Douglas N. Rutledge

Study of the heat stability of sunflower oil enriched in natural antioxidants by different analytical techniques and front-face fluorescence spectroscopy combined with Independent Components Analysis

The aim of this study was to find objective analytical methods to study the degradation of edible oils during heating and thus to suggest solutions to improve their stability. The efficiency of Nigella seed extract as natural antioxidant was compared with butylated hydroxytoluene (BHT) during accelerated oxidation of edible vegetable oils at 120 and 140 °C. The modifications during heating were monitored by 3D-front-face fluorescence spectroscopy along with Independent Components Analysis (ICA), (1)H NMR spectroscopy and classical physico-chemical methods such as anisidine value and viscosity. The results of the study clearly indicate that the natural seed extract at a level of 800 ppm exhibited antioxidant effects similar to those of the synthetic antioxidant BHT at a level of 200 ppm and thus contributes to an increase in the oxidative stability of the oil.

Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA).

Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of the proposed approach was also tested using two spectrometers with considerable differences in their sensitivities. Discrimination rates were not affected proving that the developed approach could be extrapolated to different spectrometers. MIR combined with ICA is a promising tool for plastic waste separation that can help improve performance in this field; however further technological improvements and developments are required before it can be applied at an industrial level given that all tests presented here were performed under laboratory conditions.

Independent components analysis coupled with 3D-front-face fluorescence spectroscopy to study the interaction between plastic food packaging and olive oil.

Olive oil is one of the most valued sources of fats in the Mediterranean diet. Its storage was generally done using glass or metallic packaging materials. Nowadays, plastic packaging has gained worldwide spread for the storage of olive oil. However, plastics are not inert and interaction phenomena may occur between packaging materials and olive oil. In this study, extra virgin olive oil samples were submitted to accelerated interaction conditions, in contact with polypropylene (PP) and polylactide (PLA) plastic packaging materials. 3D-front-face fluorescence spectroscopy, being a simple, fast and non destructive analytical technique, was used to study this interaction. Independent components analysis (ICA) was used to analyze raw 3D-front-face fluorescence spectra of olive oil. ICA was able to highlight a probable effect of a migration of substances with antioxidant activity. The signals extracted by ICA corresponded to natural olive oil fluorophores (tocopherols and polyphenols) as well as newly formed ones which were tentatively identified as fluorescent oxidation products. Based on the extracted fluorescent signals, olive oil in contact with plastics had slower aging rates in comparison with reference oils. Peroxide and free acidity values validated the results obtained by ICA, related to olive oil oxidation rates. Sorbed olive oil in plastic was also quantified given that this sorption could induce a swelling of the polymer thus promoting migration.

Attenuated total reflectance-mid infrared spectroscopy (ATR-MIR) coupled with independent components analysis (ICA): A fast method to determine plasticizers in polylactide (PLA)

Compliance of plastic food contact materials (FCMs) with regulatory specifications in force, requires a better knowledge of their interaction phenomena with food or food simulants in contact. However these migration tests could be very complex, expensive and time-consuming. Therefore, alternative procedures were introduced based on the determination of potential migrants in the initial material, allowing the use of mathematical modeling, worst case scenarios and other alternative approaches, for simple and fast compliance testing. In this work, polylactide (PLA), plasticized with four different plasticizers, was considered as a model plastic formulation. An innovative analytical approach was developed, based on the extraction of qualitative and quantitative information from attenuated total reflectance (ATR) mid-infrared (MIR) spectral fingerprints, using independent components analysis (ICA). Two novel chemometric methods, Random_ICA and ICA_corr_y, were used to determine the optimal number of independent components (ICs). Both qualitative and quantitative information, related to the identity and the quantity of plasticizers in PLA, were retrieved through a direct and fast analytical method, without any prior sample preparations. Through a single qualitative model with 11 ICs, a clear and clean classification of PLA samples was obtained, according to the identity of plasticizers incorporated in their formulations. Moreover, a quantitative model was established for each formulation, correlating proportions estimated by ICA and known concentrations of plasticizers in PLA. High coefficients of determination (higher than 0.96) and recoveries (higher than 95%) proved the good predictability of the proposed models.

Using pH variations to improve the discrimination of wines by 3D front face fluorescence spectroscopy associated to Independent Components Analysis

Wine composition in polyphenols is related to the variety of grape that it contains. These polyphenols play an essential role in its quality as well as a possible protective effect on human health. Their conjugated aromatic structure renders them fluorescent, which means that 3D front-face fluorescence spectroscopy could be a useful tool to differentiate among the grape varieties that characterize each wine. However, fluorescence spectra acquired simply at the natural pH of wine are not always sufficient to discriminate the wines. The structural changes in the polyphenols resulting from modifications in the pH induce significant changes in their fluorescence spectra, making it possible to more clearly separate different wines. 9 wines belonging to three different grape varieties (Shiraz, Cabernet Sauvignon and Pinot Noir) and from 9 different producers, were analyzed over a range of pHs. Independent Components Analysis (ICA) was used to extract characteristic signals from the matrix of unfolded 3D front-face fluorescence spectra and showed that the introduction of pH as an additional parameter in the study of wine fluorescence improved the discrimination of wines.

Characterization of surfactant complex mixtures using Raman spectroscopy and signal extraction methods: Application to laundry detergent deformulation.

