scholarly journals Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis

Molecules ◽  
2019 ◽  
Vol 24 (14) ◽  
pp. 2559 ◽  
Author(s):  
Pei ◽  
Zuo ◽  
Zhang ◽  
Wang
Keyword(s):  
Fourier Transform ◽  
Data Fusion ◽  
Near Infrared ◽  
Geographical Origin ◽  
Spectroscopic Techniques ◽  
Mid Infrared ◽  
Level Data ◽  
Paris Polyphylla ◽  
High Level ◽  
Geographical Origin Traceability

Origin traceability is important for controlling the effect of Chinese medicinal materials and Chinese patent medicines. Paris polyphylla var. yunnanensis is widely distributed and well-known all over the world. In our study, two spectroscopic techniques (Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR)) were applied for the geographical origin traceability of 196 wild P. yunnanensis samples combined with low-, mid-, and high-level data fusion strategies. Partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to establish classification models. Feature variables extraction (principal component analysis—PCA) and important variables selection models (recursive feature elimination and Boruta) were applied for geographical origin traceability, while the classification ability of models with the former model is better than with the latter. FT-MIR spectra are considered to contribute more than NIR spectra. Besides, the result of high-level data fusion based on principal components (PCs) feature variables extraction is satisfactory with an accuracy of 100%. Hence, data fusion of FT-MIR and NIR signals can effectively identify the geographical origin of wild P. yunnanensis.

scholarly journals Detecting Food Fraud in Extra Virgin Olive Oil Using a Prototype Portable Hyphenated Photonics Sensor

2020 ◽  
Author(s):  
Yannick Weesepoel ◽  
Martin Alewijn ◽  
Michiel Wijtten ◽  
Judith Müller-Maatsch
Keyword(s):  
Data Fusion ◽  
Olive Oil ◽  
Near Infrared ◽  
Edible Oils ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Food Fraud ◽  
Olive Oils ◽  
Level Data ◽  
High Level

Abstract Background Current developments in portable photonic devices for fast authentication of extra virgin olive oil (EVOO) or EVOO with non-EVOO additions steer towards hyphenation of different optic technologies. The multiple spectra or so-called “fingerprints” of samples are then analyzed with multivariate statistics. For EVOO authentication, one-class classification (OCC) to identify “out-of-class” EVOO samples in combination with data-fusion is applicable. Objective Prospecting the application of a prototype photonic device (“PhasmaFood”) which hyphenates visible, fluorescence, and near-infrared spectroscopy in combination with OCC modelling to classify EVOOs and discriminate them from other edible oils and adulterated EVOOs. Method EVOOs were adulterated by mixing in 10–50% (v/v) of refined and virgin olive oils, olive-pomace olive oils, and other common edible oils. Samples were analyzed by the hyphenated sensor. OCC, data-fusion, and decision thresholds were applied and optimized for two different scenarios. Results: By high-level data-fusion of the classification results from the three spectral databases and several multivariate model vectors, a 100% correct classification of all pure edible oils using OCC in the first scenario was found. Reducing samples being falsely classified as EVOOs in a second scenario, 97% of EVOOs adulterated with non-EVOO olive oils were correctly identified and ones with other edible oils correctly classified at score of 91%. Conclusions Photonic sensor hyphenation in combination with high-level data fusion, OCC, and tuned decision thresholds delivers significantly better screening results for EVOO compared to individual sensor results. Highlights Hyphenated photonics and its data handling solutions applied to extra virgin olive oil authenticity testing was found to be promising.

scholarly journals Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation

Foods ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1450
Author(s):  
Tito Damiani ◽  
Rosa M. Alonso-Salces ◽  
Inés Aubone ◽  
Vincent Baeten ◽  
Quentin Arnould ◽  
...  
Keyword(s):  
Data Fusion ◽  
Cost Benefit ◽  
Classification Performance ◽  
Spectroscopic Techniques ◽  
Food Authentication ◽  
Prediction Ability ◽  
Fast Screening ◽  
Level Data ◽  
Ft Raman Spectroscopy ◽  
High Level

