Detection of orange juice frauds using front-face fluorescence spectroscopy and Independent Components Analysis

Ammari, Faten; Redjdal, Lamia; Rutledge, Douglas N.

doi:10.1016/j.foodchem.2014.06.110

Cited by 55 publications

(24 citation statements)

References 30 publications

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“…The increasing use of this technique has been facilitated by improved instruments and new data analysis techniques such as multivariate and multiway chemometric tools. One of these tools is ICA, which is particularly useful to improve the interpretation of fluorescence data in cases of spectral overlap and changes caused by environmental and matrix effects [15][16][17][18][19][20][21][22][23][24][25]. The main application areas of ICA to handle fluorescence profiles are environmental monitoring and food control.…”

Section: Spectroscopymentioning

confidence: 99%

“…The adulteration of orange juice by addition of grapefruit juice at the level of 1% was uncovered by 3D-frontface fluorescence spectroscopy followed by ICA [20]. Multiple linear regression (MLR) was performed to estimate the added percentage of grapefruit juice from the IC proportions based on the dataset of juice mixtures and two commercial samples labeled with known composition of both juices.…”

Section: Spectroscopymentioning

confidence: 99%

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Independent components analysis (ICA) at the “cocktail-party” in analytical chemistry

Monakhova

Rutledge

2020

Talanta

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Independent components analysis (ICA) is a probabilistic method, whose goal is to extract underlying component signals, that are maximally independent and non-Gaussian, from mixed observed signals. Since the data acquired in many applications in analytical chemistry are mixtures of component signals, such a method is of great interest. In this article recent ICA applications for quantitative and qualitative analysis in analytical chemistry are reviewed. The following experimental techniques are covered: fluorescence, UV-VIS, NMR, vibrational spectroscopies as well as chromatographic profiles. Furthermore, we reviewed ICA as a preprocessing tool as well as existing hybrid ICA-based multivariate approaches. Finally, further research directions are proposed. Our review shows that ICA is starting to play an important role in analytical chemistry, and this will definitely increase in the future.

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Section: Spectroscopymentioning

confidence: 99%

Section: Spectroscopymentioning

confidence: 99%

Independent components analysis (ICA) at the “cocktail-party” in analytical chemistry

Monakhova

Rutledge

2020

Talanta

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“…Moreover, very popular type of fruit juice sophistication is the addition of cheaper fruit juices [3]. Orange juices are most often adulterated with the addition of mandarin [5, 6], tangerine [7], lemon [8], or grapefruit [9] juices.…”

Section: Introductionmentioning

confidence: 99%

Novel analytical method for detection of orange juice adulteration based on ultra-fast gas chromatography

2018

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The food authenticity assessment is an increasingly important issue in food quality and safety. The application of an electronic nose based on ultra-fast gas chromatography technique enables rapid analysis of the volatile compounds from food samples. Due to the fact that this technique provides chemical profiling of natural products, it can be a powerful tool for authentication in combination with chemometrics. In this article, a methodology for classification of Not From Concentrate (NFC) juices was presented. During research samples of 100% orange juice, 100% apple juice, as well as mixtures of these juices with known percentage of base juices were tested. Classification of juice samples was carried out using unsupervised and supervised statistical methods. As chemometric methods, Hierarchical Cluster Analysis, Classification Tree, Naïve Bayes, Neural Network, and Random Forest classifiers were used. The ultra-fast GC technique coupled with supervised statistical methods allowed to distinguish juice samples containing only 1.0% of impurities. The developed methodology is a promising analytical tool to ensure the authenticity and good quality of juices.Graphical abstract

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“…In contrast to the statistical methods based on second-order moments, ICA uses fourth-order moment of the signals to obtain the latent variables (Wang et al, 2008). Recent applications of ICA for spectroscopic data interpretation includes processing of infrared spectra of marine organic matter aggregates (Monakhova et al, 2015), detection of orange juice frauds using front-face fluorescence spectroscopy (Ammari et al, 2015), spectrophotometric analysis of polysaccharide/milk protein interactions with methylene blue (Rohart et al, 2015), near-infrared spectroscopy for analysis of bioactive components (Chuang et al, 2014a), near infrared spectroscopy for evaluation of rice freshness (Chuang et al, 2014b), analysis of Raman images of pharmaceutical drug product (Boiret et al, 2014), fluorescence spectroscopy for studying interaction between plastic food packaging and olive oil (Kassouf et al, 2014) and to characterise organic matter in soils (Ammari et al, 2014). The outcomes of these studies indicate that ICA simplified the interpretation of the results by decomposing the original spectral data into ''source signals''.…”

Section: Introductionmentioning

confidence: 99%

Application of independent components analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

Mishra

Cordella

Rutledge

et al. 2016

Journal of Food Engineering

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a b s t r a c tIn recent years, Independent Components Analysis (ICA) has proven itself to be a powerful signal-processing technique for solving the Blind-Source Separation (BSS) problems in different scientific domains. In the present work, an application of ICA for processing NIR hyperspectral images to detect traces of peanut in wheat flour is presented. Processing was performed without a priori knowledge of the chemical composition of the two food materials. The aim was to extract the source signals of the different chemical components from the initial data set and to use them in order to determine the distribution of peanut traces in the hyperspectral images. To determine the optimal number of independent component to be extracted, the Random ICA by blocks method was used. This method is based on the repeated calculation of several models using an increasing number of independent components after randomly segmenting the matrix data into two blocks and then calculating the correlations between the signals extracted from the two blocks. The extracted ICA signals were interpreted and their ability to classify peanut and wheat flour was studied. Finally, all the extracted ICs were used to construct a single synthetic signal that could be used directly with the hyperspectral images to enhance the contrast between the peanut and the wheat flours in a real multi-use industrial environment. Furthermore, feature extraction methods (connected components labelling algorithm followed by flood fill method to extract object contours) were applied in order to target the spatial location of the presence of peanut traces. A good visualization of the distributions of peanut traces was thus obtained.

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Detection of orange juice frauds using front-face fluorescence spectroscopy and Independent Components Analysis

Cited by 55 publications

References 30 publications

Independent components analysis (ICA) at the “cocktail-party” in analytical chemistry

Independent components analysis (ICA) at the “cocktail-party” in analytical chemistry

Novel analytical method for detection of orange juice adulteration based on ultra-fast gas chromatography

Application of independent components analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

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