First Application of Newly Developed FT-NIR Spectroscopic Methodology to Predict Authenticity of Extra Virgin Olive Oil Retail Products in the USA

Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice

Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.

Comparing the potential of IR-spectroscopic techniques to gas chromatography coupled to ion mobility spectrometry for classifying virgin olive oil categories

Virgin olive oil (OO) can be classified into three different categories: extra virgin, virgin and lampante. The official method for this classification, based on physicochemical analysis and sensory tasting, is considered useful and effective, although it is a costly and time-consuming process. The aim of this study was to assess the potential of some analytical techniques for classifying and predicting different OO categories to support official methods and to provide olive oil companies with a rapid tool to assess product quality. Thus, mid and near infrared spectroscopies (MIR and NIR) have been compared by using different instruments and with head-space gas chromatography coupled to an ion mobility spectrometer (HS-GC-IMS). High classification success rates in validation models were obtained using IR spectrometers (>70% and > 80% in average for ternary and binary classifications, respectively), although HS-GC-IMS showed greater classification potential (>85% and > 90%).

Magnetic thermo-responsive branched polymer for fast extraction and enrichment of phenolic acids in olive oil with tunable and enhanced performance

Magnetic thermo-responsive branched polymer (Fe₃O₄@poly(glycidyl methacrylate)@poly(N-isopropylacrylamide)) was fabricated for the first time and applied for microwave-assisted magnetic solid phase extraction of phenolic acids in olive oil samples followed by ultra-high performance liquid chromatography-tandem mass spectrometry analysis in multiple reaction monitoring mode. Owing to the controllable molecular weight of poly(glycidyl methacrylate) synthesized by atom transfer radical polymerization and the thermo-responsive characteristic of poly(N-isopropylacrylamide), extraction performance could be efficiently tuned and enhanced. The whole sample pretreatment process was accomplished within 1 min with the help of the microwave. The nanocomposites were characterized by transmission electron microscope, scanning electron microscope, Fourier transform infrared spectroscopy, thermogravimetric analysis, vibrating sample magnetometer, water contact angles and dynamic light scattering. The adsorption experimental data fitted well with the Freundlich isotherm model and followed the pseudo-second-order kinetic model. The factors affecting the extraction process including adsorbent amount, adsorption time, sample volume, desorption conditions and interferents were investigated and optimized. Under the most favorable conditions, the developed method showed good linearity (R² ≥ 97.98%) in the range of 0.2-30 μg L^-1, low limits of detection (0.005-0.030 μg L^-1) and limits of quantification (0.016-0.098 μg L^-1) as well as satisfactory precision (RSDs≤4.85%). Our proposed method was successfully used for determination of phenolic acids in olive oil samples and satisfactory recoveries at three spiked concentration levels were in the range of 84.6-108.1% with RSDs less than 9.20%. Coupled with principal component analysis, our developed method proved promising for fast and convenient differentiation between extra virgin olive oils and refined olive oils.

Instrumental Techniques to Classify Olive Oils according to Their Quality

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Authentication of the Botanical and Geographical Origin and Detection of Adulteration of Olive Oil Using Gas Chromatography, Infrared and Raman Spectroscopy Techniques: A Review

Olive oil is among the most popular supplements of the Mediterranean diet due to its high nutritional value. However, at the same time, because of economical purposes, it is also one of the products most subjected to adulteration. As a result, authenticity is an important issue of concern among authorities. Many analytical techniques, able to detect adulteration of olive oil, to identify its geographical and botanical origin and consequently guarantee its quality and authenticity, have been developed. This review paper discusses the use of infrared and Raman spectroscopy as candidate tools to examine the authenticity of olive oils. It also considers the volatile fraction as a marker to distinguish between different varieties and adulterated olive oils, using SPME combined with gas chromatography technique.

Building robust models for identification of adulteration in olive oil using FT-NIR, PLS-DA and variable selection

Being a product with a high market value, olive oil undergoes adulterations. Therefore, studies that make the verification of the authenticity of olive oil more efficient are necessary. The aim of this study was to develop a robust model using FT-NIR and PLS-DA to discriminate extra virgin olive oil samples and build individual models to differentiate adulterated extra virgin olive oil samples. The best PLS-DA-OPS classification model for olive oils showed specificity (Spe) and accuracy (Acc) values higher than 99.7% and 99.9%. For the classification of adulterants, PLS-DA-OPS models presented values of Spe at 96.0% and Acc above 95.5% for varieties. For the blend, the best PLS-DA-GA model presented Acc and Spe values greater than 98.2% and 98.8%. Reliable and robust models have been built, allowing differentiation from seven adulterants to genuine extra virgin olive oils.

