Potentialities of Rapid Analytical Strategies for the Identification of the Botanical Species of Several "Specialty" or "Gourmet" Oils

Exploring Strategies to Mitigate the Lightness Effect on the Prediction of Soybean Oil Content in Blends of Olive and Avocado Oil Using Smartphone Digital Image Colorimetry

Extra virgin olive oil (EVOO) and avocado oil (AVO) are recognized for their unique sensory characteristics and bioactive compounds. Declared blends with other vegetable oils are legal, but undeclared mixing is a common type of fraud that can affect product quality and commercialization. In this sense, this study explored strategies to mitigate the influence of lighting in order to make digital image colorimetry (DIC) using a smartphone more robust and reliable for predicting the soybean oil content in EVOO and AVO blends. Calibration models were obtained by multiple linear regression using the images' RGB values. Corrections based on illuminance and white reference were evaluated to mitigate the lightness effect and improve the method's robustness and generalization capability. Lastly, the prediction of the built model from data obtained using a distinct smartphone was assessed. The results showed models with good predictive capacities, R2 > 0.9. Generally, models solely based on GB values showed better predictive performances. The illuminance corrections and blank subtraction improved the predictions of EVOO and AVO samples, respectively, for image acquisition from distinct smartphones and lighting conditions as evaluated by external validation. It was concluded that adequate data preprocessing enables DIC using a smartphone to be a reliable method for analyzing oil blends, minimizing the effects of variability in lighting and imaging conditions and making it a potential technique for oil quality assurance.

NMR-Based Characterization of Citrus Tacle Juice and Low-Level NMR and UV-Vis Data Fusion for Monitoring Its Fractions from Membrane-Based Operations

Tacle is a citrus variety which recently gained further interest due to its antioxidant and biological properties. This study suggests using Nuclear Magnetic Resonance (NMR) imaging to characterize Tacle juice's metabolic composition as it is intimately linked to its quality. First, polar and apolar solvent systems were used to identify a significant fraction of the Tacle metabolome. Furthermore, the antioxidant capacity and the total content of flavonoids, polyphenols and β-carotene in the juice were investigated with UV-Visible spectroscopy. Tacle juice was clarified and fractionated by ultrafiltration (UF) and nanofiltration (NF) membranes in order to recover and purify its bioactive principles. Finally, the second part of this work sheds light on the spectrophotometric assays and 1H-NMR spectra of fractions coming from membrane operations coupled with a multivariate data analysis technique, PCA, to explore the impact of UF and NF processes on the metabolic profile of the juice.

A Preliminary Color Study of Different Basil-Based Semi-Finished Products during Their Storage

Basil-based semi-finished products, which are mainly used as an intermediate to produce the typical pesto sauce, are prepared and exported all over the world. Color is a fundamental organoleptic requirement for the acceptability of these semi-finished products by the manufacturers of the pesto sauce. Some alternative formulations, which adjust the typical industrial recipe by both changing the preservative agent (ascorbic acid, citric acid, or a mixture of both) and introducing a preliminary thermic treatment (blast chilling), were evaluated. In this work, a fast and non-destructive spectrophotometric analysis, to monitor the color variations in these food products during their shelf-life, was proposed. The raw diffuse reflectance spectra (380-900 nm) obtained by a UV-visible spectrophotometer, endowed with an integrating sphere, together with the CIELab parameters (L*, a*, b*) automatically obtained from these, were considered, and elaborated using multivariate statistical analysis (principal component analysis). From this preliminary study, blast chilling, together with the use of ascorbic acid, proved to be the best solution to better preserve the color of these products during their shelf-life.

Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers

Since consuming gutter oil does great harm to people's health, the Food Safety Administration has always been seeking for a more effective and timely supervision. As laboratory tests consume much time, and existing field tests have excessive limitations, a more comprehensive method is in great need. This is the first time a study proposes machine learning algorithms for real-time gutter oil detection under multiple feature dimensions. Moreover, it is deployed on FPGA to be low-power and portable for actual use. Firstly, a variety of oil samples are generated by simulating the real detection environment. Next, based on previous studies, sensors are used to collect significant features that help distinguish gutter oil. Then, the acquired features are filtered and compared using a variety of classifiers. The best classification result is obtained by k-NN with an accuracy of 97.18%, and the algorithm is deployed to FPGA with no significant loss of accuracy. Power consumption is further reduced with the approximate multiplier we designed. Finally, the experimental results show that compared with all other platforms, the whole FPGA-based classification process consumes 4.77 µs and the power consumption is 65.62 mW. The dataset, source code and the 3D modeling file are all open-sourced.

Application of UHPLC Fingerprints Combined with Chemical Pattern Recognition Analysis in the Differentiation of Six Rhodiola Species

Rhodiola, especially Rhodiola crenulate and Rhodiola rosea, is an increasingly widely used traditional medicine or dietary supplement in Asian and western countries. Because of the phytochemical diversity and difference of therapeutic efficacy among Rhodiola species, it is crucial to accurately identify them. In this study, a simple and efficient method of the classification of Rhodiola crenulate, Rhodiola rosea, and their confusable species (Rhodiola serrata, Rhodiola yunnanensis, Rhodiola kirilowii and Rhodiola fastigiate) was established by UHPLC fingerprints combined with chemical pattern recognition analysis. The results showed that similarity analysis and principal component analysis (PCA) could not achieve accurate classification among the six Rhodiola species. Linear discriminant analysis (LDA) combined with stepwise feature selection exhibited effective discrimination. Seven characteristic peaks that are responsible for accurate classification were selected, and their distinguishing ability was successfully verified by partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA), respectively. Finally, the components of these seven characteristic peaks were identified as 1-(2-Hydroxy-2-methylbutanoate) β-D-glucopyranose, 4-O-glucosyl-p-coumaric acid, salidroside, epigallocatechin, 1,2,3,4,6-pentagalloyglucose, epigallocatechin gallate, and (+)-isolarisiresinol-4′-O-β-D-glucopyranoside or (+)-isolarisiresinol-4-O-β-D-glucopyranoside, respectively. The results obtained in our study provided useful information for authenticity identification and classification of Rhodiola species.

Extraction Methods of Oils and Phytochemicals from Seeds and Their Environmental and Economic Impacts

Over recent years, the food industry has striven to reduce waste, mostly because of rising awareness of the detrimental environmental impacts of food waste. While the edible oils market (mostly represented by soybean oil) is forecasted to reach 632 million tons by 2022, there is increasing interest to produce non-soybean, plant-based oils including, but not limited to, coconut, flaxseed and hemp seed. Expeller pressing and organic solvent extractions are common methods for oil extraction in the food industry. However, these two methods come with some concerns, such as lower yields for expeller pressing and environmental concerns for organic solvents. Meanwhile, supercritical CO2 and enzyme-assisted extractions are recognized as green alternatives, but their practicality and economic feasibility are questioned. Finding the right balance between oil extraction and phytochemical yields and environmental and economic impacts is challenging. This review explores the advantages and disadvantages of various extraction methods from an economic, environmental and practical standpoint. The novelty of this work is how it emphasizes the valorization of seed by-products, as well as the discussion on life cycle, environmental and techno-economic analyses of oil extraction methods.