In the present work, the provenance discrimination of Argentinian honeys was used as case study to compare the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected among three main honey-producing regions of Argentina over four harvesting seasons. Each sample was fingerprinted by FT-MIR, NIR and FT-Raman spectroscopy. The spectroscopic platforms were compared on the basis of the classification performance achieved under a supervised chemometric approach. Furthermore, low- mid- and high-level data fusion were attempted in order to enhance the classification results. Finally, the best-performing solution underwent to SIMCA modelling with the purpose of reproducing a food authentication scenario. All the developed classification models underwent to a “year-by-year” validation strategy, enabling a sound assessment of their long-term robustness and excluding any issue of model overfitting. Excellent classification scores were achieved by all the technologies and nearly perfect classification was provided by FT-MIR. All the data fusion strategies provided satisfying outcomes, with the mid- and high-level approaches outperforming the low-level data fusion. However, no significant advantage over the FT-MIR alone was obtained. SIMCA modelling of FT-MIR data produced highly sensitive and specific models and an overall prediction ability improvement was achieved when more harvesting seasons were used for the model calibration (86.7% sensitivity and 91.1% specificity). The results obtained in the present work suggested the major potential of FT-MIR for fingerprinting-based honey authentication and demonstrated that accuracy levels that may be commercially useful can be reached. On the other hand, the combination of multiple vibrational spectroscopic fingerprints represents a choice that should be carefully evaluated from a cost/benefit standpoint within the industrial context.

scholarly journals Comparison and Identification for Rhizomes and Leaves of Paris yunnanensis Based on Fourier Transform Mid-Infrared Spectroscopy Combined with Chemometrics

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3343 ◽  
Author(s):  
Yi-Fei Pei ◽  
Qing-Zhi Zhang ◽  
Zhi-Tian Zuo ◽  
Yuan-Zhong Wang
Keyword(s):  
Fourier Transform ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Chemical Information ◽  
Accurate Identification ◽  
Mid Infrared ◽  
Model Classification ◽  
Paris Polyphylla ◽  
Mir Spectra ◽  
The Individual

Paris polyphylla, as a traditional herb with long history, has been widely used to treat diseases in multiple nationalities of China. Nevertheless, the quality of P. yunnanensis fluctuates among from different geographical origins, so that a fast and accurate classification method was necessary for establishment. In our study, the geographical origin identification of 462 P. yunnanensis rhizome and leaf samples from Kunming, Yuxi, Chuxiong, Dali, Lijiang, and Honghe were analyzed by Fourier transform mid infrared (FT-MIR) spectra, combined with partial least squares discriminant analysis (PLS-DA), random forest (RF), and hierarchical cluster analysis (HCA) methods. The obvious cluster tendency of rhizomes and leaves FT-MIR spectra was displayed by principal component analysis (PCA). The distribution of the variable importance for the projection (VIP) was more uniform than the important variables obtained by RF, while PLS-DA models obtained higher classification abilities. Hence, a PLS-DA model was more suitably used to classify the different geographical origins of P. yunnanensis than the RF model. Additionally, the clustering results of different geographical origins obtained by HCA dendrograms also proved the chemical information difference between rhizomes and leaves. The identification performances of PLS-DA and the RF models of leaves FT-MIR matrixes were better than those of rhizomes datasets. In addition, the model classification abilities of combination datasets were higher than the individual matrixes of rhizomes and leaves spectra. Our study provides a reference to the rational utilization of resources, as well as a fast and accurate identification research for P. yunnanensis samples.

