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

Sánchez, María‐Teresa; Haba, María-José De la; Torres, Irina; Pérez‐Marín, Dolores

doi:10.3390/s17112642

Cited by 36 publications

(39 citation statements)

References 25 publications

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“…Comparison with results obtained from different ring tests underline the performance of the presented approach ( z ‐scores between −0.8 and 0.9) (Table ). Garrido et al using the previously mentioned set of oils reported an RMSEP very similar (0.06%) to the one obtained in this work.…”

Section: Resultssupporting

confidence: 88%

“…Different proficiency tests resulted in good z ‐scores confirming the accuracy of the NIR‐method (Table ). Garrido et al using a set of n = 359 and n = 100 olive oils for calibration and validation reported a RMSEP of 1.31, slightly higher than the one obtained here.…”

Section: Resultscontrasting

confidence: 74%

“…For a mean value of 2.122, a reproducibility standard deviation of 0.022 is reported in the method DGF‐C‐IV 6 (13) (K 232 in cyclohexane) as well as for a mean of 0.124 the reproducibility standard deviation was 0.01 (K 268 , isooctane). Garrido et al obtained almost identical RMSEP values for K 232 (0.10) and K 270 (0.01).…”

Section: Resultsmentioning

confidence: 81%

“…Up to now there are only few reports dealing with the application of NIR techniques to predict chemical and sensorial properties of olive oils . Recently, Garrido‐Varo et al reported that NIR can successfully be used for quality control of olive oil by measuring PV, FFA, K 232 and K 270 and alkyl esters based on an analysis of 478 Spanish olive oils. Nevertheless, most studies focused on the authentication of olive oil according the geographical origin.…”

Section: Introductionmentioning

confidence: 99%

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A New Statistical Approach to Describe the Quality of Extra Virgin Olive Oils Using Near Infrared Spectroscopy (NIR) and Traditional Analytical Parameters

Willenberg

Matthäus

Gertz³

2018

Euro J Lipid Sci & Tech

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NIR prediction models are developed for the determination of relevant parameters to evaluate olive oil quality such as acidity (FFA), peroxide value (PV), UV‐absorption at 232 and at 268/270 nm, p‐anisidine value (AnV), isomeric diacylglycerols (DG), and pyropheophytin A (PPP). In addition a new NIR method to estimate the age of olive oil is presented. The relevant wavenumbers are given for the calculation of the parameters and the precision data are presented in comparison to the chemical reference methods. The calibration and validation of the methods are executed with independent data sets (test in test instead of cross‐validation) to cover the wide range of variability of the analytical parameters including the corresponding accurate analytical results from the reference chemical methods. The correctness and accuracy of the developed NIR methods are verified by analyzing certified materials. Finally, a simple statistical approach has been developed to describe the quality of olive oils using the parameters FFA, PV, K232 and K270, DG and PPP. The probability of presence of a sensory defect (100% ≙ 1; 0% ≙ 0) is calculated using the following equation: Pred (BIN) = 1/(1 + exp(−(−9 + 37 × FFA‐0.9 × PV‐2.9 × K232 + 14 × K270 + 3.7 × PPP‐0.17 × DG))). Practical Applications: The use of NIR allows the analysis of different parameters relevant for the quality of olive oil in one run in comparison to the individual chemical reference methods. That allows saving time and money, and a skilled operator is not necessary, making the method interesting for routine analysis. The use of a simple equation developed from the logistic regression using FFA, PV, K‐values, DG, and PPP measured by NIR as variables enables for the first time to describe the sensory quality of olive oils without making sensory testing. This criterion without setting limits for the individual parameters estimates the probability of the presence of sensory defects on basis of chemical parameters. This tool can easily be used to differentiate the two categories extra virgin and virgin olive oils both using the traditional laboratory methods and the corresponding NIR‐methods. In the present study, NIR prediction models are developed for the determination of free fatty acids, peroxide value, UV‐absorption at 232 and at 268/270 nm, p‐anisidine value, isomeric di‐acyl‐glycerols, and pyropheophytin A to evaluate olive oil quality. On basis of NIR, a new method is developed to estimate the age of olive oil. The parameters FFA, PV, K232 and K270, DG and PPP have been used to describe the quality of olive oils by a simple statistical approach.

