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

Abstract

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.

Multi-Spectral Imaging for Fruits and Vegetables

“…The threshold limit which was set for the "fresh" samples of vanilla cream, was a crucial factor for product delivery to the market by the manufacturer. This example gives a good outlook for the application of multi-spectral imaging in combination with the UOS algorithm in the quality management of the dairy industry [12].…”

“…The pvalues for the T-tests were plotted with the null hypothesis and it showed no variation in mean values for the six-day interval. Lianou et al [12] proposed an online feature selection grading scheme for vanilla cream quality analysis using multispectral imaging. The study was conducted for the inspection of two microbiological quality classes of cream samples, with the value of total viable counts (TVC) ≤ 2.0 log CFU/g for fresh samples and TVC ≥ 6.0 log CFU/g for spoiled samples.…”

Deciphering Cancer Cell Behavior From Motility and Shape Features: Peer Prediction and Dynamic Selection to Support Cancer Diagnosis and Therapy

“…Features that satisfied all the three criteria are then selected. The extension we propose here with respect to the standard DFS(18,30) is the inclusion of a preliminary supervised selection performed at the beginning of the model construction based on stepwise feature selection procedure (31) applied on the training samples. The fact that motion models may vary within the same experiment(12) implies the necessity to extract many kinematics descriptors.…”

Characterization of spoilage microbiota through next-generation sequencing and application of spectroscopy-based technologies for assessing the microbiological quality of fresh-cut produce

“…Advances in data science have introduced various machine learning approaches, applied in tandem with the above analytical technologies for the qualitative and/or quantitative evaluation of the microbiological spoilage of various food products, including mainly meat products (Argyri et al, 2013;Grewal et al, 2015;Oto et al, 2013;Panagou et al, 2014;Fengou et al, 2019a;Spyrelli et al 2020) but also fish (Duan et al, 2014;He & Sun, 2015a;He & Sun, 2015b;Saraiva et al, 2016;Fengou et al, 2019b), and to a lesser extent, dairy products (Nicolaou & Goodacre, 2008;Lianou et al, 2019), fruits (Di Egidio et al, 2009;De Sousa Marques et al, 2013;Al-Holy et al, 2015;Manthou et al, 2020) and vegetables (Wang et al, 2010;Tsakanikas et al, 2018). However, it is often challenging to select the appropriate combinations of analytical technologies and machine learning approaches and, thus, comparative evaluation for choosing the best approach for specific type of data is still needed (Cozzolino et al, 2015;Estelles-Lopez et al, 2017).…”

“…Depending on the type of sensor and data complexity, the data analysis involves advanced computational methods, including machine learning (Ropodi et al, 2016). Therefore, various machine learning approaches coupled with spectroscopy-based technologies have been proposed for the qualitative and/or quantitative evaluation of the microbiological quality and safety of various food products including meat and fish products, dairy products, fruits and vegetables (Al-Holy et al, 2015;Barbin et al, 2015;Sravan-Kumar et al, 2015;Tsakanikas et al, 2018;Fengou et al, 2019aFengou et al, , 2019bLianou et al, 2019;Manthou et al, 2020;Spyrelli et al 2020).…”