monitor quality of edible oils. Owing to the variety and intricacy of edible oil properties along with the alterations in attributes of the PAT tools, the reliability of the tool used and the operating factors are the crucial components which require attention to enhance the efficiency in identification of adulterants. The combination of process analytical tools with chemometrics offers a robust technique with immense chemotaxonomic potential. These involves identification of adulterants, quality control, geographical origin evaluation, process evaluation, and product categorization.

Challenges in Medicinal and Aromatic Plants DNA Barcoding-Lessons from the Lamiaceae

The potential value of DNA barcoding for the identification of medicinal plants and authentication of traded plant materials has been widely recognized; however, a number of challenges remain before DNA methods are fully accepted as an essential quality control method by industry and regulatory authorities. The successes and limitations of conventional DNA barcoding are considered in relation to important members of the Lamiaceae. The mint family (Lamiaceae) contains over one thousand species recorded as having a medicinal use, with many more exploited in food and cosmetics for their aromatic properties. The family is characterized by a diversity of secondary products, most notably the essential oils (EOs) produced in external glandular structures on the aerial parts of the plant that typify well-known plants of the basil (Ocimum), lavender (Lavandula), mint (Mentha), thyme (Thymus), sage (Salvia) and related genera. This complex, species-rich family includes widely cultivated commercial hybrids and endangered wild-harvested traditional medicines, and examples of potential toxic adulterants within the family are explored in detail. The opportunities provided by next generation sequencing technologies to whole plastome barcoding and nuclear genome sequencing are also discussed with relevant examples.

A rapid quality grade discrimination method for Gastrodia elata powderusing ATR-FTIR and chemometrics

Gastrodia elata is an obligate fungal symbiont used in traditional Chinese medicine. There are currently 4 grades of the plant based on the "Commodity Specification Standard of 76 Kinds of Medicinal Materials". The traditional discrimination methods for determining the medicinal grade of G. elata powders are complex and time-consuming which are not suitable for rapid analysis. We developed a rapid analysis method for this plant using attenuated total reflection and Fourier-transform infrared spectroscopy (ATR-FTIR) together with machine learning algorithms. The original spectroscopic data was first pre-treated using the multiplicative scatter correction (MSC) method and 4 principal components were extracted using extremely randomized trees (Extra-trees) and principal component analysis (PCA) algorithms, and different kinds of classification models were established. We found that multilayer perceptron classifier (MLPC) modeling was superior to support vector machine (SVM) and resulted in validation and prediction accuracies of 99.17% and 100%, respectively and a modeling time of 2.48 s. The methods established from the current study can rapidly and effectively distinguish the 4 different types of G. elata powders and thus provides a platform for rapid quality inspection.

Chia Oil Adulteration Detection Based on Spectroscopic Measurements

Chia oil is a valuable source of omega-3-fatty acids and other nutritional components. However, it is expensive to produce and can therefore be easily adulterated with cheaper oils to improve the profit margins. Spectroscopic methods are becoming more and more common in food fraud detection. The aim of this study was to answer following questions: Is it possible to detect chia oil adulteration by spectroscopic analysis of the oils? Is it possible to identify the adulteration oil? Is it possible to determine the amount of adulteration? Two chia oils from local markets were adulterated with three common food oils, including sunflower, rapeseed and corn oil. Subsequently, six chia oils obtained from different sites in Kenya were adulterated with sunflower oil to check the results. Raman, NIR and fluorescence spectroscopy were applied for the analysis. It was possible to detect the amount of adulterated oils by spectroscopic analysis, with a minimum R² of 0.95 for the used partial least square regression with a maximum RMSEP_range of 10%. The adulterations of chia oils by rapeseed, sunflower and corn oil were identified by classification with a median true positive rate of 90%. The training accuracies, sensitivity and specificity of the classifications were over 90%. Chia oil B was easier to detect. The adulterated samples were identified with a precision of 97%. All of the classification methods show good results, however SVM were the best. The identification of the adulteration oil was possible; less than 5% of the adulteration oils were difficult to detect. In summary, spectroscopic analysis of chia oils might be a useful tool to identify adulterations.

Mycobiome analysis for distinguishing the geographical origins of sesame seeds

Sesame (Sesamum indicum) is one of the most widely cultivated crops in Asia and Africa. The identification of the geographical origins of sesame seeds is important for the detection of fraudulent samples. This study was conducted to build a prediction model and suggest potential biomarkers for distinguishing the geographical origins of sesame seeds using mycobiome (fungal microbiome) analysis coupled with multivariate statistical analysis. Sesame seeds were collected from 25 cities in Korea, six cities in China, and five sites in other countries (Ethiopia, India, Nigeria, and Pakistan). According to the expression of fungal internal transcribed spacer (ITS) sequences in sesame seeds, 21 fungal genera were identified in sesame seeds from various countries. The optimal partial least squares-discriminant analysis model was established by applying two components with unit variance scaling. Based on seven-fold cross validation, the predictive model had 94.4% (Korea vs. China/other countries), 91.7% (China vs. Korea/other countries), and 88.9% (other countries vs. Korea/China) accuracy in determining the geographical origins of sesame seeds. Alternaria, Aspergillus, and Macrophomina were suggested as the potential fungal genera to differentiate the geographical origins of sesame seeds. This study demonstrated that mycobiome analysis could be used as a complementary method for distinguishing the geographical origins of raw sesame seeds.

Prediction of chemical parameters and authentication of various cold pressed oils with fluorescence and mid-infrared spectroscopic methods

It was aimed to compare the performances of two spectroscopic methods, fluorescence and mid-infrared spectroscopy, in terms of their adulteration detection and estimation of several chemical properties for various cold pressed seed oils. Spectroscopic profiles, fatty acid, free fatty acid and total phenol contents of pumpkin seed, grape seed, black cumin oil, and sesame seed oils were determined and these oils were mixed with sunflower oil at 1-50% (v/v). Both spectroscopic techniques provided comparable results for determination of adulteration of each oil type and the most successful prediction was obtained for pumpkin seed oil at levels >%1. Combined data set of oils resulted in successful quantification of their free fatty acid value, total phenol and major fatty acids contents with both spectroscopic methods regardless of oil type. Both techniques could be used as reliable, fast and environmentally friendly alternatives in the analyses of different types of seed oils.