# Quantification and Detection of Ground Garlic Adulteration Using Fourier-Transform Near-Infrared Reflectance Spectra

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Samples

#### 2.2. Measurement of the Fourier-Transform Near-Infrared Spectra

^{−1}and 4000 cm

^{−1}with 2 cm

^{−1}resolution, i.e., between 1000 nm and 2500 nm, directly through glass vials. Before collecting the spectra of adulterated ground garlic mixtures, moisture was removed from the samples through drying them to constant weight in a laboratory dryer at 80 °C. Then, they were carefully mixed to obtain an even distribution of an adulterant in a sample. Finally, each mixture was described using the average FT-NIR spectrum of 32 independent scans. Spectroscopic measurements were carried out at stable room temperature (22 °C) and humidity (50%). Spectra were recorded thirty minutes after switching on the instrument in order to stabilize the radiation source. The background spectrum was recorded at the beginning of the measurements. The background correction procedure was repeated automatically every hour using an internal standard (diffusely reflective gold plate).

#### 2.3. Exploratory Analysis of the FT-NIR Spectra

#### 2.4. Construction of Multivariate Calibration Models

#### 2.5. Discrimination and Classification Models

## 3. Results

#### 3.1. The FT-NIR Spectra of Pure Components

#### 3.2. Exploratory Analysis of FT-NIR of Adulterated Samples

#### 3.3. Multivariate Calibration Models Based on Latent Variables

^{2}c) and validation samples (R

^{2}v).

#### 3.4. Discrimination of Adulterated Samples Using PLS-DA

#### 3.5. Classification Models

## 4. Discussion

#### 4.1. Exploratory Analysis

#### 4.2. Construction of Multivariate Calibration Models

#### 4.3. Discrimination of Samples with Two Adulterants Using PLS-DA

#### 4.4. Classification OC-PLS Models

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Fourier-transform near-infrared spectra of pure components: ground garlic, corn flour, and corn starch.

**Figure 2.**Projection of the FT-NIR spectra of ground and dried garlic samples adulterated with corn flour onto (

**a**) PC 1 and PC 2 as well as (

**b**) PC 1 and PC 3. (

**c**) Loading values on PC 3. Projection of the FT-NIR spectra of ground and dried garlic samples adulterated with corn starch onto (

**d**) PC 1 and PC 2. (

**e**) Loading values on PC 1.

**Figure 3.**Principal component regression models constructed for (

**a**) original FT-NIR spectra of ground and dried garlic samples adulterated with different amounts of corn flour, (

**b**) original FT-NIR spectra of ground and dried garlic samples adulterated with different amounts of corn starch, and (

**c**) FT-NIR spectra of ground and dried garlic samples adulterated with different amounts of corn starch after the optimal transformation (standard normal variate).

**Figure 4.**Partial least squares regression models for (

**a**) original and (

**b**) FT-NIR spectra of ground and dried garlic samples adulterated with different amounts of corn flour after optimal transformation (standard normal variate); (

**c**) original and (

**d**) FT-NIR spectra of ground and dried garlic samples adulterated with different amounts of corn starch after optimal transformation (the first derivative, window equal to fifteen sampling points and polynomial degree equal to two).

**Figure 5.**Average correct discrimination rates obtained for the model set, test set, and external test set from partial least squares discriminant model as a function of model complexity with indicated uncertainties estimated using the Monte Carlo validation approach (500 random subsamples).

**Table 1.**Principal component regression (PCR) and partial least squares regression (PLSR) models, built with f latent variables for original and optimally preprocessed Fourier-transform near-infrared reflectance spectra of ground garlic with a selected adulterant (corn flour or corn starch).

Adulterant | Preprocessing | Model | f | RMSEC | RMSEP | R^{2}c | R^{2}v |
---|---|---|---|---|---|---|---|

Corn flour | none | PCR * | 3 | 2.5397 | 2.3115 | 0.9919 | 0.9787 |

Corn starch | none | PCR | 9 | 2.3140 | 2.6896 | 0.9933 | 0.9711 |

Corn starch | SNV | PCR * | 4 | 2.2659 | 2.1256 | 0.9936 | 0.9820 |

Corn flour | none | PLSR | 6 | 1.4365 | 1.5655 | 0.9974 | 0.9902 |

Corn flour | 1st derivative ^{1} | PLSR * | 4 | 1.8841 | 1.8844 | 0.9955 | 0.9858 |

Corn starch | none | PLSR | 8 | 1.7759 | 2.2297 | 0.9960 | 0.9802 |

Corn starch | ISC | PLSR * | 4 | 1.7679 | 1.7812 | 0.9961 | 0.9873 |

^{1}The first-derivative spectra were obtained using Savitsky–Golay smoothing with a widow containing fifteen sampling points and a second-degree polynomial. * Asterisks indicate the optimal calibration models.

**Table 2.**Correct discrimination/classification rates (CCRs), sensitivities, and specificities reported as mean values obtained based on 500 training sets drawn randomly without replacement with the uncertainty of a given figure of merit expressed as the corresponding standard deviation for the optimal partial least squares discriminant model (PLS-DA) with seven latent variables. The model was built to discriminate samples adulterated with two different components (corn flour and corn starch).

Validation Parameter | Training Set | Internal Test Set | Validation Set |
---|---|---|---|

CCR | 97.05 ± 1.38 | 94.51 ± 3.59 | 99.66 ± 0.87 |

Sensitivity | 97.86 ± 1.3 | 94.15 ± 5.94 | 99.96 ± 3.57 |

Specificity | 98.02 ± 1.46 | 94.88 ± 5.23 | 99.36 ± 1.67 |

**Table 3.**The optimal OC-PLS models with seven latent variables that were built to classify samples adulterated with corn flour or corn starch. Each model is characterized by sensitivity and specificity, and the correct classification rate (CCR) is calculated for training, test, and validation sets.

Model | Sensitivity | Specificity | CCR | ||
---|---|---|---|---|---|

Training | Test | Validation | |||

Corn flour | 97.33% | 64.00% | 96.00% | 100% | 64.00% |

Corn starch | 94.67% | 45.33% | 92.00% | 100% | 45.33% |

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**MDPI and ACS Style**

Daszykowski, M.; Kula, M.; Stanimirova, I.
Quantification and Detection of Ground Garlic Adulteration Using Fourier-Transform Near-Infrared Reflectance Spectra. *Foods* **2023**, *12*, 3377.
https://doi.org/10.3390/foods12183377

**AMA Style**

Daszykowski M, Kula M, Stanimirova I.
Quantification and Detection of Ground Garlic Adulteration Using Fourier-Transform Near-Infrared Reflectance Spectra. *Foods*. 2023; 12(18):3377.
https://doi.org/10.3390/foods12183377

**Chicago/Turabian Style**

Daszykowski, Michal, Michal Kula, and Ivana Stanimirova.
2023. "Quantification and Detection of Ground Garlic Adulteration Using Fourier-Transform Near-Infrared Reflectance Spectra" *Foods* 12, no. 18: 3377.
https://doi.org/10.3390/foods12183377