# Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSS–PLS Algorithm

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}) of prediction being 0.9922, and the root-mean-square error of prediction (RMSEP) being 1.4889 % v/v in the prediction process. The results obtained indicated that the FT-NIR spectroscopy technique has the potential to perform a rapid quantitative analysis of the adulteration content of EVOO, and the BOSS algorithm showed its superiority in informative wavenumbers selection.

## 1. Introduction

## 2. Results

#### 2.1. Variable Selection by the BOSS Algorithm

^{−1}. Thus, the 15 variables selected by the BOSS algorithm constituted the best variable subsets for building the final PLS model.

#### 2.2. Results of the PLS Model

^{2}was 0.9908 in the calibration set. The predictive accuracy and generalization performance of the constructed model were evaluated using the independent samples from the validation set. The result of the root-mean-square error of prediction (RMSEP) was 1.4889 % v/v, and the R

^{2}was 0.9922 in the validation set which, as shown in Figure 3.

## 3. Discussion

## 4. Materials and Methods

#### 4.1. Sample Preparation and Division

#### 4.2. FT-NIR Spectra Acquisition

^{−1}. The range of spectral scanning was set from 10,000 cm

^{−1}to 4000 cm

^{−1}. Thus, the original spectrum of each doped sample contained 1557 wavenumbers (i.e., 1557 wavelength variables). The absorbance data were stored as Log (1/T), T being the transmittance.

#### 4.3. Spectra Preprocessing

#### 4.4. Data Analyses Methods

#### 4.5. Model Evaluation

^{2}) were used as measures for model performance evaluation. RMSECV, RMSEP, and R

^{2}are given by the expressions

_{i}is the reference measurement value from the ith sample, and $\widehat{{y}_{\backslash i}}$ is the estimated value of the ith sample, when the model is constructed with the removed ith sample. For RMSEP, n is the number of samples in validation set, y

_{i}is the reference measurement value of the ith sample in the validation set, and $\widehat{{y}_{i}}$ is the estimated value of the ith sample in the validation set. For R

^{2}, n is the number of samples, y

_{i}is the reference measurement value from the ith sample, $\widehat{{y}_{i}}$ is the estimated value of the ith sample, and $\overline{{y}_{i}}$ is the mean of all samples.

#### 4.6. Software

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

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Sample Availability: Samples of the compounds are available from the authors. |

**Figure 1.**Evolution of the number of variables (

**a**) and root-mean-square error of cross validation (RMSECV) (

**b**) in each iteration of the sub-models using the bootstrapping soft shrinkage (BOSS) algorithm.

**Figure 2.**The weights of the variables in the optimal sub-model at the eighth iteration using the BOSS algorithm.

**Figure 3.**Reference-measured versus FT-NIR-predicted doping concentration of extra virgin olive oil (EVOO) in the validation set.

**Figure 4.**The original FT-NIR spectra (

**a**) and the standard normal variate (SNV) preprocessing FT-NIR spectra (

**b**) of all adulterated EVOO samples.

**Table 1.**Results of different partial least-square (PLS) models for the prediction of doping concentrations in EVOO. CARS: competitive adaptive reweighted sampling; MCUVE: Monte Carlo uninformative variable elimination; IRIV: iteratively retaining informative variables.

Models | Selected Wavenumbers (cm^{−1}) | Number of Variables | PLS Factors | Calibration Set | Validation Set | ||
---|---|---|---|---|---|---|---|

R^{2} | RMSECV | R^{2} | RMSEP | ||||

PLS | 9999.10-3999.64 | 1557 | 6 | 0.9421 | 3.4618 | 0.9599 | 3.2520 |

CARS-PLS | 4192.49; 4242.63; 4261.92; 4578.18; 4593.61; 4655.32; 4659.18; 4666.89; 4670.75; 4674.60; 4682.32; 4690.03; 5746.83; 5754.55; 5758.40; 5766.12; 5858.68; 5862.54; 5870.25; 5874.11; 5877.97; 5881.82; 5885.68; 5889.54; 5897.25; 5901.11; 5912.68; 5920.39; 5935.82; 8234.55 | 30 | 4 | 0.9617 | 2.9647 | 0.9683 | 2.7664 |

MCUVE-PLS | 4373.76; 4412.33; 4566.61; 4593.61 4612.89; 4632.18; 4647.61 4670.75; 4690.03; 4709.32; 5750.69; 5762.26; 5777.69; 5866.40; 5885.68; 5904.97; 5924.25; 5939.68; 6001.39; 6028.39; 8238.41; 8253.84; 8261.55; 8265.41 | 24 | 3 | 0.9694 | 2.6828 | 0.9778 | 2.3232 |

IRIV-PLS | 4373.76; 4412.33; 5750.69; 5754.55; 5758.40; 5762.26; 5769.97; 5773.83; 5777.69; 5854.83; 5858.68; 5862.54; 5866.40; 5874.11 | 14 | 2 | 0.9901 | 1.4877 | 0.9887 | 1.8471 |

BOSS-PLS | 4373.76; 4678.46; 4705.46; 5758.40; 5762.26; 5766.12; 5777.69; 5858.68; 5862.54; 5866.40; 5870.25; 5877.97; 5881.82; 5885.68; 5904.97 | 15 | 3 | 0.9908 | 1.4487 | 0.9922 | 1.4889 |

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**MDPI and ACS Style**

Jiang, H.; Chen, Q.
Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSS–PLS Algorithm. *Molecules* **2019**, *24*, 2134.
https://doi.org/10.3390/molecules24112134

**AMA Style**

Jiang H, Chen Q.
Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSS–PLS Algorithm. *Molecules*. 2019; 24(11):2134.
https://doi.org/10.3390/molecules24112134

**Chicago/Turabian Style**

Jiang, Hui, and Quansheng Chen.
2019. "Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSS–PLS Algorithm" *Molecules* 24, no. 11: 2134.
https://doi.org/10.3390/molecules24112134