Comparison of different analytical classification scenarios: application for the geographical origin of edible palm oil by sterolic (NP) HPLC fingerprinting

Pérez-Castaño, Estefanía; Ruíz-Samblás, Cristina; Medina‐Rodríguez, Santiago; Quirós-Rodríguez, Verónica; Jiménez-Carvelo, Ana M.; Valverde-Som, Lucía; González‐Casado, Antonio; Cuadros-Rodrı́guez, Luis

doi:10.1039/c5ay00168d

Cited by 43 publications

(20 citation statements)

References 35 publications

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“…Scan control and data acquisition were performed using a MS Workstation (Varian Inc.) software data system (version 6.9) in CSV format. Data treatment was performed using MATLAB, version 8.2 (Mathworks Inc., Natick, MA, USA) and the programmed MATLAB function ‘Medina’ (version 14) . This function implements several algorithms from the MATLAB Bioinformatics Toolbox and the ‘icoshift’ (interval correlation optimized shifting), which is an algorithm for aligning the chromatogram peaks .…”

Section: Methodsmentioning

confidence: 99%

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis – part II: applications

et al. 2018

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Section: Methodsmentioning

confidence: 99%

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis – part II: applications

et al. 2018

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“…This implies that a sample may in principle be assigned to multiple classes -or to none when it fits within none of the PCA models [158]. Perez-Castaño et al applied SIMCA and PLS-DA on normal-phase-LC data to classify palm oils based on their geographical origin [159]. Planinc et al applied SIMCA on LC-MS data to analyze changes in the N-glycosylation of therapeutic glycoproteins [160].…”

Section: Soft Independent Modeling Of Class Analogymentioning

confidence: 99%

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

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et al. 2020

J of Separation Science

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The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as highresolution mass spectrometry, causes an urgent need for highly efficient dataanalysis and optimization strategies. This is especially true for comprehensive twodimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from oneand two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

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“…Nonetheless, day2 and day3 are relat ively d ifficu lt to classify, es pecially for the KNN classifier due to the MC values between 10% and 50%. The performance of classifiers is assessed by applying two quality classification metrics including sensitivity (SENS) (also called true positive rate), and specificity (SPEC) [19] [28]. The value of SENS represents the probability of the target class identified correctly in the total number of one target classes.…”

Section: B Features Selectionmentioning

confidence: 99%

Machine Learning Driven Approach Towards the Quality Assessment of Fresh Fruits Using Non-Invasive Sensing

et al. 2020

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2020) Machine learning driven approach towards the quality assessment of fresh fruits using non-invasive sensing.Abstract-In agriculture science, accurate information of moisture content (MC) in fruits and vegetables in an automated fashion can be vital for astute quality and grading evaluation. This demands for a viable, feasible and cost-effective technique for the defect recognition using timely detection of MC in fruits and vegetables to maintain a healthy sensory characteristic of fruits. Here we propose a non-invasive machine learning (ML) driven technique to monitor variations of MC in fruits using the terahertz (THz) waves with Swissto12 material characterization kit (MCK) in the frequency range of 0.75 THz to 1.1 THz. In this regard, multi-domain features are extracted from time-, frequency-, and time-frequency domains, and applied three ML algorithms such as support vector machine (SVM), knearest neighbour (KNN) and Decision Tree (D-Tree) for the precise assessment of MC in both apple and mango slices. The results illustrated that the performance of SVM exceeded other classifiers results using 10-fold validation and leave-oneobservation-out-cross-validation techniques. Moreover, all three classifiers exhibited 100% accuracy for day 1 and 4 with 80% MC value (freshness) and 2% MC value (staleness) of both fruits' slices, respectively. Similarly, for day 2 and 3, an accuracy of 95% was achieved with intermediate MC values in both fruits' slices. This study will pave a new direction for the real-time quality evaluation of fruits in a non-invasive manner by incorporating ML with THz sensing at a cellular level. It also has a strong potential to optimize economic benefits by the timely detection of fruits quality in an automated fashion.

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Comparison of different analytical classification scenarios: application for the geographical origin of edible palm oil by sterolic (NP) HPLC fingerprinting

Abstract: The comparison of scenarios of classification is highlighted. This term is referred to the combination of classifiers and analytical data.

Cited by 43 publications

References 35 publications

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis – part II: applications

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis – part II: applications

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Machine Learning Driven Approach Towards the Quality Assessment of Fresh Fruits Using Non-Invasive Sensing

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