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Section: Resultssupporting

confidence: 88%

Section: Resultscontrasting

confidence: 74%

Section: Resultsmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

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A New Statistical Approach to Describe the Quality of Extra Virgin Olive Oils Using Near Infrared Spectroscopy (NIR) and Traditional Analytical Parameters

Willenberg

Matthäus

Gertz³

2018

Euro J Lipid Sci & Tech

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“…Despite this progress, little research has been done into the in situ analysis of large volumes of olive oils in bulk using near infrared spectroscopy (NIRS). 9,10 For this reason, work has been carried out within the framework of a research project at the Department of Agrolivestock Systems Engineering (University of Córdoba, Spain) to design and build a fiber-optic probe. This tool, interfaced to a NIRS instrument, could serve to sample and analyse olive oils directly in the tanker trucks.…”

Section: Introductionmentioning

confidence: 99%

Fine-tuning and cloning of a fiber-optic probe for in situ monitoring and evaluation of quality of olive oil products

Pérez‐Marín

Adame-Siles

Sánchez-Müller

et al. 2019

Proceedings of the 18th International Conference on Near Infrared Spectroscopy

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The control of conformity at reception of bulk olive oil before unloading is an essential step for packing plants to meet quality standards and ensure traceability. New approaches, different to sampling and analysis at-line, are needed to improve efficiency and decision-making processes. Near-infrared spectroscopy (NIRS) provides fast, cost-effective and in situ analytical measurements without sample preparation, which makes it an ideal technology towards this goal. The purpose of this work is to assess and optimize the spectral acquisition with a fiber-optic probe, especially designed to sample and analyze tank trucks. This probe features two fiber optic bundles, one of measurement and one of illumination. First of all, two olive oils samples, one of extra virgin and one of lampante category, were used to evaluate the noise and repeatability of the NIRS spectral signals of the target probe. A set of 20 spectra were recorded for each sample, and the sequence of measurements lampante-extra virgin was repeated three times per day for seven days spread over five weeks. The noise level was evaluated using a first derivative pre-treatment, and the optimum working range (after removing the noisy regions) was 1150-2149 nm. The Root Mean Square (RMS) statistic and the MEAN values were calculated in each case, and the spectral repeatability results showed that the RMS (MEAN) values for lampante were higher than for extra virgin olive oil (12,630 and 2998 µlog(1/R), respectively). Furthermore, a standardization procedure was performed to make a comparative study with a laboratory probe. Both probes were connected to a MATRIX-F Fourier transform-NIR instrument. A set of 42 samples of different nature were used for standardization purposes. Two matrices of standardization were developed, selecting 1 (STD1) and 10 (STD2) samples, and a validation set of 32 samples was used. The spectral adjustment was evaluated using the validation set by calculating the RMS(c) statistic. The RMS(c) values obtained in the master (laboratory probe, LP) versus satellite (in situ analysis probe, IAP) before standardization showed important differences that were significantly corrected by the best standardization matrix (STD1).

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Olive Oil

Kiritsakis¹,

Turkan²,

Kiritsakis³

2020

Bailey's Industrial Oil and Fat Products

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Olive oil is a natural juice obtained by mechanical or physical means from the fruit of the tree Olea europaea L. Many historians believe that the olive cultivation began during the Palaeolithic and Neolithic periods (5000–3500 bce ). Most of the world's olive trees grow in the Mediterranean Basin. Olive oil has always been an important component in the diet of Mediterranean people, but in the past years this oil has become more popular among consumers in Northern Europe, the United States, Canada, and Australia. Olive oil quality depends mainly on the cultivar, microclimate, soil conditions, fruit health, and processing conditions. Several studies have confirmed that extra virgin olive oil (EVOO) helps to reduce oxidative stress, high blood pressure, obesity, fatty liver, and insulin resistance, diseases that are closely related to diabetes and coronary heart disease. This chapter focuses on the origin and history of olive tree, statistics of world production, and consumption of olive oil. It also deals with olive fruit processing, olive oil classification and categories, quality and purity criteria, olive oil components, olive oil analysis, deterioration, and adulteration. Phenolics and other minor functional compounds of olive oil are discussed in detail.

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

Cited by 36 publications

References 25 publications

A New Statistical Approach to Describe the Quality of Extra Virgin Olive Oils Using Near Infrared Spectroscopy (NIR) and Traditional Analytical Parameters

A New Statistical Approach to Describe the Quality of Extra Virgin Olive Oils Using Near Infrared Spectroscopy (NIR) and Traditional Analytical Parameters

Fine-tuning and cloning of a fiber-optic probe for in situ monitoring and evaluation of quality of olive oil products

Olive Oil

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