An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer

Violino, Simona; Ortenzi, Luciano; Antonucci, Francesca; Pallottino, Federico; Benincasa, Cinzia; Figorilli, Simone; Costa, Corrado

doi:10.3390/foods9060834

Cited by 26 publications

(12 citation statements)

References 63 publications

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“…The predecessors conducted many studies on rice origin tracing or variety attribute category, and the test materials included multiple varieties from the same origin and the same variety from multiple origins [42][43][44]. The test materials in this study were three saline-alkali-tolerant varieties and three saline-alkali-sensitive varieties, and 432 samples were collected from the control field and the saline-alkali-treated field, respectively.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Identification of Saline-Alkali-Tolerant Japonica Rice Varieties Based on Raman Spectroscopy and Python Visual Analysis

Feng

Wang

et al. 2022

Agriculture

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The core of saline-alkali land improvement is planting suitable plants. Planting rice in saline-alkali land can not only effectively improve saline-alkali soil, but also increase grain yield. However, traditional identification methods for saline-alkali-tolerant rice varieties require tedious and time-consuming field investigations based on growth indicators by rice breeders. In this study, the visualization method of Python data processing was used to analyze the Raman spectroscopy of japonica rice in order to study a simple and efficient identification method of saline-alkali-tolerant japonica rice varieties. Three saline-alkali-tolerant japonica varieties and three saline-alkali-sensitive japonica varieties were collected from control and saline-alkali-treated fields, respectively, and the Raman spectra of 432 samples were obtained. The data preprocessing stage used filtering-difference method to process Raman spectral data to complete interference reduction and crests extraction. In the feature selection stage, scipy.signal.find_peaks (SSFP), SelectKBest (SKB) and recursive feature elimination (RFE) were used for machine feature selection of spectral data. According to the feature dimension obtained by machine feature selection, dataset partitioning by K-fold CV, the typical linear logistic regression (LR) and typical nonlinear support vector machine (SVM) models were established for classification. Experimental results showed that the typical nonlinear SVM identification model based on both RFE machine feature selection and six-fold CV dataset partitioning had the best identification rate, which was 94%. Therefore, the SVM classification model proposed in this study could provide help in the intelligent identification of saline-alkali-tolerant japonica rice varieties.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Identification of Saline-Alkali-Tolerant Japonica Rice Varieties Based on Raman Spectroscopy and Python Visual Analysis

Feng

Wang

et al. 2022

Agriculture

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“…Bruker GmbH developed a Fourier transform near-infrared spectroscopy (FT-NIR) protocol for routine analysis of olive oils, making quality control and the production process easy (Behmer, 2018). Violino et al (2020) instead have recently developed a method based on an open source visible-near infrared spectrophotometer coupled with a specific app relying on artificial intelligence neural networks for traceability purposes. Through this advanced approach, a correct classification of 94.6% of the samples was obtained, and specific wavelengths for origin authentication were found.…”

Section: Official Methods To Assess the Authenticity Of Evoosmentioning

confidence: 99%

Nuclear magnetic resonance spectroscopy in extra virgin olive oil authentication

Maestrello

Solovyev

Bontempo

et al. 2022

Comp Rev Food Sci Food Safe

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Extra virgin olive oil (EVOO) is a high-quality product that has become one of the stars in the food fraud context in recent years. EVOO can encounter different types of fraud, from adulteration with cheaper oils to mislabeling, and for this reason, the assessment of its authenticity and traceability can be challenging. There are several officially recognized analytical methods for its authentication, but they are not able to unambiguously trace the geographical and botanical origin of EVOOs. The application of nuclear magnetic resonance (NMR) spectroscopy to EVOO is reviewed here as a reliable and rapid tool to verify different aspects of its adulteration, such as undeclared blends with cheaper oils and cultivar and geographical origin mislabeling. This technique makes it possible to use both targeted and untargeted approaches and to determine the olive oil metabolomic profile and the quantification of its constituents.

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“…In order to extract the most informative features, among the 133 acquired ones, a feature importance analysis was conducted as follows [76]. The hidden layer matrix (9 nodes x 133 variables) was a posteriori analyzed by considering its elementwise absolute value.…”

Section: Multivariate Modeling Of Coral Activitymentioning

confidence: 99%

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions

Zuazo

Grinyó

López-Vázquez

et al. 2020

Sensors

Self Cite

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Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species—the bubblegum coral Paragorgia arborea—in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.

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An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer

Cited by 26 publications

References 63 publications

Machine Learning Identification of Saline-Alkali-Tolerant Japonica Rice Varieties Based on Raman Spectroscopy and Python Visual Analysis

Machine Learning Identification of Saline-Alkali-Tolerant Japonica Rice Varieties Based on Raman Spectroscopy and Python Visual Analysis

Nuclear magnetic resonance spectroscopy in extra virgin olive oil authentication

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions

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