Geographic Classification of Extra Virgin Olive Oils from the Eastern Mediterranean by Chemometric Analysis of Visible and Near-Infrared Spectroscopic Data

2003 ◽  
Vol 57 (2) ◽  
pp. 158-163 ◽  
Author(s):  
Gerard Downey ◽  
Peter McIntyre ◽  
Antony N. Davies
Keyword(s):  
Discriminant Analysis ◽  
Spectral Data ◽  
Near Infrared ◽  
Eastern Mediterranean ◽  
Geographic Origin ◽  
Near Infrared Reflectance ◽  
Virgin Olive Oils ◽  
Olive Oils ◽  
Sample Set ◽  
Factorial Discriminant Analysis

Visible and near-infrared reflectance spectra have been examined for their ability to classify extra virgin olive oils from the eastern Mediterranean on the basis of their geographic origin. Classification strategies investigated were partial least-squares regression, factorial discriminant analysis, and k-nearest neighbors analysis. Discriminant models were developed and evaluated using spectral data in the visible (400–750 nm), near-infrared (1100–2498 nm), and combined (400–2498 nm) wavelength ranges. A variety of data pretreatments was applied. Best results were obtained using factorial discriminant analysis on raw spectral data over the combined wavelength range; a correct classification rate of 93.9% was obtained on a prediction sample set. Though the overall sample set was limited in numbers, these results demonstrate the potential of near-infrared spectroscopy to classify extra virgin olive oils on the basis of their geographic origin.

scholarly journals Fast, Low-Cost and Non-Destructive Physico-Chemical Analysis of Virgin Olive Oils Using Near-Infrared Reflectance Spectroscopy

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2642 ◽  
Author(s):  
Ana Garrido-Varo ◽  
María-Teresa Sánchez ◽  
María-José De la Haba ◽  
Irina Torres ◽  
Dolores Pérez-Marín
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Virgin Olive Oils ◽  
Olive Oils ◽  
Physico Chemical ◽  
Non Destructive

Application of Partial Least-Squares Regression to Near-Infrared Reflectance Spectroscopic Determination of Shive Content in Flax

2003 ◽  
Vol 57 (5) ◽  
pp. 551-556 ◽  
Author(s):  
Miryeong Sohn ◽  
Franklin E. Barton ◽  
Wiley H. Morrison ◽  
Douglas D. Archibald
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Standard Error ◽  
Near Infrared ◽  
Single Sample ◽  
Calibration Model ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Sample Set

Shive, the nonfiberous core portion of the stem, in flax fiber after retting is related to fiber quality. The objective of this study is to develop a standard calibration model for determining shive content in retted flax by using near-infrared reflectance spectroscopy. Calibration samples were prepared by manually mixing pure, ground shive and pure, ground fiber from flax retted by three different methods (water, dew, and enzyme retting) to provide a wide range of shive content from 0 to 100%. Partial least-squares (PLS) regression was used to generate a calibration model, and spectral data were processed using various pretreatments such as a multiplicative scatter correction (MSC), normalization, derivatives, and Martens' Uncertainty option to improve the calibration model. The calibration model developed with a single sample set resulted in a standard error of 1.8% with one factor. The best algorithm was produced from first-derivative processing of the spectral data. MSC was not effective processing for this model. However, a big bias was observed when independent sample sets were applied to this calibration model to predict shive content in flax fiber. The calibration model developed using a combination sample set showed a slightly higher standard error and number of factors compared to the model for a single sample set, but this model was sufficiently accurate to apply to each sample set. The best algorithm for the combination sample set was generated from second derivatives followed by MSC processing of spectral data and from Martens' Uncertainty option; it resulted in a standard error of 2.3% with 2 factors. The value of the digital second derivative centered at 1674 nm for these spectral data was highly correlated to shive content of flax and could form the basis for a simple, low-cost sensor for the shive or fiber content in retted flax.

scholarly journals Prediction Model of the Key Components for Lodging Resistance in Rapeseed Stalk Using Near-Infrared Reflectance Spectroscopy (NIRS)

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Jie Kuai ◽  
Shengyong Xu ◽  
Cheng Guo ◽  
Kun Lu ◽  
Yaoze Feng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Spectral Data ◽  
Near Infrared ◽  
Soluble Sugar ◽  
Computing Time ◽  
Colorimetric Method ◽  
Mature Stage ◽  
Near Infrared Reflectance ◽  
Lodging Resistance ◽  
Physiological Basis

