Multi-block data analysis using ComDim for the evaluation of complex samples: Characterization of edible oils

Rosa, Larissa Naida; Figueiredo, Luana Caroline de; Bonafé, Elton Guntendorfer; Coqueiro, Aline; Visentainer, Jesuí Vergílio; Março, Paulo Henrique; Rutledge, Douglas N.; Valderrama, Patrícia

doi:10.1016/j.aca.2017.01.019

Cited by 24 publications

(10 citation statements)

References 21 publications

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“…There was a peak at approximately 1122.81 nm and valleys at approximately 1200.19 and 1483.46 nm. Two absorption bands at approximately 1122 and 1200 nm contributed to the second overtone of C-H stretching vibrations of carbohydrates 21 , 23 , and the band at 1483 nm was related to the N-H stretch first overtone from CONH 2 24 . The graphic also shows that the curves of the spectra from the different genotypes show consistent trends, and they have similar peaks and valleys positions.…”

Section: Resultsmentioning

confidence: 99%

Discrimination of CRISPR/Cas9-induced mutants of rice seeds using near-infrared hyperspectral imaging

Feng

Peng

Chen

et al. 2017

Sci Rep

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Identifying individuals with target mutant phenotypes is a significant procedure in mutant exploitation for implementing genome editing technology in a crop breeding programme. In the present study, a rapid and non-invasive method was proposed to identify CRISPR/Cas9-induced rice mutants from their acceptor lines (huaidao-1 and nanjing46) using hyperspectral imaging in the near-infrared (NIR) range (874.41–1733.91 nm) combined with chemometric analysis. The hyperspectral imaging data were analysed using principal component analysis (PCA) for exploratory purposes, and a support vector machine (SVM) and an extreme learning machine (ELM) were applied to build discrimination models for classification. Meanwhile, PCA loadings and a successive projections algorithm (SPA) were used for extracting optimal spectral wavelengths. The SVM-SPA model achieved best performance, with classification accuracies of 93% and 92.75% being observed for calibration and prediction sets for huaidao-1 and 91.25% and 89.50% for nanjing46, respectively. Furthermore, the classification of mutant seeds was visualized on prediction maps by predicting the features of each pixel on individual hyperspectral images based on the SPA-SVM model. The above results indicated that NIR hyperspectral imaging together with chemometric data analysis could be a reliable tool for identifying CRISPR/Cas9-induced rice mutants, which would help to accelerate selection and crop breeding processes.

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Section: Resultsmentioning

confidence: 99%

Discrimination of CRISPR/Cas9-induced mutants of rice seeds using near-infrared hyperspectral imaging

Feng

Peng

Chen

et al. 2017

Sci Rep

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“…To the second PARAFAC factor, the information provided regards the antioxidant properties of plant-based substances, TBHQ, BHT, and control samples. According to Rosa et al [ 46 ], the NIR absorptions below 1000 nm, from 1200 to 1350 nm, and above 1400 nm is attributed to the samples of edible oils that demonstrated the lower amount of tocopherol and phenolic compounds. Based on this, the scores on factor 2 ( Figure 3 E) reveal an equivalence between BHT, TBHQ, and plant-based substances added directly to the control sample.…”

Section: Resultsmentioning

confidence: 99%

“…The loadings plot from the temperature mode on factor 2 ( Figure 3 F) agrees with several studies [ 13 , 28 , 29 , 36 ], that show the antioxidant concentrations decrease over time. The spectral mode for the PARAFAC loadings on factor 2 ( Figure 3 G) presents some similarities with the tocopherol NIR spectra [ 46 ]. However, considering that the tocopherol content in vegetable oils is directly related to the type of processing applied, and that, with chemically refined oils, the content of this constituent is reduced by up to 80% [ 47 ], in this case, the recovered spectra suggest the absorption of antioxidants as a whole, i.e., tocopherol from soybean oil plus the added substances with antioxidant properties.…”