EXPRESS: Comparison of Spectroscopic Techniques for Determining the Peroxide Value of 19 Classes of Naturally Aged, Plant-Based Edible Oils

2020 ◽  
pp. 000370282097470
Author(s):  
Joshua M. Ottaway ◽  
J. Chance Carter ◽  
Kristl L Adams ◽  
Joseph Camancho ◽  
Barry Lavine ◽  
...  
Keyword(s):  
Olive Oil ◽  
Peroxide Value ◽  
Near Infrared ◽  
Edible Oils ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Spectroscopic Techniques ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Calibration Models

The peroxide value (PV) of edible oils is a measure of the degree of oxidation, which directly relates to the freshness of the oil sample. Several studies previously reported in the literature have paired various spectroscopic techniques with multivariate analyses to rapidly determine PVs using field portable and process instrumentation; those efforts presented ‘best-case’ scenarios with oils from narrowly defined training and test sets. The purpose of this paper is to evaluate the use of near- and mid-infrared absorption and Raman scattering spectroscopies on oil samples from different oil classes, including seasonal and vendor variations, to determine which measurement technique, or combination thereof, is best for predicting PVs. Following PV assays of each oil class using an established titration-based method, global and global-subset calibration models were constructed from spectroscopic data collected on the 19 oil classes used in this study. Spectra from each optical technique were used to create partial least squares regression (PLSR) calibration models to predict the PV of unknown oil samples. A global PV model based on near-infrared (8 mm optical path length – OPL) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm OPL near infrared (5.1), Raman (6.9) and 50 μm OPL mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global PV models based on low-level fusion of the NIR (8 and 24 mm OPL) data and all infrared data produced the same RMSEP of 5.1. Global subset models, based on any of the spectroscopies and olive oil training sets from any class (pure, extra light, extra virgin), all failed to extrapolate to the non-olive oils. However, the near-infrared global subset model built on extra virgin olive oil could extrapolate to test samples from other olive oil classes.

Vibrational spectroscopy in practice: Detection of mineral oil in sunflower oil with near- and mid-infrared spectroscopy

NIR news ◽  
2018 ◽  
Vol 29 (3) ◽  
pp. 6-11 ◽  
Author(s):  
Michael K-H Pfister ◽  
Bettina Horn ◽  
Janet Riedl ◽  
Susanne Esslinger ◽  
Carsten Fauhl-Hassek
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Sunflower Oil ◽  
Near Infrared ◽  
Limit Of Detection ◽  
Model Performance ◽  
Mineral Oil ◽  
Food Ingredient ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Fourier transform infrared spectroscopy becomes increasingly important for detecting adulterations in food due to a minimal sample preparation and a fast nondestructive measurement. Sunflower oil is a popular food ingredient, which might be contaminated or even adulterated by compounds with health concerns such as mineral oil. In this context a feasibility study was performed to compare the suitability of near- and mid-infrared spectroscopy for detecting mineral oil in sunflower oil. For this purpose, sunflower oils spiked with mineral oil in the concentration range of 0.001–1.0% w/w were analyzed by Fourier transform near- and mid-infrared spectroscopy, respectively, and spectra data were preprocessed prior to partial least squares regression. Hereby, the data preparation was optimized for each technique to account for model performance influences. The model performance was fairly similar for both approaches with a slightly better precision and thus limit of detection (near infrared 0.12% w/w, mid infrared 0.16% w/w) for the near-infrared-based model compared to the mid-infrared model. Consequently, both techniques are considered suitable for the determination of mineral oil in sunflower oil in the context of food authentication.

scholarly journals Tridimensional Spectroscopic Techniques: Conference Summary

1995 ◽  
Vol 149 ◽  
pp. 369-381 ◽  
Author(s):  
J. Bland-Hawthorn
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Three Dimensional ◽  
Spectroscopic Techniques ◽  
Conference Summary ◽  
Wide Range ◽  
Fabry Perot ◽  
Scientific Results ◽  
General Thrust

Over the last four days, we have enjoyed a wide range of talks on developments in three dimensional spectroscopic techniques. The conference organizing committee are to be congratulated for the artful manner in which instrumental presentations were interleaved with talks on the scientific results from these instruments. The general thrust of most talks was to advance the versatility of traditional instruments either through the Jacquinot (throughput) advantage or through the multiplex advantage, or both. A number of groups have attempted to utilize the full aperture of scanning Fabry-Perot and Fourier Transform interferometers. Arguably, Fabry-Perot interferometers have a wider application at present, although imaging Fourier Transform devices appear to have finally arrived, at least in the near infrared.

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