Potential of Near-Infrared Spectroscopy for the Determination of Olive Oil Quality

Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning

Cannabis is cultivated for therapeutic and recreational purposes where delta-9 tetrahydrocannabinol (THC) is a main target for its therapeutic effects. As the global cannabis industry and research into cannabinoids expands, more efficient and cost-effective analysis methods for determining cannabinoid concentrations will be beneficial to increase efficiencies and maximize productivity. The utilization of machine learning tools to develop near-infrared (NIR) spectroscopy-based prediction models, which have been validated from accurate and sensitive chemical analysis, such as gas chromatography (GC) or liquid chromatography mass spectroscopy (LCMS), is essential. Previous research on cannabinoid prediction models targeted decarboxylated cannabinoids, such as THC, rather than the naturally occurring precursor, tetrahydrocannabinolic acid (THCA), and utilize finely ground cannabis inflorescence. The current study focuses on building prediction models for THCA concentrations in whole cannabis inflorescences prior to harvest, by employing non-destructive screening techniques so cultivators may rapidly characterize high-performing cultivars for chemotype in real time, thus facilitating targeted optimization of crossbreeding efforts. Using NIR spectroscopy and LCMS to create prediction models we can differentiate between high-THCA and even ratio classes with 100% prediction accuracy. We have also developed prediction models for THCA concentration with a R2 = 0.78 with a prediction error average of 13%. This study demonstrates the viability of a portable handheld NIR device to predict THCA concentrations on whole cannabis samples before harvest, allowing the evaluation of cannabinoid profiles to be made earlier, therefore increasing high-throughput and rapid capabilities.

A novel method for determination of peroxide value and acid value of extra-virgin olive oil based on fluorescence internal filtering effect correction

Peroxide value (PV) and acid value (AV) are widely used indicators for evaluating oxidation degree of olive oils. Fluorescence spectroscopy has been extensively studied on the detection of oil oxidation, however, the detection accuracy is limited due to internal filtering effect (IFE). Due to the primary and secondary IFE, at least two wavelengths of absorption information are required. Least squares support vector regression (LSSVR) models for PV and AV were established based on two absorption coefficients (μa) at 375 nm and emission wavelength and one fluorescence intensity at corresponding wavelength. The regression results proved that the model based on 375 and 475 nm could reach the best performance, with the highest correlation coefficient for prediction (rp) of 0.889 and 0.960 for PV and AV respectively. Finally, the explicit formulations for PV and AV were determined by nonlinear least squares fitting, and the rp could reach above 0.94 for two indicators.

Mechanical properties, sustained release, and oxygen scavenging properties of nanocomposite films loaded with bimetallic nanoparticles (Fe2O3/TiO2) in extra virgin olive oil

The aim of this study was the synthesis of bimetallic nanoparticles based on Fe2O3/TiO2 and its use in the poly(lactic acid) (PLA) films as an oxygen scavenger in extra virgin olive oil (EVOO) packaging. Bimetallic nanocomposites were prepared by two different precipitation methods (precipitation with ammonia and sodium hydroxide). The characteristics of bimetallic nanoparticles precipitated with sodium hydroxide (Na-Ti0.01Fe0.048O0.08) and bimetallic nanoparticles precipitated with ammonia (NH-Ti0.01Fe0.022O0.09) were compared. Relative amounts of elements in bimetallic nanocomposites and their morphological characteristics were determined using field emission scanning electron microscopy coupled with energy-dispersive X-ray spectrometer. Porosity volume and surface area of bimetallic nanoparticles were calculated using adsorption-desorption isotherms and the Brunauer-Emmett-Teller method. The formation/characterization of bimetallic nanoparticles and their location in the matrix of PLA-based nanocomposite film was studied using X-ray diffraction and Fourier transform infrared. In nanocomposite films based on PLA, bimetallic nanoparticles lead to better oxidative stability (peroxide value, p-anisidine index, K232, and K270) of the EVOO and oxygen scavenging during storage compared to free nanoparticles. Mechanical properties of nanocomposite films were improved due to bimetallic nanoparticles, which were better for Na-Ti0.01Fe0.048O0.08. In vitro release modeling of the bimetallic nanoparticles in EVOO proved that Fickian diffusion is the dominant mechanism, and the Peleg model was the best description of the release behavior of nanoparticles.

Assessment of extra virgin olive oil quality by miniaturized near infrared instruments in a rapid and non-destructive procedure

Food fraud in olive oil is a major concern for consumers and authorities due to the health risks and economic impacts. Common frauds include blending with other cheaper non-olive oils, or misleading labelling. The main issue is that legislation and methods presently used in routine laboratories are not always up to date with current fraudulent practices, making detection difficult, so new analytical methods development is required. This study focuses on developing an affordable and non-destructive analysis method based on NIR spectroscopy and chemometrics for EVOO quality assessment, specifically by monitoring 7 parameters of interest in EVOO measured by official methods and used to develop calibrations through NIR data. For this, two NIR low-cost portable instruments were employed, studied in-depth and compared with a NIR benchtop instrument. Calibration results enabled detection of atypical olive oils and excellent accuracy, especially for palmitic and oleic acid predictions, demonstrating the potential of the instruments.