Section: Resultsmentioning

confidence: 99%

N-Way NIR Data Treatment through PARAFAC in the Evaluation of Protective Effect of Antioxidants in Soybean Oil

et al. 2020

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The use of chemometric tools is progressing to scientific areas where analytical chemistry is present, such as food science. In analytical food evaluation, oils represent an important field, allowing the exploration of the antioxidant effects of herbs and seeds. However, traditional methodologies have some drawbacks which must be overcome, such as being time-consuming, requiring sample preparation, the use of solvents/reagents, and the generation of toxic waste. The objective of this study is to evaluate the protective effect provided by plant-based substances (directly, or as extracts), including pumpkin seeds, poppy seeds, dehydrated goji berry, and Provençal herbs, against the oxidation of antioxidant-free soybean oil. Synthetic antioxidants tert-butylhydroquinone and butylated hydroxytoluene were also considered. The evaluation was made through thermal degradation of soybean oil at different temperatures, and near-infrared spectroscopy was employed in an n-way mode, coupled with Parallel Factor Analysis (PARAFAC) to extract nontrivial information. The results for PARAFAC indicated that factor 1 shows oxidation product information, while factor 2 presents results regarding the antioxidant effect. The plant-based extract was more effective in improving the frying stability of soybean oil. It was also possible to observe that while the oxidation product concentration increased, the antioxidant concentration decreased as the temperature increased. The proposed method is shown to be a simple and fast way to obtain information on the protective effects of antioxidant additives in edible oils, and has an encouraging potential for use in other applications.

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“…ComDim is a particular implementation of the "Common Components and Specific Weights Analysis" (CCSWA) procedure [27][28][29]. ComDim determines a common space describing the dispersion among data sets or blocks through sample scores; each block having a specific weight or "salience" associated with each dimension in this common space, the Common Components (CCs).…”

Section: Comdim Methodsmentioning

confidence: 99%

“…Significant differences in the values of saliences for a given dimension reflect the fact that the dimension contains different amounts of information from each block. In addition, and for each CC, the ComDim procedure calculates the loadings of the variables in each block, enabling further interpretation of the highlighted discriminations [28]. Here, ComDim was applied on the 3D array (173 samples × 25 excitation wavelengths × 101 emission wavelengths), which was treated as 101 blocks or matrices corresponding to the emission wavelengths.…”

Section: Comdim Methodsmentioning

confidence: 99%

Multivariate Monitoring of Surface Water Quality: Physico-Chemical, Microbiological and 3D Fluorescence Characterization

Daou

El-Hoz

Kassouf

et al. 2020

Water

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The primary objective of this study is to explore a water quality database on two Mediterranean rivers (the Kadisha-Abou Ali and El Jaouz rivers—located in north Lebanon), considering their physicochemical, microbiological and fluorescence characteristics. Principal Component Analysis (PCA) was applied to the matrix gathering physicochemical and microbiological data while the Common Components and Specific Weight Analysis (CCSWA) or ComDim was used for fluorescence excitation-emission matrices (EEMs). This approach provided complementary and valuable information regarding water quality in such complex ecosystem. As highlighted by the PCA and ComDim scores, the Kadisha-Abou Ali River is highly influenced by anthropogenic activities because its watershed districts are intensively populated. This influence reveals the implication of organic and bacteriological parameters. To the contrary, the El Jaouz watershed is less inhabited and is characterized by mineral parameters, which determines its water quality. This work highlighted the relationship between fluorescence EEMs and major water quality parameters, enabling the selection of reliable water quality indicators for the studied rivers. The proposed methodology can surely be generalized to the monitoring of surface water quality in other rivers. Each customized water quality fingerprint should constantly be inspected in order to account for any emerging pollution.

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Multi-block data analysis using ComDim for the evaluation of complex samples: Characterization of edible oils

Cited by 24 publications

References 21 publications

Discrimination of CRISPR/Cas9-induced mutants of rice seeds using near-infrared hyperspectral imaging

Discrimination of CRISPR/Cas9-induced mutants of rice seeds using near-infrared hyperspectral imaging

N-Way NIR Data Treatment through PARAFAC in the Evaluation of Protective Effect of Antioxidants in Soybean Oil

Multivariate Monitoring of Surface Water Quality: Physico-Chemical, Microbiological and 3D Fluorescence Characterization

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