Rapid Assessment of the Microbiological Quality of Pasteurized Vanilla Cream by Means of Fourier Transform Infrared Spectroscopy in Tandem with Support Vector Machine Analysis

Lianou, Alexandra; Malavazos, Christos; Triantafyllou, Ioannis; Panagou, Efstathios Ζ.

doi:10.1007/s12161-017-1063-3

Cited by 9 publications

(7 citation statements)

References 35 publications

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“…For a detailed description of the experimental procedure the reader should refer to a previously published work [27]. In brief, the cream samples employed in this work were packages (170 g) of commercially prepared product that were obtained: (a) directly from the manufacturer within three days of production (65 samples), (b) from different retail stores at various points of their shelf life (48 samples) and (c) from expired samples in retail outlets that were returned to the manufacturer (132 samples).…”

Section: Methodsmentioning

confidence: 99%

Online Feature Selection for Robust Classification of the Microbiological Quality of Traditional Vanilla Cream by Means of Multispectral Imaging

Lianou

Mencattini

Catini

et al. 2019

Sensors

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The performance of an Unsupervised Online feature Selection (UOS) algorithm was investigated for the selection of training features of multispectral images acquired from a dairy product (vanilla cream) stored under isothermal conditions. The selected features were further used as input in a support vector machine (SVM) model with linear kernel for the determination of the microbiological quality of vanilla cream. Model training (n = 65) was based on two batches of cream samples provided directly by the manufacturer and stored at different isothermal conditions (4, 8, 12, and 15 °C), whereas model testing (n = 132) and validation (n = 48) were based on real life conditions by analyzing samples from different retail outlets as well as expired samples from the market. Qualitative analysis was performed for the discrimination of cream samples in two microbiological quality classes based on the values of total viable counts [TVC ≤ 2.0 log CFU/g (fresh samples) and TVC ≥ 6.0 log CFU/g (spoiled samples)]. Results exhibited good performance with an overall accuracy of classification for the two classes of 91.7% for model validation. Further on, the model was extended to include the samples in the TVC range 2–6 log CFU/g, using 1 log step to define the microbiological quality of classes in order to assess the potential of the model to estimate increasing microbial populations. Results demonstrated that high rates of correct classification could be obtained in the range of 2–5 log CFU/g, whereas the percentage of erroneous classification increased in the TVC class (5,6) that was close to the spoilage level of the product. Overall, the results of this study demonstrated that the UOS algorithm in tandem with spectral data acquired from multispectral imaging could be a promising method for real-time assessment of the microbiological quality of vanilla cream samples.

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Section: Methodsmentioning

confidence: 99%

Online Feature Selection for Robust Classification of the Microbiological Quality of Traditional Vanilla Cream by Means of Multispectral Imaging

Lianou

Mencattini

Catini

et al. 2019

Sensors

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“…During pasteurization treatment, microbiological stability of vanilla cream can be compromised, resulting in germination of surviving bacterial spores. One study combined FTIR spectroscopy measurements of samples in addition to sensory evaluation and microbiological determination of aerobic plate count (APC) to form two classes where microbiological data was converted to log (colony-forming units) per gram of cream (log CFU/g): class 1 (accept, APC < 4.5 log CFU/g) and class 2 (reject, APC ≥ 4.5 log CFU/g) [125]. Using SVM classification model with a second-degree polynomial kernel function in tandem with FTIR, spectral fingerprints generated correct classification accuracy of 93.5% for training data sets and 99.2% for the testing data set.…”

Section: Vegetablesmentioning

confidence: 99%

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

2020

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Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

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“…We next demonstrate spectral reconstruction in the mid-to long-wave IR using our metasurface device. As discussed, this spectral range is of much interest due to numerous medical, industrial and scientific applications [21][22][23][24][25][26] . The workhorse tool for spectroscopy in a laboratory setting in this spectral range is the FTIR spectrometer.…”

Section: Spectral Reconstruction Demonstrationmentioning

confidence: 99%

“…While promising results were obtained in these previous works, one of the most important applications of the MWIR-LWIR spectral range is that of spectroscopy. This plays a vital role in areas that include biology 21 , medicine 22 , chemical analysis 23 , forensic science 24 , and food safety testing 25,26 . The conventional workhorse for MWIR-LWIR spectroscopy is the Fourier transform infrared spectrometer (FTIR).…”

mentioning

confidence: 99%

Mid- to long-wave infrared computational spectroscopy with a graphene metasurface modulator

Shrestha

Craig

Meng

et al. 2020

Sci Rep

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in recent years there has been much interest concerning the development of modulators in the midto long-wave infrared, based on emerging materials such as graphene. these have been frequently pursued for optical communications, though also for other specialized applications such as infrared scene projectors. Here we investigate a new application for graphene modulators in the mid-to longwave infrared. We demonstrate, for the first time, computational spectroscopy in the mid-to longwave infrared using a graphene-based metasurface modulator. furthermore, our metasurface device operates at low gate voltage. to demonstrate computational spectroscopy, we provide our algorithm with the measured reflection spectra of the modulator at different gate voltages. We also provide it with the measured reflected light power as a function of the gate voltage. The algorithm then estimates the input spectrum. We show that the reconstructed spectrum is in good agreement with that measured directly by a fourier transform infrared spectrometer, with a normalized mean-absolute-error (nMAe) of 0.021.

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Rapid Assessment of the Microbiological Quality of Pasteurized Vanilla Cream by Means of Fourier Transform Infrared Spectroscopy in Tandem with Support Vector Machine Analysis

Cited by 9 publications

References 35 publications

Online Feature Selection for Robust Classification of the Microbiological Quality of Traditional Vanilla Cream by Means of Multispectral Imaging

Online Feature Selection for Robust Classification of the Microbiological Quality of Traditional Vanilla Cream by Means of Multispectral Imaging

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

Mid- to long-wave infrared computational spectroscopy with a graphene metasurface modulator

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