Fusion of dielectric spectroscopy and computer vision for quality characterization of olive oil during storage

Sanaeifar, Alireza; Jafari, Abdolabbas; Golmakani, Mohammad‐Taghi

doi:10.1016/j.compag.2017.12.035

Cited by 30 publications

(19 citation statements)

References 59 publications

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“…Olive oil has unique sensory, nutritional, and health characteristics, making it highly appreciated by consumers. The olive oil quality changes during the distribution/commercialization storage have been widely investigated under the conditions of different time periods, temperature ranges, lighting, and packing (Ayyad et al, 2015;Jabeur, Zribi, & Bouaziz, 2016;Salek et al, 2017;Sanaeifar, Jafari, & Golmakani, 2018). The olive oil quality changes during the distribution/commercialization storage have been widely investigated under the conditions of different time periods, temperature ranges, lighting, and packing (Ayyad et al, 2015;Jabeur, Zribi, & Bouaziz, 2016;Salek et al, 2017;Sanaeifar, Jafari, & Golmakani, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…However, olive oil undergoes lipid oxidation and hydrolytic degradation during its storage along the distribution chain, which can dramatically change its final nutritional, health, and sensory properties, leading to the appearance of sensory defects like rancidity (Genovese, Caporaso, & Sacchi, 2015), decreasing its final quality grade. The olive oil quality changes during the distribution/commercialization storage have been widely investigated under the conditions of different time periods, temperature ranges, lighting, and packing (Ayyad et al, 2015;Jabeur, Zribi, & Bouaziz, 2016;Salek et al, 2017;Sanaeifar, Jafari, & Golmakani, 2018). In contrast, as recently pointed out by Genovese et al (2015), researchers have mostly focused on the shelf life of the bottled olive oil during the product distribution rather than on the quality changes that may occur during domestic consumption.…”

Section: Introductionmentioning

confidence: 99%

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Olive Oil Quality and Sensory Changes During House‐Use Simulation and Temporal Assessment Using an Electronic Tongue

Rodrigues

Oliveira

Mendanha

et al. 2018

J Americ Oil Chem Soc

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During domestic usage, olive oil bottle manipulation may lead to a quality decrease due to agitation and oxygenation. Therefore, assessing the domestic consumption time period during which the initial quality grade is retained may allow including this information as a recommendation, ensuring olive oil consumers' satisfaction. Temporal changes of physicochemical, chemical, and sensory parameters of extra-virgin olive oils (EVOO) were monitored during 1-month simulated house-use conditions. It was observed that K 232 (R-Pearson ≥+0.81) and ΔK increased resulting in a significant olive oil quality decrease from EVOO (during the initial 21 days of simulated usage) to lampante olive oil (after 28 days of simulated usage) as well as the appearance of rancid sensation. As lampante olive oils cannot be commercialized, it is pertinent to establish olive oil shelf life under usual home-use conditions. Principal component analysis allowed grouping the olive oils according to home-use time period and how bottles are stored after their first opening, showing that the overall olive oil physicochemical and sensory characteristics changed with the domestic-use time period. Finally, a potentiometric electronic tongue coupled with linear discriminant analysis was used to discriminate olive oils according to the domestic-use time period (leave-one-out crossvalidation sensitivities ≥95%). Thus, this device could be used to indirectly assess the quality of the remaining bottled olive oil by establishing for how long an olive oil bottle has been used under domestic conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Olive Oil Quality and Sensory Changes During House‐Use Simulation and Temporal Assessment Using an Electronic Tongue

Rodrigues

Oliveira

Mendanha

et al. 2018

J Americ Oil Chem Soc

View full text Add to dashboard Cite

show abstract

“…Torrecilla et al [113] obtained a misclassification under 1.3% with SOM based on lag-k autocorrelation coefficients grouping 120 signals into five classes. In a study comparing several different techniques for storage time classification of EVOO, Sanaeifar et al [114] obtained 100% accuracy with Bayesian network (BN) while ANN with one hidden layer produced accuracy of 97.5% and SVM with a polynomial kernel function achieved accuracy of and 96.3%.…”

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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“…This research was supported and funded by the National Key Research and Development Program of China (No.2017YFE0111200) from the China Ministry of Science and Technology. This experiment was performed in Croatia during a visit by the first author, which was supported by the Chinese-Croatian bilateral Scientific and Technological Cooperation Program (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19).…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…It is difficult to accurately assess the quality of an entire batch of fruit based on single evaluation indicators and limited samples; such assessments are affected by the experimental environment and the characteristics of the individual fruit sampled [16,17]. Fruit quality evaluation methods combined with multiple detection technologies, therefore, receive significant attention [18,19]. For example, Das et al [20] described a platform for evaluation of honey quality based on electrical impedance spectroscopy (EIS) and Fourier-transform mid-infrared spectroscopy (FT-MIR), which was used to detect the presence of sucrose as an adulterant in honey varieties from different floral origins.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption

Zhang

et al. 2020

Applied Sciences

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Featured Application: Based on principal component analysis (PCA), an evaluation and grading model for fresh peaches was developed to provide guidance for the selection of fresh peaches for the consumer market. The approach and findings presented here may be useful for effective evaluation and grading during real-world fruit production; they can potentially improve processing efficiency, reduce costs, and minimize waste in an automated quality evaluation system. Abstract: Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar-acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch "Spring Belle") were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model.

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Fusion of dielectric spectroscopy and computer vision for quality characterization of olive oil during storage

Cited by 30 publications

References 59 publications

Olive Oil Quality and Sensory Changes During House‐Use Simulation and Temporal Assessment Using an Electronic Tongue

Olive Oil Quality and Sensory Changes During House‐Use Simulation and Temporal Assessment Using an Electronic Tongue

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

A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption

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