This paper presents the analysis of surfactants in complex mixtures using Raman spectroscopy combined with signal extraction (SE) methods. Surfactants are the most important component in laundry detergents. Both their identification and quantification are required for quality control and regulation purposes. Several synthetic mixtures of four surfactants contained in an Ecolabel laundry detergent were prepared and analyzed by Raman spectroscopy. SE methods, Independent Component Analysis and Multivariate Curve Resolution, were then applied to spectral data for surfactant identification and quantification. The influence of several pre-processing treatments (normalization, baseline correction, scatter correction and smoothing) on SE performances were evaluated by experimental design. By using optimal pre-processing strategy, SE methods allowed satisfactorily both identifying and quantifying the four surfactants. When applied to the pre-processed Raman spectrum of the Ecolabel laundry detergent sample, SE models remained robust enough to predict the surfactant concentrations with sufficient precision for deformulation purpose. Comparatively, a supervised modeling technique (PLS regression) was very efficient to quantify the four surfactants in synthetic mixtures but appeared less effective than SE methods when applied to the Raman spectrum of the detergent sample. PLS seemed too sensitive to the other components contained in the laundry detergent while SE methods were more robust. The results obtained demonstrated the interest of SE methods in the context of deformulation.

Chemometric Tools to Highlight the Variability of the Chemical Composition and Yield of Lebanese Origanum syriacum L. Essential Oil.

This study deals with the variation in the yield and composition of Lebanese Origanum syriacum L. essential oil (EO) according to harvesting time, drying methods used, and geographical location. Plant material was harvested twice a month all over 2013 and 2014 from Qartaba and Achkout located at high altitude and from Byblos at low altitude. EOs of the aerial parts were obtained by hydrodistillation. The highest yields were obtained at full flowering stage and slightly reduced after flowering. The GC/MS analysis revealed the presence of 50 components representing 90.49 – 99.82%, 88.79 – 100%, and 95.28 – 100% of the total oil extracted from plants harvested from Qartaba, Achkout, and Byblos, respectively. The major components in the oils were: carvacrol (2.1 – 79.8%), thymol (0.3 – 83.7%), p‐cymene (2.8 – 43.8%), thymoquinone (0.4 – 27.7%), γ‐terpinene (0.4 – 10.0%), octan‐3‐ol (0.3 – 4.9%), caryophyllene oxide (0.2 – 4.7%), oct‐1‐en‐3‐ol (0.3 – 3.7%), β‐caryophyllene (0.7 – 3.2%), cis‐sabinene hydrate (0.1 – 2.8%), terpinen‐4‐ol (0.1 – 2.8%), and α‐terpinene (0.2 – 2.2%). Independent components analysis (ICA) revealed that two groups were discriminated, reflecting compositional differences in the EOs profiles of the Lebanese oregano samples: O. syriacum grown in Qartaba and Achkout belongs to carvacrol chemotype, while O. syriacum grown in Byblos belongs to thymol chemotype. The flowering phase was the most productive period in terms of yield, bringing marked changes in the EO composition by increasing the amounts of carvacrol or thymol, and decreasing those of thymoquinone and p‐cymene.

Iterative weighting of multiblock data in the orthogonal partial least squares framework

The integration of multiple data sources has emerged as a pivotal aspect to assess complex systems comprehensively. This new paradigm requires the ability to separate common and redundant from specific and complementary information during the joint analysis of several data blocks. However, inherent problems encountered when analysing single tables are amplified with the generation of multiblock datasets. Finding the relationships between data layers of increasing complexity constitutes therefore a challenging task. In the present work, an algorithm is proposed for the supervised analysis of multiblock data structures. It associates the advantages of interpretability from the orthogonal partial least squares (OPLS) framework and the ability of common component and specific weights analysis (CCSWA) to weight each data table individually in order to grasp its specificities and handle efficiently the different sources of Y-orthogonal variation. Three applications are proposed for illustration purposes. A first example refers to a quantitative structure-activity relationship study aiming to predict the binding affinity of flavonoids toward the P-glycoprotein based on physicochemical properties. A second application concerns the integration of several groups of sensory attributes for overall quality assessment of a series of red wines. A third case study highlights the ability of the method to combine very large heterogeneous data blocks from Omics experiments in systems biology. Results were compared to the reference multiblock partial least squares (MBPLS) method to assess the performance of the proposed algorithm in terms of predictive ability and model interpretability. In all cases, ComDim-OPLS was demonstrated as a relevant data mining strategy for the simultaneous analysis of multiblock structures by accounting for specific variation sources in each dataset and providing a balance between predictive and descriptive purpose.

Corrigendum to “Non-invasive spectrophotometric sensing of carrot quality from harvest to consumption” [Postharvest Biol. Technol. 45 (2007) 30–37]

Fig. 4. Scatter plots of carrot -carotene contents analyzed by means of destructive chromatographic analysis and non-invasive spectrometry in remission mode applying a PLS model on SNV preprocessed data (A), y = a + bx (a = 782.54465, b = 0.77334513, Rcal = 0.87); and DOSC pre-processed data (B), y = a + bx (a = 130.82529, b = 0.96472452, Rcal = 0.97). The 95% prediction (outer lines) and confidence (inner lines) intervals of linear regression are shown.