Tutorial: Items to be included in a report on a near infrared spectroscopy project

Williams, Phil; Dardenne, Pierre; Flinn, Peter

doi:10.1177/0967033517702395

Cited by 124 publications

(125 citation statements)

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“…The values obtained for the optimized NIR methods are shown in Table . They were found to be in a similar range as the repeatability standard deviation of the reference methods or even lower because many systematic errors such as changing operator, instrumentation and sample preparation and derivatization which occur within the traditional analysis process must not be considered …”

Section: Resultsmentioning

confidence: 97%

“…It is also a fact of statistics, that the number of samples and the distribution of the different classes (i.e., countries) can have a significant impact on the final result of the statistical evaluation . Some statistical methods like so‐called parametric tests like LDA require that the number of objects in each class of the training set should be approximately equal otherwise the class with most representatives will always be selected.…”

Section: Resultsmentioning

confidence: 99%

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A Systematic Chemometric Approach to Identify the Geographical Origin of Olive Oils

Gertz¹,

Gertz²,

Matthäus

et al. 2019

Euro J Lipid Sci & Tech

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The verification of the geographical origin of olive oils by analytical techniques is still a challenge. The goal of this work is to explore the application and accuracy of different chemometric tools combined with near infrared spectroscopy (NIR) based analytical methods in the field of geographical authenticity of olive oils. As olive oils associated with different geographical origins are mainly characterized by different fatty acid (FA) and triacylglycerol (TAG) compositions, NIR methods for the fast and reliable determination of these parameters are developed. Next, these NIR methods are used to characterize a comprehensive set of olive oils (n > 5000) derived from 19 different countries. This set of data is used to build a statistical workflow, which allows the determination of the geographical origin of unknown olive oil samples. First of all, the untreated data set is pretreated by k‐means clustering and the selection of the relevant analytical variables by principal component analysis (PCA) and linear discriminant analysis (LDA) and min/max normalization of all parameters. Subsequently, classification is performed with a reduced sample set of the 200 most similar samples identified by k‐nearest neighbor tool (kNN). For classification purpose kNN, LDA, naïve Bayes classifier, and logit regression are applied. Practical Applications: The established statistical workflow can be used to verify the geographical origin of olive oils. The application and usage of up to four different statistical models for classification purpose results in a superior probability of the predicted origin in comparison to the application of only one single statistical classification test. As standardized methods are used as reference methods for building the NIR methods, the FA and TAG composition and the iodine value can be either determined by the standard methods or by the described NIR method. The presented statistical approach will help to build up a system for the verification of the geographical origin of olive oils.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

A Systematic Chemometric Approach to Identify the Geographical Origin of Olive Oils

Gertz¹,

Gertz²,

Matthäus

et al. 2019

Euro J Lipid Sci & Tech

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“…This might be due to the fact that systematic errors like different operators, instrumentation and sample preparation occurring within the traditional analysis process do not have to be considered. [ 38,39 ] The MD which is an indicator whether an unknown sample fits to the population of the calibration is for all methods within an acceptable range for the validation set. All of these observations demonstrate that the presented NIR‐methods are suitable for the analysis of the distinct oxidative parameters.…”

Section: Resultsmentioning

confidence: 99%

Detection of Soft‐Deodorized Olive Oil and Refined Vegetable Oils in Virgin Olive Oil Using Near Infrared Spectroscopy and Traditional Analytical Parameters

Gertz¹,

Matthäus

Willenberg

2020

Euro J Lipid Sci & Tech

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The European Parliament identifies virgin olive oil (VOO) as one of the foods which are often subject to fraudulent activities. Possibilities of adulteration are the application of illegal soft deodorization of extra virgin olive oil (EVOO) or the commercialization of blends of EVOO with soft-deodorized EVOO or refined vegetable oils. Despite the search for possibilities to prove the illegal soft deodorization of EVOO or the addition of cheaper vegetable oils to EVOO, suitable methods are still missing. Therefore, the aim of the study is to develop a new analytical and statistical approach addressing detection of mild deodorization or addition of refined foreign oils. For this purpose, VOOs are treated in lab-scale for 1 h up to 28 days at different temperatures (20, 50, 60, 80,100, 110, and 170°C) in order to simulate and study the effect of heat treatment on known analytical parameters by near infrared spectroscopy (NIR). A logit regression model enabling the calculation of the probability for a heat treatment is developed. This new methodology allows detecting both soft deodorized olive oils and blends of EVOO with cheaper full refined vegetable oils. Adding only 10% of full refined oil could be detected in extra VOO.Practical Applications: NIR methods combined with chemometrics have become one of the most attractive analytical tools to control quality of food. It is a simple, precise, and rapid method. All relevant analytical parameters of oxidative and thermal fat degradation can be determined in a single run and be used to detect adulterated virgin olive oils (VOOs). The use of a simple equation developed from the logistic regression using peroxide value, K-values, p-anisidine value, pyropheophytine, 1,2-diacylglycerols, total polar compounds and monomeric oxidized triacylglycerols, and other well-known parameters allows to detect mild deodorized olive oils or also blends of VOO with soft-deodorized ones or the addition of low amounts of foreign vegetable oils. This technique has potential to be used as a screening method for the detection of adulterated olive oils using both the traditional laboratory methods and the corresponding NIR-methods. R 2 : coefficient of determination; d) RMSEP: root mean square error of prediction; e) MD: limit of Mahalanobis distances.

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“…R 2 is calculated from a plot of calculated results vs those predicted by the specific targeted models. RPD (Williams et al 2017) is calculated as the ratio of standard deviation of FT-IR predicted results to the standard error, in turn calculated as the standard deviation of the residuals for each targeted model. LoD (at 0.1% false positives) is calculated as noted above in this paper, with the unspiked samples FT-IR predicted results used.…”

Section: Targeted Modelsmentioning

confidence: 99%

Development and application of Fourier transform infrared spectroscopy for detection of milk adulteration in practice

Hansen¹,

Holroyd

2019

Int J of Dairy Tech

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Fourier transform infrared (FT‐IR) spectroscopy is widely applied for routine milk analysis. More recently, it has been used to detect a range of specific milk adulterants. Here, we show that the simultaneous use of targeted and untargeted models applied to FT‐IR spectra for detecting potential milk adulterants across relevant concentration ranges is an effective strategy. Using an appropriate validation strategy, limits of detection, sensitivity and specificity are determined for specific adulterants giving a useful guide to how these models would work in a practical situation. Finally, theoretical limits of detection of a range of potential adulterants are listed.

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Tutorial: Items to be included in a report on a near infrared spectroscopy project

Cited by 124 publications

References 1 publication

A Systematic Chemometric Approach to Identify the Geographical Origin of Olive Oils

A Systematic Chemometric Approach to Identify the Geographical Origin of Olive Oils

Detection of Soft‐Deodorized Olive Oil and Refined Vegetable Oils in Virgin Olive Oil Using Near Infrared Spectroscopy and Traditional Analytical Parameters

Development and application of Fourier transform infrared spectroscopy for detection of milk adulteration in practice

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