An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer

Extra virgin olive oil (EVOO) represents a crucial ingredient of the Mediterranean diet. Being a first-choice product, consumers should be guaranteed its quality and geographical origin, justifying the high purchasing cost. For this reason, it is important to have new reliable tools able to classify products according to their geographical origin. The aim of this work was to demonstrate the efficiency of an open source visible and near infra-red (VIS-NIR) spectrophotometer, relying on a specific app, in assessing olive oil geographical origin. Thus, 67 Italian and 25 foreign EVOO samples were analyzed and their spectral data were processed through an artificial intelligence algorithm. The multivariate analysis of variance (MANOVA) results reported significant differences (p < 0.001) between the Italian and foreign EVOO VIS-NIR matrices. The artificial neural network (ANN) model with an external test showed a correct classification percentage equal to 94.6%. Both the MANOVA and ANN tested methods showed the most important spectral wavelengths ranges for origin determination to be 308–373 nm and 594–605 nm. These are related to the absorption of phenolic components, carotenoids, chlorophylls, and anthocyanins. The proposed tool allows the assessment of EVOO samples’ origin and thus could help to preserve the “Made in Italy” from fraud and sophistication related to its commerce.

Non-Targeted Authentication Approach for Extra Virgin Olive Oil

The aim of this study is to develop a non-targeted approach for the authentication of extra virgin olive oil (EVOO) using vibrational spectroscopy signatures combined with pattern recognition analysis. Olive oil samples (n = 151) were grouped as EVOO, virgin olive oil (VOO)/olive oil (OO), and EVOO adulterated with vegetable oils. Spectral data was collected using a compact benchtop Raman (1064 nm) and a portable ATR-IR (5-reflections) units. Oils were characterized by their fatty acid profile, free fatty acids (FFA), peroxide value (PV), pyropheophytins (PPP), and total polar compounds (TPC) through the official methods. The soft independent model of class analogy analysis using ATR-IR spectra showed excellent sensitivity (100%) and specificity (89%) for detection of EVOO. Both techniques identified EVOO adulteration with vegetable oils, but Raman showed limited resolution detecting VOO/OO tampering. Partial least squares regression models showed excellent correlation (Rval ≥ 0.92) with reference tests and standard errors of prediction that would allow for quality control applications.

A Rapid, Univariate FT-NIR Procedure to Determine Moisture Concentration in Olive Oil

We recently observed that the weak near-infrared (NIR) band near 5260 cm^-1 was relatively more intense for extra virgin olive oil (EVOO) than for refined olive oil (ROO). We also observed that its intensity was diminished upon heating and erroneously presumed that it may be attributed to volatile carbonyl components in EVOO. In the present study we demonstrate for the first time that this band is primarily attributed to a water O-H combination band. To accurately determine the intensity of this weak band, observed on a shifted and sloping baseline, we measured the peak-to-peak (p-p) height of its first derivative. An exponential calibration curve for p-p height versus gravimetrically-determined concentration of spiked water was satisfactorily generated. The calibration curve was first evaluated by using independent sets of gravimetrically prepared test samples. Subsequently, it was used to determine the moisture content, a quality parameter, for a limited set of authenticated reference olive oils whose quality and purity were confirmed by official methods. These concentrations, 0.098-0.12% H₂O (w/w) for EVOO, 0.022-0.030% H₂O (w/w) for ROO, and 0.028-0.054% H₂O (w/w) for pomace olive oil (POO), were consistent with those reported in the literature. For 88 commercial products investigated, the moisture levels fell in the range from 0.026% to 0.13% (w/w). The correlation between moisture content and other olive oil quality parameters has been reported in the literature and has yet to be further investigated.