The chemical composition of rape stalk is the physiological basis for its lodging resistance. By taking the advantage of NIRS, we developed a rapid method to determine the content of six key composition without crushing the stalk. Rapeseed stalks in the mature stage of growth were collected from three cultivation modes over the course of 2 years. First, we used the near-infrared spectroscope to scan seven positions on the stalk samples and took their average to form the spectral data. The stalks were then crushed and sieved; then the ratio of carbon and nitrogen, ratio of acid-insoluble lignin and lignin, and the content of soluble sugar and cellulose were determined using the combustion method, weighing method, and colorimetric method, respectively. The partial least squares regression (PLSR) method was used to establish a prediction model between the spectral data and the chemical measurements, and all models were evaluated by an internal interaction verification and an external independent test set sample. To improve the accuracy of the model and reduce the computing time, some optimization methods have been applied. Some outliers were removed, and then the data were preprocessed to determine the best spectral information band and the optimal principal component number. The results showed that elimination of outliers effectively improved the precision of the prediction model and that no spectral pretreatment method exhibited the highest prediction accuracy. In summary, the NIRS-based prediction model could facilitate the rapid nondestructive detection in the key components of rapeseed stalk.

Evaluation of the bony repair in rat cranial defect using near infrared reflectance spectroscopy and discriminant analysis

2017 ◽  
Vol 33 (4) ◽  
pp. 1160-1168 ◽  
Author(s):  
Leomir A. S. de Lima ◽  
Kássio M. G. Lima ◽  
Lana S. S. de Oliveira ◽  
Aurigena A. Araújo ◽  
Raimundo Fernandes de Araújo Junior
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Cranial Defect

scholarly journals Use of Visible and Near-Infrared Reflectance Spectroscopy Models to Determine Soil Erodibility Factor (K) in an Ecologically Restored Watershed

2020 ◽  
Vol 12 (18) ◽  
pp. 3103
Author(s):  
Qinghu Jiang ◽  
Yiyun Chen ◽  
Jialiang Hu ◽  
Feng Liu
Keyword(s):  
Land Use ◽  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soil Erodibility ◽  
Infrared Reflectance ◽  
Cultivated Land ◽  
Near Infrared Reflectance ◽  
Natural Land ◽  
Soil Erodibility Factor

This study aimed to assess the ability of using visible and near-infrared reflectance (Vis–NIR) spectroscopy to quantify soil erodibility factor (K) rapidly in an ecologically restored watershed. To achieve this goal, we explored the performance and transferability of the developed spectral models in multiple land-use types: woodland, shrubland, terrace, and slope farmland (the first two types are natural land and the latter two are cultivated land). Subsequently, we developed an improved approach by combining spectral data with related topographic variables (i.e., elevation, watershed location, slope height, and normalized height) to estimate K. The results indicate that the calibrated spectral model using total samples could estimate K factor effectively (R2CV = 0.71, RMSECV = 0.0030 Mg h Mj−1 mm−1, and RPDCV = 1.84). When predicting K in the new samples, models performed well in natural land soils (R2P = 0.74, RPDP = 1.93) but failed in cultivated land soils (R2P = 0.24, RPDP = 0.99). Furthermore, the developed models showed low transferability between the natural and cultivated land datasets. The results also indicate that the combination of spectral data with topographic variables could slightly increase the accuracies of K estimation in total and natural land datasets but did not work for cultivated land samples. This study demonstrated that the Vis–NIR spectroscopy could be used as an effective method in predicting K. However, the predictability and transferability of the calibrated models were land-use type dependent. Our study also revealed that the coupling of spectrum and environmental variable is an effective improvement of K estimation in natural landscape region.

PRELIMINARY EVALUATION OF THE APPLICATION OF THE FTIR SPECTROSCOPY TO CONTROL THE GEOGRAPHIC ORIGIN AND QUALITY OF VIRGIN OLIVE OILS

2007 ◽  
Vol 30 (4) ◽  
pp. 424-437 ◽  
Author(s):  
ALESSANDRA BENDINI ◽  
LORENZO CERRETANI ◽  
FABIO DI VIRGILIO ◽  
PAOLO BELLONI ◽  
MATTEO BONOLI-CARBOGNIN ◽  
...  
Keyword(s):  
Ftir Spectroscopy ◽  
Geographic Origin ◽  
Preliminary Evaluation ◽  
Virgin Olive Oils ◽  
Olive Oils
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