Authentication of pure and adulterated edible oils using non-destructive ultrasound

At present, the quality of edible oil is evaluated using traditional analysis techniques that are generally destructive. Therefore, efforts are being made to find alternative methods with non-destructive techniques such as Ultrasound. This work aims to confirm the feasibility of non-destructive ultrasonic inspection to characterise and detect fraudulent practices in olive oil due to adulteration with two other edible vegetable oils (sunflower and corn). For this purpose, pulsed ultrasonic signals with a frequency of 2.25 MHz have been used. The samples of pure olive oil were adulterated with the other two in variable percentages between 20% and 80%. Moreover, the viscosity and density values were measured. Both these physicochemical and acoustic parameters were obtained at 24 °C and 30 °C and linearly correlated with each other. The results indicate the sensitivity of the method at all levels of adulteration studied. The responses obtained through the parameters related to the components of velocity, attenuation, and frequency of the ultrasonic waves are complementary to each other. This allows concluding that the classification of pure and adulterated oil samples is possible through non-destructive ultrasonic inspection.

Quality Analysis of Canola and Mustard Oil Using Fluorescence Spectroscopy

The potential of Fluorescence spectroscopy has been utilized for the quality analysis of canola and mustard oil along with the effect of heating on their molecular composition has been investigated. Laser diode at 405 nm has been employed directly to oil surface to excite both oil type samples and their emission spectra has been recorded by an in-house developed Fluorosensor. The emission spectra of both oil types unveiled that they contain carotenoids, isomers of vitamin E and chlorophylls that exhibit their fluorescence at 525 and 675/720 nm, and these can be used as markers for their quality assurance. Fluorescence spectroscopy is a fast, reliable and non-destructive analytical technique for the quality assessment of both oil types. Moreover, the effect of temperature on their molecular composition has been investigated by heating them at 110, 120, 130, 140, 150, 170, 180 and 200 °C, each sample for 30 min which was done because both oils are used for cooking and frying. On heating, the deterioration of carotenoids and isomers of vitamin E in both oil types occurred with an increase in the oxidised products. However, it was found that up to 150 °C, both oil types can be used safely for cooking/frying purpose where they do not lose much of their valuable ingredients and up to 180 °C for deep frying, both oils can be used with less deterioration and after that both deteriorated much due to rapid increase of the oxidized products. The portable Fluorosensor, therefore, proved as an excellent device for quality screening of edible oils based on carotenoids and vitamin E.

Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy

Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry.

Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa

Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography-mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II-Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4-100% accuracy prediction), the handheld device also performed well (83.1-100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.

Recent Advances in Analytical Methods for the Detection of Olive Oil Oxidation Status during Storage along with Chemometrics, Authenticity and Fraud Studies

Olive oil is considered to be a food of utmost importance, especially in the Mediterranean countries. The quality of olive oil must remain stable regarding authenticity and storage. This review paper emphasizes the detection of olive oil oxidation status or rancidity, the analytical techniques that are usually used, as well as the application and significance of chemometrics in the research of olive oil. The first part presents the effect of the oxidation of olive oil during storage. Then, lipid stability measurements are described in parallel with instrumentation and different analytical techniques that are used for this particular purpose. The next part presents some research publications that combine chemometrics and the study of lipid changes due to storage published in 2005-2021. Parameters such as exposure to light, air and various temperatures as well as different packaging materials were investigated to test olive oil stability during storage. The benefits of each chemometric method are provided as well as the overall significance of combining analytical techniques and chemometrics. Furthermore, the last part reflects on fraud in olive oil, and the most popular analytical techniques in the authenticity field are stated to highlight the importance of the authenticity of olive oil.

Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods

Over the past four decades, near-infrared reflectance spectroscopy (NIRS) has become one of the most attractive and used technique for analysis as it allows for fast and simultaneous qualitative and quantitative characterization of a wide variety of food samples [...].

Use of Natural Microtalcs during the Virgin Olive Oil Production Process to Increase Its Content in Antioxidant Compounds

During the olive oil production process, certain olive varieties, such as ‘Hojiblanca’ and ‘Picual’, create pastes from which it is difficult to separate the oil, resulting in low extraction yields. To improve oil extraction, one alternative is the addition of natural microtalcs (NMT). In the present study, a NMT of great purity (CaCO3 concentration less than 6 wt.%) and small average particle size (ϕ ≤ 2.1 µm) was added in the malaxation stage on an industrial scale at two olive mills. In one of them and using ‘Hojiblanca’ olives, the performance of the high-purity NMT was compared with that of a traditional NMT, while in the other, the effect of its dosage in the quality of ‘Picual’ oils was assessed. The performance of the high-purity NMT was evaluated in terms of industrial oil yield, extractability index, quality parameters and oxidative stability of the resulting oils. The addition of the high-purity NMT not only increased the extraction yields but also improved the quality of the virgin olive oils, especially in relation to antioxidant compounds (tocopherols and phenolic compounds). Increases of 10.4% in phenolic compounds and of 21.5% in the tocopherols were found, thus enhancing the oxidative stability of the oils.