Monitoring Carotenoid and Chlorophyll Pigments in Virgin Olive Oil by Visible-Near Infrared Transmittance Spectroscopy. On-Line Application

2003 ◽  
Vol 11 (3) ◽  
pp. 219-226 ◽  
Author(s):  
A. Jiménez Márquez

Visible-near infrared transmittance spectroscopy was used to determine total levels of chlorophyll and carotenoid in virgin olive oil. Calibration models were developed in the laboratory using partial least squares regression. An initial smoothing followed by a first derivative treatment was the best signal correction. The validation set gave a correlation coefficient and standard error of prediction of 0.985 and 0.66 mg kg−1 for carotene totals and 0.993 and 0.96 mg kg−1 for chlorophyll totals. These partial least squares models were used to monitor on-line levels of these compounds during virgin olive oil processing in olive oil mills. The results indicate similarity between both visible-near infrared transmittance spectroscopy and reference laboratory methods.

2019 ◽  
Vol 1056 ◽  
pp. 7-15 ◽  
Author(s):  
M. Antonietta Baldo ◽  
Paolo Oliveri ◽  
Sabrina Fabris ◽  
Cristina Malegori ◽  
Salvatore Daniele

2021 ◽  
pp. 096703352110515
Author(s):  
Marco Bragolusi ◽  
Andrea Massaro ◽  
Carmela Zacometti ◽  
Alessandra Tata ◽  
Roberto Piro

The potential of the combination of near infrared (NIR) spectroscopy and Raman spectroscopy to differentiate Italian and Greek extra virgin olive oil (EVOO) by geographical origin was evaluated. Near infrared spectroscopy and Raman fingerprints of both study groups (extra virgin olive oil from the two countries) were pre-processed, merged by low-level and mid-level data fusion strategies and submitted to partial least-squares discriminant analysis. The classification models were cross-validated. After low-level data fusion, the partial least-squares discriminant analysis correctly predicted the geographical origins of extra virgin olive oils in cross-validation with 93.9% accuracy, while sensitivity and specificity were 77.8% and 100%, respectively. After mid-level data fusion, the partial least-squares discriminant analysis correctly predicted the geographical origins of extra virgin olive oils in cross-validation with 97.0% accuracy, while sensitivity and specificity were 88.9% and 100%, respectively. In this preliminary study, improved discrimination of Italian extra virgin olive oils was achieved by the synergism of near infrared spectroscopy and Raman spectroscopy as compared to the discrimination obtained by the separate laboratory techniques. This pilot study shows encouraging results that could open a new avenue for the authentication of Italian extra virgin olive oil.


2019 ◽  
Vol 28 (3) ◽  
pp. e015
Author(s):  
José-Henrique Camargo Pace ◽  
João-Vicente De Figueiredo Latorraca ◽  
Paulo-Ricardo Gherardi Hein ◽  
Alexandre Monteiro de Carvalho ◽  
Jonnys Paz Castro ◽  
...  

Aim of study: Fast and reliable wood identification solutions are needed to combat the illegal trade in native woods. In this study, multivariate analysis was applied in near-infrared (NIR) spectra to identify wood of the Atlantic Forest species.Area of study: Planted forests located in the Vale Natural Reserve in the county of Sooretama (19 ° 01'09 "S 40 ° 05'51" W), Espírito Santo, Brazil.Material and methods: Three trees of 12 native species from homogeneous plantations. The principal component analysis (PCA) and partial least squares regression by discriminant function (PLS-DA) were performed on the woods spectral signatures.Main results: The PCA scores allowed to agroup some wood species from their spectra. The percentage of correct classifications generated by the PLS-DA model was 93.2%. In the independent validation, the PLS-DA model correctly classified 91.3% of the samples.Research highlights: The PLS-DA models were adequate to classify and identify the twelve native wood species based on the respective NIR spectra, showing good ability to classify independent native wood samples.Keywords: native woods; NIR spectra; principal components; partial least squares regression.


2018 ◽  
Vol 11 (7) ◽  
pp. e201700365 ◽  
Author(s):  
Raphael Henn ◽  
Christian G. Kirchler ◽  
Zora L. Schirmeister ◽  
Andreas Roth ◽  
Werner Mäntele ◽  
...  

2020 ◽  
pp. 000370282097470
Author(s):  
Joshua M. Ottaway ◽  
J. Chance Carter ◽  
Kristl L Adams ◽  
Joseph Camancho ◽  
Barry Lavine ◽  
...  

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.


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