FT-NIR: a tool for rapid intracellular lipid quantification in oleaginous yeasts

BACKGROUND

Lipid extraction for quantification of fat content in oleaginous yeasts often requires strong acids and harmful organic solvents; it is laborious and time-consuming. Therefore, in most cases just endpoint measurements of lipid accumulation are performed and kinetics of intracellular lipid accumulation is difficult to follow. To address this, we created a prediction model using Fourier-transform near-infrared (FT-NIR) spectroscopy. This method allows to measure lipid content in yeast.

METHODS

The FT-NIR calibration sets were constructed from spectra of freeze-dried cells of the oleaginous yeasts Rhodotorula toruloides CBS 14, Lipomyces starkeyi CBS 1807 and Yarrowia lipolytica CBS 6114. The yeast cells were obtained from different cultivation conditions. Freeze-dried cell pellets were scanned using FT-NIR in the Multi Purpose Analyser (MPA) from Bruker. The obtained spectra were assigned corresponding to total fat content, obtained from lipid extraction using a modified Folch method. Quantification models using partial least squares (PLS) regression were built, and the calibration sets were validated on independently cultivated samples. The R. toruloides model was additionally tested on Rhodotorula babjevae DBVPG 8058 and Rhodotorula glutinis CBS 2387.

RESULTS

The R ² of the FT-NIR model for R. toruloides was 98%, and the root mean square error of cross-validation (RMSECV) was 1.53. The model was validated using a separate set of R. toruloides samples with a root mean square error of prediction (RMSEP) of 3.21. The R ² of the Lipomyces model was 96%, with RMSECV 2.4 and RMSEP 3.8. The R ² of the mixed model, including all tested yeast strains, was 90.5%, with RMSECV 2.76 and RMSEP 3.22, respectively. The models were verified by predicting the total fat content in newly cultivated and freeze-dried samples. Additionally, the kinetics of lipid accumulation of a culture were followed and compared with standard lipid extraction methods.

CONCLUSIONS

Using FT-NIR spectroscopy, we have developed a faster, less laborious and non-destructive quantification of yeast intracellular lipid content compared to methods using lipid extraction.

Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSS-PLS Algorithm

This work applied the FT-NIR spectroscopy technique with the aid of chemometrics algorithms to determine the adulteration content of extra virgin olive oil (EVOO). Informative spectral wavenumbers were obtained by the use of a novel variable selection algorithm of bootstrapping soft shrinkage (BOSS) during partial least-squares (PLS) modeling. Then, a PLS model was finally constructed using the best variable subset obtained by the BOSS algorithm to quantitative determine doping concentrations in EVOO. The results showed that the optimal variable subset including 15 wavenumbers was selected by the BOSS algorithm in the full-spectrum region according to the first local lowest value of the root-mean-square error of cross validation (RMSECV), which was 1.4487 % v/v. Compared with the optimal models of full-spectrum PLS, competitive adaptive reweighted sampling PLS (CARS–PLS), Monte Carlo uninformative variable elimination PLS (MCUVE–PLS), and iteratively retaining informative variables PLS (IRIV–PLS), the BOSS–PLS model achieved better results, with the coefficient of determination (R²) of prediction being 0.9922, and the root-mean-square error of prediction (RMSEP) being 1.4889 % v/v in the prediction process. The results obtained indicated that the FT-NIR spectroscopy technique has the potential to perform a rapid quantitative analysis of the adulteration content of EVOO, and the BOSS algorithm showed its superiority in informative wavenumbers selection.

FT-NIR the method of choice to verify the authenticity of extra virgin olive oils

Fourier transform near infrared spectroscopy was recently demonstrated to be an excellent method to evaluate the authenticity and adulteration of extra virgin olive oil. Since this method is matrix dependent, it takes a chemical fingerprint of all the components which sets it apart from the targeted methods. Careful examinations of the Fourier transform near infrared spectra lead to the identification of a minor carbonyl overtone absorption at 5269 cm−1 associated with the volatile fraction in extra virgin olive oil that appears to be a reliable indicator of authenticity. The same spectra were used to identify the fatty acids present in the oil using models based on comparison to accurate GC data. Gravimetric mixtures of extra virgin olive oil with refined edible oils were then prepared to develop PLS1 calibration models to identify possible adulterants and by how much. The great varietal difference in olive oils made it necessary to develop four unique sets of PLS1 calibration models for each extra virgin olive oil variety. As a result, an extra virgin olive oil acceptance specification was established.